Data Science and Machine Learning using Python 

Day-1 An Introduction to Python.

A Brief History of Python Versions

Installing Python

Variables

Local & Global Variables

Data Types

Dynamic Types

Python Reserved Words

Naming Conventions

Your First Python Program

How Python Code Gets Executed

Difference between Compiler and Interpreter

 

Day-2 Basic Python Syntax
Instruction/Statement

Basic Syntax Comments

Receiving Input

Type Conversion/Casting

Numeric Data Types

Formatted Float

Boolean Data Types

Swapping

Strings

Formatted Strings

String Methods

 

Day-3 Language Components
Arithmetic Operations

Operator Precedence

Math Functions

Indentation

If Statements

Logical Operators

Letter Grade Program

Comparison/Relational/Conditional Operators

Leap Year Program

Assignment Operators

Ternary Operators

Weight Converter Program

 

Day-4 Loop

While Loops

Break & Continue Statement

Sum of n Numbers Program

Building a Guessing Game

Building the Car Game

For Loops

For-While Comparison

For with Range Function

Nested Loops

 

Day-5 List

Lists

Bubble Sort 

List Methods

Range Function in a list

2D Lists/Matrix

Tuples

Unpacking/Comparing

Set (Union/Intersection/Difference)

Dictionaries

 

Day-6 Functions

Functions

Parameters/Arguments

Keyword Parameters/Arguments

Default Parameter Value

xargs and xxargs

Return Statement

Lambda Function

Map and Filter function

List Comprehensions

Zip Function

Recursion

Debugging

Exception Handling

                       

Day-7 Classes, Objects & Method

Object Oriented Programing (OOP)

Classes

Objects

Introducing Method

Default Constructors
Parameterized Constructor

Pass Statement

Class/Static Variable

Instance Variable

Class Method

Static Method

Instance Method

Intro to Inheritance
Method Overloading
Method Overriding

Magic Method

 

Day-8 Data Analysis using NumPy-Part 1

A brief introduction

Installation instructions.

NumPy arrays

Built-in methods

Array methods and attributes.

Indexing, slicing

 

Day-9 Data Analysis using NumPy-Part 2

Broadcasting

Boolean masking

Arithmetic Operations

Universal Functions

Exercises Overview

Exercises Solutions

Day-10 Data Analysis using Pandas-Part 1

A brief introduction and installation instructions.

Pandas Introduction.

Pandas Data Structures - Series

Pandas Data Structures - DataFrame

Hierarchical Indexing

Handling Missing Data

Data Wrangling - Combining, Merging, Joining, Group by

Useful Methods and Operations

 

Day-11 Data Analysis using Pandas-Part 2 (Project)

Project 1 (Overview) Customer Purchases Data

Project 1 (Solutions) Customer Purchases Data

Project 2 (Overview) Chicago Payroll Data

Project 2 (Solutions Part 1) Chicago Payroll Data

Project 2 (Solutions Part 2) Chicago Payroll Data

 

Day-12 Data Visualization using Matplotlib

Part 1 - Basic Plotting & Object Oriented Approach

Part 2 - Basic Plotting & Object Oriented Approach

Part 3 - Basic Plotting & Object Oriented Approach

(Project)-Exercises Overview

(Project)-Exercises Solutions

(Optional) – Advance

 

Day-13 Data Visualization using Seaborn

Introduction & Installation

Distribution Plots

Part 1-Categorical Plots

Part 2-Categorical Plots

Axis Grids

Matrix Plots

Regression Plots

Controlling Figure Aesthetics

Exercises Overview

Exercise Solutions

 

Day-14 Data Visualization using pandas

Pandas Built-in Data Visualization

Pandas Data Visualization Exercises Overview

Panda Data Visualization Exercises Solutions

 

Day-15 Interactive & geographical plotting using Plotly and Cufflinks

Interactive & Geographical Plotting (Part 1)

Interactive & Geographical Plotting (Part 2)

Interactive & Geographical Plotting Exercises (Overview)

Interactive & Geographical Plotting Exercises (Solutions)

 

Day-16 Capstone Project - Data Analysis & Visualization

Oil vs Banks Stock Price during recession (Overview)

Oil vs Banks Stock Price during recession (Solutions Part 1)

Oil vs Banks Stock Price during recession (Solutions Part 2)

Oil vs Banks Stock Price during recession (Solutions Part 3)

Emergency Calls from Montgomery County, PA (Overview)

 

Day-17 Machine Learning - Introduction to Machine Learning

Introduction to ML - What, Why

Machine Learning Applications

Supervised Learning

Unsupervised Learning

Machine Learning with Python

What is Machine Learning Model?

Training and Test Sets: Splitting Data

Underfitting and Overfitting

 

Day-18 Machine Learning -Linear Regression- K Nearest Neighbors

Linear Regression Theory

Linear Regression Algorithm

Linear Regression Pen & Paper Exercise

K Nearest Neighbors Theory

K Nearest Neighbors Algorithm

K Nearest Neighbors Pen & Paper Exercise

 

Day-19 Machine Learning - scikit-learn - Linear Regression Model

Linear Regression Model, No Free Lunch, Bias Variance Tradeoff

A note on student’s concerns and questions on Future Warnings

Linear Regression Model - Hands-on (Part 1)

Linear Regression Model Hands-on (Part 2)

How to save and load your trained Machine Learning Model

(Project)-Linear Regression Model (Insurance Data Project Overview)

(Project)-Linear Regression Model (Insurance Data Project Solutions)

 

Day-20 Machine Learning - scikit-learn - K Nearest Neighbors

K Nearest Neighbors, Curse of dimensionality

K Nearest Neighbors - Hands-on

(Project)-K Nearest Neighbors (Project Overview)

(Project)-K Nearest Neighbors (Project Solutions)

 

Day-21 Machine Learning - scikit-learn - Principal Component Analysis (PCA)

Principal Component Analysis (PCA)

Principal Component Analysis (PCA) - Hands-on

(Project)-Principal Component Analysis (PCA) - (Project Overview)

(Project)-Principal Component Analysis (PCA) - (Project Solutions)

 

Day-22 Machine Learning - Decision Tree and Random Forests

Decision Tree Theory

Decision Tree Algorithm

Decision Tree Pen & Paper Exercise

Random Forests Theory

Random Forests Algorithm

 

Day-23 Machine Learning - scikit-learn - Decision Tree and Random Forests

D-Tree & Random Forests, Splitting

Entropy, IG, Bootstrap, Bagging

Decision Tree and Random Forests - Hands-on

(Project)-Decision Tree and Random Forests (Overview)

(Project)-Decision Tree and Random Forests (Solutions)

 

Day-24 Machine Learning-Support Vector Machines -K Means Clustering

Support Vector Machines Theory

Support Vector Machines Algorithm

K Means Clustering Theory

K Means Clustering Algorithm

K Means Clustering Pen & Paper Exercise

 

Day-25 Machine Learning - scikit-learn -Support Vector Machines (SVMs)

Support Vector Machines (SVMs)

Support Vector Machines - Hands-on (SVMs)

(Project)-Support Vector Machines (Project 1 Overview)

(Project)-Support Vector Machines (Project 1 Solutions)

(Project)-Support Vector Machines (Optional Project 2 - Overview)

 

Day-26 Machine Learning - scikit-learn - K Means Clustering

K Means Clustering, Elbow method

K Means Clustering - Hands-on

(Project)-K Means Clustering (Project Overview)

(Project)-K Means Clustering (Project Solutions)

 

 

Curriculum