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Professional Certificate in  Data Science

The Professional Certificate in Data Science provides a foundation in the key knowledge areas required to launch or strengthen  your career in one of the most in demand and fastest growing professions in the world.
SOME OF THE ORGANISATIONS OUR GRADUATES WORK AT

What skills and knowledge will I gain?

The Certificate in Data Science has been developed by practising data scientists with experience working with major international firms across a wide range of industries. They have identified key skills required for data analysts and data scientists and have also ensured the programme conforms to the Edison European Data Science Framework’s Body of Knowledge (BoK-DS).
This Professional Certificate course is credit-rated at postgraduate level on the European Qualifications framework and carries 30 ECTS credits. These credits can also be used toward's the Institute's Postgraduate Diploma and MSc in Data Science.

Key Skills and Knowledge

  • R and Python programming skills
  • Data management
  • Descriptive and inferential statistics
  • Data visualisation
  • Hypothesis testing
  • Analysis of variance
  • Predictive analytics
  • Business intelligence tools
  • Power BI

Tools and Languages

What you’ll learn?

You are provided with highly structured and detailed course content, broken down into six distinct units covering core skills and knowledge.  Through our learning management system, you will attend our live group classes, access hundreds of video lessons, data sets, and assignments. You will also benefit from tutors that are on hand to provide support and feedback for your exam preparation and project work.
Certificate Modules
MODULE 1

Exploratory Data Analysis 

Most industry analysis starts with exploratory data analysis and a thorough study of this will help you to perform data health checks and provide initial business insights. You will gain a sound understanding of Python and R programming, descriptive statistics, data management and data visualisation. You will also learn SQL for big data preprocessing and prepare data for big data analytics.
  • Programming Basics in Python and R
  • Data management
  • Measures of central tendency and variation
  • Bivariate relationships
  • Data visualisation
MODULE 2

Statistical Inference

Statistical inference is the process of drawing inferences or conclusions from data using statistical techniques. This is at the core of data analytics and data science, and a strong understanding of statistics from the beginning is the prime ingredient for a competent data analyst. In this unit, you will cover the fundamentals of sampling, statistical distribution, hypothesis testing, and variance analysis and use Python and R code to carry out various statistical tests and draw inferences from their output.
  • Fundamental principles of statistical inference 
  • Standard parametric tests 
  • Non-parametric tests
  • Analysis of Variance 
MODULE 3

Business Intelligence tools

PowerBI and Excel are fundamental parts of the data analytics toolkit. A strong understanding in these also provides a basis for more advanced data analytics with other techniques and technologies. In this unit, you will gain experience in collecting, processing, analysing, and communicating with data using Excel. In addition, data visualisation is a powerful way to communicate meaning in data and support business decision-making. You will cover the main commercial tools used in data visualisation such as Power BI, enabling you to create a wide range of graphs, charts, and dashboards and use them appropriately in context. 
  • Excel Data Analyst’s
    Toolkit
  • Data Analysis with Excel 
  • Data visualisation with PowerBI 
MODULE 4

Fundamentals of Predictive Modelling

Solutions to many business problems are related to
successfully predicting future outcomes. This module
introduces predictive modelling and provides a foundation
for more advanced methods and machine learning. You’ll
gain an understanding of the general approach to predictive modelling and then build simple and multiple linear regression models in Python and R and apply these in a range of contexts.
  • Predictive modelling principles                  
  • Linear regression models                 
  • Model validation
  • Python and R packages and functions for predictive modelling
MODULE 5

 Data Analytics Capstone Project

This module provides learners with an opportunity to apply knowledge through project work. They will be able to select a project from a specific domain and carry out various data management, exploratory data analysis, data visualisation and predictive modelling tasks to produce analysis, insights and recommendations.
  • Real world scenarios
  • Synthesise skills and knowledge
  • Presentation and communication skills 

Who is the course for?

The Certificate in Data Science is for people looking to launch a career in data analytics, as well as professionals with experience aiming to strengthen their skill set and gain official recognition. It is aimed at graduates with a bachelor’s degree or equivalent, managers seeking a deeper understanding, and those with relevant professional experience in analytics looking to enhance their employment prospects.  
Career changers
Those wishing to change careers and transition to data science and analytics
Data Analysts
Working data analysts looking to enhance their employability
Working Professionals
Those without a degree but relevant professional experience
Graduates
Graduates from a wide range of disciplines including business and finance, computing, economics, the sciences, social sciences 
Experienced managers
Experienced managers and entrepreneurs seeking to gain an understanding of data science and analytics
Career advancement
Those wanting to take the next step towards a data scientist role

How do I progress with the Professional Certificate in Data Science?

The credits you earn on the Professional certificate contribute to the Data Science Institute's Postgraduate Diploma in Data Science and MSc in Data Science. The course is credit-rated at postgraduate level on the European Qualifications framework and carries 30 ECTS credits.

Holders of the Professional Certificate in Data Science can also combine their professional experience to achieve the Institute's Certified Professional Data Analyst CPDA certification.
Professional Certificate in
Data Science 
 30 ECTS credits
*1 year minimum professional
experience
Certified Professional
Data Analyst (CPDA)
Postgraduate Diploma in
Data Science
60 ECTS credits
*2 years minimum professional experience
Certified Professional
Data Scientist (CPDS)
MSc Data Science
90 ECTS credits
*More than 3 years experience in technical leadership or strategic roles
Fellow of the
 Data Science Institute (FDSI)

What are employment opportunities will I have?

Holders of the Professional Certificate in Data Science will find opportunities in data analytics and data science roles across a wide range of sectors. Data Science and analytics is central to AI, machine learning, robotics and forecasting, and is used across a wide array of industries and professional functions such as those listed below:
  • Pharmaceuticals
  • Sports 
  • Marketing
  • Education
  • Manufacturing
  • Agriculture 
  • Utilities
  • Healthcare 
  • Healthcare 
  • Human Resources 
  • Telecommunications 
  • Banking and Finance
  • Academia
  • Insurance
  • Big Tech
  • Aerospace
  • Aerospace
  • Logistics
  • Environmental management
  • Cybersecurity
  • Engineering
  • Medical Technology

How is the course delivered?

Our unique, hands-on learning experience combined with our interactive learning platform and  industry case studies, real worlds data sets, project based assessment led by expert instructors and tutor support allows learners to build their knowledge and practice applying it before bringing it into a professional setting.
Interactive Leaning Platform
Progress through the course using our highly structured, comprehensive learning content with 100s of video lessons, lecture slides, and quizzes
Case Studies
Learn from case studies, examples and datasets from actual companies produced by data science professionals.
Live Online Classes
Regular scheduled classes delivered by practising data scientists and webinars from industry professionals 
Build a portfolio
Carry out project work based on real world scenarios and data sets and build a portfolio to support you at work or improve your employability
Exam practice
Comprehensive practice questions and guidance to help you prepare for every module of your course exams
Expert Tutor Support
Expert tutors are on hand to guide you through the course, prepare you for exams and support your project work

Some numbers….

250K

Shortage of data scientists
In the United states a study by Quanthub in 2020 estimated that there was a shortage of 250,000 data scientists in the USA alone

10K+

Open positions in UK/Ireland
Indeed.com/glassdoor/Linkedin job openings with figures 10,000 + data science and analyst open positions in UK and Ireland (January 2022)

No. 1

Data Scientist/Analyst
According to the World Economic Forum report 2020 - fastest growing job roles- 1.  Data Scientists and Analysts  2. Machine Learning Engineers 3. Big Data Specialists 

See what our students say about us

Mohammed Hamad

Analytics Lead

DXC Technologies

“The course is very strong technically and the support was excellent”

Jehad Mossa

Senior Manager

PWC

“The overall course programme is very complete and I developed my data science knowledge substantially”

Manuel Tanpoco

(Ed.D, MBA)

Professor of Mathematics La Salle University

“As a mathematician and lecturer, I was impressed with the course content and explanation of the statistical methods and algorithms. I also benefited greatly from learning Python and R”

Mohammed Hamad

Analytics Lead

DXC Technologies

“The course is very strong technically and the support was excellent”

Jehad Mossa

Senior Manager

PWC

“The overall course programme is very complete and I developed my data science knowledge substantially”

Manuel Tanpoco

(Ed.D, MBA)

Professor of Mathematics La Salle University

“As a mathematician and lecturer, I was impressed with the course content and explanation of the statistical methods and algorithms. I also benefited greatly from learning Python and R”

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