The Data Science Institute’s Professional Diploma in Data Science provides a foundation in the key knowledge required to set you on a career path in one of the most in-demand and fastest-growing professions in the world.
The course will allow you to both get a start in data science and analytics and enable you to progress to the institute’s Postgraduate Diploma in Data Science and MSc Data Science. The Professional Certificate has been developed by highly experienced data science professionals and academics and carries 30 ECTS credits on the European Qualifications Framework, ensuring that you gain a recognised, transferable qualification.
It 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 scientists and have also ensured the course content conforms to the Edison European Data Science Framework’s Body of Knowledge (DS-BoK).
Who should take this course?
The Professional Diploma in Data Science is suitable for those looking to enter data science from other fields or after graduation or those already working in a data analytics or data management role. Managers and entrepreneurs looking to gain insight into the impact and influence of data science and analytics on business will also benefit from taking this course.
Course applicants should have completed a first degree or equivalent. While having numerate and/or science subjects can be an advantage, this is not a prerequisite and candidates from all backgrounds will be considered.
Professional Diploma 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
Aim of the Program
The overall aims of the Professional Diploma in Data Science will enable you to:
Gain mathematical knowledge required for simple to complex statistical analysis
Develop programming skills in Python and R
Become familiar with and use business intelligence tools used in data analytics and data science
Understand and carry out exploratory data analysis including data management, descriptive statistics and data visualisation
Gain a strong understanding of statistical inference
Develop competence in predictive modelling methods
Communicate complex analysis to technical and non-technical audiences
Communicate complex analysis to technical and non-technical audiences
Assessment and Projects
The course is assessed by assignments and exams for each module and a guided capstone project based on real-world data and scenarios provides the opportunity to bring together elements of the overall course.