Data Science
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Data Science Syllabus — Unit-wise Content
The official GTU curriculum for Data Science (subject code 3151608) as prescribed for CS-Cyber · Semester 5 divides the course into 4 units, covering 6 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (3151608)Unit 1 Introduction to Business Analytics
- Why Analytics Business Analytics: The Science of Data-Driven Decision Making Descriptive Analytics Predictive Analytics Prescriptive Analytics Descriptive
- Predictive and Prescriptive Analytics Techniques Big Data Analytics Web and Social Media Analytics Machine Learning Algorithms Framework for Data-Driven Decision Making Analytics Capability Building Roadmap for Analytics Capability Building Challenges in Data-Driven Decision Making and Future
Unit 2 Descriptive Analytics
- Introduction to Descriptive Analytics Data Types and Scales Types of Data Measurement Scales Population and Sample Percentile
- Decile and Quartile Measures of Variation Measures of Shape − Skewness and Kurtosis Subject Code: 3151608 w.e.f
Unit 3 Introduction to Probability
- Introduction to Probability Theory Probability Theory – Terminology Fundamental Concepts in Probability – Axioms of Probability Application of Simple Probability Rules – Association Rule Learning Bayes’ Theorem Random Variables Probability Density Function (PDF) and Cumulative Distribution Function (CDF) of a Continuous Random Variable Binomial Distribution Poisson Distribution Geometric Distribution Parameters of Continuous Distributions Uniform Distribution Exponential Distribution Chi-Square Distribution Student’s t-Distribution F-Distribution
Unit 4 Sampling and Estimation
- Introduction to Sampling Population Parameters and Sample Statistic Sampling Probabilistic Sampling Non-Probability Sampling Sampling Distribution Central Limit Theorem (CLT) Sample Size Estimation for Mean of the Population Estimation of Population Parameters Method of Moments Estimation of Parameters Using Method of Moments Estimation of Parameters Using Maximum Likelihood Estimation 04 15 5 simple Linear Regression Introduction to Simple Linear Regression History of Regression–Francis Galton’s Regression Model Simple Linear Regression Model Building Estimation of Parameters Using Ordinary Least Squares Interpretation of Simple Linear Regression Coefficients Validation of the Simple Linear Regression Model Outlier Analysis Confidence Interval for Regression Coefficients b0 and b Confidence Interval for the Expected Value of Y for a Given X Prediction Interval for the Value of Y for a Given X 04 10 Logistic Regression Introduction – Classification Problems Subject Code: 3151608 w.e.f
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