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Lesson 1:  Data Science Overview
o    Data Science  Data Scientists
o    Examples of Data Science
o     Python for Data Science.

Lesson 2: Data Analytics Overview
o    Introduction to Data Visualization  Processes in Data Science
o     Data Wrangling, Data Exploration, and Model Selection
o    Exploratory Data Analysis or EDA
o    Data Visualization
o    Plotting
o    Hypothesis Building and Testing.

Lesson 3: Statistical Analysis and Business Applications
o    Introduction to Statistics  Statistical and Non-Statistical Analysis
o    Some Common Terms Used in Statistics
o    Data Distribution: Central Tendency, Percentiles, Dispersion
o    Histogram
o    Bell Curve
o    Hypothesis Testing
o    Chi-Square Test
o    Correlation Matrix
o    Inferential Statistics.

Lesson 4:Python: Environment Setup and Essentials
o    Introduction to Anaconda  Installation of Anaconda Python Distribution - For Windows, Mac OS, and Linux
o     Jupyter Notebook Installation
o      Jupyter Notebook Introduction
o      Variable Assignment
o      Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting  Creating, accessing, and slicing tuples
o    Creating, viewing, accessing, and modifying dicts
o    Creating and using operations on sets
o     Basic Operators: 'in', '+', '*'
o    Functions
o    Control Flow.

Lesson 5:  Mathematical Computing with Python (NumPy)
o    NumPy Overview  Properties, Purpose, and Types of ndarray
o    Class and Attributes of ndarray Object
o      Basic Operations: Concept and Examples
o     Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
o    Copy and Views
o     Universal Functions (ufunc)
o    Shape Manipulation
o      Linear Algebra.

Lesson 6:Scientific computing with Python (Scipy)
o    SciPy and its Characteristics  SciPy sub-packages
o    SciPy sub-packages –Integration
o    SciPy sub-packages – Optimize
o    Linear Algebra
o    SciPy sub-packages – Statistics
o    SciPy sub-packages – Weave.

Lesson 7: Data Manipulation with Python (Pandas)
o    Introduction to Pandas  Data Structures
o    Series
o     DataFrame
o     Missing Values
o    Data Operations
o    Data Standardization
o     Pandas File Read and Write Support
o    SQL Operation.

Lesson 8: Machine Learning with Python (Scikit–Learn)
o    Introduction to Machine Learning  Machine Learning Approach
o    How Supervised and Unsupervised Learning Models Work
o    Scikit-Learn
o    Supervised Learning Models - Linear Regression
o    Supervised Learning Models: Logistic Regression
o    K Nearest Neighbors (K-NN) Model
o     Unsupervised Learning Models: Clustering
o    Unsupervised Learning Models: Dimensionality Reduction
o    Pipeline
o    Model Persistence
o    Model Evaluation - Metric Functions.

Lesson 9: Natural Language Processing with Scikit-Learn
o    NLP Overview  NLP Approach for Text Data
o      NLP Environment Setup
o     NLP Sentence analysis
o      NLP Applications
o    Major NLP Libraries
o    Scikit-Learn Approach
o    Scikit - Learn Approach Built - in Modules
o    Scikit - Learn Approach Feature Extraction
o    Bag of Words
o    Extraction Considerations
o    Scikit - Learn Approach Model Training
o    Scikit - Learn Grid Search and Multiple Parameters
o    Pipeline.

Lesson 10: Data Visualization in Python using Matplotlib
o    Introduction to Data Visualization  Python Libraries
o    Plots
o    Matplotlib Features:
o     Line Properties Plot with (x, y)
o     Controlling Line Patterns and Colors
o     Set Axis, Labels, and Legend Properties
o      Alpha and Annotation
o     Multiple Plots
o      Subplots
o    Types of Plots and Seaborn.

Lesson 11: Data Science with Python Web Scraping
o    Web Scraping  Common Data/Page Formats on The Web
o    The Parser
o    Importance of Objects
o    Understanding the Tree
o    Searching the Tree
o    Navigating
o    Modifying the Tree
o    Parsing Only Part of the Document
o    Printing and Formatting
o    Encoding.

Lesson 12: Python integration with Hadoop, MapReduce and Spark