Data Science with Python Training Course Online

12 Lesson

40 Hours

Add to Wishlist

igmGuru offers the best Data Science with Python Course online is designed to help you gain the skills needed to work with Python to fulfill Data Science tasks. In our Data Science with Python online course program, we cover various essential Python libraries like Pandas, Seaborn, NumPy, and Matplotlib. You will learn data analytics, data visualization concepts, and regression models in this training program. Get a Data Science with Python certification course as you showcase knowledge in data transformation, histograms, and different types of distributions. Our experienced trainers also provide post-training support to keep you updated with the latest trends.

Enroll Now

Data Science with Python Training Online

Overview

Prerequisites:

Python Programming Fundamentals
Basic Mathematics
Basic Statistics
Familiarity with Data Structures
Basic SQL Knowledge
Problem-Solving and Logical Thinking
Curiosity for Data Analysis
Comfort with Software Installation and Management

What You Will Learn:

Introduction to Data Science in Python
Python Programming Fundamentals
Jupyter Notebooks for Data Science
Functions and Control Flow (Loops, Conditionals)
Data Manipulation with NumPy
Data Analysis with Pandas
Data Cleaning and Formatting
Data Wrangling Techniques
Data Visualization with Matplotlib and Seaborn
Hands-on Exercise – Visualize Data with Matplotlib
Exploratory Data Analysis (EDA)
Statistical Programming with Python
Descriptive and Inferential Statistics
Working with Real-world Datasets
Introduction to Machine Learning with Scikit-learn
Model Training and Evaluation
Foundational Computer Programming Concepts
Data Science Workflow and Foundations
Logical Thinking and Problem Solving in Data Tasks

Key Features

Instructor Led Training : 40 Hrs
24 X 7 Lifetime Support & Access
Experienced & Professional Trainers
1 On 1 Training Option Available
Small Batches Upto 10 Participants
Flexible Schedule

Course Curriculum

Lesson 1 - Data Science Overview

1. Data Science

2. Data Scientists

3. Examples of Data Science

4. Python for Data Science

Lesson 2 - Data Analytics Overview

1. Introduction to Data Visualization

2. Processes in Data Science

3. Data Wrangling, Data Exploration, and Model Selection

4. Exploratory Data Analysis or EDA

5. Data Visualization

6. Plotting

7. Hypothesis Building and Testing

Lesson 3 - Statistical Analysis and Business Applications

1. Introduction to Statistics

2. Statistical and Non-Statistical Analysis

3. Some Common Terms Used in Statistics

4. Data Distribution: Central Tendency, Percentiles, Dispersion

5. Histogram, Bell Curve, Hypothesis Testing

6. Chi-Square Test, Correlation Matrix, Inferential Statistics

Lesson 4 - Python: Environment Setup and Essentials

1. Introduction to Anaconda

2. Installation of Anaconda Python Distribution - For Windows, Mac OS, and Linux

3. Jupyter Notebook Installation, Jupyter Notebook Introduction

4. Variable Assignment

5. Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting

6. Creating, accessing, and slicing tuples

7. Creating, accessing, and slicing lists

8. Creating, viewing, accessing, and modifying dicts

9. Creating and using operations on sets

10. Basic Operators: 'in', '+', '*', Functions, Control Flow

Lesson 5 - Mathematical Computing with Python (NumPy)

1. NumPy Overview

2. Properties, Purpose, and Types of ndarray

3. Class and Attributes of ndarray Object

4. Basic Operations: Concept and Examples

5. Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays

6. Copy and Views, Universal Functions (ufunc)

7. Shape Manipulation, Broadcasting, Linear Algebra

Lesson 6 - Scientific computing with Python (Scipy)

1. SciPy and its Characteristics, SciPy sub-packages

2. SciPy sub-packages – Integration, SciPy sub-packages – Optimize

3. Linear Algebra

4. SciPy sub-packages - Statistics, SciPy sub-packages - Weave

5. SciPy sub-packages - I O

Lesson 7 - Data Manipulation with Python (Pandas)

1. Introduction to Pandas

2. Data Structures, Series, DataFrame, Missing Values

3. Data Operations, Data Standardization

4. Pandas File Read and Write Support

5. SQL Operation

Lesson 8 - Machine Learning with Python (Scikit-Learn)

1. Introduction to Machine Learning

2. Machine Learning Approach

3. How Supervised and Unsupervised Learning Models Work

4. Scikit-Learn

5. Supervised Learning Models - Linear Regression, Logistic Regression

6. K Nearest Neighbors (K-NN) Model

7. Unsupervised Learning Models: Clustering, Dimensionality Reduction

8. Pipeline, Model Persistence, Model Evaluation - Metric Functions

Lesson 9 - Natural Language Processing with Scikit-Learn

1. NLP Overview

2. NLP Approach for Text Data

3. NLP Environment Setup

4. NLP Sentence analysis, NLP Applications

5. Major NLP Libraries, Scikit-Learn Approach

6. Scikit - Learn Approach Built - in Modules, Scikit - Learn Approach Feature Extraction

7. Bag of Words, Extraction Considerations

8. Scikit - Learn Approach Model Training

9. Scikit - Learn Grid Search and Multiple Parameters

10. Pipeline

Lesson 10 - Data Visualization in Python using Matplotlib

1. Introduction to Data Visualization

2. Python Libraries, Plots

3. Matplotlib Features: Line Properties Plot with (x, y), Controlling Line Patterns and Colors, Set Axis, Labels, and Legend Properties, Alpha and Annotation, Multiple Plots, Subplots

4. Types of Plots and Seaborn

Lesson 11 - Data Science with Python Web Scraping

1. Web Scraping

2. Common Data/Page Formats on The Web

3. The Parser, Importance of Objects

4. Understanding the Tree, Searching the Tree

5. Navigating options, Modifying the Tree

6. Parsing Only Part of the Document

7. Printing and Formatting, Encoding

Lesson 12 - Python integration with Hadoop, MapReduce and Spark

1. Need for Integrating Python with Hadoop

2. Big Data Hadoop Architecture

3. MapReduce, Cloudera QuickStart VM Set Up

4. Apache Spark

5. Resilient Distributed Systems (RDD)

6. PySpark, Spark Tools

7. PySpark Integration with Jupyter Notebook

Talk To Us

We are happy to help you

1-800-7430-173 (US Toll Free)

+91 7240740740 (India)

Drop Us a Query

Fields marked * are mandatory

Name

Phone Number

Comments

Request For Live Demo Class

Course Fees

Online Class Room Training

US $ 599.00

100% Money Back Guarantee

Duration : 40 Hrs
Plus Self Paced

Classes Starting From

Fast Track Batch 22 Jun 2026
Weekday Batch 22 Jun 2026
Weekend Batch 27 Jun 2026

WATCH 1st CLASS FREE

SELF PACED LEARNING

US $ 299.00

Refund Policy

Duration : 40 hrs
Lifetime Free Upgrade
Reference Documents
24x7 Support & Access

Corporate Training

Customized Training Delivery Model
Flexible Training Schedule Options
Industry Experienced Trainers
24x7 Support

Trusted By Top Companies Worldwide

Want to know Today's Offer

Data Science with Python Certification Exam

Official certification exam name is Data Science with Python - Certified Associate (varies by provider)

Exam Format

Duration: 90 to 120 minutes
Number of Questions: 60–80
Passing Score: 65–70%
Type: Multiple-choice, multiple-response, and coding-based questions
Mode: Online (proctored) or in-person at certified centers

Exam Cost

Fee: $150 to $295 USD depending on certification provider

igmGuru provides a Course Completion Certificate for Data Science with Python Training. This certification validates your knowledge in Python for data analysis, visualization, and basic machine learning concepts. It also confirms your understanding of tools such as Pandas, NumPy, Matplotlib, and Scikit-learn used in data science projects. This certification can support roles such as Data Analyst, Junior Data Scientist, and Python Developer.

Data Science with Python Certification Exam

Reviews

Login

Email ID*

Password*

Forgot Password?

Data Science with Python Training Course Online