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 A tu propio ritmo · 1 año de acceso
45 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
01 / About
Acerca de este curso
Try Free →
No se requiere tarjeta de crédito
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 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
22 Lecciones interactivas · 141 topics01 Preface +
02 Data Science, Data, and Responsible Inquiry 6 topics · 2 Laboratorio en vivo +
- Data Science as a Discipline
- The Data Science Lifecycle
- Data Structures and Analytical Questions
- Human Context and Data Ethics
- Applied Data Audit
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
03 The Reproducible Data Science Workspace 5 topics · 2 Laboratorio en vivo +
- Local Computing Foundations
- Supported Installation Path
- Optional Managed Local Environment
- Git and Reproducibility
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
04 Computational Thinking with Python: Logic and Flow 7 topics · 3 Laboratorio en vivo +
- Problem Decomposition
- Values, Variables, and Types
- Expressions and Decisions
- Repetition and Accumulation
- Core Collections
- Validate the Parser Logic
- Summary
3 Laboratorio en vivo in this lesson — see the labs panel →
05 Python Abstraction, Reliability, and Data Access 6 topics · 1 Laboratorio en vivo +
- Functions and Abstraction
- Modules and Packages
- Exceptions and Defensive Programming
- Testing and Debugging
- Files and External Data
- Summary
1 Laboratorio en vivo in this lesson — see the labs panel →
06 Multidimensional Data, NumPy, and Simulation 6 topics · 2 Laboratorio en vivo +
- Array Foundations
- Vectorized Computation
- Shape, Axes, and Broadcasting
- Numerical Quality
- Monte Carlo Simulation
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
07 Data Wrangling and Tidy Data with Pandas 7 topics · 4 Laboratorio en vivo +
- Series and DataFrames
- Data Quality Assessment
- Cleaning and Transformation
- Grouping and Reshaping
- Combining Data
- Documented Data Cleaning Workflows
- Summary
4 Laboratorio en vivo in this lesson — see the labs panel →
08 Relational Databases and SQL Analytics 7 topics · 2 Laboratorio en vivo +
- Relational Design
- Retrieving and Filtering
- Calculation and Aggregation
- Joins and Multi-Table Questions
- Subqueries, CTEs, and Windows
- SQL-to-Python Pipeline
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
09 Exploratory Data Analysis and Data Quality Reasoning 7 topics · 3 Laboratorio en vivo +
- Question-Driven Exploration
- Univariate Analysis
- Bivariate and Multivariate Analysis
- Outliers and Anomalies
- Correlation, Confounding, and Causality
- EDA Portfolio Artifact
- Summary
3 Laboratorio en vivo in this lesson — see the labs panel →
10 Data Visualization, Accessibility, and Storytelling 7 topics · 1 Laboratorio en vivo +
- Visual Encoding and Chart Choice
- Static Visualization with Matplotlib
- Interactive Visualization with Plotly
- Accessibility and Ethical Design
- Data Storytelling
- Deceptive Chart Makeover
- Summary
1 Laboratorio en vivo in this lesson — see the labs panel →
11 Probability and Statistical Thinking through Simulation 7 topics · 2 Laboratorio en vivo +
- Probability Foundations
- Random Variables and Distributions
- Sampling and Variability
- Simulation as a Statistical Tool
- Bootstrap Foundations
- Simulation-Based Statistical Inference
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
12 Statistical Inference: Randomization First, Classical Methods Second 7 topics · 2 Laboratorio en vivo +
- Confidence through Bootstrap Intervals
- Hypothesis Testing by Randomization
- A/B Testing
- Classical Inference Bridge
- Errors, Power, and Multiple Testing
- Inference Reporting
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
13 The Machine Learning Workflow and Pipeline 7 topics · 2 Laboratorio en vivo +
- Problem Framing
- Training, Validation, and Test Data
- Generalization and Leakage
- Preprocessing Pipelines
- Baseline-to-Model Workflow
- Reproducible Modeling Workflows
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
14 Regression and Continuous Outcomes 7 topics · 2 Laboratorio en vivo +
- Linear Regression: The Basics
- Multiple Regression
- Diagnostics and Assumptions
- Regression Metrics
- Regularized Regression
- Real Estate Prediction Project
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
15 Classification and Decision-Making 7 topics · 3 Laboratorio en vivo +
- Classification Foundations
- Core Classification Models
- Confusion-Matrix Metrics
- Thresholds and Curves
- Probability Quality
- Cost-Sensitive Classifier Project
- Summary
3 Laboratorio en vivo in this lesson — see the labs panel →
16 Text Mining Fundamentals 5 topics · 2 Laboratorio en vivo +
- Introduction to Text Data and Text Mining
- Text Preprocessing Techniques
- Analyzing Word Frequencies and Importance
- Sentiment Analysis and Topic Discovery
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
17 Graphs and Networks 7 topics · 1 Laboratorio en vivo +
- 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 Laboratorio en vivo in this lesson — see the labs panel →
18 Unsupervised Pattern Discovery 6 topics · 2 Laboratorio en vivo +
- k-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis
- Anomaly Detection
- Customer Segmentation Project
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
19 Model Selection, Tuning, and Error Analysis 7 topics · 2 Laboratorio en vivo +
- Cross-Validation
- Hyperparameter Search
- Feature Engineering and Selection
- Bias-Variance Reasoning
- Error Analysis and Fairness Checks
- Model Championship
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
20 Responsible, Secure, and Explainable Data Science 7 topics · 2 Laboratorio en vivo +
- Bias and Fairness
- Privacy and Data Protection
- Security Practices
- Explainability and Documentation
- Human Oversight and Governance
- Model Audit Project
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
21 Communicating and Delivering a Data Product 7 topics · 1 Laboratorio en vivo +
- Analytical Communication
- Dashboard and Application Design
- Streamlit Foundations
- Model Delivery Boundaries
- Versioned Portfolio Delivery
- Interactive Data Product Project
- Summary
1 Laboratorio en vivo in this lesson — see the labs panel →
22 Integrated Data Science Capstone 9 topics · 4 Laboratorio en vivo +
- 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 Laboratorio en vivo in this lesson — see the labs panel →
Laboratorios prácticos Our edge
45 Laboratorio en vivos- Understanding Core Data Science Concepts
- 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
- Identifying and Cleaning Data Quality Issues
- Wrangling and Transforming Pandas DataFrames
- Reshaping and Integrating Datasets with Pandas
- Combining Datasets and Auditing Merges
- Completing a Customers-Orders-Products Database Schema
- Querying and Analyzing Relational Data with SQL and Pandas
- Practicing Question-Driven EDA Under Pressure
- Exploring Numerical Data and Relationships
- 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
- Framing Machine Learning Problems Before Building Any Model
- Building and Evaluating a Classification Model
- Evaluating Classification Models
- 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
- Reviewing Responsible Model Use
- 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
Los laboratorios se ejecutan en tu navegador; no hay nada que instalar.
03 / Preguntas frecuentes
Preguntas antes de empezar
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 año de acceso completo
- 45 LiveLab incluido
- Certificado de finalización
No se requiere tarjeta de crédito