How to Land a Job in Data Science and Business Analytics in India

Did you know?

The global data science market is projected to expand from $37.9 billion in 2019 to $230.8 billion by 2026, highlighting the growing importance of data across various industries. And with an average annual salary of ₹13,10,000 for data scientists, it’s clear that a career in this field is not just in high demand but also highly rewarding.

However, while the benefits are great, breaking into data science and analytics requires a strong skill set and a willingness to adapt as technology rapidly evolves. 

In this blog, we will explore the essential steps you need to take to successfully land a job in data science and business analytics. From understanding the job market to learning the foundational skills, we’ll cover everything you need to know to make your entry into this dynamic field. 

Data Science Job Market in India

According to the US Bureau of Labor Statistics, data science jobs are projected to grow by an impressive 36% between 2023 and 2033. 

Meanwhile, India has rapidly emerged as a global powerhouse for data science and analytics, with industries across sectors establishing advanced analytics hubs to use big data for better decision-making, improving efficiency, and driving innovation. Some of the major industries to look at are:

• IT and Technology:
  • Companies like TCS, Infosys, and Wipro have large data teams, which are responsible for handling the solution architecture. These companies use data to find solutions to complex problems and build solutions that align with the company’s product or service.
  • These problems can be seen as a new product or a service that the company needs to launch. For which, the company analyzes data to know the best sales times of the year. 

• E-commerce:
  • Amazon and Flipkart leverage data science to personalize shopping experiences. They recommend products, track shopping habits, and give users a personalized shopping experience. 

• Healthcare:
  • With the rise of telemedicine and health data analytics, healthcare has become a significant sector in the data science domain. In this industry, data is used regularly, often called real-time data. 
  • To track the health records of patients and give them a better understanding of body vitals, data is used with technologies like smartwatches, fit bands, etc.

• Banking and Finance:
  • Analytics are used to identify frauds, risks, and customer segmentation. Security has been a more important concern in this field and to manage risks and analyze threats, data is used.
  • Finance companies often make use of the data for predictive analytics.

Now that we’ve seen how industries are using data science, let’s look at the different roles you can pursue in this field.

Roles and Titles: 

1. Data Analyst

• A Data Analyst job covers collecting, cleaning and analyzing data and after that they identify trends which helps in business decisions. 
• They usually use tools like Excel, SQL and data visualization platforms to create a clear vision report which helps stakeholders to make data-driven decisions. 
• With skills in statistical analysis and data visualization, a data analyst can earn between 4 to 10 LPA.

To learn these tools for free, check out our selection of free courses.

• Excel Free Course
• SQL Free Course
• Data Visualization Free Course

2. Machine Learning Engineer

• Machine learning Engineer works with complex algorithms, implementing machine learning models like linear Regression, decision tree, random forest, support vector machines(SVM) and many more, those models are further used for specific tasks.
• ML engineers have strong programming skills in Python or Java, and have experience in ML frameworks like Tensorflow and PyTorch. 
• The average pay for ML engineers ranges from 6 to 17.3 LPA, depending on the seniority and experience they have.

Explore our collection of free courses to learn these languages at no cost.

• Python Free Course
• Java Free Course

3. Data Scientists

• Data Scientists analyze complex sets of large data and build predictive models to solve business problems. They often conduct exploratory data analysis and communicate with non-technical teams.
• They are proficient in Python or R, statistical methods, machine learning, and popular libraries like Scikit-learn. 
• They pay for Data Scientists typically range between 8 to 20 LPA, depending on the experience and skills they have.

Check out our free courses to master these languages and tools without any fees.

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• Machine Learning Course
• Scikit-learn

4. Data Engineer

• A Data Engineer is responsible for maintaining and designing the infrastructure needed to process large amounts of data. They focus on building seamline data pipelines and ETL processes.
• They are proficient in SQL, Python, and big data tools like Apache Spark, kafka, Cloudera, etc.
• The typical pay range for Data engineers is between 5 to 15 LPA, depending on skills and experience they have.

Avg Salary Trend

The average annual salary of a data scientist in India is 10 LPA, depending on factors like experience level, company, and city. Top-tier cities like Bangalore, Mumbai, and Delhi offer some of the highest salaries for data science roles.

Required Skills for Data Science Jobs

To land a data science job, you need to have a strong foundation in technical and soft skills. Let’s see what all those fundamentals are.

1. Technical skills

• Programming Languages: Python, R, and SQL are essential languages to learn.
• Statistics and Mathematics: Knowledge of probability, statistics, linear algebra, and calculus.
• Machine Learning: Knowledge of machine learning algorithms like decision trees, SVM, and neural networks.
• Big Data Technologies: Familiarity with tools like Hadoop, Spark, and Kafka for efficiently handling large datasets.
• Data Visualization: Experience with Tableau, Power BI, or Matplotlib to create insightful visualizations.

2. Analytical and Problem Solving Skills

Being a data scientist you should be able to think critically and solve complex problems using data-driven approaches. Solving problems related to business, and other aspects of industry can be a handful of skills.

3. Soft Skills

Communication is the ability to change situations and explain technical findings to non-technical stakeholders, which is crucial. Then comes the ability to work with a team; most data science projects are collaborative and require effective communication between team members.

Required Educational Background

While having a degree in computer science, engineering, mathematics, or statistics is advantageous, it is not always mandatory. 

What is important is to show that you have the skills to apply what data scientists use in their line of duty. 

Learning these skills requires time and effort and with correct resources and guidance you can pace-up on this path.

Recommendations for Certifications and Courses

Great Learning has various courses and certifications in the data science field which include:

1. PG Program In Data Science and Business Analytics

• With benefits like learning anytime, anywhere, and weekly online mentorships by experts, you also get a dual certification from the University of Texas at Austin.
• Program comprises 225+hours of learning content, and 15+ tools and languages like Python, SQL, Matplotlib, R, R studio, Jupyter and many more.
• The program offers hands-on case studies on various projects, like Hotel Booking cancellation prediction, Diabetes risk prediction, and Machine predictive maintenance.

2. Masters in Data Science

• This Program offers you a world class curriculum with dual Certification from 2 global universities(Deakin University, UT at Austin).
• With 3300+ hiring companies, you get a 50% average salary hike and dedicated placement and career opportunities.
• The program covers Interview preparation, resume-building sessions, and access to curated jobs.

3. Data Science and Engineering

• Program highlights include 20 weekly online mentorship sessions, Dedicated 1:1 mentorship, and dedicated career support.
• With 7+ hands-on projects and 40+ case studies, you get a PG certificate from Great Lakes.
• 150+ hours of content, 8+ languages, and tools like Seaborn, Numpy, MySQL, Docker, and Flask.

4. MS in Data Analytics

• This is a Study in USA program, with 20 months of learning and a 94% placement rate.
• You get an MS Degree in data analytics from Clark University.
• The program has a 9:1 student to faculty ratio, dedicated program manager, and alumni network of 40,000 members.
• There are 15+ programs and courses in data science with certifications that are recognized globally. Check out the top data science courses on Great Learning.

How to Get Started With Learning These Skills?

To get started with learning, you can follow the steps below:

1. Start with foundational skills and courses: Begin by learning about Python, machine learning, and data analysis through beginner courses. Which will help you to understand the basics well. 
2. Move to more advanced topics: Once comfortable with the basics, progress to more in depth topics like big data and deep learning. You can check online courses to get started with.
3. Learn through practical projects: Make the use of your skills to build some exciting real-life projects guided by tutors to gain hands-on experience.
4. Enroll in an advanced program: To achieve professional skills, move on to an advanced course. Great Learning offers PG and Master’s programs to further advance your skills.
5. Following a structured curriculum: Great Learning programs include hands-on project guidance, covering advanced topics in depth and guidance from experienced professionals. Which is well structured and flexible.

Start your learning journey and enroll now in the  data science and business analytics program.

Building a Strong Portfolio

Your projects are one of the most important assets when applying for a job in the data science and analytics domain. Employers want to see whether you are able to apply your skills on a practical level. And nothing showcases this better than a strong portfolio. Lets see some project ideas you can build on different levels.

For Freshers: 

1. Movie Recommendation System

• Technologies: Python and Scikit-learn. 
• Explanation: Use Pyhton’s library to build a system that recommends movies to users on the basis of their preferences and ratings. With this you will learn how to use personalized suggestions for filtering data.
  Checkout this project for reference.

2. Fake News Detection

• Technologies: Python, NLP(Natural Language Processing).
• Explanation: You can build a model which finds discrepancies between real and fake news articles by using text classification and machine learning algorithms in NLP. you will learn to process data and implementation of ML algorithms.
  Checkout this project for reference.

3. Customer Attrition Prediction

• Technologies: Python and Scikit-learn.
• Explanation: Build a predictive model to identify the rate at which customers are likely to leave a service using historical data data, which can be beneficial for retention strategies. 

For Mid-Level Professionals:

1. Credit Card Fraud Detection

• Technologies: Python, Machine Learning (Random Forest, SVM)
• Explanation: Apply random forest and SVM to analyze the patterns in transaction data which will help to identify fraudulent transactions. You will learn about machine learning algorithms and their implementation.
  Checkout this project for reference.

2. Sales Forecasting Model

• Technologies: Python, Time Series Analysis
• Explanation: You can use historical data to train a time series model, identify seasonality, and predict future trends. You’ll learn how to deal with non-stationary data and evaluate models using metrics like Root Mean Square Error(RMSE).
  Checkout this project for reference.

3. Market Basket Analysis

• Technologies: Python, Apriori Algorithm 
• Explanation: Use association rule learning, analyze the customer buying patterns and recommend products based on those patterns. You will get to know how big ecommerce platforms like amazon and flipkart use these to recommend products.
  Checkout this project for reference.

For Experienced Professionals:

1. Real-Time Traffic Prediction System

• Technologies: Python, Big Data(Spark)
• Explanation: Develop a system that can predict the traffic conditions in real time. You can use the historical data and machine learning models to optimize the routes for drivers, and provide an effective solution for traffic.
  Checkout this project for reference. 

2. Healthcare Predictive Analytics

• Technologies: R/Python, Machine learning
• Explanation: Use patient data to predict health outcomes or disease outbreaks by using advanced analytics, which will improve patient care strategies in the healthcare sector.

3. Conversational AI Chatbot

• Technologies: Python, NLP, TensorFlow
• Explanation: In this project will build an AI chatbot by utilizing the TensorFlow a python library. This chatbot should be capable of understanding and responding to user queries. You can use NLP and machine learning algorithms to achieve this.

A portfolio should include a detailed description of projects you have worked on. Each project should cover the following points: 

1. Problem Statement: What was the problem or business need?
2. Requirements: What tools and technologies were used?
3. Solutions: What approach or algorithm was used in the project?
4. Results: Showcase outcomes with metrics and visualizations.

Preparing for Data Science Interviews

Having skills will make you a valuable asset, but you also need to get familiar with the interview questions and patterns for data science jobs. Data Science interviews vary greatly depending on experience level. Here are some details:

For Freshers:

• Expect questions on basic Python programming, SQL, and statistics.
• Common Questions: 
  • Explain linear regression and its applications.
  • What is a p-value, and why is it important?
  • How do you perform a JOIN operation in SQL?
  • Explain the difference between supervised and unsupervised learning.
  • What are the differences between a list and a tuple in Python?
• Resources: Platforms like HackerRank, Leetcode, and Mock Interviews.

For Mid-Level Professionals:

• Focus on applied machine learning, projects you have worked on, and algorithm optimizations.
• Common Questions:
  • How do you handle overfitting in machine learning models?
  • What is cross-validation, and why is it used?
  • How would you select important features for a model when dealing with a large dataset?
  • Explain the workings of a random forest and how you can tune its hyperparameters.
  • Explain a time when you used machine learning to solve a business problem. What was the impact you made?

For Highly Experienced Professionals:

• You may be asked to design data architectures and discuss deep learning algorithms, big data solutions, and leadership in projects.
• Common Questions:
  • How would you design a scalable solution for a large company using machine learning?
  • What are the differences between LSTM and GRU in deep learning? When would you use one over the other?
  • Describe how you have implemented big data tools like Hadoop or Spark.
  • How do you ensure model interpretability and fairness when dealing with complex algorithms?
  • Can you walk through the architecture of an end-to-end data pipeline you’ve built?

Job Search Strategies

Landing a job in data science not just requires technical skills but also the right strategy to pursue. Below are the Details of good portals, Networking ways, and tips for recruiter outreaching for better chances.

• Job Portals
  • When seeking opportunities, you can utilize platforms like Naukri, LinkedIn, and Analytics Vidhya, which have specialized job listings. These portals not only feature job openings but also offer you different filters that allow you to search for open positions in a much better way. 
  • These platforms are reliable and are trusted by industry recruiters. So, make sure you update your profile on a regular basis to reflect your learnings.
• Networking
  • Mentors can introduce fresh candidates to recruiters and veterans in the field and at top companies, which will help the candidates to secure a great future or, at the very least, provide a helping hand. 
  • You can find mentors by directly approaching them on social platforms, or you can get them through different programs, events, and courses.
• Recruiter Outreach:
  • Recruiters look for those candidates who have relevant skills and are keeping themselves updated. To effectively stand out in a crowd of applicants, you can tailor your resume and cover letter for each specific position you apply for. You can highlight relevant skills and experiences which align with the job requirements. 
  • Also, you can mention your projects and the technologies you have worked on to make a good impact on the recruiter.

Conclusion

The demand for data science and analytics jobs in India is rapidly growing, but breaking into the field requires a combination of the right skills, education, and networking. You need to focus on building your portfolio and connecting with skilled people.

If you are looking to gain a competitive edge, enrolling in a course like Data Science and Business Analytics from Great Learning can give you the advantage you need, providing industry-relevant knowledge, hands-on experience, and expert guidance.

By using these procedures and using the right learning approach, it will not be difficult for someone to secure a job in the ever-growing field.

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