Course Overview

Training Options

Blended Learning

$ 599

  • 90 days of flexible access to online classes
  • num_of_days days of access to high-quality, self-paced learning content designed by industry experts
  • Classes starting from:-
13th Jun: Weekend Class

Corporate Training

Customized to your team's needs

  • Blended learning delivery model (self-paced e-learning and/or instructor-led options)
  • Course, category, and all-access pricing
  • Enterprise-class learning management system (LMS)
  • Enhanced reporting for individuals and teams
  • 24x7 teaching assistance and support

Course Curriculum

Course Content

  • 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
  • Section 03 - Practice Projects

    Preview
    • Practice Projects

      • Bike-Sharing Demand Analysis
  • 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

Reviews

  • Shuaib Kokate

    Shuaib Kokate

    Simplilearn is the best platform for e-learning. The instructors are qualified and knowledgeable and willing to help. I have started with this course, and have finished three courses in the past. I would recommend Simplilearn to my friends and colleagues.

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Why Simplilearn

Simplilearn’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.

  • Self-Paced Online Video

    A 360-degree learning approach that you can adapt to your learning style

  • Live Virtual Classroom

    Engage and learn more with these live and highly-interactive classes alongside your peers

  • 24/7 Teaching Assistance

    Keep engaged with integrated teaching assistance in your desktop and mobile learning

  • Online Practice Labs

    Projects provide you with sample work to show prospective employers

  • Applied Projects

    Real-world projects relevant to what you’re learning throughout the program

  • Learner Social Forums

    A support team focused on helping you succeed alongside a peer community

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