### Section 01 - Programming Basics and Data Analytics with Python (Self Learning Curriculum)

Preview#### Lesson 1 Learning Objective

##### Course End Objectives

#### Lesson 2 Introduction

16:02Preview##### Learning Objectives

##### Getting Started Analyzing Data in Python

04:14##### Importing and Exporting Data in Python

04:13##### Introduction to Data Analysis with Python

00:50##### Python Packages for Data Science

02:28##### The Problem

01:51##### Understanding the Data

02:26##### Introduction

#### Lesson 3 Data Wrangling

18:50##### Learning Objectives

##### Binning in Python

01:47##### Data Formatting in Python

03:23##### Data Normalization in Python

03:34##### Dealing with Missing Values in Python

05:57##### Indicator variables in Python

02:00##### Pre-processing Data in Python

02:09##### REVIEW DATA WRANGLING

#### Lesson 4 Exploratory Data Analysis

18:23Preview##### Learning Objectives

##### Analysis of Variance (ANOVA)

03:58##### Correlation - Statistics

02:37##### Correlation

02:29##### Descriptive Statistics

04:39##### Exploratory Data Analysis

01:20##### GroupBy in Python

03:20##### REVIEW: EXPLORATORY DATA ANALYSIS

#### Lesson 5 Model Development

26:07Preview##### Learning Objectives

##### Introduction

01:44##### Linear Regression and Multiple Linear Regression

06:34##### Model Evaluation using Visualization

04:44##### Polynomial Regression and Pipelines

04:25##### Measures for In-Sample Evaluation

03:37##### Prediction and Decision Making

05:03##### REVIEW-MODEL DEVELOPMENT

#### Lesson 6 Model Evaluation

22:54Preview##### Learning Objectives

##### Introduction

01:44##### Model Evaluation

07:30##### Overfitting Underfitting and Model Selection

04:20##### Grid Search

04:33##### Model Evaluation and Refinement

00:21##### Ridge Regression

04:26##### MODEL EVALUATION AND REFINEMENT

##### Unlocking IBM Certificate

### Section 02 - Programming Basics and Data Analytics with Python (Live Classes Curriculum)

Preview#### Lesson 01 Course Introduction

##### Course Objectives

##### Course Prerequisites

##### Why Python for Data Analytics?

##### Course Outline

##### Topics Covered

##### Course Features

##### Course-End Project Highlights

##### Learning Outcomes

##### Course Completion Criteria

#### Lesson 02 Introduction to Programming

##### Learning Objectives

##### Program

##### Programming language

##### Algorithm, Pseudo code, and Flowchart

##### Compiler and Interpreter

##### Key Takeaways

#### Lesson 3 Programming Environment Setup

##### Learning Objectives

##### Python

##### Environments for Python

##### Anaconda

##### Installation of Anaconda Python Distribution

##### Jupyter Notebook

##### Assisted Practice: Install Python

##### Assisted Practice: First Python Program

##### Key takeaways

#### Lesson 4 OOPs Concept with Python

##### Learning Objectives

##### Object Oriented Programming Language

##### Objects and classes

##### Methods and attributes

##### Access Modifiers

##### Assisted Practice: Objects and Classes

##### Abstraction

##### Assisted Practice: Abstraction

##### Encapsulation

##### Assisted Practice: Encapsulation

##### Inheritance

##### Assisted Practice: Inheritance

##### Polymorphism

##### Assisted Practice: Polymorphism

##### Key takeaways

#### Lesson 5 Programming Fundamentals of Python

##### Learning Objectives

##### Variables

##### Data Types with Python

##### Assisted Practice: Data Types in Python

##### Keywords and Identifiers

##### Expressions

##### Basic Operators

##### Assisted Practice: Operators in Python

##### Functions

##### Assisted Practice: Search for a Specific Element from a Sorted List

##### Assisted Practice: Create a Banking System Using Functions

##### String Operations

##### Assisted Practice: String Operations in Python

##### Tuples

##### Assisted Practice: Tuples in Python

##### Lists

##### Assisted Practice: Lists in Python

##### Sets

##### Assisted Practice: Sets in Python

##### Dictionaries

##### Assisted Practice: Dictionary in Python

##### Unassisted Practice: Dictionary and its Operations

##### Conditions and Branching

##### Assisted Practices: Check the Scores of a Course

##### While Loop

##### Assisted Practice: Find Even Digit Numbers

##### Unassisted Practice: Fibonacci Series Using While Loop

##### For Loop

##### Assisted Practice: Calculate the Number of Letters and Digits

##### Unassisted Practice: Create a Pyramid of Stars

##### Break and Continue Statements

##### Key takeaways

##### Tic-Tac-Toe Game

#### Lesson 6 File handling, Exception handling, and Package handling

##### Learning Objectives

##### File Handling

##### File Opening and Closing

##### Reading and Writing Files

##### Directories in File Handling

##### Assisted Practice: File Handling

##### Errors and Exceptions

##### Assisted Practice: Exception Handling

##### Modules and Packages

##### Assisted Practice: Package Handling

##### Key Takeaways

##### Student Data Handling

#### Lesson 7 Data Analytics Overview

##### Learning objectives

##### Data Analytics

##### Data Analytics Process

##### Hypothesis

##### Data Visualization

#### Lesson 8 Statistical Computing

##### Learning objectives

##### Statistics

##### Probability Density Function

##### Types of Probability Density Function

##### Central limit theorem

##### Confidence Intervals

##### Hypothesis Testing: Parametric

##### Hypothesis Testing: Nonparametric

##### What is A/B Testing?

##### Case Study: A/B Testing

##### Key takeaways

##### A/B Testing

#### Lesson 9 Mathematical Computing using NumPy

##### Learning objectives

##### NumPy

##### Assisted Practice: Create and Print Numpy Arrays

##### Operations

##### Assisted Practice: Executing Basic Operations in Numpy Array

##### Unassisted Practice: Performing Operations Using Numpy Array

##### Assisted Practice: Demonstrate the Use of Copy and Use

##### Assisted Practice: Manipulate the Shape of an Array

##### Key takeaways

##### Country GDP

##### Olympic 2012 Medal Tally

#### Lesson 10 - Data Manipulation with Pandas

##### Learning Objectives

##### Introduction to Pandas

##### Data Structures

##### Assisted Practice: Create Pandas Series

##### DataFrame

##### Assisted Practice: Create Pandas DataFrames

##### Unassisted Practice: Create Pandas DataFrames

##### Missing Values

##### Assisted Practice: Handle Missing Values

##### Data Operation

##### Assisted Practice: Data Operations in Pandas DataFrame

##### Unassisted Practice: Data Operations in Pandas DataFrame

##### Data Standardization

##### Assisted Practice: Pandas SQL Operations

##### Unassisted Practice: Pandas SQL Operations

##### Key takeaways

##### Analyze the Federal Aviation Authority (FAA) Dataset using Pandas

##### Analyzing the Dataset

#### Lesson 11 - Data visualization with Python

##### Learning objectives

##### Data Visualization

##### Considerations of Data Visualization

##### Factors of Data Visualization

##### Python Libraries

##### Assisted Practice: Create Your First Plot Using Matplotlib

##### Line Properties

##### Assisted Practice: Create a Line Plot for Football Analytics

##### Multiple Plots and Subplots

##### Assisted Practice: Create a Plot with Annotation

##### Unassisted Practice: Create Multiple Plots to Analyze the Skills of the Players

##### Assisted Practice: Create Multiple Subplots Using plt.subplots

##### Types of plots

##### Assisted Practice: Create a Stacked Histogram

##### Assisted Practice: Create a Scatter Plot of Pretest scores and Posttest Scores

##### Assisted Practice: Create a Heat Map to Analyze the Sepal Width, Petal Length, and Petal Width of an Iris Dataset

##### Assisted Practice: Create a Pie Chart

##### Assisted Practice: Create an Error Bar

##### Assisted Practice: Area Chart to Display the Skills of the Players

##### Assisted Practice: Create a Word Cloud of a Random Data

##### Assisted Practice: Create a Bar Chart

##### Assisted Practice: Create Box Plots

##### Assisted Practice: Create a Waffle Chart

##### Seaborn and Regression Plots

##### Introduction to Folium

##### Maps with Markers

##### Kernel Density Estimate Plots

##### Analyzing Variables Individually

##### Key Takeaways

##### Visualize the Sales Data

#### Lesson 12 - Introduction to Model Building

##### Learning objectives

##### Introduction to Machine Learning

##### Machine Learning Approach

##### Scikit-Learn

##### Supervised Learning Models: Linear Regression

##### Assisted Practice: Loading a Dataset

##### Assisted Practice: Linear Regression Model

##### Supervised Learning Models: Logistic Regression

##### Supervised Learning Models: K-Nearest Neighbors

##### Assisted Practice: K-NN and Logistic Regression Models

##### Unsupervised Learning Models: Clustering

##### Assisted Practice: K-Means Clustering to Classify Data Points

##### Unsupervised Learning Models: Dimensionality Reduction

##### Unsupervised Learning Models: Principal Component Analysis

##### Assisted Practice: Principal Component Analysis (PCA)

##### Assisted Practice: Build Pipelines

##### Assisted Practice: Persist a Model for Future Use

##### Key Takeaways

##### Create a Model to Predict the Sales Outcome

##### List the Glucose Level Readings

- Free Course
### Python for Data Science

Preview#### Lesson 1 - Welcome

02:28Preview##### Welcome

02:28##### Learning Objectives

#### Lesson 2 - Python Basics

11:55Preview##### 2.1 Learning Objectives

##### 2.2 Your first program

01:15##### 2.3 Types

02:57##### 2.4 Expressions and Variables

03:50##### 2.5 Write your First Python Code

##### 2.6 String Operations

03:53##### 2.7 String Operations

#### Lesson 3 - Python Data Structures

16:22Preview##### 3.1 Learning Objectives

##### 3.2 Lists and Tuples

08:46##### 3.3 Lists and Tuples

##### 3.4 Sets

05:12##### 3.5 Sets

##### 3.6 Dictionaries

02:24##### 3.7 Dictionaries

#### Lesson 4 - Python Programming Fundamentals

41:08Preview##### 4.1 Learning Objectives

##### 4.2 Conditions and Branching

10:13##### 4.3 Conditions and Branching

##### 4.4 Loops

06:40##### 4.5 Loops

##### 4.6 Functions

13:28##### 4.7 Functions

##### 4.8 Objects and Classes

10:47##### 4.9 Objects and Classes

#### Lesson 5 - Working with Data in Python

12:35##### 5.1 Learning Objectives

##### 5.2 Reading files with open

03:38##### 5.3 Reading Files

##### 5.4 Writing files with open

02:49##### 5.5 Writing Files

##### 5.6 Loading data with Pandas

04:07##### 5.7 Working with and Saving data with Pandas

02:01##### 5.8 Loading Data and Viewing Data

#### Lesson 6 - Working with Numpy Arrays

18:26##### 6.1 Learning Objectives

##### 6.2 Numpy One-Dimensional Arrays

11:18##### 6.3 Working with One-Dimensional Numpy Arrays

##### 6.4 Numpy Two-Dimensional Arrays

07:08##### 6.5 Working with Two-Dimensional Numpy Arrays

#### Lesson 7 - Course Summary

01:13Preview##### Course Summary

01:13##### Unlocking IBM Certificate

- Free Course
### Data Visualization With Python

Preview#### Lesson 1 Welcome

03:01Preview##### Learning Objectives

##### Welcome

03:01

#### Lesson 2 Introduction to Visualization Tools

22:05Preview##### Learning Objectives

##### Introduction to Data Visualization

04:36##### Introduction to Matplotlib

06:26##### Basic Plotting with Matplotlib

04:39##### Dataset on Immigration to Canada

02:43##### Line Plots

03:41##### Arrays & Matrices

#### Lesson 3 Basic Visualization Tools

13:13Preview##### Learning Objectives

##### Area Plots

04:45##### Histograms

04:58##### Bar Charts

03:30##### Area Plots, Histograms, and Bar Charts

#### Lesson 4 Specialized Visualization Tools

12:13##### Learning Objectives

##### Pie Charts

04:14##### Box Plots

03:42##### Scatter Plots

04:17##### Pie Charts, Box Plots, Scatter Plots, and Bubble Plots

#### Lesson 5 Advanced Visualization Tools

05:22##### Learning Objectives

##### Waffle Charts

01:28##### Word Clouds

01:28##### Seaborn Regression Plots

02:26##### Waffle Charts, Word Clouds, and Regression Plots

#### Lesson 6 Creating Maps and Visualizing Geospatial Data

09:17##### Learning Objectives

##### Introduction to Folium

02:35##### Maps with Markers

02:21##### Choropleth Maps

04:21##### Generating Maps in Python

##### Unlocking IBM Certificate

Simplilearnâ€™s Blended Learning model brings classroom learning experience online with its world-class LMS. It combines instructor-led training, self-paced learning and personalized mentoring to provide an immersive learning experience.

- Disclaimer
- PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.