AI Data Science (ETH Dozent)
exkl. MwSt.
Trainingsprogramm Fakten
Key learnings
- Programming for Data Science:
- Pandas
- Numpy
- SQL and Object Relational Databases
- Data Structures & Algorithms
- Graphing and Visualization:
- Plotly
- Dash
- Statistics for Data Science
- Hypothesis Testing
- Descriptive Statistics
- Alpha, Beta, Confidence Intervals
- Probability Distributions and Samples
- Statistical Inference
- Forecasting
- Linear Regression
- Non-Linear Regression
- Time Series
- Decomposition
- Regression techniques
- Forecast evaluation
- Data Manipulation:
- Imputation
- Fill methods
- Data Cleaning and Aggregation
- Machine Learning with Python
- Decision Trees
- Clustering Algorithms
- Model Evaluation and Cross Validation
- Feature Engineering and Selection
- Random Forest
- Dimensionality Reduction (PCA, KPCA)
- Natural Language Processing
- Large Language Models
- Retrieval Augmented Generation
- Vector Search and Nearest Neighbor Search
- Data Visualization
- Basic Principles of Data Visualization
Anforderungen
Prior Knowledge Requirements / Anforderungen
- At 1 year programming in Python.
- Solid understanding of basic mathematics (algebra, functions)
- Prior knowledge of Unix / Bash is helpful (not mandatory)
- Prior knowledge of SQL or relational databases is useful (not mandatory)
Programm Inhalt
Das Training findet sich täglich vom 08:30 bis 12:30 statt.
Trainings-Dauer: 14.04.2025 bis zum 13.06.2025 (40 Tage)
Feiertage: 18.04, 21.04, 01.05, 29.05, 09.06
Die ersten 3 Tage finden sich am Ort in Circle 6 am Zürich Flughafen statt.
Das Training in den Resttagen (bis zum 13.06.2025) findet sich nur Online statt.
| Wk. 1 | |
| Mon | (08:30) 1 hour lecture on Python, Git, and version control.
(09:30) 30 min Practice with Python and Git & 30 min solutions. (10:30) 1 hour (coding together) tutorial: Setting up SSH Keys, the Project Repository, and exercise repository. (11:30) Independent work on Homework, reading course materials and “warm-up” exercises. |
| Tue | (08:30) 1 hour Introduction Python, Git, Unix., Bash
(09:30) 30 min setup of git repo on remote and local servers. (10:30) 1 hour (coding together) tutorial on Git Flow and Management of Merge Conflicts. (11:30) Independent work on Homework and exercises. |
| Wed | (08:30) Pandas & Numpy Lecture
(09:30) 30 min exercises on Pandas (10:30) 1 hour (coding together) tutorial in advanced Pandas, introduction to MatPlotLib (11:30) 1 hour Home exercises and coding practice. |
| Thur | (08:30) 1 hour lecture on major data types, and treatment of various data types.
(09:30) 30 min exercises & 30 min solutions and explanations on the topic of Data types (10:30) 1 hour (coding together) tutorial on advanced pandas (merge, melt, concat) (11:30) 1 hour Home exercises and coding practice. |
| Fri | (08:30) 1 hour lecture on more advanced data visualization.
(09:30) 30 min exercises & 30 min solutions and explanations on the topic of advanced data visualization (10:30) 1 hour (coding together) tutorial advanced data visualization and data analysis (11:30) 1 hour Home exercises and coding practice. |
| Wk. 2 | |
| Mon | (08:30) 1 hour lecture on desc. statistics and hypothesis testing
(09:30) 1 hour exercises and solutions on data-driven hypothesis generation and descriptive statistics (10:30) 1 hour (coding together) workshop: Generating descriptive statistics from fresh datasets (11:30) 1 hour Project Work and Questions |
| Tue | (08:30) Introduction to Monte-Carlo random sampling
(09:30) 30 min exercises on monte carlo sampling (10:30) 1 hour (coding together) comparing random samples from different distributions (11:30) 1 hour Project Work & Questions |
| Wed | (08:30) 1 hour lecture Conditional Distributions
(09:30) 30 min exercises & 30 min solutions and explanations & 1 hour (coding together) tutorial on authentication & User Management (10:30) 1 hour (coding together) tutorial on implementing conditional sampling (11:30) 1 hour Project Work & Questions |
| Thur | (08:30) (lecture) Linear Regression (continued)
(09:30) 30 min exercises & 30 min solutions of exercises in multiparameter linear regression (10:30) 1 hour (coding together) tutorial on implementing linear regression estimation for large datasets (11:30) 1 hour Project Work & Questions (Add Auth to App!) |
| Fri | (08:30) (lecture): Non-Linear Regression (continued)
(09:30) 1 hour exercises + solutions for non-linear regression techniques on large datasets (10:30) 1 hour (coding together) tutorial on building non-linear regression forecasting pipeline (11:30) 1 hour Project Work & Questions |
| Wk. 3 | |
| Mon | (08:30) Normalization, Imputation and Data Cleaning
(09:30) Exercises in data cleaning: how fast can you clean your dataset? (10:30) 1 hour (coding together) tutorial: Working with massive datasets (11:30) Independent work on Projects |
| Tue | (08:30) Advanced data transformations for addressing multicollinearity
(09:30) Exercises in multicollinearity: when to transform? (10:30) 1 hour (coding together) advanced data imputation (11:30) 1 hour Project Work & Questions |
| Wed | (08:30) Dimensionality Reduction + Clustering
(09:30) 1 hour exercises PCA, kPCA (10:30) 1 hour (coding together) tutorial on dimensionality reduction and clustering (11:30) 1 hour Project Work & Questions
|
| Wed | (08:30) 1 hour lecture on Time-Series data: How is it different?
(09:30) 1-hour exercises on manipulation of dependant datasets (10:30) 1 hour (coding together) tutorial on timeseries datasets in python (pandas) (11:30) 1 hour Project Work & Questions
|
| Fri | (08:30) 1 hour lecture on Time-Series regression
(09:30) 1-hour exercises ARM and ARIMA regression models (10:30) 1 hour (coding together) tutorial on timeseries datasets in python (pandas) (11:30) 1 hour Project Work & Questions |
| Wk. 4 | |
| Mon | (08:30) Lecture on Assessing Classification Accuracy
(09:30) 30 min exercises + 30 min solutions Classification Accuracy (Predicted vs. Observed, ROC, MSE) (10:30) 1 hour (coding together) tutorial: Setting up a classification pipeline (11:30) 1 hour project work and questions |
| Tue | (08:30) 1 hour lecture on Random Forest
(09:30) 30 min exercises + 30 min solutions in using the Random Forest Algorithm (Machine Learning) (10:30) 1 hour (coding together) tutorial: Preparing data for Random Forest (11:30) Project work and questions |
| Wed | (08:30) 1 hour lecture on Bagging and Ensemble methods
(09:30) 30 min exercises + 30 min solutions on Machine Learning: how to use machine learning methods ? (10:30) 1 hour (coding together) tutorial: Setting up a machine learning model (11:30) 1 hour project work and questions |
| Thur | (08:30) Data Structures and Algorithms
(09:30) 1 hour data-structures & algorithms (10:30) 1 hour (coding together) tutorial on Data Structures and Algorithms (Technical Interview Prep) (11:30) 1 hour Project Work & Questions |
| Fri | (08:30) Technical Interview Drilling (all participants practice job interviews + reviewing interviews)
(11:30) 1 hour Project Work & Questions |
| Wk. 5 | |
| Mon | (08:30) 1 hour lecture on Web-Development and Dash
(09:30) 30 min exercises + 30 min solutions in Dash operations (10:30) 1 hour (coding together) tutorial: Setting up a Dash Application and visualizing a graph (11:30) Setup first deployment (Heroku) |
| Tue | (08:30) 1 hour lecture on Plotly and advanced Dash Callbacks
(09:30) 30 min exercises in Dash data visualization (10:30) 1 hour (coding together) tutorial: integrating statistical algorithms with a Dash Application (11:30) Setup a second deployment (Heroku) |
| Wed | (08:30) Introduction to REST and APIs
(09:30) 30 min exercises & 30 min solutions and explanations with Integrating REST api into pipeline (10:30) 1 hour (coding together) tutorial – Adding API data to our Dash app (11:30) 1 hour Project Work & Questions |
| Thur | (08:30) Error Handling and Dash pipeline integration
(09:30) 30 min exercises & 30 min solutions and explanations with Integrating REST api into pipeline (10:30) 1 hour (coding together) tutorial – Adding user selections to your Dash application (11:30) 1 hour Project Work & Questions |
| Fri | (08:30) Lecture on forecasting and confidence intervals: Drawing conclusions from your datasets.
(09:30) 1 hour exercises and solutions in data forecasting (10:30) 1 hour (coding together) tutorial on integrating forecasting into your Dash Applications (11:30) 1 hour Project Work & Questions |
| Wk. 6 | |
| Mon | (08:30) Feature Engineering for Random Forest
(09:30) 30 min exercises in implementing Feature Engineering Pipeline (10:30) 1 hour (coding together) feature engineering on a fresh dataset (11:30) 1 hour project work and questions |
| Tue | (08:30) Random Forest Deep Dive (Random Forest for continuous variables)
(09:30) 30 min exercises in Random Forest (10:30) 1 hour (coding together) tutorial: Visualizing the results of your Random Forest (11:30) Setup a training pipeline |
| Wed | (08:30) Deep Focus on advanced topics in Statistics
(09:30) 30 min exercises & 30 min solutions and explanations & 1 hour (coding together) tutorial on regression, revisited (10:30) 1 hour (coding together) tutorial – integrating regression in your feature engineering pipeline (11:30) 1 hour Project Work & Questions |
| Thur | (08:30) Advanced Dash Operations (integrating User selections with your Machine Learning algorithm)
(09:30) 1 hour exercises on Statistics Re-Cap (10:30) 1 hour (coding together) tutorial on challenge topics in statistics (11:30) 1 hour Project Work & Questions |
| Fri | (08:30) Technical Interview Drilling (all bootcamp participants practice job interviews + reviewing interviews)
(11:30) 1 hour Project Work & Questions |
| Wk. 7 | |
| Mon | (08:30) Introduction to Large Language Models
(09:30) 30 min exercises in Vector Operations (10:30) 1 hour (coding together) Installing and Fine-Tuning a Language Model (11:30) 1 hour project work and questions |
| Tue | (08:30) Introduction to Retrieval Augmented Generation (RAG)
(09:30) 30 min exercises in Vector Search (10:30) 1 hour (coding together) tutorial: Building a Vector Search Database (11:30) 1 hour project work and questions |
| Wed | (08:30) Introduction to Nearest-Neighbor Search Algorithms + Intro to FAISS
(09:30) 30 min exercises & 30 min solutions and explanations (10:30) 1 hour coding time – Nearest Neighbor Search Challenge! (11:30) 1 hour Project Work & Questions |
| Thur | (08:30) Advanced RAG Principals + Hybrid RAG
(09:30) 1 hour exercises on Statistics Re-Cap (10:30) 1 hour coding time – Nearest Neighbor Search Challenge! (11:30) 1 hour Project Work & Questions |
| Fri | (08:30) Advanced RAG Principals + Hybrid RAG
(09:30) 1 hour exercises on Statistics Re-Cap (10:30) 1 hour (coding together) tutorial: Building an advanced hybrid RAG pipeline from scratch (11:30) 1 hour Project Work & Questions |
| Wk. 8 | |
| Mon | (08:30) Technology Lecture: Docker
(09:30) 30 min exercises creating docker containers (10:30) 1 hour (coding together) feature engineering on a fresh dataset (11:30) 1 hour project work and questions |
| Tue | (08:30) Technology Lecture: Deployment
(09:30) 30 min exercises in Deployment of docker containers (10:30) 1 hour (coding together) tutorial: Building and deploying a dash app from scratch (11:30) 1 hour project work |
| Wed | (08:30) Technology Lecture: Postgres and SQL Alchemy
(09:30) 30 min exercises in SQLAlchemy + 30 solution review (10:30) 1 hour (coding together) tutorial: Database query practice (11:30) 1 hour project work
|
| Thur | (08:30) Technology Lecture: BigQuery + Spark + Big Data Methods
(09:30) 2 hour technical job-interview question practice (11:30) 1 hour Project Work & Questions |
| Fri | (08:30) Technical Interview Drilling (all participants practice job interviews + reviewing interviews)
(11:30) 1 hour Project Work & Questions |
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AI Data Science (ETH Dozent)
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AI Data Science (ETH Dozent)
14.04.2025
13.06.2025
exkl. MwSt.
Zürich Airport
