Develop the skills you need to launch a fulfilling career in Data Analytics with our 24-week part-time course or our fulltime nine-week bootcamp.
Learn Data Analytics in only nine weeks!
Learn Data Analytics in only 24 weeks!
Select one of the formats above for specific course information. Otherwise, information about the Data Analytics bootcamp (full-time) will be shown by default.
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.
- 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.
- 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.
- 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.
To ensure the safety of our students and staff, we have migrated all ongoing and upcoming classes to a remote learning environment. Learn more about Our response to COVID-19
DepositOur courses fill up fast. Secure your spot at Ironhack by making an advance deposit when you are accepted into the program. Deposit amount: 750€
Do you need help with the payment? Our financing options help you find the support you need so you can focus on your career and developing new skills.
Only pay for your Ironhack experience after you’ve graduated by using Quotanda. With a simple and quick application process, you will be able to access financing for up to €5,250 with an annual rate of 5% and a 12-month payback period.
Pay nothing until after you’ve secured your dream job! This agreement guarantees that your payments will only begin after you have secured a role making at least €25,000 per year.
You can have up to 100% of your study costs financed by the national unemployment center of the Netherlands (UWV) if you are receiving monthly alimony for unemployment. You won’t have to pay anything back – this funding from the UWV is specifically designed to help you find a job.
Want more information?Visit our Financing page
The tech community suffers from a gender gap, which is why we are proud to promote women in tech with an upfront scholarship of 10% off for any of our Web Development and Data Analytics courses.
Jan is excited to teach you about wrangling and analyzing data. He has always enjoyed working with data, be it modeling, understanding or extracting information. When he’s not working with data, he enjoys making music, drawing and playing tennis. Jan loves understanding how stuff works, and sharing his knowledge and experience with others. But most of all, he enjoys helping people learn new skills and grow professionally.data
In the bootcamp you will study from 9 a.m. to 6 p.m., Monday through Friday. The schedule may be subject to changes depending on the chosen campus.
9:00Start your day energized
Pour yourself some coffee to get your day going, as class begins with a review of yesterday’s exercises. You’ll also learn to manage a healthy workflow by incorporating the key strategies of Agile and Kanban.
9:30Lecture block 1
Your first block of lectures begins, where you’ll dive deep and learn some of the intricacies of !
11:30Data lab 1
Apply the concepts you’ve learned in the previous lecture to a new tutorial or challenge. Time to roll up your sleeves and code!
Take a well-earned break and recharge! Our campus is centrally located, so good eats are always easy to find.
14:00Lecture block 2
Afternoon lectures commence, continuing your development of even more skills and techniques.
16:00Data lab 2
Time to solve a project by yourself! This will be a hands-on exercise calling for you to apply your knowledge in real-world practice.
Once the day’s coursework has ended, explore and find innumerable opportunities to meet and network with other data professionals. Meetups, networking sessions and workshops are just down the street, or you can stick around campus to catch up on classwork.
20% increase of energy efficiency, 20% decrease in CO2 emissions, and 20% of energy production from renewable sources, all by 2020: a challenging goal to meet. Ingrid was inspired to analyze Spain’s electricity consumption in order to determine whether or not we are on the path toward a more sustainable rate of electricity consumption. She found some particularly interesting patterns that we are following as users!
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