Learn Python for Data Analysis and Visualization

Explore data science as an umbrella for analytics, mining, and machine learning. Discover how data analytics extracts patterns, insights, and decisions from structured and unstructured data across industries.

Explore the four analytics types—descriptive, diagnostic, predictive, and prescriptive—and how they guide data-driven decisions through forecasting and recommendations.

Learn the essential requirements for data analysts to deliver actionable, story-driven insights for clients, using a structured framework from discovery to visualization and clear communication.

Discover why Python dominates data science, highlighting its interpreter, readability, and libraries, and learn how to install Python and set up Anakonda and Jupiter for data analytics.

Install Anaconda to access data analytics tools such as Jupyter Notebook and Spyder, download the release, and launch a Python notebook in your browser for interactive data analysis and visualization.

Create your first Python program in Jupyter notebook, learn to print strings, assign variables, and identify string, integer, and float types with basic comments.

Learn how to read input and print output in Python, assign variables, and cast input to integers to ensure correct arithmetic and concatenation.

Explore Python's loosely typed nature and core data types—numbers (int, float, complex) and strings—plus string slicing, indexing, and concatenation, with introductory notes on lists and dictionaries.

Explore Python lists, a mutable container with square brackets holding mixed types. Learn indexing from zero, slicing, updating, extending, and removing elements to manipulate lists in data analysis workflows.

Explore Python tuples, an immutable sequence defined with parentheses and commas, unlike lists with square brackets. Learn indexing and slicing, and why tuples act as read-only data.

Learn how Python dictionaries map keys to values, acting as hash-like structures with quick access and items. Explore creating empty dictionaries, assigning values, and printing keys and values.

Discover how Python uses while, for, and nested loops to execute sequences, manage loop variables, and control repetition in data analysis and visualization.

Explore Python loops, including nested and for and while iterations, and master break and continue statements to control flow and pattern printing in data analysis and visualization workflows.

Learn how to define and use Python functions, including def syntax, parameters, return values, docstrings, indentation, and examples like adding hello or checking even numbers.

Explore NumPy, a scientific computing library in Python, for data processing, indexing, vectorized operations, array creation, and statistical functions for aggregation and linear algebra.

Learn how to import NumPy, convert Python lists to NumPy arrays, and inspect their shape and dimensionality to work with one- and two-dimensional data efficiently.

Explore numpy functions for arrays, including reshaping, creating ranges, and checking length; apply transpose, identity matrices, and basic statistics like mean, median, and standard deviation.

Explore array indexing in Python by accessing elements via zero-based and negative indices, demonstrating element retrieval, adding and removing items, and reversing sequences.

Discover pandas, the data analysis library built on numpy, and learn how its data structures—series, dataframes, and panels—enable data manipulation, reshaping, merging, sorting, slicing, and handling missing data.

Learn how to create a pandas series from lists and dictionaries, set custom indices, and understand how keys become the index in a one-dimensional data structure.

Learn to create and inspect pandas dataframes from dictionaries, access visitors data, and use head and indexing to explore business and US data.

Learn statistics for data science, including population and sample, descriptive and inferential methods, and qualitative and quantitative data types (nominal, ordinal, interval, ratio, discrete, continuous) to inform decision making.

Explore descriptive statistics to organize, summarize, and visualize data, focusing on central tendency (mean, median, mode), distribution, outliers, variance, standard deviation, and skewness.

Learn how statistics support data analysis in business with the Python toolkit, using sampling, probability, and distribution to predict outcomes and understand data confidence.

Explore the role of visualization in data analytics and learn chart types from scatterplots to heat maps, and how to present data to stakeholders using Matplotlib and Seaborn.

Explore how to use matplotlib to create line plots by importing the library, plotting x and y coordinates, adding markers, customizing colors and labels, and adjusting axis limits.

Learn to use Matplotlib scatter, bar, and pie plots to visualize data such as language usage, with x and y coordinates, heights, rotations, and percentages.

Explore subplot techniques to plot multiple charts in a 2x2 grid using a covid-19 data frame, visualizing confirmed, active, recovered, and deceased cases and building histograms of frequency distributions.

Explore seaborn's high-level interface built on matplotlib to load datasets, prepare data, and create statistical visuals such as strip plots, scatter plots, and correlation matrices.

Explore Seaborn bar plots and relational plots, showing a gender-based survival bar plot from a sample dataset, with survival by sex and class encoded as 1 or 0.

Explore seaborn countplot to visualize frequency by category, demonstrated with Titanic data and categories A, B, C, and D, showing counts such as 59, 47, and 32.

Explore exploratory data analysis on wine quality data to reveal which chemical properties like alcohol, volatile acidity, and density influence red wine quality, using heatmaps, box plots, and scatter plots.

Analyze employee performance data to identify key drivers of ratings, such as department, work-life balance, and satisfaction, and use findings to guide appraisal and development.

Trace the history and motivation of machine learning and how algorithms learn from data. Explore data preprocessing, learning types, regression and classification, and applications like sentiment analysis.

Explore supervised learning, its classification and regression tasks, and how labeled data trains models—like logistic regression or nearest neighbor—to predict house prices or gender from images as data grows.

Master linear regression, a supervised method that predicts a continuous outcome from features, including univariate, multiple, and polynomial forms, with training, testing, and assumptions such as linearity.

Learn to build a simple linear regression in Python to predict salary from years of experience using a salary dataset, with code and visuals in a notebook.

Evaluate regression performance with R squared and root mean square error, and improve models through data transformations, weighted least squares, and penalized methods like lasso and elastic net.

Learn a regression case study with sklearn, performing simple linear regression on salary data, splitting into training and testing sets, fitting and predicting, and evaluating with r2 and visualizations.