Data Science with Python Course Online

80 Lessons

40 hours

kodestree 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.

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About Course

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

Course Curriculum

Course Content

Lesson 1 – Data Science Overview

Data Science
Data Scientists
Examples of Data Science
Python for Data Science

Lesson 2 – Data Analytics Overview

Lesson 3 – Statistical Analysis and Business Applications

Lesson 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

Lesson 5 – Mathematical Computing with Python (NumPy)

Lesson 6 – Scientific computing with Python (Scipy)

Lesson 7 – Data Manipulation with Python (Pandas)

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

Lesson 9 – Natural Language Processing with Scikit-Learn

NLP Overview
NLP Approach for Text Data
NLP Environment Setup
NLP Sentence analysis, NLP Applications
Major NLP Libraries, Scikit-Learn Approach
Scikit – Learn Approach Built – in Modules, Scikit – Learn Approach Feature Extraction
Bag of Words, Extraction Considerations
Scikit – Learn Approach Model Training
Scikit – Learn Grid Search and Multiple Parameters
Pipeline

Lesson 10 – Data Visualization in Python using Matplotlib

Lesson 11 – Data Science with Python Web Scraping

Lesson 12 – Python integration with Hadoop, MapReduce and Spark

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Self Paced Learning

₹17,940.00

✓ Refund Policy

Duration: 40 hrs
80 Lessons & Practical Labs
Lifetime Full Access & Free Upgrades
Downloadable Study Materials & Code Labs
Recognized Certification of Completion
24x7 Online Support & Learner Forum

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Want to know Today's Offer

Data Science with Python Course Online Certification Exam

Upon completing the Data Science with Python Course Online, you will receive a globally recognized certification that validates your expertise in software development and programming best practices. This certification is a testament to your practical knowledge, hands-on skills, and professional readiness.

The Programming certification exam assesses your ability to apply real-world concepts, tools, and techniques learned during the course. Certified professionals are in high demand across industries, opening doors to exciting career opportunities and higher salary potential.

Our certification is recognized by top employers and organizations worldwide. Whether you are looking to advance your current career, switch to a new role, or demonstrate your expertise to clients, this certification gives you the competitive edge you need in today’s fast-paced technology landscape.

Frequently Asked Questions

The Data Science with Python Course Online is designed to provide comprehensive, in-depth training in Data Science with Python. 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 The program covers foundational concepts through to advanced techniques to ensure you gain job-ready expertise.

This course is ideal for beginners, IT enthusiasts, and working professionals who want to build or advance their career in Data Science with Python and qualify for relevant industry roles. There are no stringent prerequisites—basic computer proficiency and a willingness to learn are all that is required. Foundational topics are thoroughly covered during onboarding.

The course is delivered through interactive, live online sessions led by industry experts. You will also have 24/7 access to LMS resources, study materials, and session recordings so you can catch up or revise at your convenience if you miss any live classes.

Yes, hands-on learning is a core component of this course. You will work on practical assignments, real-life use cases, and capstone projects using Data Science with Python tools and best practices, helping you build a portfolio to showcase to hiring managers.

Upon successful completion of the course curriculum and project assessments, you will be awarded an industry-recognized Certificate of Completion from Kodestree. We also offer career assistance, including resume-building workshops, mock technical interviews, and job opportunities to help you transition into relevant industry roles.

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WhatsApp:
+91 7204614489

Email:
admissions@kodestree.com

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The trainers explained concepts through real-world attack scenarios, which I was able to apply on the job right after the course. Structured curriculum and hands-on labs were the best part.

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Agile concepts were explained clearly, especially backlog management and team facilitation. A bit more time would have made it even better, but overall a solid course.

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Even as a beginner, I never felt lost — the step-by-step labs and doubt-clearing sessions made switching careers so much easier.

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Data Science with Python Course Online

About Course

Prerequisites:

What You Will Learn:

Course Curriculum

Course Content

Lesson 1 – Data Science Overview

Data Science

Data Scientists

Examples of Data Science

Python for Data Science

Lesson 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

Lesson 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

Lesson 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

Lesson 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

Lesson 6 – 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

Lesson 7 – 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

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

Introduction to Machine Learning

Machine Learning Approach

How Supervised and Unsupervised Learning Models Work

Scikit-Learn

Supervised Learning Models – Linear Regression, Logistic Regression

K Nearest Neighbors (K-NN) Model

Unsupervised Learning Models: Clustering, Dimensionality Reduction

Pipeline, Model Persistence, Model Evaluation – Metric Functions

Lesson 9 – Natural Language Processing with Scikit-Learn

NLP Overview

NLP Approach for Text Data

NLP Environment Setup

NLP Sentence analysis, NLP Applications

Major NLP Libraries, Scikit-Learn Approach

Scikit – Learn Approach Built – in Modules, Scikit – Learn Approach Feature Extraction

Bag of Words, Extraction Considerations

Scikit – Learn Approach Model Training

Scikit – Learn Grid Search and Multiple Parameters

Pipeline

Lesson 10 – Data Visualization in Python using Matplotlib

Introduction to Data Visualization

Python Libraries, Plots