The Professional Certificate in Data Analytics will equip you with sought-after skills in data analysis, positioning you to take your analytics career to new heights.
This 25-week online programme will provide you with the skills necessary to clean, manipulate, analyse, and visualise data, and identify critical business insights. You will also establish a strong technical foundation with relevant business applications, and gain an analytical toolkit to help you succeed in a data-driven role.
Throughout this programme, you will benefit from the deep knowledge of our expert faculty, learn from video lectures and hands-on activities, and receive personal support from programme advisors to help propel your career in data analytics. Throughout the programme you will build a comprehensive portfolio for a business problem that highlights your analytics expertise, which you will be able to share with future employers.
You will finish the programme prepared to implement what you have learned, and with a verified Digital Certificate from Imperial College Business School Executive Education.
Who should attend?
This online programme is suitable for:
- Early-career data professionals who wish to drive greater impact in their day-to-day work by building on their experience and gain a set of advanced analytical skills that can aid decision making.
- Recent STEM or business graduates who are looking to launch a career in data analytics and who wish to supplement their theoretical knowledge with practical experience in extracting meaningful insights from large quantities of data.
- Business or technology professionals seeking to acquire analytical skills to help make informed business decisions that improve profitability
This hands-on programme is meticulously crafted to equip you with practical skills in modern-day analytics, enabling you to tackle real-world business challenges effectively. You will gain expertise in extracting meaningful insights and driving your organisation towards its objectives by applying cutting-edge analytics. This programme will enable you to:
- Identify the full spectrum of data analytics characteristics and applications in order to understand its significance to organisational success.
- Use industry-standard tools to conduct exploratory and descriptive analyses of large data sets in order to identify meaningful information.
- Use predictive modelling to forecast outcomes and evaluate the merits of each to help your organisation solve problems and uncover new opportunities.
- Design and solve an optimisation problem from a data set and create meaningful strategic recommendations that you can communicate to stakeholders.
- Build a comprehensive portfolio for a business problem that highlights your analytics expertise to share with potential employers.
What you will learn
Part I: Getting Started with Data Analytics
Module 1: What is Data Analytics?
Module 2: What is the Role of a Data Analyst?
Module 3: Getting Started with Python
Module 4: Solving Real-World Problems with Python
Module 5: Getting Started with SQL
Module 6: Solving Real-World Problems with SQL
Part II: Data Wrangling and Descriptive Analytics
Module 7: Gathering Data
Module 8: Web Crawling and Scraping
Module 9: Data Wrangling
Module 10: Data Visualisation Principles and Best Practices
Part III: Diagnostic Analytics and Exploratory Data Analysis
Module 11: Probabiloty Theory
Module 12: Anomaly Detection and Explanation
Module 13: Sampling, Resampling, and Hypothesis Testing
Module 14: Regression Analysis
Part IV: Predictive Analytics
Module 15: Fundamentals of Predictive Analytics
Module 16: Supervised Learning, Part 1
Module 17: Supervised Learning, Part 2
Module 18: Hyperparameter Tuning
Module 19: Unsupervised Learning
Part V: Prescriptive Analytics
Module 20: Introduction to Prescriptive Analytics
Module 21: Discrete Optimisation
Module 22: Nonlinear Optimisation
Part VI: Communicating Results
Module 23: Communicating Results with Data
Module 24: Capstone
Professor Wolfram Wiesemann
Professor of Analytics & Operations
Professor Wiesemann is Professor of Analytics & Operations at Imperial College Business School, where he also serves as the Academic Director of the MSc Business Analytics programme. His teaching focuses on linear, discrete, and nonlinear optimisation and the theory, algorithms and applications of machine learning. He also teaches prescriptive analytics.
Wolfram holds a joint master's degree in management and computing from Darmstadt University of Technology, and a PhD in operations research from Imperial College London. He is also a Fellow of the KPMG Centre for Advanced Business Analytics.
Alex Ribeiro Castro
Data Scientist, Senior Teaching Fellow
Dr Alex Ribeiro-Castro is a Data Scientist and Senior Teaching Fellow with the Operations Management Department at Imperial College Business School. He also teaches on the Global Business Analytics MSc. In addition, he has more than 10 years of teaching experience in pure and applied mathematics at institutions across the globe. He has more than five years of consultancy experience, including machine learning and optimisation problems, in industries such as fintech, health, energy, and more recently in retail.
Alex holds an MA and a PhD in mathematics from the University of California (Santa Cruz) and a professorship in mathematics from the Pontifical Catholic University (PUC-Rio)
Cognitive Scientist, Senior Teaching Fellow
Dr Fintan Nagle is a senior teaching fellow at Imperial College Business School, where he teaches data science in the context of business analytics. His current research interests focus on knowledge representation. He is a cognitive scientist and software engineer with a background in computer science vision science, and machine learning. In addition to earning his PhD at University College London, Fintan has completed postdoctoral work in automated driving and co-founded several organisations.
Become an Associate Alumni
Take your partnership with Imperial College Business School to the next level by becoming an Associate Alumni. Complete one (1) of our on-campus, online, and virtual programmes to claim 'Associate Alumni' status and join our active alumni community.