About Data Analyst
Data analysis is the craft of turning raw data into decisions, and this course walks
through it in the order most analysts actually learn it. It starts with
Foundations of Data Analysis (the analysis lifecycle, asking good
questions, and defining metrics), then Spreadsheets & Excel and
Statistics Fundamentals so you can explore and reason about real
datasets from day one.
Next comes the query layer: SQL Fundamentals and
Advanced SQL cover selecting, filtering, joining, aggregating,
CTEs, and window functions against PostgreSQL or MySQL. From there,
Python for Data Analysis introduces pandas and NumPy, while
Data Cleaning & Wrangling covers the messy reality of missing
values, duplicates, and inconsistent formats.
Exploratory Data Analysis teaches you to find patterns and
outliers, and Data Visualization covers choosing the right chart
with Matplotlib, Seaborn, and design principles. BI Tools &
Dashboards then covers Tableau, Power BI, and Looker Studio for
building dashboards that stakeholders use.
Finally, A/B Testing & Business Analytics covers hypothesis
testing, experiment design, and product and marketing metrics, and
Storytelling & Career Prep covers presenting insights,
building a portfolio, and interviewing: the skills that separate someone who can
run a query from an analyst who changes decisions.
Content
12 modules
01
Foundations of Data Analysis
The role of a data analyst, the analysis lifecycle, data types, asking the right business questions, and defining KPIs.
Analysis LifecycleKPIsData Types
02
Spreadsheets & Excel
Formulas, lookups, pivot tables, and charts in Excel or Google Sheets for quick analysis and reporting.
ExcelPivot TablesXLOOKUP
03
Statistics Fundamentals
Descriptive statistics, probability, distributions, correlation, and sampling: the maths behind trustworthy conclusions.
Descriptive StatsDistributionsCorrelation
04
SQL Fundamentals
Querying relational databases with SELECT, WHERE, GROUP BY, ORDER BY, and joins across multiple tables.
SELECTJOINsAggregations
05
Advanced SQL
Subqueries, CTEs, window functions, and query optimisation for the analytical questions real teams ask.
CTEsWindow FunctionsQuery Optimisation
06
Python for Data Analysis
Python basics, then pandas and NumPy for loading, transforming, grouping, and merging datasets in Jupyter notebooks.
PythonpandasJupyter
07
Data Cleaning & Wrangling
Handling missing values, duplicates, outliers, and inconsistent formats, plus reshaping data and working with dates and text.
Missing DataDeduplicationData Validation
08
Exploratory Data Analysis
Profiling datasets, spotting patterns, trends, and anomalies, and forming hypotheses worth testing.
ProfilingOutlier DetectionSegmentation
09
Data Visualization
Choosing the right chart for the question, building plots in Python, and applying design principles that make data readable.
MatplotlibSeabornChart Design
10
BI Tools & Dashboards
Connecting data sources and building interactive dashboards and reports that stakeholders can explore themselves.
TableauPower BIDAX
11
A/B Testing & Business Analytics
Hypothesis testing, experiment design, and the product, marketing, and finance metrics analysts report on every day.
Hypothesis TestingA/B TestsCohort Analysis
12
Storytelling & Career Prep
Turning findings into recommendations, presenting to non-technical audiences, building a portfolio, and preparing for interviews.
Data StorytellingPortfolioInterview Prep
What You Can Do After This Course
By the end of the curriculum you'll have worked through 5 hands-on projects and
covered the core of what a junior data analyst role expects. Concretely,
you'll be able to:
Frame the right question: translate a vague business request into a measurable question, with clear metrics and a plan to answer it.
Analyse in spreadsheets: build pivot tables, lookups, and quick charts for fast, ad hoc analysis.
Query data with SQL: write joins, CTEs, and window functions to pull exactly the data a question needs.
Wrangle data in Python: clean, reshape, and analyse datasets with pandas and NumPy in reproducible notebooks.
Reason statistically: summarise distributions, test hypotheses, and tell real signal from noise.
Visualise and build dashboards: choose the right chart and ship interactive dashboards in Tableau or Power BI.
Influence decisions: run and interpret A/B tests, present recommendations clearly, and show your work in a portfolio.