Introduction to Data Science

Get oriented with the end-to-end data science process and set up your working environment. You'll see how a question becomes data, analysis, and a result.

• 1.1 What Is Data Science?
  Preview 18:40
• 1.2 The Workflow: Ask, Collect, Clean, Explore, Model, Communicate
  42:15
• 1.3 Setting Up Python, Anaconda, and Jupyter
  36:50
• 1.4 Tools of the Trade: Notebooks, Libraries, and Datasets
  22:05
• 1.5 Exercise: Your First Notebook
  48:30

Learn just enough Python to be productive, then dive into NumPy arrays and pandas DataFrames, the workhorses of data manipulation.

• 2.1 Python Essentials: Variables, Types, and Control Flow
  55:10
• 2.2 Functions, Lists, and Dictionaries
  47:35
• 2.3 NumPy Arrays and Vectorized Operations
  51:20
• 2.4 pandas Series and DataFrames
  58:45
• 2.5 Selecting, Filtering, and Indexing Data
  53:00
• 2.6 Exercise: Exploring a Real Dataset with pandas
  72:15

Real data is messy. Learn to diagnose and fix data quality problems and reshape datasets into a tidy, analysis-ready form.

• 3.1 Handling Missing Values and Duplicates
  49:25
• 3.2 Data Types, Parsing, and Conversion
  44:10
• 3.3 Merging, Joining, and Concatenating
  52:30
• 3.4 Reshaping: GroupBy, Pivot, and Aggregation
  56:40
• 3.5 Exercise: Cleaning a Messy Survey Dataset
  68:50

Use summary statistics and visual inspection to understand a dataset before modeling, identifying distributions, relationships, and anomalies.

• 4.1 Descriptive Statistics and Distributions
  46:15
• 4.2 Correlation and Relationships Between Variables
  43:50
• 4.3 Detecting Outliers and Anomalies
  38:20
• 4.4 Feature Engineering Basics
  50:05
• 4.5 Exercise: EDA on a Housing Dataset
  65:30

Build the statistical intuition that underpins data analysis and machine learning, from probability basics to hypothesis testing.

• 5.1 Probability Fundamentals
  44:40
• 5.2 Distributions: Normal, Binomial, and More
  48:15
• 5.3 Sampling and the Central Limit Theorem
  41:30
• 5.4 Hypothesis Testing and Confidence Intervals
  53:25
• 5.5 Exercise: Running an A/B Test Analysis
  60:10

Turn data into clear, honest visuals. Learn the grammar of charts and how to choose the right plot for each question.

• 6.1 Plotting Fundamentals with Matplotlib
  47:00
• 6.2 Statistical Plots with Seaborn
  44:35
• 6.3 Choosing the Right Chart for Your Data
  36:50
• 6.4 Customization, Annotation, and Storytelling
  42:20
• 6.5 Exercise: Building a Dashboard of Charts
  58:15

Meet the core families of machine learning models and train your first predictors with scikit-learn across regression, classification, and clustering.

• 7.1 Supervised vs. Unsupervised Learning
  39:10
• 7.2 Linear and Logistic Regression
  54:25
• 7.3 Classification with Decision Trees and k-NN
  51:40
• 7.4 Clustering with K-Means
  45:15
• 7.5 Building a scikit-learn Pipeline
  49:50
• 7.6 Exercise: Predicting Customer Churn
  70:30

Learn how to tell whether a model is actually good. Cover the metrics, validation strategies, and pitfalls that separate reliable models from misleading ones.

• 8.1 Train/Test Splits and Cross-Validation
  43:00
• 8.2 Regression Metrics: MAE, RMSE, R²
  38:25
• 8.3 Classification Metrics: Accuracy, Precision, Recall, ROC
  47:40
• 8.4 Overfitting, Underfitting, and Regularization
  44:55
• 8.5 Exercise: Tuning and Comparing Models
  62:20

Bring it all together. Take a dataset of your choice from raw files to a documented, evaluated model and a presentation of your findings.

• 9.1 Framing a Project and Choosing a Dataset
  35:30
• 9.2 End-to-End Walkthrough: Clean, Explore, Model
  68:10
• 9.3 Communicating Results to a Non-Technical Audience
  40:45
• 9.4 Capstone: Build Your Own Data Science Project
  120:00
• 9.5 Building a Portfolio and Next Steps
  28:35
• 9.6 Course Wrap-Up and Certificate
  14:20