Data Analyst Course: Job-Oriented Live Training with Power BI & PL-300

Excel is still where most analyst work starts and where most interviews open. We treat it as a serious tool rather than a warm up.

• Lookups, dynamic arrays, XLOOKUP and the formula patterns that survive real workbooks
• Pivot tables, slicers and interactive summary reports
• Power Query in Excel: cleaning and shaping without manual edits
• Data validation, named ranges and building workbooks other people can use
• Charting choices that communicate rather than decorate
• Hands on: a management KPI dashboard built end to end

SQL is the single most tested skill in analyst interviews. Two full weekends go to it rather than one.

• SELECT, WHERE, ORDER BY, GROUP BY and HAVING used properly
• Joins of every type, and reading a schema before writing a line
• Subqueries, set operations and filtering logic that holds up
• Aggregation patterns and the mistakes that quietly produce wrong numbers
• Database design basics an analyst is expected to understand
• Hands on: answer real business questions against an e commerce database

This is the weekend that separates candidates who pass the SQL round from candidates who do not.

• Window functions: ROW_NUMBER, RANK, LAG and LEAD, running totals and moving averages
• Common table expressions and structuring a query somebody else can read
• Cohort analysis, retention curves and funnel drop off queries
• Time series aggregation, period over period and year to date comparisons
• Query performance basics: indexes, execution plans and why your query is slow
• Hands on: the exact question patterns SQL interview rounds use

Taught for people who have never programmed. The goal is not software engineering, it is doing in twenty lines what would take an afternoon by hand.

• Python essentials for non programmers: types, control flow, functions
• pandas: loading, filtering, joining, grouping and reshaping data
• NumPy for numerical work and vectorised thinking
• Visualisation with matplotlib and seaborn
• Working in Jupyter notebooks, and keeping them reproducible
• Hands on: a repeatable notebook analysis workflow

Real data arrives broken. Employers care far more about whether you can handle that than whether you know an algorithm.

• Missing values, duplicates, outliers and inconsistent categories
• Type coercion, date handling and the encoding problems that waste hours
• A repeatable EDA framework that takes you from raw file to first insight
• Distributions, correlations and segment comparisons
• Knowing when a finding is real and when it is noise
• Hands on: a full exploratory analysis on a dataset you have never seen

Enough statistics to be trusted with a recommendation, taught without the notation that makes people give up.

• Descriptive statistics, distributions, sampling and bias
• Confidence intervals and what they genuinely tell a stakeholder
• Hypothesis testing in plain language, including when not to use it
• A/B testing: designing an experiment, sizing it and reading the result
• Correlation against causation, and the traps that produce embarrassing decks
• Hands on: judge a real product experiment and write the recommendation

Modelling is the heaviest weighted skill area on PL-300 and the thing that separates a real analyst from a chart maker.

• Connecting to sources, Power Query transformations and query folding
• Star schema design, relationships, cardinality and cross filter direction
• DAX foundations: calculated columns, measures and the filter context
• CALCULATE, time intelligence, iterators and variables
• Model optimisation and performance for reports that do not crawl
• Hands on: a clean semantic model over a multi table sales dataset

Microsoft refreshed this exam on 15 January 2026, with major changes to the report creation objective. We teach the current version, not the 2023 syllabus still circulating elsewhere.

• Report design: layout, colour, accessibility and telling a story with a page
• Drill through, bookmarks, tooltips and report navigation
• Identifying patterns and trends, and the analytics features that surface them
• Row level security, workspaces, apps and sharing content safely
• Refresh schedules, gateways and lifecycle management
• Where Power BI sits inside Microsoft Fabric, and what that means for your next role
• Hands on: an executive dashboard published with row level security applied

The weekend no competitor programme includes. Analyst hiring has already shifted toward people who work this way, and the gap is currently wide open.

• AI assisted exploratory analysis: from raw file to first insight in minutes
• Claude for SQL and DAX: generating, explaining, debugging and reviewing queries
• Turning an analysis into an executive narrative and a chart set automatically
• Verification discipline: how to catch the mistakes an AI assistant will make
• Documenting analysis so a colleague can rerun it without you
• Hands on: redo one of your earlier projects with Claude and measure the time saved

The final weekend is about conversion. A portfolio and a credential are only useful if you can get them in front of the right people and defend them.

• End to end capstone: business brief, data, model, dashboard and recommendation
• A full length PL-300 style mock with a domain scored report and a revision plan
• SQL interview rounds under time, plus analytics case study practice
• Guesstimate and product sense questions that appear in analyst interviews
• Resume, LinkedIn and portfolio review with mentors
• Mock interviews with structured written feedback