Data Analyst Level 3 Career Academy with Work Experience

Building the next generation of data analysts to transform business insights into powerful decisions. 

Module 1: Foundations of Data in Business 

• 1.1 Data’s Role in Business

  • Introduction to data-driven decision-making.

  • Data types and classifications (qualitative, quantitative, structured, unstructured).

  • Applications of data analysis in various business functions (marketing, finance, operations).

  • Key business metrics and KPIs.

• 1.2 From Data to Insights

  • Difference between data, information, and actionable insights.

  • The data-to-decision pipeline, and the role of stakeholders

  • Importance of context in transforming data into insights.

  • Case studies illustrating data insights that influenced business strategy.

• 1.3 Ethics and Compliance

  • Key principles of data privacy laws (GDPR, CCPA).

  • Data handling best practices (anonymisation, consent, data minimisation).

  • Understanding data security basics (encryption, access control).

  • Ethical concerns in data analysis: Bias, accuracy, and transparency.

Module 2: Essential Data Handling with Excel

• 2.0 Introduction to Excel

  • Overview of Excel interface and navigation.

  • Understanding workbooks, worksheets, and cell referencing.

  • Basics of formulas and cell formatting.

  • Importance of Excel in data handling and analysis.

  • Real-world scenarios showcasing Excel’s role in business.

• 2.1 Organizing Business Data

  • Structuring data for analysis (headers, data types, naming conventions).

  • Sorting and filtering data.

  • Excel tables for structured data handling.

  • Practical scenarios for data organization (sales, finance, inventory data). Structuring and managing large datasets for analysis.

• 2.2 Key Excel Functions for Analysis

  • Basic functions: SUM, AVERAGE, MIN, MAX, COUNT.

  • Logical functions: IF, AND, OR.

  • Lookup functions: VLOOKUP, HLOOKUP, INDEX/MATCH.

  • Text functions: CONCATENATE, LEFT, RIGHT, TRIM, FIND.

  • Date functions: TODAY, YEAR, MONTH, DATE.

  • Business applications of these functions (e.g., sales analysis, payroll calculations).

• 2.3 Data Cleaning and Preparation

  • Handling missing data (deletion, imputation).

  • Removing duplicates and identifying outliers.

  • Data formatting for consistency (numbers, dates, text).

  • Using Text to Columns, Flash Fill, and Find/Replace for data cleaning.

• 2.4 Visual Reporting

  • Types of charts (line, bar, pie, scatter, histogram).

  • Adding data labels, legends, and trendlines.

  • Best practices for chart design (avoiding clutter, choosing the right chart type).

  • Real-world examples of visual reporting for business data.

Module 3: Advanced Data Analysis in Excel

• 3.1 Data Summarisation with PivotTables

  • Creating PivotTables from raw data.

  • Adding fields to rows, columns, and values.

  • Summarising data with PivotTables: SUM, COUNT, AVERAGE.

  • Using slicers and filters in PivotTables.

  • Practical business applications: Monthly sales summaries, region-based analysis.

• 3.2 Enhanced Data Visualisation

  • Advanced chart types: Histograms, heat maps, combo charts.

  • Customising visuals with conditional formatting.

  • Creating dynamic charts with linked data and slicers.

  • Business examples: Financial trend analysis, customer segmentation.

• 3.3 Efficiency and Productivity

  • Excel shortcuts for fast navigation and operations.

  • Using Macros to automate repetitive tasks.

  • Creating custom templates for repeatable reports.

  • Time-saving tips for report generation.

Module 4: Database Fundamentals and SQL for Business 

• 4.1 Introduction to Databases

  • Types of charts (line, bar, pie, scatter, histogram).

  • Adding data labels, legends, and trendlines.

  • Best practices for chart design (avoiding clutter, choosing the right chart type).

  • Real-world examples of visual reporting for business data.

• 4.2 SQL for Business Insights

  • Basic SQL syntax: SELECT, WHERE, ORDER BY.

  • Filtering data with WHERE clauses.

  • Sorting and ordering results.

  • Practical examples: Retrieving sales data, filtering customer records.

• 4.3 Data Structuring Basics

  • Primary and foreign keys.

  • Data normalisation basics.

  • Entity-relationship diagrams (ERD) and designing simple data models.

  • Real-world example: Structuring customer and transaction tables.

Module 5: Advanced SQL for Real-World Applications 

• 5.1 Aggregating and Summarising Data

  • Using aggregate functions: SUM, COUNT, AVG, MIN, MAX.

  • Applying GROUP BY for data segmentation.

  • HAVING clause for filtering grouped data.

  • Business scenarios: Summing monthly revenue, calculating average order values.

• 5.2 Linking Data Across Tables

  • Types of JOINs: INNER, LEFT, RIGHT, FULL OUTER.

  • Combining data across multiple tables.

  • Practical examples: Joining customer and transaction tables for analysis.

  • Using subqueries for nested data retrieval.

• 5.3 Window Functions

  • Introduction to window functions and their applications.

  • Using ROW_NUMBER, RANK, and DENSE_RANK to add row numbers and ranks.

  • Calculating running totals and moving averages.

  • Practical examples: Ranking top customers, cumulative sales analysis.

• 5.4 CASE Statements

  • Using CASE statements for conditional logic in queries.

  • Creating calculated columns based on conditional criteria.

  • Practical examples: Segmenting customers by purchase amount, categorising products based on price range.

• 5.5 Optimising SQL for Performance

  • Indexing basics and when to use indexes.

  • Query optimisation techniques for large datasets.

  • Best practices for writing efficient SQL queries.

  • Example: Optimising query performance on large customer tables.

Module 6: Data Exploration and Visualisation in Python 

• 6.1 Python and Programming Essentials

  • Introduction to Python and Jupyter Notebooks: Installing Python, setting up Jupyter Notebook, and navigating the interface.

  • Python data types and structures: integers, floats, strings, lists, dictionaries, and tuples.

  • Control flow basics: conditional statements and loops for data processing.

  • Functions in Python: Creating and using functions, parameters, return values, and modular code benefits.

  • Practical exercises to reinforce Python syntax, data structures, and basic calculations.

• 6.2 Object-Oriented Programming (OOP) Basics

  • Introduction to OOP: Understanding classes, objects, and the basics of object-oriented programming in Python.

  • Core OOP principles: encapsulation, inheritance, polymorphism, and abstraction.

  • Creating Classes and Objects: Defining attributes, methods, and creating instances of classes.

  • Practical examples: Real-world applications of OOP concepts, like creating classes for customers or transactions.

  • Hands-on exercises to apply OOP concepts in small, manageable programs.

• 6.3 Data Manipulation and Exploratory Data Analysis (EDA) with Pandas

  • DataFrames and Series: Creating DataFrames and Series, understanding indexes, and navigating data.

  • Loading and exploring datasets from sources like CSV and Excel.

  • Data cleaning: Handling missing values, removing duplicates, converting data types, and basic text manipulation.

  • Data transformation: Filtering, sorting, grouping data, and creating calculated columns.

  • Descriptive statistics and aggregation with groupby for data summarisation.

  • Practical examples on real-world datasets (e.g., customer demographics, sales records) and hands-on EDA exercises.

• 6.4 Data Visualisation with Matplotlib and Seaborn

  • Introduction to Matplotlib: Creating basic visualisations like line, bar, and scatter plots.

  • Customising charts: Adding titles, labels, legends, colors, and adjusting styles for clarity.

  • Advanced chart types: Histograms, box plots, pie charts, and scatter plots for deeper insights.

  • Introduction to Seaborn: Statistical plotting with Seaborn, including pair plots, heatmaps, and categorical plots (bar, box, violin).

  • Data storytelling with visuals: Choosing effective chart types and ensuring clarity in data communication.

  • Practical visualisation exercises for sales trends, customer segments, and more.

• 6.5 Data Wrangling, Advanced Analysis, and Mini-Project

  • Data Wrangling: Techniques for combining datasets (merge, join, concatenate) and reshaping data (pivot, melt).

  • Binning and discretisation for numerical data segmentation (e.g., age groups, income brackets).

  • Case study: Data wrangling and reshaping with a comprehensive dataset to answer key business questions.

  • Mini-Project: End-to-end analysis of a real-world dataset, from data cleaning and EDA to visualisation and reporting.

  • Project presentation: Structuring findings in a report, using visuals effectively, and making actionable recommendations.

Module 7: Business Intelligence with Power BI and Tableau 

• 7.1 Power BI for Dynamic Insights

  • Overview of Microsoft Power BI and its importance in business intelligence.

  • Loading and connecting data to Power BI from various sources.

  • Data transformation with Power Query (e.g., removing duplicates, changing data types).

  • Building visualisations: Bar charts, line charts, maps, and tables.

  • Creating calculated columns, measures, and DAX basics.

  • Designing interactive dashboards: Using slicers, drill-downs, and filters.

  • Setting up and managing relationships in data models.

  • Optimising reports for performance.

    Practical examples: Sales performance dashboard, customer demographics report.

• 7.2 Power BI Advanced Features

  • Using AI visuals for advanced analytics (e.g., Key Influencers, Decomposition Tree).

  • Understanding row-level security and its implementation.

  • Publishing and sharing reports on Power BI Service.

  • Creating paginated reports with Power BI Report Builder.

  • Practical examples: Budget vs. Actuals analysis, advanced segmentation.

• 7.3 Introduction to Tableau as an Alternative

  • Overview of Tableau as a business intelligence tool.

  • Brief comparison with Power BI (strengths, key features).

  • Use cases where Tableau might be preferred (e.g., data storytelling, interactive reports).

  • Connecting data sources in Tableau and creating basic visualisations.

• 7.4 Data-Driven Decisions with Interactive Elements

  • Adding filters, drill-downs, and cross-highlighting in Power BI/Tableau.

  • Creating parameter controls for user interaction.

  • Practical use case: Interactive dashboard for product performance insights.

Module 8: Capstone Project – Real-World Data Challenge 

• 8.1 Integrated Project

  • Project planning and objectives: Defining a business question.

  • Data acquisition, cleaning, and preparation.

  • Analysis and visualisation based on chosen tools (Excel, SQL, Python, Power BI/Tableau).

  • Example project ideas: Customer segmentation, financial performance analysis.

• 8.2 Presentation and Storytelling

  • Structuring insights for business presentations.

  • Choosing effective visuals for clear communication.

  • Drafting a storyline that links data to actionable insights.

• 8.3 Stakeholder Engagement

  • Tailoring communication to technical and non-technical stakeholders.

  • Presenting recommendations and justifications based on data.

  • Role-play exercise: Simulating stakeholder presentations and Q&A.

Microsoft Certified: Power BI Data Analyst Associate Exam

• Exam Format: Computer-based, multiple-choice, and multiple-response questions.

• Number of Questions: 40 – 60 questions.

• Duration: 100 minutes.

• Pass Mark: 70% (700 out of 1000 marks).

• Material Allowed: Closed book; no materials permitted during the exam.

To learn more about exam duration and experience, visit: Exam duration and exam experience.

Additional Information:

• Proctored Exam: This exam is monitored to ensure a fair testing environment.

• Retake Policy: If you do not pass on the first attempt, you may retake the exam after 24 hours. For further retakes, the waiting period may vary. For full details, visit: Exam retake policy.

• Available Languages: The exam is offered in multiple languages, including English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil), Arabic (Saudi Arabia), Russian, Chinese (Traditional), Italian, and Indonesian.

No prior experience in data analysis is required for this course, making it ideal for those eager to start their journey in data and analytics. Data Analysis Specialist Level 3 course is designed as a strong entry-level pathway into the world of data analysis, equipping you with essential skills, knowledge, and certifications.

This course is ideal if you are:

• Beginners in Data Analysis: If you’re new to the data field, this programme provides a strong foundation, covering essential skills and certifications to help you get started confidently.

• Recent Graduates: Stand out in today’s competitive job market by adding industry-recognised certifications and practical data skills to your CV, setting yourself up for success in data-driven roles.

• Job Seekers: Enhance your employability with a blend of training and real-world experience, equipping you with in-demand data analysis skills valued across industries.

• Career Changers: Ready for a new direction? Transition smoothly into the rapidly growing field of data analysis with comprehensive technical training and hands-on experience.

• Tech Enthusiasts: Fuel your passion for data and technology by gaining foundational skills in data analysis tools and practices, preparing you for future growth in the digital economy

Guaranteed remote work experience

Gain hands-on experience with a guaranteed remote placement lasting between 4 and 16 weeks. Work part-time or full-time with one of our international partner companies, allowing you to apply your data analysis skills in a real-world setting and build confidence in your abilities.

Job placement opportunities

Boost your employment chances—1 in 4 students receive a job offer from their work placement host. Our extensive network of employers spans 100+ countries, including some of the world’s most innovative companies, giving you valuable exposure and potential job prospects aligned with your career goals.

Award-winning training excellence

Benefit from our reputation for high-quality training. With over 630,000 students trained globally and an “Excellent” rating on Trustpilot from over 14,515 learners, e-Careers is dedicated to providing the best in professional education, delivered by experienced trainers and industry experts.

Flexible virtual weekend classes

Study alongside your other commitments with structured virtual weekend classes. Spread over four months, these sessions provide flexible learning to suit your schedule while ensuring comprehensive coverage of data analysis concepts and skills.

Comprehensive coaching and support sessions

Benefit from mandatory coaching and practical workshops designed to reinforce your learning and develop your skills. Additionally, optional drop-in Q&A sessions are available, offering you the chance to clarify doubts, receive personalised support, and stay on track with your studies. This hands-on approach ensures that you feel confident and fully prepared throughout the programme.

Professional CV, LinkedIn, and cover letter support

Stand out in the job market with expertly crafted application materials. Receive professional guidance to enhance your CV, optimise your LinkedIn profile, and craft effective cover letters, setting you up for success in your applications.

Career support programme

Prepare confidently for the job market with our extensive career support services. From employability preparation to ongoing guidance, we support your journey into a data-focused career with valuable tools and resources.

Expert UK-based instructors

Learn from experienced UK-based instructors who bring industry expertise and a passion for teaching into the virtual classroom. Our trainers are committed to engaging and supporting you through every step of the course.

Price match guarantee

We offer unbeatable value with our price match guarantee. The price you see is the final cost, with VAT included and no hidden fees, ensuring transparency and affordability.

Flexible financing options

Access interest-free financing and flexible payment plans, making quality education affordable regardless of your financial situation. Our payment plans are designed to fit a variety of budgets, so you can focus on your future without financial strain.

What is Data Analysis Specialist Level 3 course?

The Data Analysis Specialist Level 3 course is an entry-level programme designed to build a solid foundation in data analysis. This course covers essential skills such as data cleaning, data management, visualisation, and reporting, providing you with the expertise needed to begin a career in data analysis. It combines comprehensive training with a guaranteed work experience placement, giving you both theoretical knowledge and practical skills.

What is a level 3 data analyst?

A Level 3 Data Analyst is a professional equipped with foundational skills in data analysis. This role involves collecting, cleaning, and analysing data to uncover insights that support business decision-making. The Level 3 framework serves as an entry point into the field, suitable for beginners or those looking to make a career transition into data analysis.

Which entry course is best for data analysis?

The Data Analysis Specialist Level 3 course is an ideal entry point if you are new to data analysis. This programme is structured to provide beginners with essential skills in data analytics, data quality, and data management. Additionally, it offers hands-on experience, setting you up for success in data-related roles upon completion.

How long does a Data Analysis Level 3 Career Academy take?

This programme spans 4 months and includes structured training, practical workshops, and guaranteed remote work experience. The schedule includes weekend virtual classes, weekly coaching sessions, and optional Q&A support, allowing you to balance learning with other commitments.

Do I need any prior experience to enrol in this course?

No prior experience in data analysis is required to enrol in the Data Analysis Level 3 Career Academy. This course is designed for beginners and covers all the fundamental skills needed to start your career in data analysis. Whether you’re a recent graduate, career changer, or tech enthusiast, this course provides the foundational knowledge and skills necessary to succeed.

What support will I receive during the course?

Throughout the course, you will have access to ongoing support from our experienced instructors, career coaches, and the e-Careers team. You’ll participate in structured workshops, receive feedback on your progress, and attend optional Q&A sessions. You’ll also benefit from employability support services, including CV writing, LinkedIn optimisation, and cover letter support, preparing you for the job market.

Is this qualification globally recognised?

Yes, this qualification is globally recognised. Upon completion, you’ll receive two industry-recognised certifications:

• Microsoft Certified: Power BI Data Analyst Associate – This certification, awarded by Microsoft, validates your skills in data analysis and visualisation using Power BI, a leading tool in business intelligence.

• e-Careers Data Analysis Specialist Level 3 Certification – This certification from e-Careers acknowledges your foundational skills in data analysis, demonstrating your readiness for roles in the field.

What are the career paths in data analysis?

Data analysis offers a wide range of career paths across various industries. Some typical roles include:

• Junior Data Analyst: Entry-level role involving data collection, cleaning, and basic analysis using tools like Excel and SQL.

• Microsoft Office Specialist: Provides data entry support to superiors, prepares reports, presentations, and other documents using Microsoft Word, Excel, and PowerPoint, making it a vital role for data organisation and communication.

• Business Intelligence (BI) Analyst: Focuses on analysing data to help companies make strategic decisions, often using tools like Power BI or Tableau.

• Data Scientist: A more advanced role that often involves statistical modelling, machine learning, and programming skills in Python or R.

• Data Engineer: Specialises in building and maintaining the infrastructure that stores and processes data, often using SQL, Python, and big data technologies like Hadoop.

• Data Analyst/Consultant: Works with clients to analyse their data and make strategic recommendations based on insights.

• With the right mix of technical skills (data cleaning, analysis, Python/SQL) and the ability to communicate insights effectively, data analysts can progress rapidly to senior roles such as Data Scientist, Business Intelligence Manager, or even Chief Data Officer (CDO).

What is the job growth and demand in the field of data analysis?

Demand for data analysis professionals is surging across industries as companies increasingly use data to drive decisions and enhance customer experiences.

• High-Growth Field: The World Economic Forum (2023) ranks data analysis roles, like Data Analysts and Business Intelligence Analysts, among the top emerging jobs globally, with demand set to grow.

• Industry-Specific Demand: IBM’s 2023 Report highlights strong demand in finance, healthcare, and e-commerce:

  • Finance: Data analyst roles in UK financial services have grown by 15% year-on-year.

  • Healthcare: Data analytics optimises patient care, making analysts essential to healthcare teams.

  • Retail/E-commerce: Data analysts are key in personalising customer experiences and driving targeted marketing.