Advanced Python for Data Science and Machine Learning (PYTN104)

COURSE CONTENTS 

Module 1 – Python Programming

• Install Anaconda, Spyder
• Hello Python and Know the Environment
• Variable Types and Operators
• Decision Making – If-Else
• While Loops and For Loops
• Lists Basic Operations
• Slicing Multidimensional Lists
• Python Tuples
• Access the Dictionary Data
• Dictionary Methods and Functions
• Open and Read Files
• Process Data and Write to Files
• Process Data Using Loops
• Example Python Project

 Module 2 – Introduction to Data Science and Machine Learning

• Type of Analytics
• Data Science Project Lifecycle
• What Does a Data Scientist Do and the Skill Required?

 Module 3 – Essential Statistics

• Understanding the Data and Its Elements
• Measure of Central Tendency using Mean, Median, Mode
• Measure of Dispersion using Standard Deviation and variance
• Measure of Dispersion using Percentile, Range and IQR

 Module 4 – Data Visualization

• Importance of Data Visualization
• Frequency Table, Histogram and Bar Chart
• Understanding Boxplot for Numerical Data
• Creat Line Plots
• Understand Plot Figure Menu
• Create Bar Chart
• Create Histogram of Data
• Plotting Blox Plot
• Data Visualization for Categorical Data
• Pie Charts
• Scatter Plots
• MatplotLib Figures for Creating Multiple Plots
• Subplots for Plotting Multiple Plots in One Figure
• Customization of Plot Elements

 Module 5 – Data Pre-Processing

• Import Library to Read and Slice the Data
• Understand the Data You are Dealing With
• Handling Missing Values
• Label-Encoding for Categorical Data
• Hot-Encoding for Categorical Data
• Data Normalization Using Standard Scaler and minmax
• Train and Test Data Split

 Module 6 – Simple Linear Regression

• What is Simple Linear Regression
• Ordinary Least Squre and Regression Errors
• Test the Model and Predict Y Values
• R-Squared and Its Importance
• Score and Get Coefficients
• Calculate RMSE (Root Mean Squared Error)
• Plot the Predictions

 Module 7 – Multiple Linear Regression

• Understanding the Multiple Linear Regression
• Degrees of Freedom
• Adjusted R-Squared
• Plot Autocorrelation
• Create Shifted or TimeLag Data
• Normality of Residuals

Module 8 – Decision Trees

• Intuition Behind Decision Trees
• Adult Income Prediction Problem
• Decision Trees Parameters
• Run and Evaluate Model
• Classification Evaluation Measures
• Adjusting Thresholds
• AUC ROC Curve

 Module 9 – Feature Selection

• F-Test for Regression and Classification
• Chi-Squared Intuition
• Scikitlearn – What are Feature Selection Transforms
• SelectKBest
• SelectPercentile
• Recursive Feature Elimination (RFE)
• Get Feature Importance Score

 Module 10 – Regularization

• Ridge Regression or L2 Penalty
• Plot Ridge Regression Line
• Lasso Regression or L1 Penalty
• Elasticnet Regularization

 Module 11 – Model Selection

• How Cross Validation Works
• Parameter and Implementation of Cross Validation
• Hyperparameter Tuning
• Grid Search and Randomized Search Approach
• GridSearchCV
• Select Best Model
• Randomized Search