Data Analysis Specialist Level 3

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

1. Introduction to data-driven decision-making.

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

3. Data analysis applications in various business functions (marketing, finance, operations).

4. Key business metrics and KPIs.

• 1.2 From Data to Insights

1. Difference between data, information, and actionable insights.

2. The data-to-decision Pipeline and the Role of stakeholders

3. Importance of context in transforming data into insights.

4. Case studies illustrating data insights that influenced business strategy.

• 1.3 Ethics and Compliance

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

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

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

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

Module 2: Essential Data Handling with Excel

• 2.0 Introduction to Excel

1. Overview of Excel interface and navigation.
2.  Understanding workbooks, worksheets, and cell referencing.
3.  Basics of formulas and cell formatting.
4.  Importance of Excel in data handling and analysis.
5.  Real-world scenarios showcasing Excel's role in business.

• 2.1 Organising Business Data

1. Structuring data for analysis (headers, data types, naming conventions).
2. Sorting and filtering data.
3. Excel tables for structured data handling.
4. Practical scenarios for data organisation (sales, finance, inventory data).Structuring and managing large datasets for analysis.

• 2.2 Key Excel Functions for Analysis

1. Basic functions: SUM, AVERAGE, MIN, MAX, COUNT.
2. Logical functions: IF, AND, OR.
3. Lookup functions: VLOOKUP, HLOOKUP, INDEX/MATCH.
4. Text functions: CONCATENATE, LEFT, RIGHT, TRIM, FIND.
5. Date functions: TODAY, YEAR, MONTH, DATE.
6. Business applications of these functions (e.g., sales analysis, payroll calculations).

•  2.3 Data Cleaning and Preparation

1. Handling missing data (deletion, imputation).
2. Removing duplicates and identifying outliers.
3. Data formatting for consistency (numbers, dates, text).
4. Using Text to Columns, Flash Fill, and Find/Replace for data cleaning.

• 2.4 Visual Reporting

1. Types of charts (line, bar, pie, scatter, histogram).
2. Adding data labels, legends, and trendlines.
3. Best practices for chart design (avoiding clutter, choosing the right chart type).
4. Real-world examples of visual reporting for business data.

Module 3: Advanced Data Analysis in Excel

• 3.1 Data Summarisation with PivotTables

1. Creating PivotTables from raw data.
2. Adding fields to rows, columns, and values.
3. Summarising data with PivotTables: SUM, COUNT, AVERAGE.
4. Using slicers and filters in PivotTables.
5. Practical business applications: Monthly sales summaries, region-based analysis.

• 3.2 Enhanced Data Visualisation

1. Advanced chart types: Histograms, heat maps, combo charts.
2. Customising visuals with conditional formatting.
3. Creating dynamic charts with linked data and slicers.
4. Business examples: Financial trend analysis, customer segmentation.

• 3.3 Efficiency and Productivity

1. Excel shortcuts for fast navigation and operations.
2. Using Macros to automate repetitive tasks.
3. Creating custom templates for repeatable reports.
4. Time-saving tips for report generation.

Module 4: Database Fundamentals and SQL for Business

• 4.1 Introduction to Databases

1. Types of charts (line, bar, pie, scatter, histogram).
2. Adding data labels, legends, and trendlines.
3. Best practices for chart design (avoiding clutter, choosing the right chart type).
4. Real-world examples of visual reporting for business data.

• 4.2 SQL for Business Insights

1. Basic SQL syntax: SELECT, WHERE, ORDER BY.
2. Filtering data with WHERE clauses.
3. Sorting and ordering results.
4. Practical examples: Retrieving sales data and filtering customer records.

• 4.3 Data Structuring Basics

1. Primary and foreign keys.
2. Data normalisation basics.
3. Entity-relationship diagrams (ERD) and designing simple data models.
4. Real-world example: Structuring customer and transaction tables.

Module 5: Advanced SQL for Real-World Applications

• 5.1 Aggregating and Summarising Data

1. Using aggregate functions: SUM, COUNT, AVG, MIN, MAX.
2. Applying GROUP BY for data segmentation.
3. HAVING clause for filtering grouped data.
4. Business scenarios: Summing monthly revenue and calculating average order values.

• 5.2 Linking Data Across Tables

1. Types of JOINs: INNER, LEFT, RIGHT, FULL OUTER.
2. Combining data across multiple tables.
3. Practical examples: Joining customer and transaction tables for analysis.
4. Using subqueries for nested data retrieval.

• 5.3 Window Functions

1. Introduction to window functions and their applications.
2. Using ROW_NUMBER, RANK, and DENSE_RANK to add row numbers and ranks.
3. Calculating running totals and moving averages.
4. Practical examples: Ranking top customers and cumulative sales analysis.

• 5.4 CASE Statements

1. Using CASE statements for conditional logic in queries.
2. Creating calculated columns based on conditional criteria.
3. Practical examples: Segmenting customers by purchase amount and categorising products based on price range.

• 5.5 Optimising SQL for Performance

1. Indexing basics and when to use indexes.
2. Query optimisation techniques for large datasets.
3. Best practices for writing efficient SQL queries.
4. Example: Optimising query performance on large customer tables.

Module 6: Data Exploration and Visualisation in Python

• 6.1 Python and Programming Essentials

1. Introduction to Python and Jupyter Notebooks: Installing Python, setting up Jupyter Notebook, and navigating the interface.
2. Python data types and structures: integers, floats, strings, lists, dictionaries, and tuples.
3. Control flow basics: conditional statements and loops for data processing.
4. Functions in Python: Creating and using functions, parameters, return values, and modular code benefits.
5. Practical exercises to reinforce Python syntax, data structures, and basic calculations.

• 6.2 Object-Oriented Programming (OOP) Basics

1. Introduction to OOP: Understanding classes, objects, and the basics of object-oriented programming in Python.
2. Core OOP principles: encapsulation, inheritance, polymorphism, and abstraction.
3. Creating Classes and Objects: Defining attributes, methods, and creating instances of classes.
4. Practical examples: Real-world applications of OOP concepts, like creating classes for customers or transactions.
5. Hands-on exercises to apply OOP concepts in small, manageable programs.

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

1. DataFrames and Series: Creating DataFrames and Series, understanding indexes, and navigating data.
2. Loading and exploring datasets from sources like CSV and Excel.
3. Data cleaning: Handling missing values, removing duplicates, converting data types, and basic text manipulation.
4. Data transformation: Filtering, sorting, grouping data, and creating calculated columns.
5. Descriptive statistics and aggregation with the group by for data summarisation.
6. 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

1. Introduction to Matplotlib: Creating basic visualisations like line, bar, and scatter plots.
2. Customising charts: Adding titles, labels, legends, and colours and adjusting styles for clarity.
3. Advanced chart types: Histograms, box plots, pie charts, and scatter plots for deeper insights.
4. Introduction to Seaborn: Statistical plotting with Seaborn, including pair plots, heatmaps, and categorical plots (bar, box, violin).
5. Data storytelling with visuals: Choosing effective chart types and ensuring clarity in data communication.
6. Practical visualisation exercises for sales trends, customer segments, and more.

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

1. Data Wrangling: Techniques for combining datasets (merge, join, concatenate) and reshaping data (pivot, melt).
2. Binning and discretisation for numerical data segmentation (e.g., age groups, income brackets).
3. Case study: Data wrangling and reshaping with a comprehensive dataset to answer key business questions.
4. Mini-Project: End-to-end real-world dataset analysis, from data cleaning and EDA to visualisation and reporting.
5. 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

1. Overview of Microsoft Power BI and its importance in business intelligence.
2. Loading and connecting data to Power BI from various sources.
3. Data transformation with Power Query (e.g., removing duplicates, changing data types).
4. Building visualisations: Bar charts, line charts, maps, and tables.
5. Creating calculated columns, measures, and DAX basics.
6. Designing interactive dashboards: Using slicers, drill-downs, and filters.
7. Setting up and managing relationships in data models.
8. Optimising reports for performance.
9. Practical examples include a sales performance dashboard and a customer demographics report.

• 7.2 Power BI Advanced Features

1. Using AI visuals for advanced analytics (e.g., Key Influencers, Decomposition Tree).
2. Understanding row-level security and its implementation.
3. Publishing and sharing reports on Power BI Service.
4. Creating paginated reports with Power BI Report Builder.
5. Practical examples: Budget vs. Actuals analysis and advanced segmentation.

• 7.3 Introduction to Tableau as an Alternative

1. Overview of Tableau as a business intelligence tool.
2. Brief comparison with Power BI (strengths, key features).
3. Use cases where Tableau might be preferred (e.g., data storytelling, interactive reports).
4. Connecting data sources in Tableau and creating basic visualisations.

•  7.4 Data-Driven Decisions with Interactive Elements

1. Adding filters, drill-downs, and cross-highlighting in Power BI/Tableau.
2. Creating parameter controls for user interaction.
3. Practical use case: Interactive dashboard for product performance insights.

Module 8: Capstone Project – Real-World Data Challenge

• 8.1 Integrated Project

1. Project planning and objectives: Defining a business question.
2. Data acquisition, cleaning, and preparation.
3. Analysis and visualisation based on chosen tools (Excel, SQL, Python, Power BI/Tableau).
4.  Example project ideas: Customer segmentation and financial performance analysis.

• 8.2 Presentation and Storytelling

1. Structuring insights for business presentations.
2. Choosing effective visuals for clear communication.
3. Drafting a storyline that links data to actionable insights.

• 8.3 Stakeholder Engagement

1.  Tailoring communication to technical and non-technical stakeholders.
2. Presenting recommendations and justifications based on data.
3. 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.
• Proctored Exam: The exam is monitored to ensure a fair testing environment.
• Retake Policy: Retakes are allowed after 24 hours of the first attempt; further retakes may vary. For full details, visit the exam retake policy.

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

No prior experience is required to enrol in the Data Analysis Level 3 Course, making it ideal for:

• Beginners in Data Analysis: Gain foundational data skills and certifications to start your career confidently.
• Recent Graduates: Add practical data analysis skills and industry certifications to stand out in the job market.
• Career Changers: Transition smoothly into the data analysis field with comprehensive technical training.
• Tech Enthusiasts: Expand your knowledge of data tools and practices to fuel your passion for technology.

Globally Recognised Certifications

Earn two valuable certifications to boost your career. The Microsoft Certified: Power BI Data Analyst Associate Certification highlights your expertise in data modelling and visualisation using Power BI. The e-Careers Data Analysis Specialist Level 3 Certification also validates your ability to analyse data sets and apply statistical methods effectively.

Flexible Learning Options

Study with virtual weekend classes, access materials 24/7, and join optional Q&A sessions for personalised support. Designed for convenience, the course fits into any schedule.

Expert Guidance and Support

Learn from experienced UK-based tutors who provide practical insights and hands-on coaching to help you confidently analyse and present data findings.

Hands-On Training with Industry Tools

Gain practical skills using Power BI, Google Data Studio, Python, SQL, and Excel to clean, format, and model data while creating professional visualisations and reports.

Comprehensive Career Support

Receive expert support to craft a professional CV, optimise your LinkedIn profile, and write tailored cover letters, ensuring you stand out in the job market.

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What is the Data Analysis Specialist Level 3 course?

The Data Analysis Specialist Level 3 Course is an entry-level programme covering essential data analysis skills such as cleaning, managing, and visualising data. The course includes certifications from Microsoft and e-Careers, providing a strong foundation for beginners.

What is a Level 3 Data Analyst?

A Level 3 Data Analyst is skilled in collecting, cleaning, and analysing data to uncover insights for business decision-making. This foundational role is perfect for beginners or those transitioning into data analysis.

What are the three levels of data analysis?

The three levels of data analysis are:

1. Descriptive Analysis – Focuses on what has happened, summarising data with statistics like averages and trends. Tools include Excel and Power BI for reports and dashboards.
2. Predictive Analysis – Forecasts what might happen by identifying patterns and trends using tools like Python and advanced Power BI features.
3. Prescriptive Analysis – Recommends what actions to take based on descriptive and predictive analysis insights, using optimisation models and tools like Tableau and AI platforms.

These levels help businesses move from understanding past data to making informed future decisions. This is why looking for a reliable Data Analysis Specialist Level 3 Coaching is essential when starting your Data Analysis career.

Do I need prior experience to enrol?

No prior experience is required. The course is designed for beginners and provides all the training needed to start a career in data analysis.

What certifications are included?

This Data Analysis Specialist Level 3 for beginners includes two industry-recognised certifications:

• Microsoft Certified: Power BI Data Analyst Associate Certification – Validates your skills in using Power BI for data modelling, visualisation, and analysing data sets to identify trends and present actionable insights.
•  e-Careers Data Analysis Specialist Level 3 Certification – Confirms your ability to apply basic statistical methods, format data, and use tools like Google Data Studio to manage and analyse data effectively.

These certifications are essential to a Data Analytics Diploma bundle, preparing you to excel in data-focused roles

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What career opportunities are available after completing the course?

This course prepares you for roles such as Junior Data Analyst, Data Analyst, Business Intelligence Analyst, and Data Technician.

Here are the salary expectations for these roles:

• Junior Data Analyst: £23k to £32k    
• Data Analyst:  £28k to £42k
• Business Intelligence Analyst: £30k to £45k