Machine Learning/Data Science

Data Science Syllabus

1.) Python for Data Science

 2.) Introduction to Statistics 

Ø Types of Statistics

Ø Analytics Methodology and ProblemSolving Framework

Ø Populations and samples

Ø Parameter and Statistics

Ø Uses of variable: Dependent and Independent variable

Ø Types of Variable: Continuous and categorical variable

3.) Descriptive Statistics

4.) Probability Theory and Distributions

5.) Picturing your Data 

Ø Histogram

Ø Normal Distribution

Ø Skewness, Kurtosis

Ø Outlier detection

6.) Inferential Statistics 

7.) Hypothesis Testing 

8.) Analysis of variance (ANOVA) 

Ø Two sample t-Test

Ø F-test

Ø One-way ANOVA

Ø ANOVA hypothesis

Ø ANOVA Model

Ø Two-way ANOVA

9.) Regression

Ø Exploratory data analysis

Ø Hypothesis testing for correlation

Ø Outliers, Types of Relationship,scatter plot

Ø Missing Value Imputation

Ø Simple Linear Regression Model

Ø Multiple Regression

Ø Model Building and Evaluation

10.)  Model post fitting for Inference 

Ø Examining Residuals

Ø Regression Assumptions

Ø Identifying Influential Observations

Ø Detecting Collinearity

11.)  Categorical Data Analysis

Ø Describing categorical Data

Ø One-way frequency tables

Ø Association

Ø Cross Tabulation Tables

Ø Test of Association

Ø Logistic Regression

Ø Model Building

Ø Multiple Logistic Regression and Interpretation

12.)  Model Building and scoring for Prediction

Ø Introduction to predictive modeling

Ø Building predictive model

Ø Scoring Predictive Model

Ø Introduction to Machine Learning and Analytics

13.)  Introduction to Machine Learning

Ø What is Machine Learning?

Ø Fundamental of Machine Learning

Ø Key Concepts and an example of ML

Ø Supervised Learning

Ø Unsupervised Learning

14.)  Linear Regression with one variable

Ø Model Representation

Ø Cost Function

Ø Parameter Learning

Ø Gradient Descent

15.)  Linear Regression with Multiple Variable

Ø Computing parameter analytically

Ø Ridge, Lasso, Polynomial Regression

16.)  Logistic Regression

Ø Classification

Ø Hypothesis Testing

Ø Decision Boundary

Ø Cost Function and Optimization

17.)  Multiclass Classification

18.)  Regularization

Ø Overfitting, Under fitting

19.)  Model Evaluation and Selection

Ø Confusion Matrix

Ø Precision-recall and ROC curve

Ø Regression Evaluation

20.)  Support Vector Machine

21.)  Decision Tree, Random Forest

22.)  Unsupervised Learning 

Ø Clustering

Ø K-mean Algorithm

23.)  Dimensionality Reduction

Ø Principal Component Analysis and applications

24.)  Introduction to Neural Network