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Data Analytics Fundamentals

This course will cover key topics like ‘What does a data scientist do?’ and ‘How can I look at my data in different ways?’.

Data Analytics Fundamentals
  • Duration4 weeks
  • Weekly study12 hours
This course is part of the Data Analytics for Decision Making program, which will enable you to Take the first steps to becoming a highly-skilled data scientist.

This course is part of the Data Analytics for Decision Making Microcredential. On this microcredential you will:

Boost your data science career potential

Alongside world-class computer science experts from Queen Mary, on this microcredential you’ll learn the process for collating and cleansing data. You’ll discover how to interpret and communicate data to others and gain valuable insights to inform your decision-making process.

You’ll also explore the essential ethical and legal issues that need to be considered when generating, analysing, and disseminating data.

Gain data analytics certification

Ultimately, you’ll come away with the accredited skills you need to apply for roles as a data scientist, or to enhance your current organisation’s capacity to interpret and manage data to solve complex problems and predict future trends.

Syllabus

  • Week 1

    Fundamentals of data science

    • Welcome to the course

      Welcome to Data Analytics Fundamentals. Let's get started and find out more about what you can expect from this course.

    • What are the basic principles of data analytics?

      Next, we will familiarise with key concepts and terminology used in data analytics. We will regularly refer back to these principles throughout the course.

    • Setting up your data environment

      Next, we will set up the software environment we will be using throughout the course for data analysis using Python. You will be given the option to install it locally or use it remotely.

    • Put it into practice: use Python to analyse data

      Next, we will learn about Pandas, a Python package for data analytics, and we will get our hands on some datasets to start practising.

    • Weekly wrap-up

      This will conclude the first week of the course and we will take a sneak peek of what we will be seeing in Week 2.

  • Week 2

    Data visualisation insights

    • Welcome to Week 2

      Welcome to Week 2. Learn about the contents we will be learning in this week.

    • How can we visualise data?

      Next, we will be digging into data visualisation, looking at the main benefits of using visualisation as well as learning the pros and cons of different data visualisation methods.

    • The randomness of data

      Next, we will see that datasets present unexpected patterns sometimes, which may need additional data cleaning before we create our visualisations. We will be seeing how to achieve this.

    • Put it into practice: produce your own notebook

      Next, you will have the opportunity to practise your data visualisation skills by using your own data environment.

    • Weekly wrap-up

      This will conclude the second week of the course, briefly introducing the contents that we will cover in Week 3.

  • Week 3

    Gaining value from data

    • Welcome to Week 3

      Welcome to Week 3. Learn about the contents we will be learning in this week.

    • How might data drive business decisions?

      Next, we will be exploring the possibilities of using datasets for data-driven decision making, using well-established methods for measuring success, such as key performance indicators.

    • Applying data processing techniques to make data valuable

      Next, we will be looking into the particular case of temporally evolving datasets and the need to regularly update our statistics and visualisations. You will be learning key skills for producing temporal data analytics.

    • Put it into practice: telling a story through data

      Next, we'll complete our data analytics skills by looking into methods for comparative data analytics (comparing patterns across variables). We'll also put our skills into practice by working on an exercise on our own environment.

    • Weekly wrap-up

      This will conclude the third week of the course, and will introduce the fourth and last week of the course, focused on revision of contents and preparing to work on the final coursework.

  • Week 4

    Revision and assessment preparation

    • Welcome to Week 4

      Welcome to the fourth and final week of the first course. We will revise the contents learnt throughout the previous three weeks and we will describe the final coursework for this course.

Who is the course for?

This course is part of the Data Analytics for Decision Making Microcredential. This microcredential would appeal to anyone looking to apply for roles as a data scientist, improve their current organisation’s data analysis, or looking to apply for higher-level study in data science.

Who developed the course?

Queen Mary University of London

Queen Mary University of London is an established university in London’s vibrant East End committed to high-quality teaching and research.

  • Established1887
  • LocationLondon, UK
  • World rankingTop 110Source: Times Higher Education World University Rankings 2020