Complete Data Science Course Bootcamp

Master Python, Statistics, Probability, EDA, Machine Learning, NLP, Time Series Analysis & Build End-to-End Data Science

Created byTutorac Inc

Last updated 7/2026

English

What you'll learn

Master Python programming for Data Science and data analysis.
Apply Probability, Statistics, and Exploratory Data Analysis techniques.
Build supervised and unsupervised Machine Learning models.
Analyze text data using Natural Language Processing (NLP).
Understand Time Series Analysis for forecasting applications.

Course content

9 sections • 47 lectures • 8h 54m total length

Introduction and Installation of Python15:30
Learn how to install Python and set up the development environment.
Data Structure24:28
Understand the basic data structures used in Python programming.
Basic of Python , Python Programming15:05
Learn the fundamentals of Python programming through practical examples.
Panda15:40
Learn the basics of Panda for data manipulation and analysis.
Numpie MatplotLib11:30
Learn the fundamentals of NumPy and MatplotLib for numerical computing and data visualization.
Problems and Solutions16:19
Practice Python concepts by solving common programming problems.

Introduction to Probability3:47
Learn the fundamentals and importance of probability.
Addition Probability8:37
Understand the addition rule of probability with examples.
Multiplication Probability11:56
Learn the multiplication rule of probability and its applications.
Probability Distribution10:35
Understand probability distributions and their significance.
Normal Distribution2:16
Learn the concepts of normal distribution and its applications.

Supervised Learning I19:12
Understand the basics of supervised learning and its applications.
Supervised Learning II12:15
Explore advanced concepts and workflow of supervised learning.
Linear Regression Problem - 0117:00
Solve a practical linear regression problem using machine learning techniques.
Linear Regression Problem- 0222:26
Continue implementing linear regression with another practical example.
Linear Regression Problem - 0314:00
Practice solving another real-world linear regression problem.
Logistic Regression15:22
Learn the fundamentals of logistic regression for classification problems.
Logistic Regression Coding17:54
Implement logistic regression using Python through a practical coding session.
Idea Behind KNN9:01
Understand the intuition and working principle of the K-Nearest Neighbors algorithm.
Idea Behind KNN Coding13:21
Implement the K-Nearest Neighbors algorithm using Python.
Support Vector Machines15:34
Learn the fundamentals of Support Vector Machines for classification tasks.

Unsupervised Learning4:17
Understand the fundamentals of unsupervised learning and its applications.
Dimentionality Reduction7:25
Learn the concept of dimensionality reduction for simplifying datasets.
K-Means19:18
Understand the K-Means clustering algorithm and its working process.
Dimentionality Reduction Coding7:50
Implement dimensionality reduction techniques through coding examples.
K-Means Coding7:52
Implement the K-Means clustering algorithm using Python.
Hierarchical Clustering11:37
Learn the concepts and workflow of hierarchical clustering.
Hierarchical Clustering Coding5_HIERARCHICAL CLUSTERING CODING11:12
Implement hierarchical clustering through a practical coding demonstration.

N L P7:44
Learn the basics of Natural Language Processing and its applications.
Tools of NLP10:56
Explore the commonly used tools and libraries in Natural Language Processing.
NLP Framework10:33
Understand the NLP framework and its workflow.
Vectorization11:44
Learn how vectorization converts text data into numerical representations.
Visualization of TFID Vectorizer6:51
Understand the TFID Vectorizer and visualize how it represents text data.
Vectorization Coding7:39
Implement text vectorization techniques using practical coding examples.
N Grams and Parts of Speech8:43
Learn N-Grams and Parts of Speech used in Natural Language Processing.
N Grams and Parts of Speech Coding5:33
Implement N-Grams and Parts of Speech concepts through coding examples.

Requirements

Basic computer knowledge.
No prior Data Science experience is required.
A computer with internet access.
Willingness to learn Python and data analysis concepts.

Description

"The Data Science Course: Complete Data Science Bootcamp" is an all-encompassing program designed to transform you into a proficient data scientist. This intensive bootcamp covers the entire data science pipeline, from data collection and preprocessing to advanced machine learning and deep learning techniques. Whether you're a beginner or looking to deepen your expertise, this course provides you with the skills and knowledge to excel in the data science field.

You'll start by mastering the fundamentals of Python programming and essential libraries such as NumPy, pandas, and Matplotlib for data manipulation and visualization. The course progresses to statistical analysis, probability theory, and exploratory data analysis to help you uncover insights from data. Delving deeper, you'll explore machine learning algorithms, including regression, classification, clustering, and dimensionality reduction techniques.

Course Highlights:

Fundamentals of Python programming
Essential libraries: NumPy, pandas, Matplotlib
Statistical analysis and probability theory
Exploratory data analysis

The bootcamp also covers advanced topics like neural networks, deep learning with TensorFlow and Keras, natural language processing, and time series analysis. Throughout the course, you'll engage in hands-on projects and real-world exercises that simulate industry scenarios, allowing you to apply your skills in practical contexts. By the end of the bootcamp, you'll be equipped to handle complex data science challenges, from building predictive models to deploying data-driven solutions.

Join us and embark on a journey to become a data science expert, ready to tackle real-world problems and drive innovation in any organization.

Who this course is for:

Beginners who want to become Data Scientists.
Python developers interested in Data Science.
Data Analysts looking to upgrade their skills.
Students preparing for Data Science and Machine Learning careers.

Complete Data Science Course Bootcamp

What you'll learn

Explore related topics

Course content

Python6 lectures • 1hr 39min

Probability5 lectures • 37min

Statistics3 lectures • 39min

Exploratory Data Analysis1 lecture • 16min

Supervised Learning10 lectures • 2hr 36min

Unsupervised Learning7 lectures • 1hr 10min

(NLP)8 lectures • 1hr 10min

Time Series4 lectures • 39min

End to End Project3 lectures • 9min

Requirements

Description

Who this course is for: