Data Analytics
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Follow on Instagram for UpdatesData Analytics Syllabus — Unit-wise Content
The official GTU curriculum for Data Analytics (subject code ME01033041) as prescribed for C(RE · Semester 1 divides the course into 5 units, covering 52 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 (ME01033041)Unit 1 Introduction to Data Analytics
- The Science of Data-Driven Decision Making
- Prescriptive and Diagnostic Analytics
- Analytics Techniques
- Big Data Analytics
- Machine Learning Algorithms
- Framework for Data-Driven Decision Making Descriptive Analytics: Data Types and Scales
- Population and Sample
- Decile and Quartile
- Measures of Variation
- Measures of Shape − Skewness and Kurtosis
Unit 2 Probability
- Probability Theory
- Axioms of Probability Application of Simple Probability Rules
- Association Rule
- Learning Bayes’ Theorem
- Binomial Distribution
- Poisson Distribution
- Geometric Distribution
- Parameters of Continuous Distributions
- Uniform Distribution
- Exponential Distribution
- Chi-Square Distribution
- Student’s t- Distribution
Unit 3 Sampling and Estimation
- Population Parameters and Sample Statistic
- 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
- Maximum Likelihood Estimation Program Name: Master of Engineering Level: PG Branch: Civil (Water Resource Engineering) Course / Subject Code: ME01033041 Course / Subject Name: Data Analytics w.e.f. 2024-25 http://syllabus.gtu.ac.in/ Page no. 1 of 2
Unit 4 Simple Linear Regression
- Simple Linear Regression Model Building
- Estimation of Parameters Using Ordinary Least Squares
- Interpretation of Simple Linear Regression Coefficients
- Validation of the Model
- Outlier Analysis
- Confidence Interval for Regression Coefficients
- Confidence Interval for the Expected Value of Y for a Given X, Prediction Interval for the Value of Y for a Given X
Unit 5 Logistic Regression
- Classification Problems
- Binary Logistic Regression
- Estimation and Interpretation of Logistic Regression Parameters
- Logistic Regression Model Diagnostics
- Classification Table
- Sensitivity, and Specificity
- Optimal Cut-Off Probability
- Variable Selection
- Application in Credit Rating
- Gain Chart and Lift Chart Decision Trees: Chi-Square Automatic Interaction Detection (CHAID)
- Classification and Regression Tree
- Cost-Based Splitting Criteria
- Ensemble Method
- Random Forest
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