One account
Many possibilites
M.Sc. in Data Analytics is a postgraduate degree meant to provide the students with knowledge in Data Analytics. By and large, it takes two years, divided into four semesters, and comprises analytical training in quantitative skills, software tools, statistical modeling, machine learning techniques and visualization tools for Data Analysts. It prepares students through comprehensive academic education and culminating projects to apply the domain knowledge in order to deal with large databases and analyse them in order to provide recommendations for business strategies or organisational development.
Some of the entrance examinations that are normally considered and allowed for admission to the M.Sc. The entrance tests for admission in the Data Analytics program are GATE, CAT , GRE, XAT, CMAT and state level entrance tests. In these entrance exams, candidates are evaluated on the basis of their arithmetical skills, logical thinking, and analytics skills.
| Highlight | Detail |
|---|---|
| Full Form | Master of Science (Data Analytics) |
| Duration | 2 years |
| Course Level | Postgraduate |
| Eligibility | Bachelor’s degree in Mathematics, Statistics, Computer Science, Engineering, Economics, or related fields with minimum required marks |
| Top Colleges | Indian Institute of Technology (IITs), Indian Statistical Institute (ISI), University of Hyderabad, BITS Pilani, University of Delhi, Chennai Mathematical Institute (CMI) |
1. Graduation in any stream with minimum 50% marks or its equivalent grade in the graduation from any University recognized by the UGC.
2. Preferably the students are from the science and technology majors such as Mathematics, Statistics, Computer Science or Information Technology.
3. A valid score in the competitive management entrance tests like CAT/MAT/XAT or the GATE/NET or the university conducted test.
4. On self-finance, some institutes give direct admission based on marks obtained in the bachelor’s degree or an interview.
5. Possession of prior work experience in any of the fields related to data analytics may be considered as preference during the hiring process.
6. IELTS/TOEFL for non English medium university if graduated University.
| Subject | Important Topics |
|---|---|
| Mathematics for Data Analytics | Linear Algebra, Probability Theory, Statistical Inference, Optimization Techniques |
| Programming for Data Analytics | Python Programming, R Programming, Data Structures and Algorithms, SQL and NoSQL Databases |
| Data Visualization | Data Visualization Principles, Tools and Techniques (Tableau, Power BI), Dashboard Design |
| Data Mining and Machine Learning | Supervised and Unsupervised Learning, Decision Trees, Regression Analysis, Clustering Techniques |
| Big Data Analytics | Hadoop Ecosystem, MapReduce, Spark Framework, Distributed Computing |
| Statistical Methods for Analytics | Hypothesis Testing, Multivariate Analysis, Time Series Analysis, Experimental Design |
| Data Ethics and Privacy | Ethical Issues in Data Analytics, Data Privacy Laws, Bias and Fairness in Machine Learning |
| Business Analytics | Predictive Analytics, Customer Analytics, Marketing Analytics, Supply Chain Analytics |
| Data Warehousing and Business Intelligence | Data Warehouse Concepts, ETL Processes, OLAP, Data Mining in BI |
| Capstone Project | Real-world Data Analytics Project, Problem Identification, Data Cleaning, Analysis and Presentation |
| Specialization | Job Title | Average Salary |
|---|---|---|
| Business Analytics | Business Analyst (Data-driven), Marketing Analyst, Market Research Analyst | 4.5-7 |
| Financial Analytics | Quantitative Analyst, Risk Analyst, Credit Analyst (with data analytics skills) | 5-8 |
| Sports Analytics | Sports Analyst, Performance Analyst (data-driven) | 4-6 |
| Healthcare Analytics | Healthcare Data Analyst, Medical Research Analyst (with data skills) | 4-6.5 |
| Big Data Analytics | Big Data Engineer, Data Architect, Big Data Analyst | 5-8 |
