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Master Data Science with R & Python
Course Content
Coding will be on R & Python
Module 1 : Basic of R & Python Duration : 10 Hrs
1. Why R & Python ?
2. R & Python Installation
3. Basics of R & Python
4. Package Installation / Libraries
5. Operators in R
6. Data Types in R & Python
7. Importing Data in R & Python
8. Plots in R & Python
9. Descriptive Statistics
Module 1 : Basic Statistics Duration : 10 Hrs
1. Introduction to Data Science.
2. Data Types.
3. Central Tendency.
4. Probability.
5. Normal Distribution & Standardization.
6. Sampling Theory.
7. Assignments
Module 2 : Adv. Statistics Duration : 10 Hrs
1. Z-Distribution & T- Distribution.
2. Hypothesis Testing.
3. Correlation Analysis.
4. Regression Types.
5. Assignments
Module 3 : Data Mining (Supervised) Duration : 10 Hrs
1. Linear Regression.
2. Logistic Regression.
3. Decision Tree.
4. Random Forest.
5. K- Nearest Neighbor.
6. Naïve Bayes.
7. Assignments.
Module 4 : Data Mining (Unsupervised) Duration : 10 Hrs
1. Hierarchical Clustering.
2. K-means Clustering.
3. Dimension Reduction.
4. Random Forest.
5. Market Basket Analysis.
6. Assignments.
Module 5 : Timeseries / Forecasting Duration : 10 Hrs
1. What is Forecasting?
2. Why Forecasting ?
3. Forecasting Strategies ?
4. Plots.
5. Partitioning.
6. Model Building.
7. Evaluating.
Module 6 : Project Work
1. One Supervised Learning Project
2. One Unsupervised Learning Project
3. Dimension Reduction and Regression Project.

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Course content dsfs

  • 1. Master Data Science with R & Python Course Content Coding will be on R & Python Module 1 : Basic of R & Python Duration : 10 Hrs 1. Why R & Python ? 2. R & Python Installation 3. Basics of R & Python 4. Package Installation / Libraries 5. Operators in R 6. Data Types in R & Python 7. Importing Data in R & Python 8. Plots in R & Python 9. Descriptive Statistics Module 1 : Basic Statistics Duration : 10 Hrs 1. Introduction to Data Science. 2. Data Types.
  • 2. 3. Central Tendency. 4. Probability. 5. Normal Distribution & Standardization. 6. Sampling Theory. 7. Assignments Module 2 : Adv. Statistics Duration : 10 Hrs 1. Z-Distribution & T- Distribution. 2. Hypothesis Testing. 3. Correlation Analysis. 4. Regression Types. 5. Assignments Module 3 : Data Mining (Supervised) Duration : 10 Hrs 1. Linear Regression. 2. Logistic Regression. 3. Decision Tree. 4. Random Forest. 5. K- Nearest Neighbor. 6. Naïve Bayes. 7. Assignments. Module 4 : Data Mining (Unsupervised) Duration : 10 Hrs 1. Hierarchical Clustering. 2. K-means Clustering. 3. Dimension Reduction. 4. Random Forest. 5. Market Basket Analysis. 6. Assignments. Module 5 : Timeseries / Forecasting Duration : 10 Hrs
  • 3. 1. What is Forecasting? 2. Why Forecasting ? 3. Forecasting Strategies ? 4. Plots. 5. Partitioning. 6. Model Building. 7. Evaluating. Module 6 : Project Work 1. One Supervised Learning Project 2. One Unsupervised Learning Project 3. Dimension Reduction and Regression Project.