Section 1: Data Science Overview

• Data Science

• Data Analytics & Business Analytics

• Data Scientists / Data Analysts / Business Analysts- What they do?

• Examples of Data Science

• Python for Data Science

Section 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

Section 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

Section 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

Section 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

Section 6: 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

Section 7: 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

Section 8: Machine Learning with Python – Part 1

• 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

Section 9: Regression

Learning objectives: This lesson will take you into your past and help you brush up on those math and statistics concepts highly necessary to understand the Machine Learning algorithms.

• Regression and its types

• Linear regression: Equations and algorithms

Section 10: Classification

Learning objectives: In this lesson, you will learn about classification, logistic regression, K-nearest neighbors, support vector machines, and Naive Bayes.

• Meaning and types of classification

• Logistic regression

• K-nearest neighbors

• Support vector machines

• Kernel support vector machines

• Naive Bayes

• Decision tree classifier

• Random forest classifier

Section 11: Unsupervised learning: Clustering

Learning objectives: In this lesson, you will learn and implement a few more algorithms within the unsupervised learning category.

• Clustering algorithms

• K-means clustering

Section 12: Introduction to Deep Learning

Learning objectives: This last lesson of the course, gives you a peek into the world of deep learning and how it is related to machine learning.

• Meaning and importance of Deep Learning

• Artificial Neural Networks

• TensorFlow

Section 13: Capstone Project 1: Predicting Customer Churn

Churn prediction is essential for businesses as it helps you detect customers who are likely to cancel a subscription, product or service. You will use variety of tools learned in the class to predict the customers who are likely to churn in future.

Section 14: Capstone Project 2: Text Analytics

Text Data from Social Media platforms could be used to analyze various things. It’s one such use is sentiment analysis. You will extract the data from twitter to work on the sentiment analysis.