Data Analytics
Learn to collect, clean and explore data, and to present what it shows clearly.
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About this course
Data analytics turns raw figures into answers a business can act on: what sold, who left, where costs rose, and why. This course builds the full toolkit of a working analyst: Excel, SQL, statistics, Python for analysis, and two business-intelligence tools for dashboards.
You practise on realistic business datasets and finish each project with a clear message for a non-technical audience. The aim is to describe and explain what has happened, with sound numbers and honest charts, and to point to what to do next.
What you will be able to do
- Turn a business question into measures (KPIs) and a plan for analysing them.
- Clean and prepare data in Excel, SQL and Python.
- Write SQL queries with joins, aggregations, subqueries and window functions.
- Apply basic statistics, such as summaries, correlation, confidence intervals and A/B tests, and read the results correctly.
- Build interactive dashboards in Power BI and Tableau.
- Choose charts that tell the truth, and present findings to non-technical colleagues.
- Run common business analyses: cohort, funnel, customer segmentation and trend analysis.
Syllabus
10 modules. Select a module to see its topics.
Module 1: Analytics foundations
- Types of analytics (descriptive, diagnostic, predictive, prescriptive)
- KPIs and metrics
- The analytics workflow
- Data types and sources
- Asking good business questions
Module 2: Excel for analysis
- Formulas and functions (lookups, logic, text, dates)
- Cleaning data
- Pivot tables and pivot charts
- Power Query in outline
- Dashboards in Excel
Module 3: SQL for analysts
- SELECT, WHERE and ORDER BY
- Joins
- GROUP BY and HAVING
- Subqueries and CTEs
- Window functions
- Building an analysis dataset
Module 4: Statistics for analysts
- Descriptive statistics
- Distributions
- Sampling
- Correlation
- Confidence intervals
- Hypothesis tests
- A/B tests
- Outliers
Module 5: Python for analytics
- Python basics
- pandas for cleaning and aggregation
- NumPy
- Matplotlib and Seaborn
- Jupyter notebooks
Module 6: Data cleaning and preparation
- Missing and duplicate data
- Standardising formats
- Combining sources
- Data-quality checks
- Documenting assumptions
Module 7: Visualisation and storytelling
- Choosing the right chart
- Colour and layout
- Avoiding misleading visuals
- Narrative structure
- Presenting to non-technical audiences
Module 8: Power BI
- Data model and relationships
- DAX measures
- Visuals and slicers
- Dashboards
- Publishing and sharing in outline
Module 9: Tableau
- Connecting to data
- Calculated fields
- Interactive dashboards
- Maps
- Comparing Tableau with Power BI
Module 10: Business analytics and capstone
- Cohort, funnel, retention and RFM analysis
- Basic forecasting
- KPI reporting
- Capstone analysis and presentation
Tools and technologies
- Excel
- SQL (PostgreSQL or MySQL)
- Python (pandas, NumPy, Matplotlib, Seaborn)
- Jupyter
- Power BI
- Tableau
- Git
Projects you will build
Sales performance dashboard in Power BI, with drill-down by region and product.
Customer segmentation using RFM (recency, frequency and monetary value).
E-commerce funnel and cohort analysis in SQL and Python.
Marketing campaign A/B test report with a recommendation.
Finance or HR KPI dashboard in Tableau.
Capstone: an end-to-end analysis of a real dataset, presented as a short deck and an interactive dashboard.
Before you start
- This outline starts from the basics: Excel, SQL and Python are taught from the first modules, so no earlier coding is assumed.
- Helpful but not required: basic Excel and comfort with numbers.
- Entry requirements, the BI tools taught, licences and operating-system needs are confirmed by Pathway Career when you enquire. Power BI Desktop runs on Windows.
Career paths
Examples of roles that use these skills. They are not a promise of a job.
- Data analyst
- Business analyst
- Reporting or MIS analyst
- BI developer (Power BI or Tableau)
- Analytics associate
- Operations, marketing or finance analyst
Questions about this course
Do I need to know coding?
This outline assumes no coding experience: SQL and Python are taught from scratch. Entry requirements are confirmed by Pathway Career when you enquire.
What is the difference between Data Analytics and Data Science?
Analytics describes and explains what has happened, using SQL, Excel and dashboards. Data Science adds prediction with statistics and machine learning. Many learners start with analytics and continue to data science.
Are Power BI and Tableau both taught?
The outline covers both, so that you can choose and see how the ideas transfer. Pathway Career confirms the tools it will teach when you enquire.
Do I need to buy software?
Power BI Desktop is free to download and Tableau offers free and trial options, but terms can change. Pathway Career confirms how licences are handled when you enquire.
Can people from non-IT backgrounds join?
Commerce, science and management graduates are a common route into analytics. Pathway Career confirms its entry policy when you enquire.
What data will we use?
Realistic public business datasets. Pathway Career confirms any datasets it supplies when you enquire.
Next step
Want to turn data into decisions? Send an enquiry to ask for the full syllabus, batch dates and fees.
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