Data Analysis using Python. Learn from Basic to Advance

Learn how to create Python lists using square brackets, store mixed types, including nested lists, and access elements via indexing, including reverse indexing.

Explore Python lists through examples: create, index from zero and with negative indices, append and remove elements, and convert strings to lists using split, then join and analyze list operations.

Explore Python comprehensions as an elegant way to write a loop in a single statement, turning for loops into concise lists.

Learn python list and dictionary comprehensions that replace nested loops, producing squared numbers and word initials from sentences, and creating key-value pairs in a single line.

Learn the basics of map, reduce, and filter, including how to map a function over list elements and break complex tasks into smaller, pairwise operations.

Explore map, reduce, and filter with lambda functions in Python to transform lists, apply arithmetic operations like squaring numbers, and aggregate results through functional patterns.

Analyze data with Pandas and NumPy by inspecting shape and memory usage, slicing 2D arrays. Compare for loops with vectorized lambdas, measuring performance to show faster and more efficient computation.

Explore the world of pandas for data analysis, learning how statistics and visualization turn data into insights, from median and mean replacements to bar charts and loss plots.

Upload a market data file, inspect with head and tail, view columns and data types, select columns and rows by name, and sort by profit and sales in descending order.

Explore indexing a data frame to select values by rows and columns, with label-based indexing and boolean filtering demonstrated for single and multiple row and column queries.

upload multiple files and merge market fact data with customer, product, and shipping dimensions using key ids, then perform aggregations and fill nulls for filtered results.

Learn practical missing-value imputation and data cleansing with pandas and numpy, using Melbourne real estate data to handle blank values, detect unrealistic entries, and decide when to drop columns.

Explore Seaborn correlation matrix plots to reveal relationships among sales, discounts, and profit; interpret pairwise variable connections and histogram insights using Python with Pandas and NumPy.

Explore a car dataset by uploading a csv, inspecting data types and columns, and using pandas and seaborn to uncover which factors influence price through density plots and scatter analyses.

Analyze covid-19 data through a guest study to identify common symptoms and highly related variables, then verify findings with hospital data and draw conclusions.