Online it class  Data Science with Python Syllabus

SECTION I — Python Basics

Lesson 1: Overview

• Why do we need Python?

• Program structure

Environment Setup

• Python Installation

• Execution Types

• What is an interpreter?

• Interpreters vs Compilers

• Using the Python Interpreter

• Interactive Mode

• Running python files

• Working with Python shell

• Integrated Development Environments (IDES)

• Interactive Mode Programming

• Script Mode Programming

Lesson 2 : Basic Concepts

• Basic Operators

• Types of Operator

• Python Arithmetic Operators

• Python Comparison Operators

• Python Assignment Operators

• Python Bitwise Operators

• Python Logical Operators

• Python Membership Operators (in, not in)

• Python Identity Operators (is, is not)

• Python Operators Precedence

• Data Types 

• Variables

• Assigning Values to Variables

• Multiple Assignment

• Python Numbers

• Python Strings

• Accessing Values in Strings

• String Special Operators

• String Formatting Operator

• Triple Quotes

• Built-in String Operations

• Python Lists

• Accessing Values in Lists

• Updating Lists

• Delete List Elements

• Basic List Operations

• Indexing, Slicing, and Matrixes

• Built-in List Functions & Methods

• Python Tuples

• Accessing Values in Tuples

• Updating Tuples

• Delete Tuple Elements

• Basic Tuples Operations

• Indexing, Slicing, and Matrixes

• No Enclosing Delimiters

• Built-in Tuple Functions

• Python Dictionary

• Accessing Values in Dictionary

• Updating Dictionary

• Delete Dictionary Elements

• Properties of Dictionary Keys

• Built-in Dictionary Functions & Methods

Lesson 3:Loops and Decision Making

• if statements

• if…else statements

• nested if statements

• while loop

• for loop

• nested loops

• Loop Control Statements

• 1) break statement

• 2) continue statement

• 3) pass statement

Lesson 4 :Functions

• Defining a Function

• Syntax

• Calling a Function

• Pass by reference vs value

• Function Arguments

• Required arguments

• Keyword arguments

• Default arguments

• Variable-length arguments

• The return Statement

• Scope of Variables

• Global vs. Local variables

Lesson 5: Basic OOPs Concept 

• Creating class in Python

• Documented String 

• Private Identifier 

• Constructor 

• Inheritance 

• Polymorphism 

Lesson 6 : Python Modules and Packages

• Framework vs Packages

• Folium Introduction

• Why are modules used?

• Creating modules

• The import Statement

• The from…import Statement

• The from…import * Statement

• Locating Modules

• The PYTHONPATH Variable

• Namespaces and Scoping

• The dir( ) Function

• The globals() and locals() Functions

• The reload() Function

• Packages in Python

Lesson 7: Advance Python 

• Decorator, Iterator and Generator 

• Anonymous Function 

• Lambda

• Map

• Filter

• Reduce

• Errors and Exception Handling

• Standard exceptions

• Assertions in Python

• The assert Statement

• What is Exception?

• Handling an exception

Section II — Statistics and Data Science Overview

Lesson8: Data Science Overview

• Data Science Disciplines

• Data Science and Business Buzzwords Why are there so many

• What is the difference between Analysis and Analytics

• An Introduction–Business Analytics, Data Analytics, and Data Science 

• Data Science Diagram 

• Introduction — BI, ML and AI 

• Careers in Data Science Fields 

• Data Overview 

• What is Data 

• Measuring Data 

• Measurement of Central Tendency 

• Measurements Dispersion 

• Measurement Quartile 

• Bi-variate Data and Co-variance

• Pearson Correlation Coefficient

• Lesson 9 : Probability 

• What is Probability

• Permutations

• Combinations

• Intersections Unions and Complements

• Independent and Dependent Events

• Conditional Probability

• Addition and Multiplication Rules

• Bayes Theorem

• Lesson 10: Distributions 

• Introduction to Distributions

• Uniform Distribution

• Binomial Distribution

• Poisson Distribution

• Normal Distribution

• Lesson 11:Statistics 

• What is Statistics

• Sampling 

• Central Limit Theorem

• Standard Error

• Hypothesis Testing

• Hypothesis Testing Example Exercise 

• Type 1 and Type 2 Errors

• Students T Distribution

• Practical Example Descriptive Statistics Exercise

• What are Confidence Intervals

• Correlation Matrix

• Lesson 12:Anova

• Introduction to ANOVA

• Two Way ANOVA Overview

• F- Distribution 

• Lesson 13:Chi Square Analysis

• Chi-Square Analysis

• Chi Squared Analysis – Exercise Example

Section III — Python for Data Analysis

• Lesson 14 : Python: Environment Setup and Essentials

• Introduction to Anaconda

• Installation of Anaconda Python Distribution – For Windows, Mac OS, and Linux

• Jupyter Notebook Installation

• Jupyter Notebook Introduction

• Lesson 15:Data Analysis- Numpy

• Introduction to Numpy

• Numpy Array 

• Numpy Indexing 

• Numpy Operations 

• Broadcasting Numpy Array 

• Lesson 16:Data Analysis — Pandas 

• Introduction to Pandas 

• Series 

• Data Frames

• Missing Data

• Groupby

• Operations

• Merging, Joining and concatenating 

• Missing Data 

• Data Input and Output 

• Lesson 17:Pandas Exercise 

• Salaries Exercise 

• Ecommerce Purchases Exercise

• Lesson 18:Numpy Exercise

• Solving Linear System

• Problem Set 

Section IV — Python for Data Visualization

• Lesson 19:Matplotlib

• Introduction 

• Matplotlib Drawing Graph — Histogram, Plotting, Box Plot etc 

• Exercise 

• Lesson 20:Seaborn

• Introduction 

• Distribution 

• Categorical Plots

• Matrix Plots 

• Regression Plots 

• Grids

• Style and Colors

• Exercise 

• Lesson  21:Data Visualization with Pandas

• Pandas Built-in Data Visualization

• Pandas Data Visualization Exercise

• Lesson  22:Data Visualization – Geographical Plotting

• Introduction to Geographical Plotting

• Choropleth Maps – Part 1 – USA

• Choropleth Maps – Part 2 – World

• Choropleth Exercises

• Capstone Project I 

• Calls Data Capstone Project

• Finance Project 

• Lesson 23:Time Series Analysis 

• Pandas for Time Series

• Introduction to Time Series with Pandas

• Date time Index

• Time Re-sampling 

• Time Shifts

• Pandas Rolling and Expanding

• Time Series Analysis

• Introduction to Time Series

• Time Series Basics

• Introduction to Statsmodel

• Capstone Project II

• Stock Market Analysis Project

• Lesson 24:Scientific computing with Python (Scipy)

• SciPy and its Characteristics

• SciPy sub-packages

• SciPy sub-packages –Integration

• SciPy sub-packages – Optimize

• Linear Algebra

• SciPy sub-packages – Statistics

• SciPy sub-packages – Weave

• SciPy sub-packages – I O

• Lesson 25: Data Science with Python Web Scraping
  
  

• Web Scraping

• Common Data/Page Formats on The Web

• The Parser

• Importance of Objects

• Understanding the Tree

• Searching the Tree

• Navigating options

• Modifying the Tree

• Parsing Only Part of the Document

• Printing and Formatting

• Encoding

Section V — Machine Learning 

• Lesson 26: Machine Learning with Python (Scikit–Learn)

• Introduction to Machine Learning

• Machine Learning Approach

• How Supervised and Unsupervised Learning Models Work

• Scikit-Learn

• Supervised Learning Models – Linear Regression

• Supervised Learning Models: Logistic Regression

• K Nearest Neighbors (K-NN) Model

• K Means Algorithm 

• SVMs

• Unsupervised Learning Models: Clustering

• Unsupervised Learning Models: Dimensionality Reduction

• Pipeline

• Model Persistence

• Model Evaluation – Metric Functions

• Lesson 27: Natural Language Processing with Scikit-Learn

• NLP Overview

• NLP Approach for Text Data

• NLP Environment Setup

• NLP Sentence analysis

• NLP Applications

• Major NLP Libraries

• Scikit-Learn Approach

• Scikit – Learn Approach Built – in Modules

• Scikit – Learn Approach Feature Extraction

Section VI: Project Works

Project 1– Board Game Review Prediction— To perform a Linear Regression Analysis by predicting the average reviews in a board game 

Project 2 — Credit Card Fraud Detection — To focus on Anomaly Detection by using probability densities to detect credit card fraud

Project 3 – Stock Market Clustering – Learn how to use the K-means clustering algorithm to find related companies by finding correlations among stock market movements over a given time span

Project 4 – Getting Started with Natural Language Processing in Python – This project will focus on Natural Language Processing (NLP) methodology, such as tokenizing words and sentences, part of speech identification and tagging, and phrase chunking.