The Data Science Course: Complete Data Science Bootcamp 2025
$0.99
*Update 2025: Intro to Data Science module updated for recent AI developments*
The Problem
Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace.
However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.
And how can you do that?
Universities have been slow at creating specialized data science programs. (not to mention that the ones that exist are very expensive and time consuming)
Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete picture
What you’ll learn
- The course provides the entire toolbox you need to become a data scientist
- Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
- Impress interviewers by showing an understanding of the data science field
- Learn how to pre-process data
- Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
- Start coding in Python and learn how to use it for statistical analysis
- Perform linear and logistic regressions in Python
- Carry out cluster and factor analysis
- Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
- Apply your skills to real-life business cases
- Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
- Unfold the power of deep neural networks
- Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
- Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
Course content
6666 sections • 525 lectures • 32h 7m total length
Part 1: Introduction
The Field of Data Science - The Various Data Science Disciplines
The Field of Data Science - Connecting the Data Science Disciplines
The Field of Data Science - The Benefits of Each Discipline
The Field of Data Science - Popular Data Science Techniques
The Field of Data Science - Popular Data Science Tools
The Field of Data Science - Careers in Data Science
The Field of Data Science - Debunking Common Misconceptions
Part 2: Probability
Probability - Combinatorics
Probability - Bayesian Inference
Probability - Distributions
Probability - Probability in Other Fields
Part 3: Statistics
Statistics - Descriptive Statistics
Statistics - Practical Example: Descriptive Statistics
Statistics - Inferential Statistics Fundamentals
Statistics - Inferential Statistics: Confidence Intervals
Statistics - Practical Example: Inferential Statistics
Statistics - Hypothesis Testing
Statistics - Practical Example: Hypothesis Testing
Part 4: Introduction to Python
Python - Variables and Data Types
Python - Basic Python Syntax
Python - Other Python Operators
Python - Conditional Statements
Python - Python Functions
Python - Sequences
Python - Iterations
Python - Advanced Python Tools
Part 5: Advanced Statistical Methods in Python
Advanced Statistical Methods - Linear Regression with StatsModels
Advanced Statistical Methods - Multiple Linear Regression with StatsModels
Advanced Statistical Methods - Linear Regression with sklearn
Advanced Statistical Methods - Practical Example: Linear Regression
Advanced Statistical Methods - Logistic Regression
Advanced Statistical Methods - Cluster Analysis
Advanced Statistical Methods - K-Means Clustering
Advanced Statistical Methods - Other Types of Clustering
ChatGPT for Data Science
Case Study: Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis
Part 6: Mathematics
Part 7: Deep Learning
Deep Learning - Introduction to Neural Networks
Deep Learning - How to Build a Neural Network from Scratch with NumPy
Deep Learning - TensorFlow 2.0: Introduction
Deep Learning - Digging Deeper into NNs: Introducing Deep Neural Networks
Deep Learning - Overfitting
Deep Learning - Initialization
Deep Learning - Digging into Gradient Descent and Learning Rate Schedules
Deep Learning - Preprocessing
Deep Learning - Classifying on the MNIST Dataset
Deep Learning - Business Case Example
Deep Learning - Conclusion
Appendix: Deep Learning - TensorFlow 1: Introduction
Appendix: Deep Learning - TensorFlow 1: Classifying on the MNIST Dataset
Appendix: Deep Learning - TensorFlow 1: Business Case
Software Integration
Case Study - What's Next in the Course?
Case Study - Preprocessing the 'Absenteeism_data'
Case Study - Applying Machine Learning to Create the 'absenteeism_module'
Case Study - Loading the 'absenteeism_module'
Case Study - Analyzing the Predicted Outputs in Tableau
Appendix - Additional Python Tools
Appendix - pandas Fundamentals
Bonus Lecture