Learn Data Analytics in Amsterdam

During your first 60 hours as an official Ironhack Data Analytics student you will be laying the foundation for success in Amsterdam. During the prework phase you will:

1. learn the fundamentals of command line, Git, Python, MySQL and statistical analysis

2. familiarize yourself with the basics of programming and statistics and

3. connect with your peers and expert academic staff by utilizing our Slack channel.

Once you’ve completed the prep materials and synced up with your class, you’ll be ready to dive into the course!

The first two weeks will introduce you and your classmates to the world of data analytics. Then you will establish your development environment for the classroom as you settle into our tight-knit data community.

Topics include:

- Introductions to data wrangling/cleaning.

- APIs and web scraping.

- Intermediate levels of Git, SQL and Python.

Week three calls for your first project as an apprentice data analyst! Apply your new Python skills by conducting data analysis with real datasets.

In weeks four and five you’ll take a deeper look into the mathematics behind data analytics.

Topics include:

- Utilizing Python to understand inferential statistics and probability.

- Incorporating Python into the fundamentals of business intelligence.

- Learning story-telling techniques in order to visualize your data and insights in presentations.

Week six will mark the start of your second project: a complete data analysis. This will be constructed from data that you will have processed, cleaned and visualized from real datasets!

The final module will introduce you to the fundamentals of machine learning in weeks seven and eight. We’ll start things off by teaching you to understand the machine workflow, and the lessons will only expand from there.

Topics include:

- Both supervised and unsupervised learning.

- The essentials of popular machine learning algorithms.

- Building, training and evaluating models with the Scikit-Learn machine learning library.

For the last week of this module and course, you will face your final and most challenging task: building an end-to-end machine learning project. You will need to process a dataset, extract features, train a model, and use that model to make predictions on new data. When your project is complete, you will compete alongside your fellow students in our Hackshow.