Python for Data Analysts: From Zero to Job-Ready Skills

Learn how to use Python variables in Google Colab, create and print values, and explore data types—integers, floats, strings, and booleans—while practicing snake_case naming and understanding dynamic typing.

Discover how Python logical operators not, and, or combine conditions to produce boolean results. Apply practical examples like age checks for adulthood and discounts to illustrate precise rule evaluation.

Learn to work with lists in Python, the ordered data structure, by creating lists with square brackets, indexing, slicing, membership checks with in or not in, and appending items.

Learn how tuples in Python store multiple values in a specific order, provide indexing and slicing, and remain immutable to protect fixed data like a Christmas date.

Learn how sets in Python store unique values, are unordered, do not support indexing, support membership tests and mutations with add and remove, and differ from lists and tuples.

This course teaches dictionaries in Python, showing how to store data as key-value pairs, access values by keys, add or remove entries, and use get with defaults for safe lookups.

Learn how the Python for loop repeats code across lists, dictionaries, and ranges, iterating over elements, keys, values, and key-value pairs.

Learn how the Python while loop runs when a condition is true and stops when it becomes false. Use break and continue to control flow and avoid infinite loops.

Load an ecommerce data csv into a pandas dataframe, upload via the files panel, and preview with df head and df tail in Google Colab.

Learn to select columns and rows with pandas, using square brackets for single or multiple columns, and iloc with zero-based indexing to inspect rows and combine selections.

Master boolean filtering in pandas to select rows by conditions, such as sales > 100 and electronics category, for focused analysis with multiple criteria.

Export the cleaned data to csv without an index, then create new columns for revenue from quantity and price, date components via .dt, and an order size label with numpy.where.

Group data with pandas group by to generate business metrics such as total revenue by product category and average order value by customer using aggregation and sorting.

Automate data workflows in Python by loading, cleaning, transforming, grouping, and merging datasets with a script that runs. Create new columns, standardize dates, calculate total revenue, and export clean reports.