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Course Overview

Training Options

online Bootcamp

$ 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:-
3rd May: Weekday Class
11th May: Weekend Class
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  • Blended learning delivery model (self-paced e-learning and/or instructor-led options)
  • Course, category, and all-access pricing
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  • 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

      21:10
      • Learning Objectives
      • 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 01: Introduction

      • Python for Data Science
  • Free Course

  • Data Visualization With Python

    Preview

    • Lesson 01: Data Visualization with Python

      • Data Visualization with Python

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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 Online Bootcamp

  • Develop skills for real career growthCutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills
  • Learn from experts active in their field, not out-of-touch trainersLeading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.
  • Learn by working on real-world problemsCapstone projects involving real world data sets with virtual labs for hands-on learning
  • Structured guidance ensuring learning never stops24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts

Related Programs

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