Over 98% of participants from the January 2024 cohort rate their course good or excellent

Leveraging Big Data for Business Intelligence

Learn how to make data-driven decisions using big data, data science and machine learning.

Certified by the CPD Certification Service

This course equals 48 hours of CPD time

  • Duration

    6 weeks

  • Commitment

    6-8 hours per week

  • Price


  • Study mode

    Tutor guided

  • Certificate of Achievement

    Evidence your learning with a Certificate of Achievement from the University of Cambridge on successful completion.

    • Start

      15 July 2024

    • Finish

      26 August 2024

    • Enrol by

      08 July 2024

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Discover more about this course from the expert(s) behind it

Course overview

Making data-driven decisions to stay ahead of the curve is imperative to the success of any business. In this course, you will learn how to understand big data, user-driven events, data science techniques and machine learning and apply this in order to inform successful product feature development and strategic business solutions.

This course takes a practical approach using BigQuery and Colab notebook to identify how you can recognise business problems, obtain data as the building blocks of problem-solving, and how to use data science techniques to help understand, build and validate possible solutions. You will be able to apply your learning immediately in the workplace, making proactive changes that secure strategic and financial success based on real data.

What will I learn?

  • communicate data-driven decisions with authority to key stakeholders
  • determine the main components of Big Data, Data Science and Machine Learning and how they work in a practical web tech environment
  • undertake data analysis, data cleansing and data visualisation for data-driven product development to resolve strategic business issues
  • interpret what the tools are telling you in terms of data trends and how to modify your approach
  • strategically apply a range of data tools and methods to analyse and resolve common business issues.

Who is this course for?

  • those interested in learning how to effectively harness the new technologies of machine learning and data science in a business context
  • individuals seeking practical, business-orientated applications for big data to improve strategy, productivity, or decision-making.

Course delivery

Our certificated courses reflect the Cambridge experience and values, with low student to tutor ratios and academically rigorous standards. Our learning model is designed to help you advance your skills and specialise in emerging areas that address global challenges. We will help you build your network through an engaging and impactful learning journey that encourages collaboration. Courses are delivered in weekly modules, allowing you to plan your time effectively. The assessment criteria will be presented to you at the start of the course, so you can approach your studies with confidence and motivation, knowing what is expected of you and how to meet those expectations. 

Throughout your online learning experience, you will have access to your course tutor, who will help facilitate your learning and provide you with support and guidance during your studies. You can interact with your tutor through a range of media, such as live sessions, discussion forums, email or canvas messaging. 

Each course includes a balance of: 

  • interactive learning and real-world application so you can directly apply what you’re learning to your own context
  • diverse teaching methods to enhance learning outcomes which will be delivered via learning activities such as University of Cambridge academic led videos, quizzes and group work  
  • optional live sessions (1 hour) with University of Cambridge academics and tutors to deepen your understanding of the week's material. These sessions may include an informal Q&A, a short lecture or a breakout activity that builds on the content introduced that week. All sessions are recorded and made available to stream so you can catch up whenever suits you
  • guided critical thinking via our reflective workbook so you can collect, structure and summarise information and your thoughts as you progress through the course. 

What will I get on completion?

Evidence your learning with a Certificate of Achievement from the University of Cambridge on successful completion.

Course dates

  • 2024

    15 Jul - 26 Aug

    Places available

    Enrol by 08 Jul


Level of knowledge

  • although you do not need to know how to use data analytics tools to join this course (as the course will introduce you to these), it is recommended that you have a general understanding of data and data analysis
  • experience with SQL and Python is desirable but not essential to be able to interpret the results of statistics and visualisations and undertake analysis of regression models
  • a level of spoken and written English sufficient to allow you to participate and succeed in the course (we recommend that you have an English Language level equivalent to an IELTS score of 7, as outlined in section 5 of our Terms of Purchase (Opens in a new window)).

Materials & equipment

  • participants will need to register for a free Google Cloud (Opens in a new window) account to access 'BigQuery'
  • where possible we ensure all of our course content is compliant to Web Content Accessibility Guidelines (WCAG) and is subject to regular review. In some cases however, our course process requires the use of proprietary third party tools such as Google Colab, which are subject to their own accessibility compliance. If you have any individual requirements, specifically relating to the use of a screen reader, please contact us at uoc.online@cambridge.org (Opens in a new window) to discuss further
  • sufficient internet speed and stability for video streaming (2 Mbps up/down)
  • please see our recommendations on web browsers (Opens in a new window).

What our learners are saying

All the video materials and presentations were outstanding. The user interface was great and it made the learning process very enjoyable.

I enjoyed the hands-on, practical approach.

A good balance between understanding some technical details but not getting bogged down in too much technical detail.

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