Data Science Using Python Training

Data science with Python:

Lesson 1: Data Science Overview

Data Science

Data Scientists

Examples of Data Science

Python for Data Science

Lesson 2: Data Analytics Overview

Introduction to Data Visualization

Processes in Data Science

Data Wrangling, Data Exploration, and Model Selection

Exploratory Data Analysis or EDA

Data Visualization

Plotting

Hypothesis Building and Testing

Lesson 3: Statistical Analysis and Business Applications

Introduction to Statistics

Statistical and Non-Statistical Analysis

Some Common Terms Used in Statistics

Data Distribution: Central Tendency, Percentiles, Dispersion

Histogram

Bell Curve

Hypothesis Testing

Chi-Square Test

Correlation Matrix

Inferential Statistics

Lesson 4: 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

Variable Assignment

Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting

Creating, accessing, and slicing tuples

Creating, accessing, and slicing lists

Creating, viewing, accessing, and modifying dicts

Creating and using operations on sets

Basic Operators: 'in', '+', '*'

Functions

Control Flow

Lesson 5: Mathematical Computing with Python (NumPy)

NumPy Overview

Properties, Purpose, and Types of ndarray

Class and Attributes of ndarray Object

Basic Operations: Concept and Examples

Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays

Copy and Views

Universal Functions (ufunc)

Shape Manipulation

Broadcasting

Linear Algebra

Lesson 6: 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 7: Data Manipulation with Python (Pandas)

Introduction to Pandas

Data Structures

Series

DataFrame

Missing Values

Data Operations

Data Standardization

Pandas File Read and Write Support

SQL Operation

Lesson 8: 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

Unsupervised Learning Models: Clustering

Unsupervised Learning Models: Dimensionality Reduction

Pipeline

Model Persistence

Model Evaluation - Metric Functions

Lesson 9: 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

Bag of Words

Extraction Considerations

Scikit - Learn Approach Model Training

Scikit - Learn Grid Search and Multiple Parameters

Pipeline

Lesson 10: Data Visualization in Python using Matplotlib

Introduction to Data Visualization

Python Libraries

Plots

Matplotlib Features:

- Line Properties Plot with (x, y)

- Controlling Line Patterns and Colors

- Set Axis, Labels, and Legend Properties

- Alpha and Annotation

- Multiple Plots

- Subplots

Types of Plots and Seaborn

Lesson 11: 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

Lesson 12: Python integration with Hadoop, MapReduce and Spark

Need for Integrating Python with Hadoop

Big Data Hadoop Architecture

MapReduce

ClouderaQuickStart VM Set Up

Apache Spark

Resilient Distributed Systems (RDD)

PySpark

Spark Tools

PySpark Integration with Jupyter Notebook