This week we will...

cover the following topics:

• Talk about the tasks from last week

• Statistical Significance

• Introduction to Version Control with Git

• Introduction to Data Preparation

Learning Resources

Until next week you should...

• Complete on DataCamp about merging DataFrames.

• Watch (33 minutes) on Git in VS Code.

• For an introduction to the possibilities of regular expressions, check out this 11-minute video.

This week we will...

cover the following topics:

• Important terms in machine learning

• Overfitting and regularization

• Model quality criteria

• Introduction to neural nets

Learning Resources

• for the definition and estimation of neural networks for different example datasets

• of the effect of overfitting and regularization

Until next week you should...

• watch the videos of the sections “” and “” from week 1 of the course Advanced Learning Algorithms from DeepLearning.AI

• further extend the dataset with additional variables that could be relevant for estimating revenue.

• further test your baseline model's predictive performance on Kaggle!

Presentation (Powerpoint, Keynote or similar)

Prepare an 8 to 10-minute presentation including:

• Your team members’ names on the title slide

• List and brief description of self-created variables

• Bar charts with confidence intervals for two self-created variables

Each team member should have a part in the presentation!

This week you will...

get an introduction to the following topics:

• What is data science?

• R vs. Python vs. SPSS vs. ...

• Jupyter Notebooks

Learning Resources

Until next week you should...

• Register for GitHub Copilot as described or download Cursor (includes a 1 week Pro trial) as described to use it instead of VS Code (Cursor and VS Code are mainly identical). As a student or teacher, you get free access to GitHub Copilot Pro following the instructions given .

• watch on working with strings (16 minutes)

• watch on working with numbers (“Numbers” and “Working With Numbers”; 5 minutes)

This week we will...

• get to know about the importance of baseline models

• learn about naïve forecasting

• see how linear regressions are defined

• understand the role of cost functions

• get an introduction into optimization functions

Learning Resources

• on Linear Regression

Until next week you should...

• Watch the videos in the section "" from Week 3 of the course "Supervised Machine Learning: Regression and Classification" on DeepLearning.AI.

• Use a simple linear model to make a prediction for the test dataset and evaluate it on Kaggle. (Ensure that the number of rows and the defined IDs match those in the sample_submission file!)

• Further enrich the dataset with additional variables that may be relevant for estimating revenue and formulate a linear model equation that maximizes the adjusted R² for your training dataset.

This week we will...

cover the following topics:

• VSCode and GitHub Code Spaces

This week we will...

cover the following topics:

• Versioning with Git in a Team

• Important Issues to Consider for Feature Engineering

• Introduction into Analyzing Time Series Data

Learning Resources

Until next week you should...

• meet with your team to discuss potential additional variables to be created for sales prediction (including whether there are any additional data sources you can use).

• analyze the time series data for the 6 product groups using moving averages, percent change, or segmentation (see learning material).

• update the "Data Import and Preparation" directory in your team repository to include:

(You need to create a free account with DeeplLearning.AI.)

Before the first course session, you should ...

• create accounts for GitHub, ChatGPT and Claude.

• install VS Code on your computer using .

• work through the first chapter of the "" course at datacamp. For this, you also need to register a (free) account at datacamp. You only need to complete the first chapter of the course, which is free.

This week we will...

• learn about different libraries for implementing neural nets

• review example notebooks for the data preparation and training of neural net using Pandas and TensorFlow

• get to know additional types of layers in neural nets

Learning Resources

• Additional (12 Minuten) on neural nets

• for a neural net

• for training a neural net

Until next week you should...

• watch (7 minutes) to better understand the properties of dropout layers.

• watch (5 minutes) to better understand the benefits of normalization.

• complete the first chapter of on DataCamp to learn about identifying missing values.

This week we will...

• learn about different patterns in times series

• walk through the general procedure for training machine learning algorithms

• get to know how to test predictions on Kaggle

• get an impression of current developments in AI

Learning Resources

• for graphical analyses of time series

Until next week you should...

• complete the sections on „“ and „“ from Module 4 of the course Python for Data Analytics by Deeplearning.ai (about 2 hours).

• download the test data set from Kaggle and merge it with your current data set.

• write code to split your dataset into a training dataset from 01.07.2013 to 31.07.2017, a validation dataset from 01.08.2017 to 31.07.2018, and the test set from 01.08.2018 to 31.07.2019. (Ensure that the number of rows and the defined IDs match those in the downloaded test dataset!)

All participants are expected to pursue a certificate of achievement or ECTS credits, that is to fulfill the following conditions to complete the course:

• Attendance at at least 80% of the course sessions (you may miss a maximum of two sessions)

• Completion of mandatory milestones defined by the course instructors

• Presentation of the given data science project (see description )

• Documentation of the project via an open source GitHub repository (also see description ), to which all team members must have contributed.

Online Attendance:

If you attend via Zoom, please make sure to use your full name, which should be the same that you used to register at edu.opencampus.sh. Otherwise your attendance will not be recorded!

This week we will...

• learn how to use dropout layers

• get to know different ways to handle missing values

Learning Resources

• for handling missing values

• Chapter 1 of course at datatcamp

• on the Transformers library

Until next week you should...

• choose one (or several) methods to replace the missing values in your dataset.

• (if you haven't already) divide the tasks well within your team: Who will work on data optimization, and who on model optimization?

• using your best model, generate predictions for the Kaggle competition test dataset and upload them there.

• prepare your final presentation (see the guidelines for Week 10 ).