Python Data Analysis - Matplotlib, Seaborn, Pandas & NumPy

Learn to use pandas for data analysis and manipulation, create dataframes from dictionaries, access data with iloc, and read or write csv files with read_csv and to_csv.

Learn to use Pandas series and data frames, create a series with a custom index, build a data frame from a dictionary, and filter salaries above 55,000.

Load data into pandas, clean missing values, remove duplicates, and fix data types to prepare a structured dataset for analysis.

master pandas filtering and selecting by applying age and salary conditions, filtering the it department, and choosing columns like name and salary for efficient, scalable data analysis.

Learn how to work with time series data in pandas, including creating date ranges, converting to datetime, and using a date index for resampling and filtering.

Learn to create basic plots with Matplotlib for Python data analysis, including line plots, scatter plots, and bar charts to visualize data clearly.

Explore how to customize plots in matplotlib by adjusting colors, line style, markers, titles, labels, and legends for clearer data visualization.

Create customized subplots in Matplotlib to display multiple graphs within a single figure, plotting sine and cosine waves with two rows and one column, and adjust layout for clear comparison.

Explore seaborn, a powerful Python visualization library built on Matplotlib, to load sample datasets and create scatter plots and histograms with just a few lines.

Explore exploratory data analysis with seaborn by visualizing the tips data set through histoplot, scatter plots, and heat maps to reveal distribution, relationships, and correlations in total bill and tip.

Create and manipulate NumPy arrays to perform fast numerical operations, and understand shape and size. Explore zeros, ones, arange, and 2d matrices for data science and machine learning.

Learn how NumPy enables fast element-wise array operations and broadcasting across shapes, with practical examples of addition, subtraction, multiplication, division, and functions like sqrt, exp, and log.

Create matrices with numpy, perform matrix multiplication with the dot function, and compute determinants and inverses. Solve linear systems ax=b with numpy to boost data analysis.

Explore random number generation with NumPy to produce floats and integers, create random matrices, and perform shuffling and random sampling for data science simulations and AI weather, generating synthetic data.

Master the three core machine learning types: supervised, unsupervised, and reinforcement learning, through practical Python examples with linear regression, clustering, and data prediction.

Train supervised learning models on input-output pairs to predict continuous values with linear regression and classify outcomes with logistic regression, using train-test splits on salary and exam data.

Discover unsupervised learning techniques like clustering and dimensionality reduction, including k-means clustering and PCA for visualization, through a hands-on implementation on synthetic data and centroid plotting.

Evaluate and select machine learning models using train-test split, accuracy, classification report, and cross-validation, demonstrated with a random forest on the iris dataset.

Master how application programming interfaces enable apps to communicate and exchange data by making basic api requests in python with the requests library, using json placeholder to fetch posts.

Learn to make API requests with the Python requests library by performing get and post calls to fetch and send data from a JSON placeholder API, including status code checks.

Learn to fetch data from an API and parse JSON with Python's built-in json module, and parse XML with xml.etree.ElementTree, extracting username and email for structured data handling.