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Fundamentals of Data Science (Technical) - an online course

About this course

This course equips beginners with the theoretical knowledge and technical skills to apply the powerful insights of data science to their work, as well as providing the foundations for a career in Data Science.

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This course has been exclusively designed from the ground up by the world-leading Data Science team at the University of Southampton.

The course emphasises a hands-on approach to learning data skills, offering a number of interactive, online exercises that will let you try out many of the techniques and concepts covered in the taught material. The technical aspects of the course are implemented using Python, and we strongly recommend you have some experience with Python or similar languages.

Structure

The course is broken into six weeks.

In week 1, you will meet your tutor and the other participants on this course and find out more about what you will be doing over the next 6 weeks and how we will be supporting you. You will get "hands-on" experience of Jupyter the web-based environment which you will use for the course exercises and assignments.  This week also contains a Python Primer activity for those of you who are unfamiliar with the programming language or would like a refresher.

In week 2, you will learn about the fundamental terminology and processes in data science, discovering the technology landscape that has helped fuel the data explosion, and the tools that data scientists use to unlock the hidden value in these vast amounts of data. This week also contains an introduction to using Python for data science.

You will begin gaining hands-on experience of data science in week 3, focusing on collecting, storing and managing data, and you will learn about the different sources of data and how they can be combined in order to increase the potential insights available.

Week 4 will then help you understand how this data is analysed, covering a range of techniques that a data science team would typically use, from statistics to machine learning. You will use Python to apply these analytical techiques to a real-world dataset.

In week 5, you will learn about how the findings from data science work can be reported using different data visualisation techniques. You will discover the various ways in which particular types of data can be displayed in order to highlight a key finding and improve the impact of your reports.

In week 6, you will be looking at the future of data science and we will be focusing on supporting you to finish off your assignments.

Hands-on experience and assignments

Each week contains a mix of taught material, self-study material, activities and practical online exercises.

Week 1 includes an (optional) introduction/'refresher' on Python. This includes online exercises for you to work through at your own pace.  This not graded. Week 2 contains further Python practice exercises.  You are encouraged to do these as these will help you with your assignments. Again, these practice exercises are not graded.

Weeks 3, 4 and 5 each include online exercises (ungraded) and a related graded coursework assignment.

To find out more, fill out the enquiry form or email us on info@southamptondata.science

This course will provide you with the knowledge and expertise to become a proficient data scientist.

Having successfully completed this module, you will be able to:

  • Understand the key concepts in data science, including their real-world applications and the toolkit used by data scientists;
  • Explain how data is collected, managed and stored for data science;
  • Implement data collection and management scripts using MongoDB;
  • Demonstrate an understanding of statistics and machine learning concepts that are vital for data science;
  • Produce Python code to statistically analyse a dataset;
  • Critically evaluate data visualisations based on their design and use for communicating stories from data;
  • Plan and generate visualisations from data using Python and Bokeh.

Upon successful completion of the course, you will be awarded with a Certificate of Completion and a graded transcript.

Visualising Bokeh (Python)
Stats / Analysis NumPy/SciPy/Pandas
Management / Querying MongoDB (using Python)
Base Python
Week 1: Welcome and course information
 
Topics
  • Welcome and introduction
  • Learning outcomes of the week
  • What data science is and why it's important
  • Course syllabus and learning outcomes
  • Using discussion forums 
  • Introduce yourself
  • Help and tutoring support
  • Course assignment details
  • A 'hands-on' Jupyter familiarisation activity
  • Python Primer
  • Glossary of terminology
 
Week 2: Introduction to core concepts and technologies
 
Topics
  • Introduction
  • Learning outcomes of the week
  • Data science in a nutshell
  • Terminology
  • The data science process
  • A data science toolkit
  • Types of data
  • Example applications
  • Further reading
  • Summary
 
Week 3: Data collection and management
 
Topics
  • Introduction
  • Learning outcomes of the week
  • Sources of data
  • Data collection and APIs
  • Exploring and fixing data
  • Data storage and management
  • Using multiple data sources
  • Further reading
  • Summary
 
Week 4: Data analysis
 
Topics
  • Introduction
  • Learning outcomes of the week
  • Terminology and concepts
  • Introduction to statistics
    • Nature of statistics and introduction
    • Central tendencies and distributions
    • Variance
    • Distribution properties and arithmetic
    • Samples/CLT
  • Basic machine learning algorithms
    • Linear regression
    • SVM
    • Naive Bayes
  • Further reading
  • Summary
 
Week 5: Data visualisation
 
Topics
  • Introduction
  • Learning outcomes of the week
  • Types of data visualisation
    • Exploratory
    • Explanatory
  • Data for visualisation
    • Data types
    • Data encodings
    • Retinal variables
    • Mapping variables to encodings
    • Visual encodings
  • Technologies for visualisation
    • Bokeh (Python)
  • Further reading
  • Summary
 
Week 6: Future of data science
 
Topics
  • Introduction
  • Learning outcomes for the week
  • The future of data science

Fees

The Fundamentals of Data Science (Technical) course is £1500 per person, inclusive of VAT.

For corporate packages, please see here.

How to Pay

You can pay by phone, email or Flywire, using the application form here.

Paying Online

  • To make a payment using this method, fill out the application form and select "Pay by Cebit / Debit Card".
  • A 2% fee is charged for payment by credit card and we do not accept American Express. 
  • Fees paid by this method will be charged in British pounds sterling.

Paying by Phone

  • To make a payment using this method, fill out the application form and select "Pay by Phone". 
  • A 2% fee is charged for payment by credit card and we do not accept American Express. 
  • Fees paid by this method will be charged in British pounds sterling.

Pay by Email

  • To arrange a payment using this method, please contact us once you have received confirmation of your place. Contact your course advisor, agent, or email us on payments@southamptondata.science 

Flywire

  • To make a payment using this method, fill out the application form and select "Pay by Flywire".
  • Best for international participants: accepting over 70 currencies via credit card, debit card, or bank transfer.

  • Simply visit www.flywire.com/southamptondata and follow the instructions on the website. Please use the same email address that you used when you applied to the course.