Welcome to Data-X, a course for Data, AI, and Information Technology & Systems

Ikhlaq Sidhu, UC Berkeley (contact)
Derek S. Chan, UC Berkeley (contact)

Data-X is a framework designed at UC Berkeley for learning and applying AI, data science, and emerging technologies. Data-X fills a gap between theory and practice to empower data and AI in current projects and new initiatives. Data-X projects create new ventures, new research, and corporate innovations all over the world.

Course Modules

Lecture Materials Homework Resources
00: Getting Started
010 Introduction Basics Slides Code Video References Page
020 Project: Getting Started Slides Video Syllabus
030A Installation Instructions Slides References Page
030B Using Jupyter Notebooks Code References Page
030C Python Basics Code References Page
01: Fundamentals
100A Prediction and Linear Regression Pt. I Slides Video
100B Prediction and Linear Regression Pt. II Slides Video HW
110 NumPy Code Video HW Introduction to NumPy
101 NumPy Exercises
NumPy Cheatsheet
120 Pandas Code Video HW Intro to Pandas Video
Jupyter Notebook
10 Minutes to Pandas
Pandas Cheatsheet
Intro to Pandas Slides
125 Dataset initial checks (diversity, quality, quantity) Code
130A Data Visualization I Code Video HW Slides List of Resources
130B Data Visualization II: Plotly Code Video Slides
130C Data Visualization III: Tableau Tableau Video
Tableau Slides
130D Data Visualization IV: Interactive Visualization H2O Wave
Data-Driven Documents (D3)
D3 Gallery
141 Precision and recall for defining success Code
140 Logistic Regression Code Video HW Basics
160A Predictive Classification with Titanic Pt. I Code Video HW
160B Predictive Classification with Titanic Pt. II Code Video HW
160C Predictive Classification with Titanic Pt. III Code Video HW
160D Predictive Classification with Titanic Pt. IV Code Video HW
161 H2O.ai and Ensemble Method Introduction Code HW H2O.ai Tutorials
170 ML Algorithm Overview Slides Video
180 Cross-Validation and Regularization Slides Code HW
190A Systematic Pt 1: Bayesian optimization Code TensorFlow ConvNets on a Budget
Loop API Example for optimization in Ax
190B Systematic Pt 2: Image models, random search Code
190C Systematic Pt 3: Ensemble methods Code
02: Data Signals
200 Correlation Slides Code Video HW
215 Time Series Slides Code Video Video HW ARIMA
Prophet Library
Exponential Smoothing
250 Spectral Signals Slides Code HW
03: Data Handling
310 Web Scraping Slides Code Video HW Extra Code 1
Extra Code 2
Extra Video
320 Flask Video Video HW
330 YOLO Object Detection Video Video HW Coursera
340 Face and Emotion Recognition HW
360 AI Data System: Anvil, Colab, and APIs Code Hugging Face
Google Cloud Vision API
04: Deep Learning
410 Intro to TensorFlow Slides Video HW TensorFlow Crash Course
TensorFlow Tutorial
What is TensorFlow?
420 Neural Networks Slides Code Video Video HW What is a Neural Network?
Neural Networks and Deep Learning
TensorFlow Playground
Deep Learning
Deep Learning book
430A Convolution Neural Networks (CNNs) Pt. I Slides Code Video Video HW CNNs
CNN Tutorial
CNN Examples
Image Classification
Image Data Augmentation in Tensorflow
CNNs for Time Series Forecasting
430B Convolution Neural Networks (CNNs) Pt. II Code Video HW Please see the cell above
440 Recurrent Neural Networks (RNNs) Slides Code Illustrated Guide to LSTM’s and GRU’s
Sequence Models
Not RNNs: "Transformers Explained Visually"
05: Natural Language Processing
500 Text Processing Slides Code Video HW NLP Toolkit
510 Feature Engineering & Text Representation Slides Video Video HW
520 Learning Models Slides Video Video Text Cleaning Using the NLTK Library
A Code-First Introduction to NLP
530 Deep learning NLP with pretrained BERT Code Colab Code Fine-tuning a pretrained model
06: Data-X Library
610A Stock Market Data and Real Time Quotes Code Video
610B Stock Market Data and Historical Quotes Code Video
07: Data Strategy and Process
700 Developing Story for Professionals Slides Video HW
710 Principles of Innovation Engineering Slides Video


Projects are a key component of Data-X. Click to view project guidelines and past projects.