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 the Python dictionary as key-value pairs stored in curly braces, with examples like name, age, and date of birth, and learn essential dictionary functions through hands-on programming.

Explore decision making in python by mastering if, elif, and else constructs, including nested ifs and practical examples with user input and simple conditions like health checks and movie reviews.

Learn how to use for and while loops in Python, including range, enumerate, and string iteration, to process sequences, test conditions, and count items in lists.

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.

A function is a core building block in programming that enables teams to write and reuse code for easier maintenance, with formats ranging from no return to those with output.

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.

Explore how NumPy enables efficient data analysis and machine learning by replacing slow Python loops with NumPy-based approaches to handle millions of records with higher performance.

Learn the basics of NumPy, including creating arrays from lists, understanding differences between lists and NumPy arrays, and performing vectorized, element-wise operations that avoid loops for faster computation.

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.

Learn core NumPy array operations for data analysis, including reshape, transpose, vertical or horizontal stacking, and vectorized computations with ufuncs.

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 how to scrape reviews from websites, extract user sentiment using html parsing, and locate data with elements like title, company, and location via html classes and id search result.

data cleaning dominates data workflows, taking about 60 percent of a data scientist's time, and uses imputation with mean, median, or mode and row removal to improve accuracy.

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.

Compare and interpret scatterplots, bar charts, histograms, line charts, and pie charts to reveal trends, distributions, and outliers using color, form, and positioning.

Explore matplotlib basics by creating and labeling plots with X and Y data, customizing titles, axes, and colors, and arranging multiple plots with subplots.

Learn to beautify plots with seaborn basics, creating histograms, density plots, bar charts, box plots, scatter plots, and joint plots from market data.

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 bi-variate analysis with aggregators plots, using bar charts, boxplots, and histograms to compare sales across product categories, customer segments, and profit-filtered data.

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 car data with Pandas in Python to manipulate data frames, examine distributions and median prices, and visualize relationships with scatterplots to explore how features relate to price.

Explore data manipulation and analysis with pandas and numpy to examine car data, visualize distributions with box plots and heat maps, and uncover relationships between price, horsepower, and compression ratio.

Learn to present data insights to business users by outlining the objective, selecting key variables, and using density plots, scatter plots, and heat maps to show correlations and outcomes.

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.