Study
Data Science & A.I. with Python Program

Module 1: Data Science with Python Basics

Python for Data Science
A beginner-friendly module that teaches you how to code in Python and work efficiently with big datasets using Google’s Colab. Understand how businesses store, extract and manipulate data through industry use cases and case studies.

Analyzing Data with Python
Learn how to analyze and harvest clean data sets and create data frames to run basic analysis as well as to perform data reporting using powerful data science libraries like Pandas and Numpy to gain actionable insights for your business.

Web Scraping with APIs
Learn how to navigate, collect and organize data from various sources like CSV files, APIs. You’ll leverage existing datasets, scrape web data and learn to access useful information in these data structures.

Intro to Data Visualization
Understand how data scientists present and visualize large datasets across industries. Leverage Matplotlib to create descriptive and interpretable visuals, enabling you to easily extract relevant information, better understand the data, and make effective decisions.

Module 2: Advanced Data Analysis and Data Engineering

Data Crawling and Data Mining

Learn how to extract and save any data on any websites like Google and Yahoo, process the HTML codes, and build automated tools to crawl the web at scale. From texts to images, fetch and display desired information for your own use with libraries like BeautifulSoup.

Advanced Data Visualisation

Bridge the gap between data and insights and create informative and striking statistical graphics with libraries like Seaborn and Plotly. Draw effective conclusions and analysis by visualizing the multidimensional relationships among the data samples, conduct correlation analysis and even statistical data exploration.

Module 3: Data Engineering and Regression Modeling

Learn how to explore, clean and model data for supervised machine learning using preprocessing techniques. Implement linear and logistic regression, and build classification models with decision trees like Random Forest to make predictions with Python machine learning library sci-kit learn and k-nearest neighbours algorithm.

Determine whether a model is good or not by implementing both a training set and a test set to ensure its ability to adapt and generalized to new, unseen data. Learn how the reward-punishment mechanism works to penalized models to prevent overfitting using regularization and cross-validation methods to improve model accuracy.

Learn how to find associations, patterns, and relationships present in data using clustering techniques and dimensionality reduction techniques as well as matrix factorization to break data set in groups.