INTRO-DS.KZ1 ISBN: 979-8-90059-165-0
Introduction to Data Science: Python, Statistics, Machine Learning, and Applied Analytics
Master data science through 22 chapters, 40 labs, and 138 quizzes. Build professional-grade analytical models using Python and statistics.
What you will be able to do
- Python Programming: Mastery of NumPy, Pandas, and Scikit-Learn for efficient data manipulation, cleaning, and complex numerical simulation.
- Statistical Inference: Proficiency in applying bootstrap methods, hypothesis testing, and A/B testing to derive actionable insights from noisy data.
- Machine Learning Pipelines: Ability to design, tune, and validate predictive models while managing bias, variance, and feature engineering constraints.
- Data Storytelling: Expertise in visualizing multidimensional datasets and deploying interactive dashboards using Streamlit for stakeholder communication and product delivery.
Intermediate Self-paced · 1 year access
40 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
01 / About
About This Course
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No credit card required
This comprehensive Introduction to Data Science course training bridges the gap between theory and production-ready code. You will navigate 22 chapters covering everything from NumPy and Pandas data wrangling to advanced machine learning pipelines and ethical AI governance. Unlike surface-level tutorials, this curriculum forces you to confront real-world constraints like data leakage, bias, and model explainability. With 40 hands-on labs, 61 exercises, and 235 flashcards, you gain the technical muscle memory required for modern industry roles. We prioritize reproducible workflows and rigorous statistical thinking, ensuring you don't just build models, but deliver reliable, secure, and actionable data products that solve actual business problems effectively.
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
22 Interactive Lessons · 141 topics01 Preface +
02 Data Science, Data, and Responsible Inquiry 6 topics · 1 LiveLab +
- Data Science as a Discipline
- The Data Science Lifecycle
- Data Structures and Analytical Questions
- Human Context and Data Ethics
- Applied Data Audit
- Summary
1 LiveLab in this lesson — see the labs panel →
03 The Reproducible Data Science Workspace 5 topics · 2 LiveLab +
- Local Computing Foundations
- Supported Installation Path
- Optional Managed Local Environment
- Git and Reproducibility
- Summary
2 LiveLab in this lesson — see the labs panel →
04 Computational Thinking with Python: Logic and Flow 7 topics · 3 LiveLab +
- Problem Decomposition
- Values, Variables, and Types
- Expressions and Decisions
- Repetition and Accumulation
- Core Collections
- Validate the Parser Logic
- Summary
3 LiveLab in this lesson — see the labs panel →
05 Python Abstraction, Reliability, and Data Access 6 topics · 1 LiveLab +
- Functions and Abstraction
- Modules and Packages
- Exceptions and Defensive Programming
- Testing and Debugging
- Files and External Data
- Summary
1 LiveLab in this lesson — see the labs panel →
06 Multidimensional Data, NumPy, and Simulation 6 topics · 2 LiveLab +
- Array Foundations
- Vectorized Computation
- Shape, Axes, and Broadcasting
- Numerical Quality
- Monte Carlo Simulation
- Summary
2 LiveLab in this lesson — see the labs panel →
07 Data Wrangling and Tidy Data with Pandas 7 topics · 3 LiveLab +
- Series and DataFrames
- Data Quality Assessment
- Cleaning and Transformation
- Grouping and Reshaping
- Combining Data
- Messy Data Portfolio Lab
- Summary
3 LiveLab in this lesson — see the labs panel →
08 Relational Databases and SQL Analytics 7 topics · 2 LiveLab +
- Relational Design
- Retrieving and Filtering
- Calculation and Aggregation
- Joins and Multi-Table Questions
- Subqueries, CTEs, and Windows
- SQL-to-Python Pipeline
- Summary
2 LiveLab in this lesson — see the labs panel →
09 Exploratory Data Analysis and Data Quality Reasoning 7 topics · 2 LiveLab +
- Question-Driven Exploration
- Univariate Analysis
- Bivariate and Multivariate Analysis
- Outliers and Anomalies
- Correlation, Confounding, and Causality
- EDA Portfolio Artifact
- Summary
2 LiveLab in this lesson — see the labs panel →
10 Data Visualization, Accessibility, and Storytelling 7 topics · 1 LiveLab +
- Visual Encoding and Chart Choice
- Static Visualization with Matplotlib
- Interactive Visualization with Plotly
- Accessibility and Ethical Design
- Data Storytelling
- Deceptive Chart Makeover
- Summary
1 LiveLab in this lesson — see the labs panel →
11 Probability and Statistical Thinking through Simulation 7 topics · 2 LiveLab +
- Probability Foundations
- Random Variables and Distributions
- Sampling and Variability
- Simulation as a Statistical Tool
- Bootstrap Foundations
- Simulation Portfolio Lab
- Summary
2 LiveLab in this lesson — see the labs panel →
12 Statistical Inference: Randomization First, Classical Methods Second 7 topics · 2 LiveLab +
- Confidence through Bootstrap Intervals
- Hypothesis Testing by Randomization
- A/B Testing
- Classical Inference Bridge
- Errors, Power, and Multiple Testing
- Inference Reporting
- Summary
2 LiveLab in this lesson — see the labs panel →
13 The Machine Learning Workflow and Pipeline 7 topics · 2 LiveLab +
- Problem Framing
- Training, Validation, and Test Data
- Generalization and Leakage
- Preprocessing Pipelines
- Baseline-to-Model Workflow
- Pipeline Blueprint Lab
- Summary
2 LiveLab in this lesson — see the labs panel →
14 Regression and Continuous Outcomes 7 topics · 2 LiveLab +
- Linear Regression: The Basics
- Multiple Regression
- Diagnostics and Assumptions
- Regression Metrics
- Regularized Regression
- Real Estate Prediction Project
- Summary
2 LiveLab in this lesson — see the labs panel →
15 Classification and Decision-Making 7 topics · 2 LiveLab +
- Classification Foundations
- Core Classification Models
- Confusion-Matrix Metrics
- Thresholds and Curves
- Probability Quality
- Cost-Sensitive Classifier Project
- Summary
2 LiveLab in this lesson — see the labs panel →
16 Text Mining Fundamentals 5 topics · 2 LiveLab +
- Introduction to Text Data and Text Mining
- Text Preprocessing Techniques
- Analyzing Word Frequencies and Importance
- Sentiment Analysis and Topic Discovery
- Summary
2 LiveLab in this lesson — see the labs panel →
17 Graphs and Networks 7 topics · 1 LiveLab +
- Introduction to Graph Theory and Networks
- Nodes, Edges, and Network Structures
- Modeling Social Networks
- Measuring Connectivity and Centrality
- Network Visualization
- Practical Applications of Network Analysis
- Summary
1 LiveLab in this lesson — see the labs panel →
18 Unsupervised Pattern Discovery 6 topics · 2 LiveLab +
- k-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis
- Anomaly Detection
- Customer Segmentation Project
- Summary
2 LiveLab in this lesson — see the labs panel →
19 Model Selection, Tuning, and Error Analysis 7 topics · 2 LiveLab +
- Cross-Validation
- Hyperparameter Search
- Feature Engineering and Selection
- Bias-Variance Reasoning
- Error Analysis and Fairness Checks
- Model Championship
- Summary
2 LiveLab in this lesson — see the labs panel →
20 Responsible, Secure, and Explainable Data Science 7 topics · 1 LiveLab +
- Bias and Fairness
- Privacy and Data Protection
- Security Practices
- Explainability and Documentation
- Human Oversight and Governance
- Model Audit Project
- Summary
1 LiveLab in this lesson — see the labs panel →
21 Communicating and Delivering a Data Product 7 topics · 1 LiveLab +
- Analytical Communication
- Dashboard and Application Design
- Streamlit Foundations
- Model Delivery Boundaries
- Versioned Portfolio Delivery
- Interactive Data Product Project
- Summary
1 LiveLab in this lesson — see the labs panel →
22 Integrated Data Science Capstone 9 topics · 4 LiveLab +
- Project Definition and Governance
- Data Acquisition and Management
- Preparation and Exploration
- Inference and Modeling
- Responsible Review
- Reproducible Project Package
- Communication and Portfolio
- Reflection and Progression
- Summary
4 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
40 LiveLabs- Defining Business Problems and Building Data-Driven Decisions
- Creating a Reproducible Python Workspace
- Establishing Reproducibility and a Git Workflow
- Implementing Python Logic and Collections
- Completing a Conditional Statement Flowchart
- Reproducible Data Science Workspace
- Building Functions and Abstraction in Python
- Performing NumPy Array Operations
- Investigating Randomness and Probability with NumPy
- Wrangling and Transforming Pandas DataFrames
- Identifying and Cleaning Data Quality Issues
- Reshaping and Integrating Datasets with Pandas
- Querying and Analyzing Relational Data with SQL and Pandas
- Completing a Customers-Orders-Products Database Schema
- Practicing Question-Driven EDA Under Pressure
- Exploring Data Distributions and Relationship
- Building and Auditing Data Visualizations
- Simulating Probability and Sampling Variability
- Applying Bootstrap Resampling and Simulation
- Constructing and Interpreting Bootstrap Confidence Intervals
- Conducting Randomization Tests for A/B Decisions
- Framing an ML Problem & Establishing a Baseline
- Building a Leakage-Free ML Pipeline
- Performing Regression Diagnostics and Error Analysis
- Building and Interpreting a Regression Model
- Building and Evaluating a Classification Model
- Framing Machine Learning Problems Before Building Any Model
- Constructing TF-IDF Text Representations
- Discovering Sentiment and Latent Topics
- Constructing and Interpreting Network Structure
- Performing and Interpreting Clustering and PCA
- Evaluating Anomalies and Designing Human Review Workflows
- Applying Cross-Validation and Hyperparameter Search
- Engineering Features and Analyzing Model Errors
- Auditing Bias, Fairness, and Privacy in ML Models
- Evaluating Production Readiness and Release Controls
- Defining Capstone Problems and Assessing Project Risks
- Acquiring, Assessing, and Exploring Project Data
- Translating and Interpreting Statistical Evidence
- Developing and Validating Predictive Models
Labs run in your browser — nothing to install.
03 / FAQs
Questions before you start
Is this course suitable for absolute beginners?+
Yes, but be prepared for a steep learning curve. We start with Python logic and move rapidly into advanced statistical modeling.
How does this course handle data ethics and bias?+
We dedicate specific modules to audit bias, data protection, and explainability, ensuring you understand the legal and moral trade-offs in AI.
Will I be job-ready after completing these 22 chapters?+
You will have a portfolio-ready capstone project and the technical skills to pass rigorous interview questions, though industry experience remains key.
Build Practical Data Science Skills
Master Python, statistics, machine learning, and data visualization through hands-on learning.
- 1 year of full access
- 40 LiveLab included
- Certificate of completion
No credit card required