Applied Data Science Remote Learning

Applied Data Science Remote Learning

Become a Data Scientist in 9 months, taught online with immediate support

Applied Data Science Remote Learning

Become a Data Scientist in 9 months, taught online with immediate support

Interactive, online tuition
Delivered by experts
Part-time over 9-months
with constant support
Next cohort
March 2018 Enroll now

How it works

Unique Curriculum

  • ✓You’ll complete nine structured modules designed to rapidly develop your knowledge and understanding.
  • ✓Each module comprises of project-based coursework and video lectures taught by expert tutors.
  • ✓You'll implement state-of-the-art libraries and learn industry best practices to produce production-ready code.
  • ✓Throughout you’ll learn by doing and build a portfolio of projects to demonstrate your skills.

Immediate Support

  • ✓You’ll be continually assessed through practical project work and get immediate feedback on your code.
  • ✓Our expert tutors offer regular office hours to provide guidance and stimulate discussion to ensure you can reason about your methodology.
  • ✓Interact with like-minded professionals via slack and peer-learn through mutual code reviews.
  • ✓Throughout we provide online support to help with your technical questions and and challenges you are facing.

Skills Development

  • ✓Receive personalised, data-driven improvement plans based on your code submissions to keep on track.
  • ✓You'll also get a bi-weekly report of common error feedback to support your learning progress.
  • ✓Develop the industry specific skills to communicate and work effectively with data science and data analyst teams.
  • ✓Understand the capabilities of machine learning models, and when/where to apply each technique to solve complex problems.

Career Progression

  • ✓Upon completion you’ll earn an Applied Data Science certification supported by our academic and industry backers.
  • ✓We're dedicated to supporting your success and provide introductions to hiring partners for new job opportunities.
  • ✓In the final 8-weeks you’ll work on a capstone project to demonstrate your knowledge and capabilities as part of your portfolio.
  • ✓Make meaningful connections as part of the Cambridge Spark community of data scientists, students and alumni.

What you will learn

Online Learning
  • Weeks 01-03 Essentials of Computer Science, Probability and Statistics
  • Weeks 04-06 EDA, Feature Engineering and Unsupervised Learning
  • Weeks 07-10 Supervised and Semi-Supervised Learning, Training and Validation
  • Weeks 11-13 Regression Models and Time Series
  • Weeks 14-16 Ensemble Models and Support Vector Classifiers
  • Weeks 17-19 Cloud Computing and Databases
  • Weeks 20-22 Big Data Systems
  • Weeks 23-25 Neural Networks and Deep Learning
  • Weeks 26-28 Natural Language Processing
  • Weeks 29-36 Capstone Project

To receive further information about our course curriculum and advisory board

Request course outline

Course Breakdown

160+ hours expert-led tuition

120+ hours completing projects with instant support

90+ hours contributing to a real-world problem

Performance feedback

Verified Certification of Completion

Career support and advice

Next Cohort: March 2018

This course is very popular and there are limited spots. Apply early to secure your place. Deadline for applications: 01 February 2018.


Standard Price £2,950
Early-bird Price (until 01 January) £2,450
Deposit at time of enrolment £295
9 monthly payments £295

Satisfaction guaranteed receive a full refund within 7 days if you are not happy with the course.


  • Elementary Python programming and use of the command line. You can acquire these skills at our Python bootcamp.
  • Basic probability and linear algebra.
  • Familiarity with Git.

Got questions? Get in touch

Our team are here to help. Get in touch to discuss your background and clarify any questions you have about the course.

Request a call

Find out More

Simply register your interest. Our team will be in touch with the full curriculum and course information.

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