Study Material / Data Analytics with Python (April 2026)
🐍 Data Analytics with Python
We are looking forward to sharing many exciting stories and examples of analytics with all of you using python programming language. This course includes examples of analytics in a wide variety of industries, and we hope that students will learn how you can use analytics in their career and life. One of the most important aspects of this course is that you, the student, are getting hands-on experience creating analytics models; we, the course team, urge you to participate in the discussion forums and to use all the tools available to you while you are in the course!
Last updated in April 2026.
Week 1
Introduction to data analytics and Python fundamentals
- Introduction to data analytics
- Python Fundamentals - I
- Python Fundamentals - II
- Central Tendency and Dispersion - I
- Central Tendency and Dispersion - II
- Important Data Files
Week 2
Introduction to probability
- Introduction to Probability- I
- Introduction to Probability- II
- Probability Distributions - I
- Probability Distributions - II
- Probability Distributions - III
Week 3
Sampling and sampling distributions
- Python Demo for Distributions
- Sampling and Sampling Distribution
- Distribution of Sample Means, population, and variance
- Confidence interval estimation: Single population - I
- Confidence interval estimation: Single population - II
Week 4
Hypothesis testing
- Hypothesis Testing- I
- Hypothesis Testing- II
- Hypothesis Testing- III
- Errors in Hypothesis Testing
- Hypothesis Testing: Two sample test- I
- Important Data Sets
Week 5
Two sample testing and introduction to ANOVA
- Hypothesis Testing: Two sample test- II
- Hypothesis Testing: Two sample test- III
- ANOVA - I
- ANOVA - II
- Post Hoc Analysis(Tukey’s test)
- Important Data files
Week 6
Two way ANOVA and linear regression
- Randomize block design (RBD)
- Two Way ANOVA
- Linear Regression - I
- Linear Regression - II
- Linear Regression - III
- Important Data files
Week 7
Linear regression and multiple regression
- Estimation, Prediction of Regression Model Residual Analysis
- Estimation, Prediction of Regression Model Residual Analysis - II
- MULTIPLE REGRESSION MODEL - I
- MULTIPLE REGRESSION MODEL - II
- Categorical variable regression
- Important data Files
Week 8
Concepts of MLE and Logistic regression
- Maximum Likelihood Estimation- I
- Maximum Likelihood Estimation- II
- LOGISTIC REGRESSION- I
- LOGISTIC REGRESSION- II
- Linear Regression Model Vs Logistic Regression Model
- Important data files
Week 9
ROC and Regression Analysis Model Building
- Confusion matrix and ROC- I
- Confusion matrix and ROC- II
- Performance of Logistic Model-III
- Regression Analysis Model Building - I
- Regression Analysis Model Building (Interaction)- II
- Important data files
Week 10
c² Test and introduction to cluster analysis
- Chi - Square Test of Independence - I
- Chi-Square Test of Independence - II
- Chi-Square Goodness of Fit Test
- Cluster analysis: Introduction- I
- Clustering analysis: part II
- Important data files
Week 11
Clustering analysis
- Clustering analysis: Part III
- Cluster analysis: Part IV
- Cluster analysis: Part V
- K- Means Clustering
- Hierarchical method of clustering -I
- Important data files
Week 12
Classification and Regression Trees (CART)
- Hierarchical method of clustering- II
- Classification and Regression Trees (CART : I)
- Measures of attribute selection
- Attribute selection Measures in CART : II
- Classification and Regression Trees (CART) - III
While all answers are verified using multiple sources, 100% accuracy cannot be guaranteed. Please independently verify the answers before submitting assignments. If you find any discrepancy in any of the question, option or answer, please report it here.
Courses
Cloud ComputingForests and their ManagementEducation for Sustainable DevelopmentIntroduction to Internet of ThingsMarketing AnalyticsConservation EconomicsIntroduction to Cognitive PsychologyIntroduction to Industry 4.0 and Industrial Internet of ThingsIntroduction to Political TheoryData Analytics with PythonHuman Computer InteractionLeadership and Team EffectivenessFood Microbiology for Safe and Sustainable Food SystemsNanotechnology In Agriculture
Made by Arsh
Copyright © 2026 Arsh Saxena.
Made with 
in India.
in India.