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Data Analytics & Data Science Training in UK

The Data Analytics & Data Science (incl. Power BI) Training in the UK is aimed for professionals who want to work more confidently with data, uncover meaningful patterns, and turn information into useful business insights. The programme brings together core data analytics and data science concep...

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Data Analytics & Data Science Training in UK

training

Domain / Vendor

data analytics concepts and understanding

Instructor-led Data Analytics and Data Science training

Practical Power BI reporting and dashboard development

Hands-on data preparation, analysis, and visualisation

Business-focused datasets and analytical scenarios

Overview Learning Objectives Who Should Attend Prerequisite Outline

Course Overview

The Data Analytics & Data Science (incl. Power BI) Training in the UK provides a practical learning experience for professionals seeking to understand how data can be explored, interpreted, visualised, and used to support business outcomes. The programme combines fundamental analytical concepts with practical application, giving participants an opportunity to work with data throughout different stages of the analysis process.

The training introduces key principles of data analytics, including data collection, preparation, cleaning, exploration, interpretation, and presentation. Participants learn how to work with structured datasets, identify relevant trends and relationships, and use appropriate analytical techniques to answer business questions. The programme also introduces selected data science concepts to help learners understand how more advanced analytical approaches can be applied.

A significant component of the programme focuses on Microsoft Power BI, enabling participants to work with data from different sources, transform datasets, build data models, and create interactive reports and dashboards. Learners explore how visualisations can be selected and structured to communicate information clearly rather than simply displaying large volumes of data.

The programme also examines how analytical findings can contribute to business planning and decision-making. Participants consider how to interpret results, identify meaningful insights, communicate findings to stakeholders, and distinguish useful information from misleading or incomplete conclusions. Depending on the programme level, additional areas may include statistical analysis, predictive techniques, data modelling, and introductory machine learning concepts.

By the end of the training, participants will have developed practical knowledge of data analytics, greater familiarity with data science concepts, and the ability to use Power BI to create informative reports and dashboards. They will be better prepared to work with business data and present insights that can support operational, tactical, and strategic decisions.

Course Objectives

Explain fundamental concepts and principles associated with data analytics and data science.
Understand the different stages involved in preparing, analysing, interpreting, and presenting data.
Identify appropriate analytical approaches for addressing common business and organisational questions.
Clean, organise, transform, and prepare datasets for meaningful analysis.
Explore data to identify trends, patterns, relationships, and potential anomalies.
Develop practical skills in Microsoft Power BI for data visualisation, reporting, and dashboard creation.
Create interactive Power BI reports using appropriate charts, filters, measures, and visual elements.
Understand fundamental data modelling concepts and how structured data supports effective reporting.
Apply basic statistical and data science concepts to understand and interpret analytical results.
Communicate data-driven findings clearly to business users, managers, and other stakeholders.
Evaluate analytical outputs critically and recognise issues such as incomplete data, misleading visualisations, or unreliable conclusions.
Apply data analytics techniques to practical workplace scenarios and support informed decision-making.

Target Audience

Data Analysts and Aspiring Data Analysts
Business Analysts and Business Intelligence Professionals
Reporting and MI Professionals
Power BI Users and Dashboard Developers
Data Science Professionals and Aspiring Data Scientists
Finance and Commercial Professionals
Marketing and Customer Insight Teams
Operations and Performance Management Professionals
HR and People Analytics Professionals
Project and Programme Professionals
Managers and Business Leaders Working with Data
Professionals Seeking to Develop Data-Driven Decision-Making Skills

Eligibility Criteria

No formal data science or programming background is required for introductory-level training.
The programme is suitable for professionals who regularly work with business information, reports, spreadsheets, or organisational data.
Basic computer literacy and familiarity with common workplace applications are recommended.
Prior experience with Microsoft Excel, Power BI, databases, statistics, or programming may be beneficial for more advanced programmes but is not always required.

Course Outline

Module 1: Introduction to Data Analytics and Data Science

Understanding data analytics, business intelligence, and data science
Exploring the data analytics lifecycle
Different types and sources of organisational data
Roles of data in operational and strategic decision-making

Module 2: Data Collection, Preparation and Quality

Working with structured and unstructured data
Identifying relevant data sources for analysis
Cleaning and organising datasets
Handling missing, duplicate, inconsistent, and inaccurate data

Module 3: Exploratory Data Analysis

Examining datasets to identify meaningful patterns
Understanding distributions, relationships, and trends
Using descriptive statistics for data exploration
Identifying anomalies and areas requiring further investigation

Module 4: Data Analysis Techniques

Applying analytical methods to business questions
Comparing performance across categories and time periods
Analysing relationships and key performance indicators
Drawing evidence-based conclusions from data

Module 5: Microsoft Power BI Fundamentals

Understanding the Power BI environment and workflow
Connecting Power BI to different data sources
Importing and organising data for analysis
Navigating reports, datasets, visualisations, and dashboards

Module 6: Data Transformation with Power Query

Preparing and transforming data using Power Query
Combining information from multiple sources
Filtering, sorting, splitting, and restructuring data
Creating a consistent dataset for reporting

Module 7: Data Modelling and DAX

Understanding relationships between tables
Building effective data models in Power BI
Introduction to measures, calculated columns, and DAX
Applying calculations to support meaningful analysis

Module 8: Data Visualisation and Dashboard Design

Selecting appropriate charts and visual elements
Designing clear and informative Power BI reports
Using filters, slicers, drill-downs, and interactive features
Applying visualisation principles for better data communication

Module 9: Business Intelligence and Insight Generation

Turning analytical findings into business insights
Identifying trends, opportunities, and performance gaps
Using dashboards to monitor business metrics
Presenting insights to support informed decisions

Module 10: Introduction to Statistical and Predictive Analysis

Understanding key statistical concepts used in data analysis
Exploring correlation, trends, and relationships
Introduction to forecasting and predictive analytics
Understanding the role of models in data-driven decision-making

Module 11: Data Science and Machine Learning Fundamentals

Understanding the data science workflow
Introduction to supervised and unsupervised learning
Exploring common machine learning applications
Understanding model evaluation and the importance of quality data

Module 12: Practical Data Analytics Project

Working with a realistic business dataset
Preparing, analysing, and modelling data
Developing an interactive Power BI report or dashboard
Presenting findings and recommendations based on the analysis

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Instructor-led Online Training
Experienced Subject Matter Experts
Approved and Quality Ensured Training Material
24*7 Learner Assistance and Support

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Customized Training Across Various Domains
Instructor-Led Skill Development Program
Ensure Maximum ROI for Corporates
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FAQ’s

What does the Data Analytics & Data Science (incl. Power BI) Training in the United Kingdom cover?

The programme covers data analytics fundamentals, data preparation, exploratory analysis, statistical concepts, data visualisation, business intelligence, Power BI, Power Query, data modelling, DAX, dashboards, predictive analysis, and introductory data science and machine learning concepts.

Who is the Data Analytics & Data Science Training programme designed for?

The training is suitable for data analysts, business analysts, BI professionals, Power BI users, reporting professionals, aspiring data scientists, finance and operations professionals, marketing and HR analysts, managers, and other professionals who work with business data.

Is previous experience in data analytics or data science required?

Entry requirements depend on the specific programme and level. Introductory training does not generally require a formal data science or programming background. Basic computer skills and familiarity with business data or spreadsheets can be helpful when starting the programme.

What practical skills will participants develop during the training?

Participants develop skills in preparing and cleaning datasets, exploring information, identifying trends, analysing business data, creating visualisations, building Power BI reports and dashboards, interpreting analytical results, and communicating data-driven findings to stakeholders.

Does the training include Microsoft Power BI?

Yes. Power BI is a key component of the programme. Participants can explore connecting to data sources, transforming information with Power Query, creating data models, using DAX, developing interactive reports, applying filters and slicers, and designing dashboards for business reporting.

Does the course cover data cleaning and transformation?

Yes. Participants learn why data quality is important for reliable analysis and explore practical approaches to cleaning, organising, filtering, transforming, and combining datasets. Power Query can be used to prepare information for analysis and reporting within Power BI.

Will the programme cover data science and machine learning?

Yes. The course introduces key data science concepts and provides an overview of areas such as statistical analysis, predictive analytics, forecasting, supervised and unsupervised learning, and machine learning applications. The depth of these topics depends on the specific programme level.

Can the training help professionals create better business dashboards?

Yes. Participants learn how to structure reports, select appropriate visualisations, use interactive features, and present information in a way that makes important trends and performance indicators easier to understand. The focus is on communicating useful insights rather than simply displaying data.

Can the training support data-driven decision-making?

Yes. The programme helps participants understand how to move from raw data to analysis and then to actionable insights. Through business scenarios and practical exercises, learners consider how analytical findings can be interpreted and communicated to support operational, tactical, and strategic decisions.

Why choose Vinsys for Data Analytics & Data Science Training in the United Kingdom?

Vinsys provides instructor-led training that combines analytical concepts with practical workplace application. The programme uses hands-on exercises, realistic datasets, Power BI activities, case studies, and business scenarios to help participants strengthen their data skills and apply analytics more effectively within their professional environment.

Why Vinsys

Seasoned Instructors

Official Vendor Partnerships

Authorized Courseware

3,000+ Courses & 2,000+ Modules

In Synch with Tech-advancements

Customizable Blended Learning Options

Reviews

The Data Analytics & Data Science Training gave me a much better understanding of how raw information can be turned into useful business insights. We worked through data preparation, exploratory analysis, visualisation, and reporting, with Power BI forming an important part of the practical sessions. Building dashboards from realistic datasets was particularly useful because it showed how analytical findings can be presented clearly to different stakeholders.

Alexander ParkerBusiness Intelligence Consultant

I found the Power BI exercises especially valuable for my day-to-day reporting work. The training covered data transformation, relationships between datasets, visualisations, filters, dashboards, and basic DAX concepts. Rather than simply creating charts, we discussed how to select visuals that communicate the right message. The hands-on approach has made me more comfortable working with data and presenting performance information.

Matthew CookData Visualisation Specialist

The data science topics added useful context to the analytics side of the programme. We explored statistical concepts, patterns and relationships in data, predictive analysis, forecasting, and an introduction to machine learning. I appreciated that these areas were connected to practical business questions instead of being treated as purely technical subjects. The course helped me understand where different analytical approaches can be useful.

Florence KellyReporting & Insights Manager

What stood out for me was the complete process covered during the training, from preparing a dataset through to interpreting and presenting the results. The exercises highlighted the importance of data quality, appropriate analysis, and clear visual communication. Working with business scenarios also helped me think more critically about the insights produced from data rather than accepting results at face value. I now have a more practical approach to using analytics in my role.

Samuel MorganData Quality Analyst

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