Data Science Training Program

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Data Science Training Program

Name: Data Science Training Program - Online Course
Rating: 4.3 (114 reviews)
Author: Tutorialspoint

Advance rapidly in the field of Data Science by learning the skills practically in this comprehensive certification.

updated on icon Updated on Sep, 2025

language icon Language - English

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category icon Development ,Data Science,Python

Lectures -32

Resources -17

Quizzes -166

Duration -73.5 hours

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Course Description

This course takes you on a complete journey into the world of Data Science, starting from the very basics and progressing to advanced topics like Machine Learning, Deep Learning, Big Data, and MLOps.

You’ll begin by mastering Python programming, mathematics, statistics, and data visualization, then move on to machine learning algorithms, natural language processing, and computer vision. Finally, you’ll explore cloud computing, big data tools, and model deployment techniques to become industry-ready.

By the end of this course, you’ll have a portfolio of real-world projects that demonstrate your skills to employers and clients, making you fully prepared for Data Scientist, ML Engineer, or AI Specialist roles.

Goals

By completing this course, learners will be able to:

Build strong foundations in Python, mathematics, statistics, and SQL for data science.
Analyze and visualize data using tools like Pandas, NumPy, Matplotlib, and Seaborn.
Apply machine learning algorithms for regression, classification, and clustering tasks.
Work with real-world datasets, perform feature engineering, and evaluate models effectively.
Develop deep learning models using TensorFlow and PyTorch for NLP and Computer Vision.
Handle large-scale data with Spark and cloud-based platforms (AWS/GCP/Azure).
Implement MLOps practices including model deployment, monitoring, and pipeline automation.
Build and showcase end-to-end data science projects for job readiness.

Prerequisites

This course is designed for absolute beginners. No prior experience in coding or data science is required. However, the following will help:

Basic Computer Skills → familiarity with operating systems, installing software, and file management.
High School Mathematics → basic knowledge of algebra, probability, and functions.
Curiosity & Problem-Solving Mindset → willingness to learn, experiment, and analyze data-driven solutions.

Curriculum

Check out the detailed breakdown of what’s inside the course

Phase - 1 || Week -1 || Day - 1 || Python setup and basic syntax

32 Lectures

Introduction to Computer Organization 14:34 14:34
Memory System 11:17 11:17
I/O Devices And Operating System 09:18 09:18
Background part 1 (IO devices, CPU, and Memory )
Quiz for Background part 1 (IO devices, CPU, and Memory )
Background part 2 ( Computer Organization and OS )
Quiz for Background part 2 ( Computer Organization and OS )
Why Do We Need Programming Languages 12:02 12:02
Readings on Why Do We Need Programming Languages?
More about Programming Language 07:13 07:13
Readings on More about Programming Languages
Python Introduction 18:00 18:00
Readings on Python Introduction
Quiz for Python - Introduction
Python - Environment Setup 06:03 06:03
Readings on Python Installation and First Program
Quiz for Python - Environment Setup
Python - Execution Process 16:16 16:16
Readings on How Python Programs are Executed
Quiz for How Python Programs are Executed
Python - Basic Syntax - 1 17:10 17:10
Python - Basic Syntax - 2 16:07 16:07
Readings on Python - Basic Syntax
Quiz for Python - Syntax
Sign In & Dashboard Explaination 08:19 08:19
Print( ) and input( ) Methods 07:33 07:33
Readings on Print() in Python
Readings on Input() in Python
Quiz for Print() in Python
Quiz for Input() in Python

Phase - 1 || Week -1 || Day - 2 || Python variables and datatypes with practice problems

19 Lectures

Phase - 1 || Week -1 || Day - 3 || Python Operators and related concepts

29 Lectures

Phase - 1 || Week -1 || Day - 4 || Python Conditional Statements & Match Statemets and Practice Problems

26 Lectures

Phase - 1 || Week -1 || Day - 5 || Python loops and related problems

31 Lectures

Phase - 1 || Week -1 || Day - 6 || Python Control statements

15 Lectures

Phase - 1 || Week -2 || Day - 1 || Introductions to data structures & Practice problems on Data Structure

50 Lectures

Phase - 1 || Week -2 || Day - 2 || Practice Problems on Data structure

28 Lectures

Phase - 1 || Week -2 || Day - 3 || Pre defined Finctions in Python

21 Lectures

Phase - 1 || Week -2 || Day - 4 || Global and Local Scope / Exception handling

21 Lectures

Phase - 1 || Week -2 || Day - 5 || String and its applications

24 Lectures

Phase - 1 || Week -2 || Day - 6 || Practice Problems on String Operations

26 Lectures

Phase - 1 || Week -3 || Day - 1 || List basics and Related Applications

26 Lectures

Phase - 1 || Week -3 || Day - 2 || Tuples basics and Related Applications

33 Lectures

Phase - 1 || Week -3 || Day - 3 || Set basics and Realted Applications

26 Lectures

Phase - 1 || Week -3 || Day - 4 || Dictionary Basics and Its Applications

31 Lectures

Phase - 1 || Week -3 || Day - 5 || Arrays Basics and Its Applications

29 Lectures

Phase - 1 || Week -3 || Day - 6 || Python OOPs and Related concepts

35 Lectures

Phase - 1 || Week -4 || Day - 1 || OOPs Access modifiers and their properties Encapsulation and Inheritance

50 Lectures

Phase - 1 || Week -4 || Day - 2 || File Handeling and its properties

24 Lectures

Phase - 1 || Week -4 || Day - 3 || Python Quick Revision Guide + Small Project Work

27 Lectures

Phase - 1 || Week -4 || Day - 4 || Numpy Basics till numpy math function

26 Lectures

Phase - 1 || Week -4 || Day - 5 || Pandas basics till data manipulation

36 Lectures

Phase - 1 || Week -4 || Day - 6 || Data cleaning till capstone exercise

56 Lectures

Phase - 1 || Week -5 || Day - 1 || Introduction to Course

4 Lectures

Phase - 1 || Week -5 || Day - 2 || Probability vs Statistics

1 Lectures

Phase - 1 || Week -5 || Day - 3 || Sets

9 Lectures

Phase - 1 || Week -5 || Day - 4 || Experiment

4 Lectures

Phase - 1 || Week -5 || Day - 5 || Probability Model

16 Lectures

Phase - 1 || Week -5 || Day - 6 || Random Variables

11 Lectures

Phase - 1 || Week -6 || Day - 1 || Continous Random Variables

10 Lectures

Phase - 1 || Week -6 || Day - 2 || Expectations

7 Lectures

Phase - 1 || Week -6 || Day - 3 || Project Bayes Classifier

1 Lectures

Phase - 1 || Week -6 || Day - 4 || Multiple Random Variables

8 Lectures

Phase - 1 || Week -6 || Day - 5 || Optional Estimation

7 Lectures

Phase - 1 || Week -6 || Day - 6 || Mathematical Derivations for Math Lovers (Optional)

5 Lectures

Phase - 1 || Week -7 || Day - 1 || Basics of MySQL

3 Lectures

Phase - 1 || Week -7 || Day - 2 || MySQL Functions

2 Lectures

Phase - 1 || Week -7 || Day - 3 || MySQL Operators

2 Lectures

Phase - 1 || Week -7 || Day - 4 || Basics of PySpark

14 Lectures

Phase - 1 || Week -7 || Day - 5 || Databricks SQL

32 Lectures

Phase - 1 || Week -7 || Day - 6 || Conclusion

4 Lectures

Phase - 2 || Week -8 || Day - 1 || Matplotlib for Data Visualization

28 Lectures

Phase - 2 || Week -8 || Day - 2 || Seaborn for Data Visualization

10 Lectures

Phase - 2 || Week -8 || Day - 3 || Plotly for 3D Interactive Plotting

6 Lectures

Phase - 2 || Week -8 || Day - 4 || Cleaning data

11 Lectures

Phase - 2 || Week -8 || Day - 5 || Exploring data (Exploratory Data Analysis)

12 Lectures

Phase - 2 || Week -8 || Day - 6 || Capstone practice project

2 Lectures

Phase - 2 || Week -9 || Day - 1 || Introduction to Machine Learning

7 Lectures

Phase - 2 || Week -9 || Day - 2 || Machine Learning Methods

8 Lectures

Phase - 2 || Week -9 || Day - 3 || Data Preparation and Preprocessing

5 Lectures

Phase - 2 || Week -9 || Day - 4 || Machine Learning Models and Optimization

5 Lectures

Phase - 2 || Week -9 || Day - 5 || Building Machine Learning Model from Scratch

6 Lectures

Phase - 2 || Week -9 || Day - 6 || Overfitting, Underfitting and Generalization

6 Lectures

Phase - 2 || Week -10 || Day - 1 || Dimensionality Reduction

4 Lectures

Phase - 2 || Week -10 || Day - 2 || Deep Learning Overview

3 Lectures

Phase - 2 || Week -10 || Day - 3 || Hands-on Machine Learning Project Using Scikit-Learn

4 Lectures

Phase - 2 || Week -10 || Day - 4 || Introduction to Recommender System

16 Lectures

Phase - 2 || Week -10 || Day - 5 || Basic of Recommender System - Part 1

16 Lectures

Phase - 2 || Week -11 || Day - 1 || Machine Learning for Recommender System - Part 1

22 Lectures

Instructor Details

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