Start working as a Data Scientist
CodeOp’s Data Science bootcamp was designed to provide women+ with a background in STEM, Finance, Economics or Business Intelligence the skills they need to build a career in the analytics industry.
Full-time (11 weeks) | Part-time (30 weeks)
Who we are looking for and how we help
- Researchers looking to transition into industry
- Business & financial professionals looking to upskill
- Obtain the job security and a salary you deserve
- Translate your career to the field of data science
- Polish your skillset with advanced analytics tools
- Get your portfolio industry-ready
- Tap into our +21k international network
- Learn with a supportive community of women+
Get practical experience with the most in-demand tools
Take your career to the next level by learning the latest in machine learning, data infrastructure and visualisation.
Boost your earning potential
Our hiring network is constantly looking to diversify their analytics team with CodeOp talent; 77% of CodeOp students see an increase to their salary after the bootcamp.
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Get qualified quickly
Our Data Science qualification means you’ll have the skills you need to start working as a Data Scientist, Data Analyst or Product Analyst after 2 – 8 months.
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The structure and guidance needed to excel
Our part-time, evening course was designed to support working students with the regiment and guidance needed to become industry-ready. We also offer the full-time course for those who prefer a more compact learning schedule and majority of their time commitment is thus reserved for the bootcamp.
We are always there to support our students excel.Don’t believe us: pay us nothing until you get a job after the bootcamp in our Study Now, Pay Later plan.
Learn MoreYour route to build a complete data science skill set
Our Data Science course provides you with an industry-ready toolkit to build your data science career, while creating multiple portfolio pieces to showcase your new skill set. Learn the fundamental concepts of Statistics and Machine Learning
- Access and create datasets from Relational Databases using SQL
- Get hands-on training using libraries in Python: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, and more
- Learn the basics of Machine Learning as well as advanced machine Learning algorithms commonly used in academia and industry
- Get exposed to processing alternative data formats including: time series, NLP (Natural Language Processing), and Geospatial data
Why this course is different
We put a lot of emphasis on learning the complete Data Analysis toolkit, which prepares you to become both a successful data analyst. Here’s how we offer our students that little bit more to give them the industry edge:
- We don’t just cover the fundamental concepts, but also hands-on, real-life application cases
- We expose students to expert instructors from various backgrounds, both from the industry and academia
- Our students get the opportunity to finish personal data science projects guided by experts in the field
- We offer customized career support and preparation during and after the bootcamp
- We offer a rich student cohort experience that encourages student participation, foster creativity, build leadership skills and generate a sense of community
- We offer customized career support, preparation and a life-long CodeOp community during and after the bootcamp
SCOPE & SEQUENCE
Our ten-topic curriculum guarantees our graduates are industry-ready
Topic
1:
Introduction to Programming
Learn the foundations of programming and how to code in Python. It is designed for people with no prior coding experience.
Activity: Write a simple application in Python
Topic
2:
Programming for Data Analytics
Understand the fundamentals of programming using Python for Data Analytics. It will cover everyday functions and applications, including how to use Python to do basic arithmetic, understanding variables and types, and building Python lists. It will also cover how to use functions, methods, and packages to use code that other Python developers have written.
Activity: Write Python codes to store, access, and manipulate data
Topic
3:
Infrastructure and SQL
Learn how to use Bash, write SQL code and understand relational databases
Activity: Create and query a relational database
Topic
4:
Exploratory Data Analysis
Understand the data analytics pipeline, review the foundations of Data Analytics using probability, statistics & basic data analysis, and learn classic data analytics methods as well as data visualisation using popular Python packages built for Exploratory Data Analysis.
Activity: Use statistical methods to analyse datasets using Jupyter notebooks.
Topic
5:
Data Visualization Presentation (Dashboards & Storytelling)
You will learn the best practices for designing dashboards, including how to choose the appropriate visualizations for your data. We will also cover the most commonly used tools and techniques like Tableau and Power BI software.
Topic
6:
Decision Science
Learn how to use frameworks such as AB testing to statistically analyze different hypotheses and make informed business decisions e.g. which variant of a website to launch
Topic
7:
Machine Learning
Learn the differences between supervised and unsupervised machine learning methods, and the different families of algorithms within each group (e.g.: regression, classification, clustering).
Activity: Create a predictive model for a given labelled dataset.
Topic
8:
Advanced Machine learning
Learn more advanced methods such as Neural Networks, Natural Language Processing (NLP) and Recommendation systems to process complex data types like images, text, geospatial data and time-series.
Activity: Use advanced machine le to analyse complex datasets.
Topic
9:
Project Phase
Apply the knowledge gathered in the previous modules to real use-cases, implementing end-to-end Data Science projects.
Project: Propose a problem and solve it using the techniques learnt in the previous modules. Cover all stages of the Data Science lifecycle in both an individual and a collaborative project.
Topic
10:
Career preparation
Prepare for job interviews through logical puzzles, data challenges, and practice sessions. Receive career coaching & support.
PRICE & FINANCING
Part-time and full-time (remote + live)
€7200
+€600 deposit
- €1,000 discount when paid upfront
- Break up the cost of tuition
- Low-interest financing options available
We offer a range of options to minimize the cost of tuition for all students, including scholarship opportunities for anyone who’s eligible. You can explore the flexible payment options available on our detailed student financing and scholarships page.
THE BOOTCAMP JOURNEY
Discover the Data Science learning schedule
Our Data Science bootcamp runs both full time and part time.
The full time course is 11 weeks long, Monday to Friday from 9:00am to 6:00pm CET. You can do this on campus live instructors.
The part time course is 30 weeks long, and virtual, synchronous classes are held Monday and Thursday from 6:30pm to 9:30pm CET.
During Lecture & Activity Phase, students work on familiarizing concepts through hands-on examples with instructors. Then, instructors guide students through their projects. The last part focuses on developing a career in Data Science.
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Week 6-10
Week 11
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PROJECTS
Showcase your new skill set with a top portfolio
You’ll work on at least two projects during the bootcamp, one individual and one team-based. Your data science instructor will spend 6-weeks guiding you through your project-work. Throughout the project phase, you’ll have feedback sessions (twice a week) to help you develop your project from start to finish.
INSTRUCTORS
Meet the team behind our industry leading course
Our instructors bring a ton of experience and energy to your technical education. They’ll act as your teacher, adviser, and guide throughout the course, providing personalized feedback to help you build confidence in developing your analytics mindset.
Filipa
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Nicholas
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Priyanka
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Guillem
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Ana
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Pilar
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COURSE SUPPORT
Get total support from start to finish
Enjoy unrivalled attention and support with class sizes of only 15 students. A ratio of 1 instructor for every 5 students guarantees one-on-one attention, faster learning, and stronger relationships.
Your support team
SENIOR INSTRUCTOR
These are the real experts; they come with varied backgrounds, from PhDs to years of rich industry experience. Their role is to direct you through the course content and teach you best practices so that you can grow in your knowledge, skills and confidence in the best, most efficient way possible.
TEACHING ASSISTANT
The bridge between the instructor and the students. In most cases they've been through the bootcamp themselves and so can relate to and empathise with the students' experience. Their role is to support you during activity time as well as with any additional technical support needed.
CAREER COACH
The role of the career coach is to prepare you for post-bootcamp life in the best way possible. They play an important role in helping you identify your strengths, weaknesses and transferable skills from your previous career and guiding you on the options available to you upon graduation.
CAREERS SUPPORT
Our dedication to yourcareer goals is second to none
We’re committed to helping our students advance their careers in tech. Our support team works hard to make sure you get where you want to go in your data science career, and can hit the ground running once you’re there.
360 CAREER SUPPORT
Students are supported with careers coaching and training over the duration of the bootcamp. After, we continue to provide ongoing career support, as well as access to our graduate network of recruiters, job opportunities and recruitment fairs, mentors, events and more.
Industry TALKS
Through interactive workshops and lectures, students learn best practices and the latest tech from professionals in the industry. You’ll have opportunities to learn about Agile methodologies, D3.js, Big Data, Open Source and privacy and ethics.
CAREERS WEEK
An intensive week of technical and career coaching workshops, presentations, and professional talks. The week culminates in a #IamRemarkable session—a Google initiative empowering women and underrepresented groups to celebrate their achievements in the workplace and beyond.
HIRING NETWORK
Because of our strong commitment to diversity, our community is one that recruiters and companies come to directly to find highly-trained candidates. We have a large hiring partner network to ensure our students can gain experience in the field, secure better jobs and further advance their tech careers.
NEXT COURSE DATES
Join our courses in-person or remotely
All courses are taught in Central European Time (CET).
PRICE & FINANCING
Part-time and full-time (remote + live)
€7200
+€600 deposit
- €1,000 discount when paid upfront
- Break up the cost of tuition
- Low-interest financing options available
We offer a range of options to minimize the cost of tuition for all students, including scholarship opportunities for anyone who’s eligible. You can explore the flexible payment options available on our detailed student financing and scholarships page.
ADMISSIONS CRITERIA
You can do it, put your back into it
Discover what it takes to apply. We’ve pretty sure you’ve got it.
WHAT YOU NEED
- A statistical, technical, or Business Intelligence background
- A good understanding in at least one of these areas:
– Computer programming – Statistical analysis – Business intelligence
- An analytical edge: Curiosity to ask questions and seek answers through delving into small and large data sets. The data does not speak.
- B2 level of English
- Empathy: It’s not going to be an easy ride, in fact, it will be an emotional rollercoaster. Which is why we want to encourage you to take care of each other and most importantly take care of yourself.
- The motivation to upskill or transform your career
- A computer and a stable internet connection
WHAT YOU DON'T NEED
- Industry experience: We’ve supported many women+, from research and academia, to gain the practical experience and portfolios needed to transition into the tech industry
- Unlimited free time:Our courses are available both part-time and full-time so that you can find a learning pace that works best for you.
- A competitive/toxic attitude
