July 2024 - Page 3 of 4 - Online Manipal

How do I prepare for a data science interview?

With the increase in the amount of data created every day and its importance in business growth and development, data science is growing rapidly. The US Bureau of Labor Statistics projects that by 2026, there will be a 27.9% increase in employment requiring data science skills. Given the above statistics and the speed at which data is doubling, data scientists are currently the most in demand.

However, to land a lucrative job, you need to pass the interview. You need to be prepared for the data science interview questions if you want to leave a great first impression. This blog offers practical advice and an organized strategy for data scientist interview questions to help you positively present yourself. We will walk you through each phase of the multi-part process for data science interview preparation. By the end, you will have everything you need to ace your interview.

1. Understand the role and requirements

The first step to acing the data science interview is understanding the role and job description. So, when applying for a particular role, after submitting the application, you must start detailed research about the job requirements and the company itself. It will help you get a gist of what the company’s expectations are, what kind of work culture it has, and what products or services it delivers.

Identify critical hard or soft skills and required technologies, such as Python, R, SQL, machine learning algorithms, etc. Enhance your skills and customize your response accordingly. Assess the company’s customer segment, the competitive landscape, the type of data it collects, the specific problems data science helps address, and so on. You can successfully align data science with the company’s objectives and prove its worth by understanding the business context and how data science can help these goals.

2. Strengthen your technical skills

Technical skills are an important aspect of a data science job as a data scientist will apply statistics, advanced mathematics, data visualization, and more to gather, analyze, and interpret data. Examples of technical skills that data scientists should focus on strengthening include the following:

Programming languages

Programming skills are essential for data scientists because this is how they communicate with computers. Some of the most important programming skills data scientists should practice are coding in Python, R, and/or SQL. A focus on writing clean, efficient, and readable code is also required to avoid any miscommunication or errors.

Data manipulation and analysis

For a data scientist or analyst, proficiency in data manipulation and analysis is essential. Such abilities will let you handle large datasets and transform your raw data into meaningful insights that drive decisions. You will need practical experience with libraries like Pandas and NumPy, as well as data visualization libraries like Matplotlib and Seaborn.

Statistics and probability

Calculus, algebra, probability, and statistics are the four most significant mathematical disciplines in data science. Brush up on fundamental concepts, including hypothesis testing, distributions, and statistical significance. Armed with these skills, you’ll be more than ready to assess data distributions, test hypotheses, and come to appropriate conclusions confidently.

Machine learning

With the growing usage of data, the demand for data scientists proficient in machine learning (ML) has increased. Reviewing key algorithms like Regression, Classification, and Clustering, and understanding model evaluation metrics and techniques of cross-validation, precision, recall, etc., will help develop expertise in ML.

Big data tools

Proficiency with tools like Spark, Kafka, and Apache Hadoop enables data scientists to handle and process large amounts of data effectively.

3. Work on projects and a portfolio

Build a portfolio

Developing a good data science portfolio is the third step to handling data science interview questions with ease. You can do this on platforms like GitHub by showing your skills through projects. The platform offers tools for tracking issues, reviewing codes, and so on.

Here you can include varied data science projects you worked on, such as exploratory data analysis, machine learning models, and data visualization.

Kaggle competitions

Participate in data science competitions, such as Kaggle, to prove your skills and show some hands-on experience. You will be competing for the models that best solve a particular problem or working with other data scientists for some real exposure.

4. Prepare for behavioral and situational questions

Behavioral questions

You will have to practice answering behavioral questions to prove your abilities, experiences, and eligibility for any position. The more times you can communicate your strengths and crisis management skills in an interview or any professional setting, the greater your chance of success. Practice frequently asked behavioral questions like leadership, teamwork, and conflict-resolution type questions. Make sure to use the STAR (Situation, Task, Action, Result) method to organize your answers.

Situational questions

Getting comfortable with situational inquiries improves interview performance by giving applicants the confidence to address circumstances related to the job, showcasing their capacity for critical thinking, problem-solving, and fit for the position.

5. Practice problem-solving and case studies

Technical challenges

Practicing problem-solving and case studies are equally important as practicing behavioral and situational questions. Use platforms like LeetCode, HackerRank, and CodeSignal to tackle coding challenges and overcome technical obstacles easily.

Case studies

Also, study and practice case interviews focusing on data-driven problem-solving. Analyze data science case studies to understand the end-to-end process of data analysis and model building.

6. Mock interviews and feedback

Conduct mock interviews

Once you’re done with your problem-solving and case study practices, the next step you need is to conduct mock interviews. This will reduce your nervousness and get you familiar with the pattern of the interview. This also helps in clearly articulating your thoughts. Conducting mock interviews helps you to maintain a professional demeanour as well. Practice with peers and mentors, or use professional services for your mock interview. It also replicates the real interview situation and enables one to gain confidence and improve performance.

Seek feedback

A mock interview is an ideal way of looking at your strengths or weaknesses and working on the areas you need to improve upon. Get constructive feedback on technical and behavioral reactions. If you have a mock interview and then seek feedback, you maximize the chance of success.

7. Stay updated and network

Follow industry trends

Technology is upgrading at a fast pace, and employers are looking for candidates who can follow the industry trends. Therefore, if you wish to crack data science interview questions, you should be updated about the recent happenings in the field of data science. Stay updated about the recent developments in the field through blogs, podcasts, and research papers.

Network with professionals

Professional networking is another essential part of enhancing skills and growing careers. It creates new opportunities, offers support and mentorship, and acts as a means of staying updated and inspired. Build a strong network of professionals in the field by joining data science communities on LinkedIn, Reddit, or through local meetups. Also, build connections with professionals to gain insights and tips about the interview process.

How does MAHE’s MSc in Data Science help you prepare for a data science career?

The Manipal Academy of Higher Education (MAHE) offers a two-year Master of Science (MSc) in Data Science, which is designed to equip students with knowledge and practical experience in the relevant fields. The course covers all major aspects of data science, such as data mining, statistical analysis, machine learning, and big data technologies, apart from some advanced topics like deep learning and artificial intelligence.

The course also encourages students to participate in data science research initiatives guided by faculty members.

Conclusion

Interviews can cause anxiety and weaken your confidence, regardless of your experience level. But by being prepared, you can walk into your next interview with confidence. By incorporating these pointers into your data science interview process, you’ll be ready to demonstrate your abilities, expertise, and excitement for the position, which will eventually raise your chances of getting the job.

Choosing the right data science program for your career: Tips and strategies

The diverse applications of data science in every field have made it one of the most in-demand disciplines in the current employment industry. As the amount of information increases exponentially and more organizations require professionals to analyze this information and use it for a business’s benefit, business organizations are increasingly demanding data scientists.

Consequently, more and more programs in data science have appeared in many universities, each with its own curriculum and outcomes. However, it is essential that you select the right program that will help you achieve your career objectives.

Understanding your career goals

It is crucial to identify your career goals before choosing a data science program. Do you want to become a data analyst or machine learning engineer or explore the AI research field?

First, determine your area of interest within data science:

Machine Learning: Courses that focus on algorithms, statistical modeling, and artificial intelligence.
Data Analysis: Subjects based on data preprocessing, presentation, and analysis.
AI: Other complex topics including neural nets, deep learning, as well as robotics.

Then, classifying your objectives as short-term, such as attaining the first job in your industry, and long-term, such as becoming a lead data scientist, will assist you in eliminating certain programs. To achieve short-term goals, concentrate on attaining skills and knowledge that will enable you to get a job as soon as possible. Select programs that provide certificates or boot camps. When focusing on long-term goals, look at courses that provide the full scope of study and the chance to focus on a certain direction, for example, master’s degrees.

Types of data science programs

Several types of data science programs address particular objectives, subject areas, formats, and learning preferences.

Online Programs are flexible and sometimes asynchronous, allowing working professionals to attend classes in the evenings and on the weekends. In-person programs provide a more engaging learning environment where students can easily engage with the faculty and fellow students.

Overview of different types of programs

Certificates and Bootcamps: Certifications are concise, skills-oriented, intense courses that last for a short time. They are best for individuals who want to advance their skills and careers in the shortest time possible.
Degree Programs: These provide a broader learning curriculum, which can be associated with a bachelor’s, master’s, or even a doctorate in data science.

Full-time vs. Part-time options

Full-time programs: Most effective for students who can devote undivided focus to studying; often finished more quickly.
Part-time programs: Permit flexibility in periods of work and study and may be more prolonged at times.

Program type	Duration	Mode	Ideal for
Certificate	3-6 months	Online/In-person	Professionals seeking quick upskilling
Bootcamp	2-12 weeks	Online/In-person	Fast-tracking into data science
Bachelor’s Degree	3-4 years	In-person	Undergraduates pursuing data science
Master’s Degree	1-2 years	Online/In-person	Specialization in advanced topics
Ph.D.	3-5 years	In-person	Research and academia

Data science curriculum and course content

The curriculum is probably one of the most crucial factors to consider when selecting a data science program. The course should teach subjects of your interest and requirements and offer appropriate specializations.

Key topics to look for

Statistics and probability: Crucial for working with data and its behaviors such as time series analysis. Builds analytical skills and critical thinking.
Programming: Languages such as Python, R, and SQL. Important for coding and software development.
Machine learning: Fundamental models and model-building methods. Essential for model creation and optimization.
Data visualization: Dashboard and visualization tools such as Tableau and Power BI to present data insights. Builds communication and storytelling skills.

Additionally, look for programs that require you to work on hands-on programs. These programs allow you to apply concepts learned in classrooms to real-life situations in projects. This can help you build your portfolio and show your practice and dedication to potential employers.

Ultimately, look for programs that provide a balanced mix of theory, practical applications, and industry-relevant skills.

Accreditation and reputation of data science programs

Selecting an accredited data science program ensures that the program chosen meets specific educational requirements and is acknowledged in the job market. It signifies that the program meets national or international quality standards, which can enhance your resume.

Gather as much information about the institution as possible, including student feedback and discussions on online communities, as well as its position in national or international rankings. Read alumni reviews to understand what past students have to say about the program.

Faculty and industry experts

Learning from experienced faculty members and industry experts can enhance the quality of education. Teachers with extensive experience share valuable lessons from their practice and use targeted techniques that help students grasp concepts easily. Check the faculty’s credentials and achievements and their academic and working experience to meet your learning objectives.

Additionally, participating in guest lectures and networking sessions allows participants to communicate with representatives of the desired field and possible employers.

Resources and support services

Student support committees and resources are valuable for developing their knowledge and skills and for preparing them for future jobs. Career services, which include job interview preparation, resume writing, and placement services, can assist in finding employment upon graduation. Moreover, provisions for libraries, databases, and other research and study resources can help students learn from diverse materials.

Ultimately, it is recommended that students should enroll in programs that provide tutoring, mentoring, and academic advising.

Cost and financial considerations

Students should compare different programs’ costs in relation to their opportunities and benefits. Assess the cost of tuition and other related fees and the total financial investment expected. Research available scholarships, grants, and the possibilities of financial aid that may lighten the load.

Finally, the potential ROI can be ascertained by evaluating the cost of the program against the projected salaries, increments, and career progression after graduation.

Flexibility and convenience

Availability or flexibility is often a crucial criterion for many learners, especially those with other responsibilities such as work or home management. Institutions and programs that offer evening, weekend, or self-schedule classes can help accommodate additional responsibilities.

As a student, you must consider your daily schedule and decide whether it is easier for you to attend classes online or offline. Then, select a program that is flexible enough to accommodate work, family, and study needs.

Networking and Community

A good professional circle helps find job vacancies and can lead to credible references. Students should search for opportunities to interact with people: colleagues, graduates, and professionals in the field. You can also join a strong learning community or organizational program that can support knowledge sharing and peer support.

Additionally, participate in data science competitions and attend conferences to demonstrate your proficiency and make a name for yourself.

Internships and practical experience

By completing internships, students obtain practical experience, which helps showcase their knowledge and dedication when applying for jobs.

Find out if the program provides or arranges internships with affiliated corporations or organizations. Programs with strong industry connections can offer real-world projects that enhance learning and employability.

Evaluating program outcomes

Evaluating different programs’ placement rates and alumni success stories can help you compare which program offers better employment opportunities. Additionally, check that the program’s outcomes suit your particular employment purposes and goals.

Making the final decision

Once you have gathered enough information, you must now create a list of schools that meet your requirements and preferences. Choose programs that fit your career interests, financial capacity, and other needs. Compare the pros and cons of each program in terms of costs, courses offered, flexibility, and reputation.

Make your final decision after conducting all the necessary research and considering your career aspirations and life situation. For example, if you need to keep working but want to study further, you can opt for an online certification program or a part-time postgraduate degree program.

Conclusion

Selecting an appropriate data science program is a critical decision on the way to attaining a fulfilling career in this rapidly evolving industry. Through proper evaluation, you can make the right choice depending on your career needs, available program types, curriculum, and resources. By choosing the right program, you will be in a good position to succeed in the ever-growing field of data science and make a positive impact in India and beyond. If you’re looking for a full-scope master’s program to master data science, enroll in MAHE’s online MSc in Data Science program today!

How to become a data science expert

In today’s technologically advanced world, data is essential to the expansion and advancement of various sectors. Data is used everywhere, including in healthcare, oil and gas, agriculture, telecom, insurance, finance, and other industries. Data scientists, the designers of modern data interpretation and utilization, are crucial as businesses and organizations attempt to navigate the complex pathways of big data.

The need for data scientists is expected to rise by 36% between 2021 and 2031, far higher than that of software engineers. The data clearly shows the rising demand for data scientists across a range of businesses. We will provide an in-depth guide in this blog on how to become an expert in data science through real-world experience and skills.

Understanding Data Science

Data science is a prominent field that involves deriving intelligent conclusions from complex data to solve problems in practical applications. It is the foundation that can revolutionize a variety of sectors, including healthcare and finance, and boost business output.

A data scientist is someone who uses data to make discoveries and unlock its potential. Their job is to effectively explain the information that has been retrieved to influence company goals and policies. They must also move toward advancement and development and tackle challenging issues with data-driven solutions.

Data scientists are always in demand because data is critical to all industries. For example, data is used to construct fraud detection systems in the banking industry, forecast illness patterns in the healthcare industry, collect weather data in agriculture, and so forth. Their adaptability to many industries makes them valuable assets that can positively impact decision-making and help formulate corporate strategy.

Building a strong data science foundation

To become a data scientist, you should establish a solid foundation with the required knowledge and credentials.

Educational background

Having the necessary education, experience, and at least an undergraduate degree in mathematics, statistics, or computer science are prerequisites for employment as a data scientist. BCA, BSc, and B.Tech are a few degrees you can pursue. Furthermore, data scientists use statistical and mathematical techniques to extract information and understanding from data. Thus, a strong knowledge of mathematics and statistics is a must.

Essential skills

Programming languages (e.g., Python, R)
Data manipulation and analysis (e.g., SQL, Excel)
Machine learning and AI basics
Data visualization tools (e.g., Tableau, Power BI)

Gaining practical experience

The third step is to gain practical exposure through projects, internships, online platforms, and more.

Projects and internships

In data science, practical skills are essential as they allow practitioners to convert theoretical knowledge into useful applications. This strengthens their knowledge and enables them to comprehend how theories apply to actual situations. Additionally, practicing data science fosters the growth of analytical and problem-solving abilities.

To find and choose among the best relevant projects and internships, you must:

Identify your goals and interest.
Attend career fairs and networking events.
Build a strong portfolio.
Use online job portals.
Seek mentorship.
Leverage university career services.

Online platforms and resources

To gain practical experience in data science, you must take advantage of online platforms and resources. Some of the recommended online courses and certifications you must explore are available at Online Manipal, Coursera, edX, Udacity, and more. You must also participate in data science competitions on various online and offline platforms to identify your mistakes and understanding. Kaggle, Topcoder, and CodaLab are a few such platforms.

Mastering Advanced Topics

Basic skills and education make you eligible for the data science field. Mastering advanced topics makes you appealing to employers and universities. Some of the areas a data scientist must master include the following:

Specialized Areas

Deep learning and neural networks: Deep learning facilitates the processing of unstructured data. It enables data scientists to see patterns in the data that are challenging or impossible for people without expertise to find. To draw valuable and accurate insights from the data, data scientists must master deep learning and neural networks.

Natural Language Processing (NLP): Data scientists utilize NLP techniques, such as text classification and sentiment classifiers, to analyze user sentiment. This enables businesses to gauge customer satisfaction and make data-driven decisions. These techniques are crucial in the functioning of chatbots and virtual assistants, too.

Big data technologies (e.g., Hadoop, Spark): By 2027, the big data market is predicted to grow to $103 billion globally. Gaining expertise in big data technology ensures that data scientists can collect, manage, and analyze massive amounts of data effectively.

Research and continuous learning

Sitting still after mastering a data science topic of your choice is not enough; it will not get you the job you want. You should keep up with the newest trends and technologies if you wish to stay current. Reading academic papers, going to conferences and networking gatherings, and taking more advanced courses can help you do this.

Building a data science portfolio

Developing a solid data science portfolio is the next stage in becoming a data scientist. How? Let’s discover.

Showcasing your work

It’s time for you to highlight your work, whether you have years of expertise in the sector or have just completed any data science internships. Make a strong CV by emphasizing your qualifications, experience, projects, and other highlights. You must create a professional portfolio website and describe all your projects in detail for recruiters to see. You can share projects using platforms like GitHub, as here you can store, track, or collaborate easily on projects.

Networking and personal branding

To reach your target audience, you must showcase your work and engage in networking and personal branding. If you have no networking experience, you should seek guidance from your mentor or coach. Build a presence on LinkedIn and other professional networks, join data science communities and forums, and attend workshops and conferences.

Preparing for the data science job market

Now that all else is done, it’s time to prepare to enter the job market. Here are some tips to stand out among other top candidates:

Create an engaging CV by emphasizing your relevant experiences and skills.
Mention the best and most relevant information first.
Customize applications for different job roles.
Keep it concise.
Put experience first, education later.
Keep your resume format simple.
Make a compelling opening to your cover letter to capture the reader’s interest.
Show genuine excitement about the position.
Be authentic and convey what makes you unique.
Explain how this role fits into your long-term career goal.
Highlight relevant skills and experiences.

Here are a few often-asked data science interview questions you should be aware of:

How would your past employers describe you?

Possible answer: Highlight your skills, achievements, and personality traits that are relevant to the data science position.

When evaluating data, which tools are your favorites?

Possible answer: Mention the tools that you are proficient in and explain how you use them for data analysis.

To be considered for a data science position, you will also need to pass technical evaluations. To prepare for technical assessments, refresh your theoretical knowledge, practice, work on your reasoning skills, and revise the basics.

Continuous improvement and growth

To thrive in the current work market, you must constantly grow and improve. You can do so with the help of career goals. Establish specific goals for your profession, both short- and long-term, and strive toward achieving them. Remember that your objective ought to be attainable, quantifiable, and reasonable. If you’re having trouble putting your career goals together, ask your seniors, mentors, co-workers, or even family members for advice.

Conclusion

Becoming a data scientist takes a combination of technical expertise, professional abilities, and education. We hope the tips mentioned above can help you build a successful data scientist career. However, the first step to becoming a data scientist is your education. Investing in basic education or enhancing your certification is one of the best and most essential decisions you will have to make. Enrol in Online Manipal data scientist courses to upgrade your skills and pave the way for a compelling data science career.

MSc Data Science vs. MSc Business Analytics

In the present era, where information technology is rapidly growing, there is a high need for qualified data personnel. Businesses across industries have started relying on data for decision-making, and thus, the best option for a student looking to enter the industry is to study a postgraduate program. Two of the best ones are the MSc Data Science and the MSc Business Analytics programs. Each has its advantages and disadvantages, and knowledge of the disparity between the two is important in directing you toward the right educational choice in relation to your desired career.

Understanding MSc Data Science

Data science is a cross-disciplinary field that involves applying science, statistics, computations, and technology tools to study data in order to drive insights and knowledge from it. It includes a wide spectrum of algorithms, from mathematics to probability theory to computer science. This field is especially dedicated to creating and deploying computational models to find and identify patterns in data.

An MSc Data Science program provides students with knowledge in programming, statistical modeling, data mining, and machine learning. Students learn to work with large datasets, perform complex analyses, and create forecast models. The program includes knowledge of computer programming languages such as Python and R, machine learning algorithms, and data visualization tools.

This program helps students learn the following skills:

Programming: Fluency in languages such as Python, R, and SQL.
Statistical Analysis: Ability to work with hypothesis testing and regression analysis, as well as using other statistical techniques.
Machine Learning: Awareness of supervised and unsupervised machine learning methods and artificial intelligence.
Data Handling: Cleaning, preprocessing, and transformation skills.

Typical curriculum and course structure

A typical MSc Data Science curriculum includes:

Core Courses: Data analysis, artificial intelligence, and data science technologies.
Programming Languages: Python and R.
Statistical Analysis and Data Visualization: Software like Tableau and Power BI.
Elective Courses: Specific niches such as deep learning, NLP, or computer vision.

Typically, the course format includes lectures, laboratory sessions, as well as assessments and projects to enable students to practice theory and codes.

Common career paths

MSc Data Science graduates can seek employment in a variety of roles, such as data scientists, machine learning engineers, and data engineers. They are employed in technology companies, healthcare organizations, financial firms, and retail trade companies, where they use algorithms and models to find solutions.

Understanding MSc Business Analytics

Business analytics focuses on using data analysis and statistical models to make informed business decisions. It bridges the gap between data science and business strategy, emphasizing the interpretation and application of data insights to improve business performance.

An MSc Business Analytics program focuses on analytical, business, and decision-making aspects. Students learn data visualization techniques, predictive analysis, and business intelligence systems for business decision-making.

This program helps students learn the following skills:

Analytical Skills: Thorough knowledge of processing and analyzing data.
Business Acumen: Knowledge of primary business activities and plans.
Data Visualization: Proficiency in utilizing tools such as Tableau and QlikView to share insights derived from data.
Decision-making: Ability to leverage data analysis in the development of business solutions.

Typical curriculum and course structure

A typical MSc Business Analytics curriculum includes:

Core courses: Forecasting, database management, programming, and financial management.
Data interpretation: Classes that revolve around the process of analyzing and converting data into business-related decisions.
Courses on Business Strategy & Operations: To comprehend how data can enhance various operations within the enterprise.

Some of these courses incorporate case scenarios, projects, and internships, which enable learners to gain hands-on experience using analytics to solve organizational issues.

Common career paths

Employment opportunities for MSc Business Analytics graduates include business analysts, data analysts, research associates, and business intelligence analysts. They work with management teams to evaluate business prospects and enhance operations in areas like finance, consulting, and marketing. For instance, while a business analyst might be working on discovering new business opportunities using data, a data analyst might be working on improving business processes.

Key differences between data science and business analytics

Aspect	MSc Data Science	MSc Business Analytics
Focus and Application	Analytical and technical, with a focus on algorithms and data manipulation	Business-oriented, focusing on data-driven decision-making
Skill Sets	Programming, statistical modeling, data analysis, artificial intelligence	Data analysis, business strategy, decision-making
Projects and Applications	Creating forecasting tools and algorithms	Evaluating business performance and enhancing business strategies

Data science is more related to data processing and algorithmization techniques, while business analytics is more strategic and oriented toward using these techniques to solve business tasks.

Curriculum comparison

Core subjects

MSc Data Science Core Subjects	MSc Business Analytics Core Subjects
Machine Learning	Predictive Analytics
Data Mining	Business Intelligence
Big Data Technologies	Strategic Management

Electives and specialization options

MSc Data Science	MSc Business Analytics
Deep Learning	Marketing Analytics
Computer Vision	Financial Analytics
Natural Language Processing	Supply Chain Analytics

Electives allow students to tailor their education to their specific career interests, providing opportunities to delve deeper into specialized areas.

Career opportunities and job roles

Typical job titles for both programs include:

MSc Data Science Graduates	MSc Business Analytics Graduates
Data Scientist	Business Analyst
Machine Learning Engineer	Data Analyst
Data Engineer	Business Intelligence Analyst

Both fields are growing rapidly as organizations continue to leverage analytical tools to drive their operations. Data science roles involve developing complex algorithms for data analysis, while business analytics roles involve analyzing data to inform business decisions. The U.S. Bureau of Labor Statistics anticipates that jobs for data scientists will increase by 11% from 2019 to 2029. The employment of business analysts is projected to increase by 14% between 2020 and 2030.

Skills and competencies acquired

MSc Data Science(Technical Skills)	MSc Business Analytics(Analytical and Strategic Skills)
Coding	Problem-Solving
Data Wrangling	Data-Driven Decision-Making
Statistical Analysis	Understanding of Business Processes

One advantage of both fields is that they are rooted in domain knowledge. Data scientists must grasp the context of the data and the business to which the data belongs, while business analysts must possess knowledge concerning sectors and markets.

Program prerequisites and admission requirements

Program	Prerequisites
MSc Data Science	Background in computer science or mathematics Familiarity with programming languages such as Python or R Basic knowledge of machine learning concepts
MSc Business Analytics	Background in business, economics, or a related field Proficiency in quantitative analysis and data interpretation Knowledge of business operations and management principles

Admission criteria and application process

Application to the programs usually involves submission of a bachelor’s degree, letters of recommendation, a statement of purpose, and sometimes previous work experience or a strong field background. Some programs may also require acceptable scores on standardized tests such as the GRE or GMAT, especially for international students.

Choosing the right program for your career goals

Self-assessment: Identifying your interests and strengths

Think about whether your interests are technical or business-related. Are you into coding and modeling, or do you prefer making decisions at the strategic level and focusing on business issues? Knowing this could help you make the right decision between the two programs depending on your preference.

Long-term career aspirations and industry preferences

Think about your ultimate professional objectives and the sectors that interest you. Data science may be better suited for technical positions, while business analytics may be more appropriate for strategic positions.

Matching program outcomes with your career objectives

Assess the extent to which the curriculum and skills from each program are relevant to your career goals. Select courses that have either elective courses or specializations that appeal to you. Secondly, it is necessary to pay attention to the type of career services and networking opportunities provided by each program to help in finding a job after graduation.

Alumni success stories and testimonials

Example of successful MSc Data Science learners — Example of a successful MSc Data Science learners

Example of a successful MSc Business Analytics learner

Conclusion

It is ultimately up to the student to decide whether to go for an MSc in Data Science or an MSc in Business Analytics based on their aspirations, passion, and talent. Altogether, both programs represent great opportunities in today’s dynamic field of data analysis. When comparing the differences and examining your goals, you can choose the way that will open the path to success in the data world. Regardless of whether your goal is to develop state-of-the-art algorithms or to inform business strategy, there exists a place for you in the data world.

Business analytics courses for freshers

There is a ton of data produced every year. It is anticipated that the amount of data created globally will surpass 180 zettabytes in 2025. Data is used in various industries as it helps improve the overall business process by innovating and monitoring business growth. The data must be carefully analyzed and used in order for it to realize its full potential. Business analytics analyze large volumes of data and pull actionable insights from them. It helps in increasing business productivity, faster decision making, better financial performance, and minimizes risk.

Formal education in business analytics is important to equip professionals with the skills and tools needed to draw actionable insights. Investing in business analytics education is a strategic and much-needed move. In this blog, we will explore the best business analytics courses available for freshers in detail.

Understanding business analytics

Business analytics is the process of transforming data into valuable information that helps companies make wise decisions. Numerous approaches are used in it, such as forecasting, optimization, data management, data visualization, predictive modelling, and data mining.

Business analytics assist the company in formulating strategies and making well-informed decisions based on real information and insights rather than guesswork.

Data analyst, financial analyst, business intelligence analyst, and other positions are among the career options available to specialists in business analytics.

Key skills required for business analytics

Business analytics have formed a key role in the analysis and interpretation of huge volumes of data and the resultant recommendations for performance enhancement. Understanding technology, statistical analysis, data visualization, and the development of programming skills will be very important in being great at this.

As a business analyst, you would be in constant contact with both developers and clients regarding the project expectations and solutions. Critical thinking is another important virtue since this allows a person to take into consideration all of the available information and analyze its viability in order to come up with the best approach that would lead one to an informed decision.

These business analytics courses are designed to create an explicit learning journey that combines theory with practice. You will engage in a number of different styles of learning activities, such as software training and projects, to give you a comprehensive skill set in business analytics roles.

Types of business analytics courses

Certificate courses

Certificate programs are short-term training courses that last for 3-6 months. These programs are intended to assist you in understanding a specific topic or necessary skill.

Some of the popular certificate courses you can pursue are Google Data Analytics Professional Certificate, IBM Data Science Professional Certificate, Harvard Business Analytics Program, and more.

Benefits of these certification courses include:

Skill development
Career advancement
Practical experience
Networking opportunities

Diploma courses

Diploma programs (lasting one to two years) offer more in-depth training and cover a broader range of subjects than certification programs.

Examples of well-regarded diploma courses include a Diploma in Business Analytics and a Diploma in Data Science & Business Analytics.

Degree programs

Bachelor’s and master’s degree programs in business analytics offer comprehensive knowledge. These programs are designed to cater to different career goals, with specialized knowledge and skills in business analytics.

Career opportunities in business analytics

Data analyst: It is a highly demanding career all over the globe. As a data analyst, your role is to focus on data analysis and problem-solving within a business system, or data warehouse.
Business analyst: With the increasing amount of data, the need for business analysts has also surged.
Data scientist: A data scientist collects structured and unstructured data from complex sources, uses computer programming and mathematical models to analyze it, and then passes it to a data analyst for trend identification and decision-making.

Industries that heavily rely on business analytics include:

Finance: Helps in fraud detection, risk management, and customer segmentation
Healthcare: Patient care optimization, streamlining administrative processes, forecasting disease outbreaks, and more.
Retail: Demand forecasting, customer insights, and supply chain optimization.

Companies actively hiring business analytics professionals are:

Amazon
Google
JP Morgan
Citibank
Walmart
McKinsey & Company

Benefits of knowing business analytics for career growth

Here are some of the benefits of understanding business analytics for your career growth.

High demand and job security

Between 2022 and 2032, there is a projected 9.7% growth in the demand for business analysts. Highly qualified business analysts are needed in a variety of industries to evaluate data, make conclusions, and provide recommendations. This field provides opportunities for growth and promotion, competitive pay, and work stability.

Skill development

Business analytics significantly enhances critical thinking, problem-solving, and decision-making skills by providing a structured, evidence-based approach to analyzing data and solving complex problems.

Technical skills like statistical analysis, programming, and data visualization are integral to business analytics. They empower professionals to handle complex data, derive meaningful insights, and communicate findings effectively.

Versatility and adaptability

The ability to work across various industries and roles is one of the key strengths of a career in business analytics. Whether in finance, healthcare, retail, or technology, organizations need to analyze data to drive strategic decisions and optimize operations.

Educational pathways in business analytics

To build a rewarding career in business analytics, you should have a basic education in the field. Various business analyst courses are available for beginners and professionals. Depending on your interest, you can pursue certification, diplomas, or degree programs.

You can take advantage of various online platforms offering business analytics courses, such as Online Manipal, Coursera, edX, Udacity, and more.

Real-world applications of business analytics

Data-driven decision making

Business analytics helps organizations make informed decisions. For example, Coca-Cola uses analytics to evaluate marketing campaigns’ effectiveness, analyzing customer engagement to determine the most effective ROI and refine marketing strategies.

Operational efficiency

Business analytics optimizes business processes and operations. Let’s take the example of Siemens. The company utilizes analytics to optimize energy usage in its industrial operations, implementing energy-saving measures, reducing costs, and promoting sustainability goals.

Market and customer insights

Business analytics helps understand market trends and customer behavior. Netflix employs business analytics to segment its customer base based on viewing habits, demographics, and preferences, thereby enhancing content recommendations, optimizing marketing campaigns, and enhancing customer satisfaction.

Tips for building a career in business analytics

Building a successful career in business analytics requires a mix of practical experience, networking, and staying informed. Here are some key strategies:

Get hands-on experience: Internships and projects that provide hands-on experience and skills should make your resume quite attractive.
Network actively: Putting time into attending industry events and being part of online forums will increase your base of contacts and, therefore, learn about job openings before they are advertised publicly.
Stay current: Be up-to-date with trends and new technologies in business analytics and maintain your competitiveness by reading industry news and completing online courses.

Choosing the right course

When choosing a business analytics course, consider the content, duration, and cost. By carefully evaluating these factors, you can choose a course that offers valuable education, fits your schedule, and offers good value for your investment.

Ensure business analytics course certification from an accredited institution to enhance your resume, demonstrate expertise, and ensure high-quality education, as accredited programs are more likely to be recognized by employers.

Challenges and how to overcome them

Common challenges faced by business analytics professionals include:

Changing business needs
Frequent technological advancements
Lack of stakeholder involvement
Unrealistic expectations
Selecting the right analytical tool
Budget limitations
Data security

Strategies for overcoming these challenges include:

Conduct regular security audits and risk assessments
Seek guidance and stay updated with new tools and technologies
Engage in continuous learning and professional development
Invest in training and certifications for both yourself and your team
Develop strong analytical and critical thinking skills to interpret data better

How to succeed in a business analytics course

Tips for freshers to make the most of their learning experience are:

Set clear goals
Choose the right learning resources
Develop technical and soft skills
Gain practical experience
Network with professionals
Stay curious and keep learning
Seek feedback and mentorship

Conclusion

Business analytics is a dynamic field that evolves rapidly. By carefully selecting business analytics courses that align with your career goals, offer practical experience, and fit your learning preferences, you can build a strong foundation for a successful career in business analytics. The tips above can help you select the right business analyst certification course to prepare for the professional world.

Key takeaways

Data is becoming increasingly important to businesses as a result of major advancements in both technology and business operations.
Business analytics strategies can enhance company performance by identifying patterns, forecasting future trends, and making informed decisions.

How is data science used in finance?

The rise of modern technologies has led to an explosion of data. To use this large amount of data for organizational efficiency, data science is crucial. Data science unlocks data’s hidden potential, driving growth and innovation across various industries, including finance. By leveraging the power of data, finance professionals can reduce risk, generate valuable insights, and make more informed decisions.

Stay tuned to get a better understanding of the application of data science in finance as we dig deeper in this blog.

The role of data science in finance

Data science plays a crucial role in analyzing large volumes of financial data by leveraging a range of techniques and tools to extract meaningful insights and drive decision-making. Data science professionals, with the help of techniques like ETL (Extract, Transform, Load), clean, transform, and merge data from different sources.

Financial institutions now have access to enormous volumes of data that can be evaluated and utilized to make well-informed choices owing to the development of big data in financial industries. These days, financial organizations can use technology like machine learning, predictive analysis, data visualization, and more to forecast stock prices, identify fraudulent activity, and improve investment portfolios.

Applications of data science in finance

Some of the applications of data science in finance is discussed below –

Risk management

Financial risk management is all about protecting organizations from potential threats, including credit and market risk, inflation, and so on. Data science plays a crucial role in identifying potential risks and developing strategies to minimize them.

Risk management data science professionals use predictive models to assess and manage financial risks. For example, machine learning technology helps detect credit card fraud by identifying anomalies in customers’ spending. Some risk management tools and techniques include Value at Risk (VaR), credit risk models, risk-adjusted return metrics, and more.

Fraud detection and prevention

The financial sector is vulnerable to fraud in several ways, such as identity theft, bank account fraud, loan application fraud, money laundering, attempted tax evasion, and speculative trading.

Machine learning systems develop algorithms to find correlations between user behavior and the probability of fraudulent acts by processing enormous datasets with various variables. These algorithms create fraud detection models that automatically identify suspicious transactions, preventing financial losses.

Algorithmic trading

Algorithmic trading executes trades at speeds and volumes that are unmatched by humans. These algorithms use real-time analysis of massive volumes of market data to spot patterns and trends that let them make quick choices on the purchase and sale of assets.

Data science, when combined with algorithmic trading, may increase investment returns while lowering risk. It has also made it possible for financial institutions to create complex trading models that study market trends and automatically execute trades—all of which increase trading profitability and performance.

Credit scoring and loan underwriting

Data science significantly enhances credit scoring and loan underwriting processes by leveraging advanced analytics, machine learning, and big data technologies. It provides more accurate risk assessments, automates processes, personalized offerings, and improves overall decision-making.

Additionally, data science enables lenders to make more informed, data-driven decisions, leading to better risk management and improved customer experiences.

Customer analytics

Knowing their customers’ behavior is essential for every bank or financial services provider to make the best choices. Using data is the most effective way to understand customers.

Market segmentation is a tool that financial data scientists are using more and more to generate highly complicated profiles. Banks, insurance companies, pension funds, and credit card providers can obtain highly accurate insights by combining many data sources and utilizing demographics such as age and geography.

These financial institutions might adjust their direct marketing and customer relationship management strategy in light of these insights. This could include leveraging data to enhance customer service or promote specific products.

Case studies

To understand the applications of data science better, let’s explore its utilization by renowned financial institutions.

American Express

Implementation: To segment customers, target marketing strategies, and detect fraud, American Express makes use of data science. Machine learning is utilized by them to examine spending trends and forecast client actions.

Challenges: Making sure that sensitive consumer data remains secure and private. American Express responded to this by putting strict compliance guidelines and data security procedures in place.

Impact: Personalized offers and recommendations from American Express are now possible because of data science, which has led to an increase in consumer engagement and conversion rates. Losses from fraudulent transactions have also decreased thanks to improved fraud detection technologies.

Goldman Sachs

Implementation: Goldman Sachs uses data science for algorithmic trading, risk management, and portfolio optimization. They apply machine learning to identify trading opportunities and manage investment risks.

Challenges: The complexity of integrating and analyzing large volumes of financial data. Goldman Sachs tackled this by developing advanced analytics platforms and investing in high-performance computing infrastructure.

Impact: The implementation of data science has led to more efficient trading strategies and better risk management. Goldman Sachs has been able to achieve higher returns on investments and manage portfolio risks more effectively.

Challenges and considerations

Data privacy and security concerns

Personal information and financial transactions are just two of the many incredibly sensitive data sets handled by financial firms. Unauthorized entry and data breaches can result in fraud, identity theft, and monetary losses. Financial institutions are also prime targets for cyberattacks, including phishing, ransomware, and hacking.

To avoid this, make sure to obtain customer consent for data collection and usage. You must also protect this data with strong encryption protocols to safeguard against unauthorized access.

Regulatory compliance and ethical considerations

Institutions must adhere to Anti-Money Laundering (AML) laws to prevent money laundering and terrorist financing. In 2023, an estimated $3.1 trillion in illicit money flowed through the global financial system. This highlights the importance of methods like Customer Due Diligence (CDD) and transaction monitoring.

Data science models must be designed to avoid biases that could lead to unfair treatment of individuals based on race, gender, or other protected characteristics. Ensuring that AI and machine learning models are used ethically, particularly in sensitive areas such as credit scoring and loan underwriting, is crucial to maintaining public trust.

The necessity of data science experts and ongoing education

Skilled professionals, including data scientists, data engineers, and cybersecurity experts, play a crucial role in drawing actionable insights, building robust data infrastructure, ensuring data privacy, and adhering to regulatory compliance and ethical considerations.

Thus, continuous learning is vital for keeping up with technological advancements, regulatory changes, and best practices in data science and security.

Future trends in data science for finance

Financial institutions are utilizing AI and quantitative finance tools to make data-driven decisions and obtain important insights.
Banks may begin using cloud technology as a scalable and affordable alternative to harness the power of data to save operational expenses.
The finance sector is changing because of Blockchain technology, which offers improved efficiency, security, and transparency.
Sentiment analysis can facilitate better investing decisions by offering valuable information on investor sentiment and market trends.
The role of data scientists in finance is expanding beyond traditional data analysis to include strategic decision-making, real-time analytics, regulatory compliance, and ethical considerations.
Data scientists in finance will play a critical role in driving innovation, ensuring regulatory adherence, and enhancing customer experiences.

Conclusion

Data science in finance is transforming the industry by providing deeper insights, enhancing decision-making, and driving innovation. Data science integration leads to greater financial results and more effective tactics, as well as increased operational efficiency and compliance.

As technology continues to advance, the role of data science in finance will become even more critical, enhancing the ability of financial institutions to navigate complex and dynamic markets.

Top affordable UG degrees offered by SMU

The pursuit of higher education is a dream for many people and can prove to be a pivotal step in shaping one’s intellectual landscape and professional future. The choice of course will help you get in the right direction, and the university you choose will help you grab the right skills without getting burdened with exorbitant fees.

Whether you are looking for the best UG courses after 12th science, arts, or commerce, you will get them all at a very affordable fee structure at the renowned Sikkim Manipal University (SMU). The Bachelor of Arts (BA), Bachelor of Commerce (BCom), and Bachelor of Science (BSc) are the three types of best UG courses after 12th preferred by students after finishing their schooling under 10+2. Online BA, BCom, and BSc courses from Sikkim Manipal University can pave the path for a bright career while allowing you to conveniently continue your other personal or professional commitments without poking a hole in your pocket with its affordable fee structure.

Also read: Frequently asked questions about online BA

If you are still trying to decide which undergraduate course to pursue after class 12th, stay tuned to learn more about the affordable and best UG courses in India.

SMU’s online BA degree: An overview

The world is going digital, so why do you want to stay behind? Opt for an online Bachelor of Arts (BA) degree course and pursue the same course as taught in the on-campus programs, sparing yourself time for other commitments in life.

An online BA course is a well-crafted undergraduate course that can be pursued in various subjects, including English, Sociology, and Political Science.

The experienced and qualified faculty, in combination with a comprehensive course curriculum, increases the value of any course by manifolds. It is one of the reasons why an online BA course at Sikkim Manipal University through the Online Manipal platform at a very affordable price is considered an investment in one’s future.

Have a look at the main reasons to choose Sikkim Manipal University for pursuing an online BA degree program:

Association with glorious Manipal legacy: Do not leave the golden chance to associate with the glorious Manipal legacy by choosing an online BA course from Sikkim Manipal University through the Online Manipal platform. Manipal Education and Medical Group (MEMG) is a name that is associated with excellence in higher education through top-class universities like Sikkim Manipal University.

Cost-effective degrees: When compared, online degree programs are cheaper than traditional on-campus degree programs, and if you wish to get a more affordable online degree program, then Sikkim Manipal University is the ideal choice for you. An online BA degree program comprising of 6 semesters costs just INR 75,000 with a no-cost EMI starting at INR 2,083 per month. The cost of one semester is INR12,500.

Accreditation and recognition: Sikkim Manipal University is recognized by the UGC and NAAC. Both these accreditations add more credibility to the degrees awarded by the university. Moreover, students who pursue courses from SMU are also widely accepted by employers across the globe.

Flexible class schedule: Offering the ultimate freedom to students to attend live classes at a time and place convenient to them, Sikkim Manipal University is much better than other institutions when it comes to pursuing an online BA degree course. Students can attend not just the live sessions but also doubt-clearing sessions with the expert faculty.

Online BA program curriculum

Analyze your interests and then choose the suitable Bachelor of Arts degree course. In case, you are keen to grasp the key skills taught in three popular BA courses – English, Sociology, and Political Science, then enroll for the unique combination online BA degree course from Sikkim Manipal University that comprises a total of 6 semesters.

The table below will acquaint you with the online BA program curriculum of this combination program:

*Semester Name*	*Semester Subjects*
Semester 1	Indian English Literature Introduction to Sociology Communicative English Natural Science Foundations of Political Science
Semester 2	Environmental Science Functional English Sociology in India World Literature Political Theory
Semester 3	Digital Fluency Rural Sociology Essays and Poetry Academic Writing Comparative Political Analysis
Semester 4	Urban Sociology New Literature in English Women Writing in English Classical Indian Political Thought Literary Terms, Forms, and Movements
Semester 5	Human Rights Social Problems in India Poetry: Evolution, Elements, and Genres Children’s Literature Victorian and Modern Literature
Semester 6	Novels and Short Stories United Nations and Global Conflicts, American Literature Comparative Literature Sociology of Change and Development

Top 5 careers you can explore after online BA

The diverse skills gained during an online BA course from a university like Sikkim Manipal University can help a graduate move forward in a wide range of professional paths. Whether it is an online BA in English, Sociology, or Political Science, each online undergraduate course from Sikkim Manipal Univesity through the Online Manipal platform prepares an individual to emerge a winner in various career fields associated with that specialization.

The table below highlights the top 5 career options after an online BA:

*Job Role*	*Job Description*	*Approximate Salary*
Entrepreneur	Start own business based on personal interest and area of specialization done through an online BA course	INR 9.9 lakh per annum
Content Writer	Writing engaging content for various online platforms or books and magazines	INR 6,70,995 per annum
Social Worker	Helping individuals navigate their personal challenges in society	INR 8 lakh per annum
Human Resource Assistant	Using strong communication and interpersonal skills to build employee and organizational relations	INR 10 lakh – INR 12 lakh per annum
Digital Marketing Specialist	Manage social media marketing and online advertising	INR 7,50,000 per annum

SMU’s online BCom degree: An overview

When looking for the best UG courses after 12th commerce, the first one to click is a Bachelor of Commerce (BCom) degree. Going a step further, you have a flexible option to pursue the same course online at prestigious universities like Sikkim Manipal University (SMU), which offers students and working professionals the opportunity to pursue an online BCom degree program. Worry not about its value; an online BCom degree holds the same value and authenticity as a campus degree for the same course.

An online BCom degree program is one of the most popular undergraduate programs preferred by those from the commerce stream. The key skills taught during this course are all related to finance, management, business, tax, accounting, economics, and marketing.

Options are galore when it comes to pursuing an online BCom course, so why choose Sikkim Manipal University? So, we thought to elaborate a bit on this by highlighting a few points about the benefits of pursuing an online BCom degree program from Sikkim Manipal University:

1. UGC-entitled degrees: Sikkim Manipal University is an NAAC A+ accredited institution. All the degrees rewarded after course completion from this university are UGC-entitled and are valued worldwide.

2. Affordable courses: You can gain the highest quality education at the most affordable fee by opting for an online BCom course from Sikkim Manipal University. The entire course fee for an online BCom at SMU is INR 75,000, and the no-cost EMI starts at INR 2,083 per month, with each of the six semesters costing just INR 12,500

3. Attractive scholarships: If you are a student from Sikkim or the Northeast region, you can avail of up to 30% scholarship for pursuing an online BA course from SMU. Similarly, those with a disability or are defense personnel or alumni of the university can get up to 20% scholarships. While meritorious students can obtain up to 10% scholarships if they have secured 80% or above in 10+2.

4. Advanced digital learning platform: Last but not least, students who choose to pursue an online BA course from Sikkim Manipal University tend to enjoy a completely personalized learning experience through an excellent Learning Management System (LMS). This allows them to access recorded sessions, attend live classes, and easily submit their assignments and projects on a single platform.

Online BCom program curriculum

An Online BCom program from Sikkim Manipal University is a 6-semester course during which the students are taught key skills required to run a business or deal with different aspects of the various popular commercial fields.

The table below will acquaint you with the online BCom program curriculum:

*Semester Name*	*Semester Subjects*
Semester 1	Digital Fluency Corporate Legal Environment Communicative English Financial Accounting – I Business Organization and Management
Semester 2	Functional English Economic Theory Financial Literacy Financial Accounting – II Environmental Studies
Semester 3	Basics of Statistics & Mathematics Personality Development and Leadership Principles of Marketing Corporate Accounting Corporate Governance and Corporate Social Responsibilities
Semester 4	E-Business Management Accounting Cost Accounting Entrepreneurship Development Financial Markets, Institutions and Services
Semester 5	Direct Taxation and GST Law and Practice Principles and Practice of Auditing International Financial Reporting Standards Human Resource Management Fundamentals of Financial Management
Semester 6	Project Work Business Research Methodology International Finance Strategic Financial Management Investment Options and Mutual Funds

Top 5 careers you can explore after online BCom

Earning an online BCom degree from Sikkim Manipal University is like stepping into a treasure trove of career opportunities. With this one undergraduate degree, you can move forth through various in-demand professional fields of commerce. From running a small business to becoming a financial planner, from a marketing manager to an accountant, several promising career avenues await an online BCom graduate from Sikkim Manipal University.

The table below will reveal the top 5 career opportunities for online BCom graduates:

*Job Role*	*Job Description*	*Approximate Salary*
Business Consultant	To assist businesses with a strong understanding of business concepts	INR 7 lakh – 20 lakh per annum
Accountant	Use financial expertise to deal with the company’s accounts	INR 4 lakh – 15 lakh per annum
Small Business Owner	Start a startup or a small business with business acumen gained during an online BCom degree	INR 6 lakh per annum
Marketing Manager	Implement effective marketing strategies and campaigns to grow the business	INR 9 lakh – INR 20 lakh per annum
Tax Consultant	Help people and businesses navigate through the complex tax regulations	INR 6 lakh – 8 lakh per annum

Easy financing options offered by Sikkim Manipal University

Sikkim Manipal University offers the best UG courses at affordable fees without compromising the quality of education. Students can easily avail of financing options with the university’s no-cost EMI, which helps them focus only on their studies without stressing about financing the course. For this, the flexi-payment option allows the students to pay fees in installments.

Conclusion

After going through this post, we assume you know which undergraduate degree is the best for you. The right choice of a UG course is crucial, and so is the right selection of the institution to pursue it. If it is the financial stress that is stopping you from pursuing any of the UG courses mentioned in this post, then avail easy financing options offered by Sikkim Manipal University, like no-cost EMI. The university also offers a range of exciting scholarships for its online programs to support and recognize outstanding students in pursuing the best online courses.

Skills to cultivate in the age of AI

Since its emergence, Artificial Intelligence, or AI, as we popularly know it, has been riding the revolutionary wave all across the globe. Its impact can particularly be felt in various industrial sectors – healthcare, software, digital marketing, and e-commerce – to name a few. The technology has been able to bring in mass automation, thereby improving productivity and efficiency in various work arenas. By creating personalized shopping experiences, AI has even succeeded in creating a never-before shopping feel for customers around the world.

It is only natural to feel threatened whether AI will soon take over each industry and its workers will be rendered jobless. The reality is thankfully not so grim as yet. The new technology can still not replace the value of a human mind nurtured by education. A Bachelor’s or Master’s degree still holds relevance in today’s competitive world and AI cannot replicate the human skills that these courses impart.

This blog highlights the current relevance and importance of pursuing an online BA/MA degree and how these degrees can equip us with the skills required in an AI-led world.

Industries that are AI-proof

We know that AI has successfully brought massive change in various sectors and industries, some of which we pointed out at the beginning of this blog. AI algorithms can analyze data and even provide suggestions to make optimum use of available resources in a business. This helps prevent large-scale wastage and avoid grave errors.

That said, can AI take over every industry in the world? Does technology have the potential to completely replace human effort which has always been regarded as valuable and irreplaceable by businesses worldwide?

The answer is no, it cannot. AI technology is still far from replicating human effort or completely displacing it. Certain industries remain “AI-proof”, as we put it, untouched by the storm that the technology is creating across the globe. Let’s take a look at some of these industries in this section.

Creative Jobs: Creating a piece of art, music, or writing a story – they still require the emotion, empathy, and imagination of a human mind to create a breathtaking effect on the audience. It is difficult for AI technology to replicate the same originality and impact.

Teaching: E-learning may have become the new reality in the face of the COVID-19 pandemic; however, it still cannot replace the place of a teacher in a real classroom. The ambiance, the learning, and the one-on-one interaction between the teacher and the student in a physical school are far different from what technology can ever create. Not to forget, the motivation teachers provide to their students cannot be done by any technology.

Skilled Jobs: Can you imagine AI technology fixing a bulb or tap in your house? Even today, an electrician or plumber is the first person that comes to mind when there is a repair in the house that needs immediate attention. These professionals are skilled in their trade and have the required technical knowledge and attention to detail, something AI cannot match up to.

Nursing: This very profession has empathy at its core – which AI cannot possibly simulate. Nurses care for their patients and empathize with their pain. Besides, there is no scope for automation in this industry since nurses have specialized medical knowledge and need to make complex decisions for the patient’s overall health, which technology cannot automate and simplify.

Social Work: This is another industry that stays AI-proof in today’s times primarily because it thrives on human connection and emotional intelligence – a competence that AI will find difficult to imitate or replace.

Skills you need to learn in an AI-led world

Now that we know which industries are likely to remain untouched by AI, let us look at some of the critical skills we can develop that can be immensely useful in resisting the effect of technology, especially in a world led by AI.

Skill parameters	How they can help compete with AI
Active Learning	Make sure that you are always learning and up-to-date with the recent developments in AI, ML, and other latest technologies. Continuous learning helps you stay on top of the game at all times.
Strong Verbal & Written Communication	Interpersonal skills are one area where AI lacks the intelligence to compete with. The technology does not have the Emotional Quotient (EQ) or social skills that humans possess.
Creativity	AI can help generate innovative ideas but the creativity involved in expressing them in a manner that strikes a chord with your audience is not something that the technology can simulate.
People Management	Developing people management skills can be beneficial as you cannot only understand your clients’ exact requirements through human interaction but also address their complaints and grievances effectively by offering empathy and personalized service. All this is not possible for AI to replicate.
Decision-making	Effective communication can help in this process and also enable a person to understand concerns from the other side before arriving at a crucial decision. Humans, and not AI technology, can do this task best.
Leadership skills	A good leader can understand their team well, motivate them, assess risks, and communicate to achieve common goals. All of these are not possible with the AI technology.

Conclusion

Now, we know that AI is not capable of taking over the industries that have been run by humans for ages. Human effort is not entirely replaceable. The human mind still holds value and potential over that of Artificial Intelligence. Learning and using certain skills can help nourish the human mind more. This is why Sikkim Manipal University (SMU) offers a host of undergraduate and postgraduate study programs through their online BA and online MA degrees. These courses are imparted on the Online Manipal platform to students sitting in the comfort of their homes.

Online BA or MA programs from Sikkim Manipal University are designed to equip students with the desired creative skills to help foster their imagination and innovation to compete with the AI era. All degrees from Sikkim Manipal University are UGC-entitled and equivalent to a campus degree. The university is also accredited by NAAC and its degrees hold recognition and relevance all over India.

Expert tips for excelling in virtual job interviews

In the contemporary professional milieu, virtual job interviews have emerged as an integral part of the recruitment process due to the popularity of digital communication platforms. Navigating the intricacies of virtual job interviews necessitates a nuanced understanding and application of tailored strategies. This article offers expert insights and tips to help you excel in virtual job interviews and secure your dream job.

Also read: Virtual placement drives: Breaking barriers for online students

The surge of virtual job interviews: Unveiling its profound significance

The surge of virtual job interviews signifies a transformative change in the recruitment landscape, propelled by technological progress and the global adoption of remote working mode. Beyond the convenience of transcending geographical barriers, virtual interviews highlight an individual’s adaptability and digital prowess. This shift aligns with the contemporary work environment, where remote or hybrid working models prevail.

Excelling in virtual interviews goes beyond showcasing qualifications; it demonstrates proficiency in digital communication, attention to detail, and the ability to establish connection through screens. Moreover, virtual interviews emphasize the need for enhanced soft skills like effective virtual communication and emotional intelligence.

As this trend continues, candidates must curate a compelling online presence and professionalism across digital platforms. In essence, the rise of virtual job interviews highlight a fundamental shift in recruitment dynamics, emphasizing digital fluency, adaptability, and the evolving nature of professional interactions.

Also read: Virtual hiring is the new normal

Mastering virtual job interviews: Expert tips for success

Excelling in a virtual job interview requires a unique set of skills and strategies that differ from traditional face-to-face interactions. Following are the expert tips to help you navigate and excel in virtual job interviews.

1. Preparing your virtual environment

Selecting the right location: Creating a suitable interview environment is crucial. Choose a quiet space where you won’t be interrupted. Select a neutral and clutter-free background, free from personal items that might divert the interviewer’s attention.

Testing equipment and internet connection: Technical glitches can cause inconveniences in an otherwise flawless interview session. Get your camera, microphone, and internet connection tested beforehand. Make sure the video conferencing platform is updated and familiarize yourself with it. Have a backup device on hand in case of unexpected issues.

2. Polishing your online presence

Reviewing and updating your social profiles: Employers often browse candidates’ social media profiles to gain insights beyond the resume. Review and eliminate any content that can be considered demeaning or unprofessional. Create a LinkedIn presence that supports your professional objectives.

Managing screen names and profile pictures: When using video conferencing platforms, your screen name and profile picture should reflect your professionalism. Use your real name as your screen name and select a profile picture that exudes professionalism and approachability.

3. Crafting a compelling digital persona

Showcasing positive body language: Non-verbal cues speak volumes in a virtual interview. Sit up straight, maintain a good posture, and smile genuinely. Avoid excessive fidgeting or looking away from the camera, as these actions can convey nervousness.

Mastering verbal and non-verbal communication: Clear communication is essential. Speak at a moderate pace, articulate your words, and avoid using excessive casual terms. Use gestures carefully and purposefully to emphasize points. Maintain eye contact by looking directly into the camera, creating a connection with your interviewer.

Also read: Tips for landing your dream job after your online course

4. Preparation and research

Studying the company and role: Thorough research of the organizations is a key to interview success. Understand the company’s values, products or services, recent achievements, and any recent news. Tailor your responses to show that you’re not just interested in any job but genuinely excited about this specific opportunity.

Formulating well-structured responses: Use the STAR method (Situation, Task, Action, Result) to structure your responses to behavioral questions. This framework helps you provide a clear and concise answer that showcases your problem-solving abilities and achievements.

5. Technical proficiency

Familiarizing yourself with the interview platform: Practice using the video conferencing platform to navigate its features. Familiarity reduces the likelihood of technical mishaps during the interview, such as accidentally muting yourself or not being able to share your screen.

Utilizing screen sharing and other tools: If the interview involves sharing your work or a presentation, practice using the screen-sharing feature. Ensure that you’re comfortable with the tools and can seamlessly transition between your camera feed and shared content.

6. Building rapport in a virtual space

Maintaining eye contact through the camera

Creating a sense of connection in a virtual interview can be challenging. Make an effort to look directly into the camera when speaking. This simulates eye contact and enhances the perception of engagement.

Active listening and thoughtful responses: Successful communication is a two-way street, so pay close attention to the interviewer’s questions, and before responding, give yourself some time to collect your thoughts. Craft thoughtful and concise answers that directly address the question asked.

Asking intelligent and relevant questions: Asking insightful questions demonstrates your genuine interest in the role and the company. Inquire about the team’s dynamics, growth opportunities, or the company’s future goals. Thoughtful questions emphasize your commitment to making an informed decision.

7. Handling challenges appropriately

Dealing with distractions and interruptions: Despite your preparations, unforeseen distractions can occur. Inform your household about your interview schedule and request minimal disruptions during that time. If an interruption does occur, address it politely and professionally.

Managing nervousness and anxiety: Interview nerves are common, but they can be managed. Practice deep breathing exercises and visualize a successful interview beforehand. Embrace nervous energy as a sign of your enthusiasm for the role.

8. Follow-up and thank you notes

Sending a personalized thank you email: After the interview, send a thank-you email within 24 hours to express gratitude for the opportunity to interview. Summarize your enthusiasm for the role and reiterate a key point that highlights your suitability for the role.

Expressing gratitude for the opportunity: Conclude your thank-you email with a genuine expression of appreciation for the interviewer’s time and consideration. A gracious conclusion reinforces your professionalism and leaves a positive final impression.

Also read: Strategies for overcoming placement-related anxiety

Wrapping Up

In conclusion, virtual job interviews demand a unique set of skills, blending technical prowess with polished communication and meticulous preparation. By adhering to these expert tips, you can confidently navigate the virtual interview landscape and distinguish yourself as a top candidate poised for success. Remember, every detail, from your virtual environment to your follow-up communication, contributes to a holistic and impressive interview experience.

Also read: Top hiring trends for 2023, and what do they mean for job seekers

How mock interviews enhance your job success

In today’s competitive job market, it is essential for job seekers to stand out from the crowd and present themselves as the best candidate for the position. One way to enhance your job success is through mock interviews, which provide an opportunity to practice and improve your interviewing skills before facing a real job interview. This article will discuss the importance of mock interviews and how they can help you secure your dream job.

Also read: Why placement assistance is crucial for online students: Support and guidance in landing the right job

Ways mock interviews help in job success

Preparing for a job interview can be a nerve-wracking experience, especially if you are unsure about how to present yourself effectively. That’s where mock interviews come in. Here are some key benefits of participating in mock interviews:

Skill enhancement: One of the primary advantages of mock interviews is the chance to practice and refine your interview skills. These simulated interviews allow you to familiarize yourself with common questions, handle challenging situations, and develop effective strategies for different interview formats. Regular practice is essential for honing your responses and becoming more comfortable with the interview process.

Constructive feedback: Mock interviews provide an excellent opportunity to receive constructive feedback from experienced interviewers. By listening to their suggestions and insights, you can identify areas for improvement and work on strengthening your weaknesses. This feedback is invaluable in preparing you for real interviews and increasing your chances of success.

Boosting confidence: Confidence plays a crucial role in interview success. By participating in mock interviews, you can build confidence in your abilities, reduce anxiety about the interview process, and feel more prepared for real interviews. The more comfortable you feel during an interview, the better impression you will make on potential employers.

Preparation for specific job interviews: Mock interviews can help you prepare for specific job interviews by conducting thorough research on the company and the position beforehand. This research enables you to tailor your responses to the company’s needs and demonstrate your understanding of the role and organization. The more knowledgeable you appear during the interview, the more likely you are to be considered for the position.

Identifying weaknesses: Mock interviews help you identify areas where you may need improvement, such as communication skills, body language, or technical knowledge. By recognizing these weaknesses, you can work on strengthening them and enhancing your overall interview performance.

Fine-tuning your responses: Mock interviews offer the opportunity to refine your responses to common interview questions. Through practice and feedback, you can develop compelling and effective answers that highlight your strengths and experiences. This will make you a more attractive candidate to potential employers.

Practicing asking questions: Mock interviews also allow you to practice asking thoughtful questions that demonstrate your interest in the company and the position. By asking relevant and engaging questions, you can show potential employers that you are genuinely engaged and enthusiastic about the opportunity.

Also read: The role of placement support services in bridging the job readiness gap

Tips to crack an interview successfully

. Here are some tips to help you prepare and successfully crack an interview:

Research the company and position: Before your interview, invest time in researching the company and the specific position you are applying for. Familiarize yourself with the company’s mission, values, products or services, and culture. This knowledge will enable you to tailor your responses accordingly and demonstrate your understanding of the role.

Go through common interview questions: Prepare for your interview by reviewing common interview questions and practicing your responses. Be ready to provide examples that showcase your skills and experiences. Consider using the STAR method (Situation, Task, Action, Result) to provide real-life examples that effectively highlight your abilities.

Dress appropriately: Regardless of whether your interview is in-person or virtual, it is crucial to dress appropriately, taking into account the company culture and dress code. Your attire should reflect professionalism and create a positive impression on the interviewer.

Arrive early: Punctuality is essential for any job interview as it highlights your professionalism. Aim to arrive at least a bit early to allow yourself time to check in, use the restroom, and compose yourself before the interview. This demonstrates your respect for the process and helps you start on the right foot.

Carry all the necessary documents and materials: Before the interview, gather all your essential documents and materials, such as an updated resume, a notebook for note-taking, and any other relevant documents like a portfolio or references. Having these items on hand showcases your preparedness and professionalism.

Maintain eye contact and demonstrate professionalism: During the interview session, make a conscious effort to maintain appropriate eye contact with the interviewer. This shows that you are engaged and attentive. Additionally, exhibit professionalism in your demeanor and responses, emphasizing your preparedness and ability to handle yourself well in a professional setting.

Follow-up after the interview: Following the interview, it is important to express gratitude by sending a thank-you note or email to the interviewer within 24 hours. Use this opportunity to thank them for their time, reiterate your interest in the position, and leave a positive impression of your professionalism and enthusiasm.

Also read: Are you industry-ready? How placement training can help you stand out

Online Manipal provides placement training to help students crack job interviews and land their dream jobs

Online Manipal is a platform through which esteemed institutions such as Manipal University Jaipur, Manipal Academy of Higher Education, and T.A. Pai Management offer online courses. Besides offering high-quality education, Online Manipal provides placement support services which include mock interviews as well. Additionally, learners are provided with resume-building and LinkedIn profile-creation sessions to help students display their strengths, knowledge, and experiences to employers to help them land their desired roles. Expert career counselors guide students and assist them in carving out their career paths and altering their professional journeys to help them become skilled professionals.

Also read: How online learning bridges the skills gap and improves placement outcomes