UPES Online | Post Graduate Program in Data Science

• Bachelor's degree or equivalent in any discipline from a recognised university/institution with minimum 50 percent aggregate marks.
• Candidates who have completed their graduation through online learning or open and distance learning mode from any university/institution recognized by the UGC DEB (Distance Education Bureau) are also eligible to apply.

OR

• Candidates who have completed professional qualifications viz. CA, CS or ICWA are eligible to apply.

OR

• Relaxation in eligibility criteria for candidates with work experience.
• Candidates with minimum 2 years of work experience must have Bachelor's degree or equivalent in any discipline from a recognised university/institution with minimum 45 percent aggregate marks.
• Candidates with minimum 4 years of work experience must have Bachelor's degree or equivalent in any discipline from a recognised university/institution with minimum 40 percent aggregate marks.

The duration of the program is 10 Months. Program is divided into six modules. The program is delivered with hands-on learning methods - all important concepts are explained in detail with real-life case studies.

Module I:

• Introduction to Analytics and Data Science
• Introduction to Business Analytics

Module II:

• Fundamentals of Python
• Data Visualization in Python
• Data Preparation and cleaning using Python
• Understanding the data using univariate statistics in Python
• Hypothesis testing and ANOVA in Python to guide decision making

Module III

• Predictive Analytics in Python
• Correlation and Linear regression
• Logistic regression
• Techniques of customer segmentation
• Time series forecasting techniques
• Decision trees and Random Forests
• Boosting Machines
• Cross Validation and Parameter
• Tuning Final Case Study

Module IV

• Introduction to Machine Learning and Python
• Introduction to Python
• Iterative Operations and Functions in Python
• Data Summary; Numerical and Visual in Python
• Data Handling in Python using NumPy and Pandas
• Mathematical Concepts for Machine Learning

Module V:

• Advanced Machine Learning
• Generalised Linear Models in Python
• Tree Models using Python
• Boosting Algorithms using Python
• Support Vector Machines (SVM), Naive
• Bayes and KNN in Python
• Unsupervised learning in Python
• Neural Networks
• Text Mining in Python
• Ensemble Methods in Machine Learning

Module VI

• Data Analysis in SQL
• Introduction to SQL
• SQL Data Types and Operators
• Useful Operations in SQL
• Aggregating Data in SQL
• Writing Sub-Queries in SQL
• Common function in SQL
• Analytic Functions in SQL
• Writing DML Statements
• Writing DDL Statements
• Using Constraints in SQL SQL
• Joins
• Views in SQL