What Is Data Analysis
Understand what data analysis is, why it matters, and the workflow analysts follow from question to decision.
Data Analysis
Learn to turn raw data into clear, reliable answers: from cleaning and spreadsheets to SQL, statistics, visualization and forecasting.
Types of Data
Tell apart categorical, numerical and ordinal data so you pick the right summary and chart for each.
Collecting and Importing Data
Bring data into your analysis correctly from surveys, exports, APIs and files without losing or corrupting it on the way…
Data Cleaning and Missing Values
Find and fix duplicates, inconsistent entries and missing values before they quietly bias every result you compute.
Spreadsheets for Analysis
Use formulas, references and sorting in a spreadsheet to explore data before reaching for heavier tools.
Descriptive Statistics: Mean, Median, Mode
Summarize a column of numbers with the right measure of central tendency and know when each one misleads.
Measures of Spread
Go beyond the average with range, variance and standard deviation to describe how consistent or scattered data really is…
Data Visualization Principles
Design charts that show the truth quickly: the right encoding, honest axes and minimal clutter.
Choosing the Right Chart Type
Match the chart to the question: comparison, trend, distribution, relationship or composition.
Working with Pivot Tables
Summarize and reshape large tables into quick answers using rows, columns, values and filters.
Introduction to SQL for Analysts
Query a database directly with SELECT, WHERE, ORDER BY and GROUP BY to answer questions a spreadsheet cannot handle.
Joins in SQL
Combine data spread across multiple tables using INNER, LEFT and other join types.
Filtering and Sorting Data
Combine conditions precisely and order results so the rows that matter surface first.
Correlation Versus Causation
Measure how strongly two variables move together, and understand why that alone never proves one causes the other.
Basic Hypothesis Testing Concepts
Understand what a p-value actually means and how analysts decide whether a difference is real or just noise.
Sampling and Bias
Draw a sample that actually represents the population you care about, and recognize the biases that quietly wreck it.
Time Series Basics and Trends
Read data ordered over time by separating trend, seasonality and noise.
Cohort and Segmentation Analysis
Group users by shared traits or start date to see patterns a single overall average hides.
Working with Dates and Time Data
Store, parse and compare dates correctly, including time zones and calendar quirks that silently break analysis.
Text and Categorical Data Basics
Clean, standardize and summarize text and category columns so they can be counted and compared reliably.
Outlier Detection
Find unusual values with simple rules like the IQR method and decide whether to keep, fix or remove them.
Data Normalization and Scaling
Rescale numeric columns to comparable ranges so no single unit dominates an analysis by accident.
A/B Testing Fundamentals
Design a controlled experiment that isolates one change and tells you honestly whether it worked.
Introduction to Regression Analysis
Fit a line through data to quantify how one variable predicts another, and read the result honestly.
Dashboards and KPIs
Design a dashboard around a small set of well-defined KPIs that drive real decisions instead of vanity numbers.
Storytelling with Data
Turn an analysis into a clear narrative with one message, the right evidence and a recommendation a reader can act on.
Data Ethics and Privacy in Analysis
Handle personal data responsibly and recognize when an analysis, even an accurate one, can still cause harm.
Introduction to Python for Data Analysis
Understand, at a conceptual level, how DataFrames, filtering and grouping work in pandas so code reads like your spreads…
Forecasting Basics
Project a trend forward with simple, defensible methods and communicate the uncertainty honestly.
Capstone: Structuring a Real Analysis Project
Plan and run a full analysis end to end, from a vague business question to a defensible, communicated answer.