Understand what data analysis means, what analysts do, and how Python helps turn raw data into useful answers.

Learn a practical step-by-step workflow for starting, organizing, and finishing a data analysis project.

Install Python and the core libraries used for beginner data analysis: pandas, NumPy, Jupyter, and openpyxl.

Learn how notebooks help analysts experiment, document steps, and run code in small pieces.

Review the Python essentials you need for data analysis: variables, lists, dictionaries, loops, and functions.

Meet NumPy, the numeric library behind much of the Python data analysis ecosystem.

Create arrays, inspect their shape, select values, and run useful numeric operations.

Meet pandas, the main Python library for loading, cleaning, exploring, and summarizing table data.

Understand the two main pandas objects: Series for one column and DataFrame for a full table.

Load CSV data into pandas and understand common options such as separators, headers, and selected columns.

Load data from Excel files, JSON data, and other common sources with pandas.

Use the most important first-look pandas methods to understand a DataFrame before analyzing it.

Select one column, multiple columns, and columns by name to focus your analysis.

Use conditions to keep only the rows that match your analysis question.

Sort rows and rank values to find top products, highest scores, and lowest performers.

Find, understand, remove, and fill missing values safely in pandas.

Detect and remove duplicate rows so repeated records do not distort your analysis.

Inspect and convert pandas data types so numbers, dates, and categories behave correctly.

Use pandas string methods to clean spaces, casing, categories, and simple patterns in text columns.

Rename unclear columns, reorder columns, reset indexes, and set meaningful indexes in pandas.

Use groupby to split data into groups and calculate summaries for each group.

Calculate single and multiple summary statistics with pandas aggregation methods.

Combine related tables with shared keys using pandas merge and understand common join types.

Stack similar DataFrames with concat and replace old append-style workflows.

Put the beginner skills together in a small exploratory data analysis project using pandas.

Create clear static charts with Matplotlib and understand the figure, axes, and label workflow.

Choose between line, bar, and pie charts based on the question your analysis needs to answer.

Understand numeric distributions with histograms, box plots, spread, skew, and outlier clues.

Use Seaborn to build attractive statistical charts from tidy pandas DataFrames.

Explore relationships between numeric variables and interpret correlation carefully.

Build interactive charts for exploration and sharing with Plotly Express.

Summarize data across categories with pandas pivot_table and crosstab patterns.

Choose the right pandas method for element mapping, row logic, and group-level calculations.

Parse, clean, and extract date features from pandas datetime columns.

Set a datetime index, resample events into periods, and calculate simple time series trends.

Identify unusual values, investigate their causes, and choose transparent handling strategies.

Create useful analysis columns from dates, numeric values, text, and business rules.

Work with categorical columns, clean category labels, and introduce simple encoding patterns.

Use SELECT, WHERE, GROUP BY, and JOIN to answer common analyst questions.

Bridge SQL databases and pandas DataFrames with safe, focused read_sql workflows.

Connect conceptually to PostgreSQL, use environment-based URLs, and query data safely from pandas.

Request JSON data from APIs, handle pagination basics, and normalize results into pandas.

Evaluate data source permissions, privacy, robots.txt, and respectful collection practices.

Follow a practical exploratory data analysis workflow from question to findings.

Turn analysis results into a clear narrative with context, evidence, and action.

Export cleaned data, summary tables, and charts in practical formats for stakeholders.

Organize analysis projects so others can rerun, review, and trust your results.

Turn exploratory notebook work into a maintainable Python script that can be rerun.

Learn the statistical thinking analysts use to make decisions, quantify uncertainty, and avoid overclaiming from data.

Understand distributions and summary statistics so you can describe data accurately and avoid misleading averages.

Use hypothesis tests carefully to compare groups, interpret p-values, and decide when an observed difference is likely meaningful.

Plan, analyze, and communicate simple A/B tests with clear metrics, clean experiment data, and responsible conclusions.

See how data analysis connects to machine learning through features, labels, baselines, evaluation, and responsible model use.

Improve pandas workflows with profiling, vectorization, better dtypes, chunking, indexing, and memory-aware habits.

Meet Polars, a fast DataFrame library with eager and lazy APIs that can speed up large analytical workflows.

Learn what changes when data no longer fits comfortably on one machine: storage, compute, sampling, partitioning, cost, and reliability.

Design useful reports and simple dashboards that communicate metrics clearly, refresh safely, and support decisions.

Practice responsible analysis by identifying bias, protecting privacy, communicating limits, and reducing harm from data decisions.

Build a complete sales performance analysis from sample data: load, clean, explore, visualize, and conclude with recommendations.

Segment customers with RFM-style features, compare groups, visualize behavior, and recommend actions for each segment.

Create a time series trends report with sample data, cleaning, rolling averages, seasonal comparison, visualization, and conclusions.

Combine SQL and pandas to produce a business report from relational tables with cleaning, exploration, visualization, and conclusions.

Recognize and fix common analysis mistakes involving metrics, data quality, joins, time, visualization, statistics, and communication.

Create a portfolio that shows practical analysis skill through clear questions, clean code, strong communication, and real project structure.

Choose your next learning path after data analysis: business intelligence, analytics engineering, data science, machine learning, or data engineering.