CH 01 – Overview

·         Introduction and basics of Data Science.

·         Python Basics Refresher.

CH 02 – Data Analytics Overview

·         Data Analytics Process

·         Knowledge Check

·         Exploratory Data Analysis (EDA)

·         Quiz

·         EDA-Quantitative Technique

·         EDA - Graphical Technique

·         Data Analytics Conclusion or Predictions

·         Data Analytics Communication

·         Data Types for Plotting

·         Data Types and Plotting

CH 03 – Statistics Overview

·         Introduction to Statistics

·         Statistical and Non-statistical Analysis

·         Major Categories of Statistics

·         Statistical Analysis Considerations

·         Population and Sample

·         Statistical Analysis Process

·         Data Distribution

·         Dispersion

·         Histogram

·         Testing

·         Correlation and Inferential Statistics

Ch 04 – Environment Setup

·         Anaconda

·         Installation of Anaconda Python Distribution (contd.)

·         Data Types with Python

·         Basic Operators and Functions

Ch 05 – Mathematical Computation with Python

·         Introduction to Numpy

·         Activity-Sequence it Right

·         Class and Attributes of ndarray

·         Basic Operations

·         Activity-Slice It

·         Copy and Views

·         Mathematical Functions of Numpy

CH 06 – Scientific Computing with Python

·         Introduction to SciPy

·         SciPy Sub Package - Integration and Optimization

·         SciPy sub package

·         Demo - Calculate Eigenvalues and Eigenvector

·         SciPy Sub Package - Statistics, Weave and IO

CH 07 – Data Manipulation with Pandas

·         Introduction to Pandas

·         Understanding DataFrame

·         View and Select Data Demo

·         Missing Values

·         Data Operations

·         File Read and Write Support

·         Knowledge Check-Sequence it Right

·         Pandas Sql Operation

CH 08 – Machine Learning

·         Machine Learning Approach

·         Understand data sets and extract its features

·         Identifying problem type and learning model

·         How it Works

·         Train, test and optimizing the model

·         Supervised Learning Model Considerations

·         Supervised Learning Models - Linear Regression

·         Supervised Learning Models - Logistic Regression

·         Unsupervised Learning Models

·         Pipeline

·         Model Persistence and Evaluation

CH 09 – Natural Language Processing( NLP)

·         NLP Overview

·         NLP Applications

·         NLP Libraries-Scikit

·         Extraction Considerations

CH 10 – Data Visualization in Python using MatPlotLib

·         Introduction to Data Visualization

·         Line Properties

·         (x,y) Plot and Subplots

·         Types of Plots

Ch 11- Web Scrapping with BeautifulSoup

Web Scraping and Parsing

·         Understanding and Searching the Tree

·         Navigating options

·         Demo3 Navigating a Tree

·         Modifying the Tree

·         Parsing and Printing the Document

CH 12 -Python Integration with Hadoop MapReduce and Spark

·         Why Big Data Solutions are Provided for Python0

·         Hadoop Core Components

·         Python Integration with HDFS using Hadoop Streaming

·         Demo 01 - Using Hadoop Streaming for Calculating Word Count

·         Python Integration with Spark using PySpark

·         Demo 02 - Using PySpark to Determine Word Count