Data Science

This course enables participants to model and interpret data. With a comprehensive combination of theory and practice, participants will learn how to construct, analyse and present integrated data sets and deliver valuable insights for their organisations.

Digital Skills Academy
Course Starts:
Course Ends:
Registration Closes:
12 weeks plus final assessment
Effort Per Week:
11 hours
Delivery Mode:
Data Science
Single Payment:
Total fees
Two Instalments:
On purchase
After 5 weeks
Total fees

Course Introduction

The course introduces participants to Data Science concepts, principles of statistical analysis, data interpretation techniques, aspects of probability models, data cleaning and manipulation, pattern recognition and learning and statistical methods.

Participants will use the Python Programming language to solve work-based big data problems, learn how to classify data sets into structured, semi-structured and unstructured data and apply their own code to work with this data. The course also introduces predictive analytics and Machine Learning

To ensure the analysed data is comprehensible to other parts of the business, participants will learn how to present compound data sets through data visualisation for reporting and plot types and develop their critical thinking skills.

Impact On Upskilling Talent Pools

This course is for participants looking to gain a range of skills necessary to structure data in a concise and creative manner. This delivers high value benefits for organisations that are looking to expand digital skills amongst their existing talent pool. Firstly, the field of Data Science fosters data-driven approaches to strategy formulation, enabling organisations to strengthen competitive advantage and achieve growth objectives. Secondly, upskilling is a vital enabler of recognition and retention for critical staff.

On completion of this course, participants will have covered:

  • Introduction to Python Programming
  • Data scraping, parsing and down-sampling
  • Data set fusion, filtering, slicing and dicing
  • Schema validation and reconciliation
  • Data manipulation, array programming, data frame
  • Sources of data problems
  • Statistical distribution fitting and statistical significance of fitting
  • Co-occurrence grouping of data
  • Introduction to Machine Learning
  • Introduction to plot types and visualisation to large data sets

On completion of the course, participants will have gained the following set of skills immediately applicable to their business environment:

  • Statistics
  • Python Programming

On completion of this course, participants will have created the following assets:

  • A variety of Python programmes
  • A selection of Data Science models for specific data sets

Essential Requirements

  • Strong numeracy
  • Experience working with Excel
  • Knowledge of programming, preferably in Python
  • Coding experience in a least one language
  • Experience of database technology including the relational data model and NoSQL
  • Experience of statistics and statistical modelling
  • Excellent written and oral communications skills
  • Experience with business and technical reporting

As part of our application process, participants will also take a simple, online suitability assessment for their chosen course. Based on their answers, this will confirm that participants have the skills and experience required for a successful outcome.

11 hours (comprising a weekly average of 5.5 taught hours plus 5.5 additional hours on average for self-study, research and group work)

Participants will be given a number of course-specific assignments that both reinforce the learning and provide work-based experience of applying new skills. All assignments are subject to a rigorous assessment framework. Assessments for this specific course will include weekly quizzes.

Key Benefits Of Online Short Courses

Work-Based Experience

Participants work on assignments that present work-based business challenges. They experience and meet the exacting demands of digitally-enabled change.

Collaboration Skills

Participants develop both the digital skills required by your organisation and the team-working skills needed to exploit them successfully.

Expand Your Talent Potential

The learning experience enhances the wider business skills required by your organisation. These include a digital mind-set, agile thinking and user-centred design.

Reinforcing The Learning

On Live Syncs and Mentoring sessions, participants tap into the knowledge of lecturers in real-time. They provide expert advice on assignments and support the development of group-working skills.

Flexible Learning Methodology

Digital Skills Academy short courses are based on a proven, flexible learning methodology. Content is accessible from anywhere, at any time via a computer, tablet or smartphone.

Peer Learning

Participants are part of an online international community of working professionals. So they and your organisation benefit from the insights and experiences shared by peers from across the globe.

Participant Employers

Participants represent a significant cross-section of global brands from a wide mix of industries, including the following:

Corporate Partner Feedback

“We are on the search for new skills - we are rolling out new functionality in Corporate Banking, Retail Banking, Insurance and Finance in all of the nineteen countries that we’re in. Digital Skills Academy will help significantly to fill those digital skills gaps.” Fanie Van Heerden - Head of IT Solutions, Architecture and Design, Standard Bank Group

Frequently Asked Questions

There are currently no blog posts

Related Courses

This course builds on the theoretical knowledge gained in the Data Science course to focus on the practical applications of working with Big Data. ...

This course builds on the theoretical knowledge gained in the Data Science and the Programming for Big Data Part I courses and prepares participant...