Data Analytics

Data Analytics - A two day Training Course designed for beginners. This short, focused, training course is designed for those who need practical skills in collecting, cleaning, analysing, and interpreting data. It is ideal for those who are relatively new to the world of data, analysis and data-driven decision-making, including analysts-in-training as well as managers and their teams taking their first steps into telling stories from data.

WHO SHOULD ATTEND?

This training is suitable for

• Business professionals with little analytics experience

• Teams transitioning to data-driven decision-making

• Business analysts, QA staff and junior data professionals

• Non-technical managers working with data teams

• Anyone needing a practical intro to data storage, queries and visualisation

LEARNING OBJECTIVES

By the end of the training, participants will

• Understand basic data concepts and terminology

• Understand database structures, including tables, rows, columns, keys, and relationships

• Carry out simple queries for data retrieval, filtering, cleaning and extraction

• Identify, collect, and prepare data for analysis

• Perform simple descriptive analytics and visualizations

• Interpret results and communicate findings effectively

• Apply analytical approaches to business led questions

COST

The registration fee of the workshop will be £495 Plus VAT (VAT UK only) which includes course notes.

PAYMENT

We will send you an invoice for the course fee after you have registered on the course. The payment can be made via bank transfer or online credit/debit card payment. If you need any further information, please contact us by email: info@mam.engineer 

PROGRAMME (All times listed refer to local London time)

LECTURER BIO

Dr. Rosie McNiece is a senior lecturer in statistics and data science at Kingston University, London, with over 20 years’ experience in education, training and research gained in academia and across various industries and professional bodies. As a committed educator she is passionate about promoting high quality data exploration and meaningful data analysis, applying statistical methodologies and machine learning approaches to achieve data driven solutions to complex problems. She leads a research group working on novel applications of statistical and analytical methods, with a particular focus on achieving data driven solutions from large and complex routinely collected national survey data. She has successfully delivered and supervised research projects in education, transport and health care monitoring.

She is a fellow of the Royal Statistical Society, an External Research Associate for the General Medical Council, an active participate in ‘Women in Data’ and has previously held advisory roles with professional bodies such as the Health Research Authority and is an experienced peer reviewer.

Beryl Jones is a Computer Scientist specialising in databases and data science with extensive global industrial experience and a strong academic track record. Having worked for several years at Oracle Corporation as a Senior Consultant, she brings substantial industry insight into her teaching, research and academic leadership.

As a committed, student-focused academic, she is dedicated to delivering high quality teaching through innovative curriculum design and thoughtful pedagogical practice. She has led the design, development and review of undergraduate and postgraduate programmes ensuring alignment with industry expectations. Her approach combines theoretical depth with practical application, equipping students with the analytical and technical skills required to address complex, real-world data challenges.

Her research interests centre on database systems, scalable data architectures and the application of data science methodologies to organisational and societal problems. She has made contributions to pedagogic research within computing, particularly in the areas of assessment design, technology-enhanced learning and curriculum innovation.

She has received multiple student-led awards in recognition of her teaching excellence and academic leadership.