Python for Data Science: Core to Cutting Edge
Start a learning journey in Python and Data Science. Learn basic Python concepts, explore advanced topics like OOP and data manipulation, and try dynamic analysis. Finish with machine learning projects, expert guidance, and talks about the future of data science.
Course Overview
Embark on a transformative journey in our comprehensive Python and Data Science course. The introduction provides a foundational understanding of Python programming, covering syntax, data types, and essential concepts. Participants explore the dynamic Python ecosystem, gaining practical insights through step-by-step instructions for installation and development environment setup. Emphasis is placed on mastering conditional statements and error-handling techniques. As the course progresses, participants delve into advanced Python topics including Object-Oriented Programming (OOP), data manipulation with Pandas, and creating dynamic visualizations with Matplotlib, Seaborn, and Plotly. Culmination lies in a machine learning module, guiding participants through supervised and unsupervised learning, model implementation, and advanced data analysis, setting the stage for the future of data science.
Duration
3 Months
Level
01
Who is it for
Ideal for beginners and new enthusiasts, this course covers Python basics, advanced concepts, and machine learning applications, providing a comprehensive foundation for data science exploration.
To Apply you’ll need
Basic computer skills are essential.
Understanding of internet fundamentals is required.
Access to a computer and internet connection.
Interest in learning Python and data science.
Enthusiasm for exploring the digital world.
Qualifications Recieved
An intermediate degree is beneficial. Enthusiasm for data science and artificial intelligence is the primary requirement, making the course accessible to learners with varied educational backgrounds.
Course Journey
Three Modules
4 Weeks each
Assessments
Every Week
Career counselling
Two Lectures
Final Assessment
In the middle and end of the course
MODULE 1
Introduction to Python Programming
In this foundational module, participants embark on a comprehensive journey into Python programming. The exploration begins with an overview of Python and its versatile applications across various domains. Participants delve into the Python ecosystem, gaining insights into its role and vibrant community. The practical aspect involves step-by-step instructions for installing Python and configuring a development environment. Emphasis is placed on understanding fundamental Python syntax and basic programming concepts, including variables, data types, and basic operations. The module culminates in a mastery of conditional statements, data structures such as arrays, lists, and dictionaries, as well as essential looping constructs and error-handling techniques. The importance of functions and modules for code organization is also underscored.
DURATION
4 Weeks
MODULE 2
Advanced Python Topics and Data Science
Building on the foundational knowledge, this module delves into advanced Python topics and their applications in data science. Participants are introduced to Object-Oriented Programming (OOP) concepts, including classes, objects, inheritance, polymorphism, encapsulation, and abstraction. The journey continues with an exploration of NumPy for numerical computing and leveraging Pandas for data manipulation and analysis. Participants learn to create static visualizations using Matplotlib and Seaborn and build interactive visualizations with Plotly. Advanced topics include handling missing data, employing data cleaning techniques with Pandas, and preparing data for machine learning through transformation and feature engineering. The module concludes with participants presenting and discussing their individual and group projects.
DURATION
4 Weeks
MODULE 3
Machine Learning and Conclusion
In the final module, participants transition to the realm of machine learning. The distinction between supervised and unsupervised learning sets the stage, followed by practical applications of clustering with K-Means. The Scikit-Learn library takes center stage for overview and hands-on usage. Participants implement regression models, explore cross-validation techniques, delve into decision trees and random forests, and apply dimensionality reduction with PCA. Association rule learning with Apriori is explored for pattern discovery. The module includes guidance on final projects, hyperparameter tuning, and in-depth analysis of model evaluation metrics. A showcase of advanced projects demonstrates various machine learning concepts. The course concludes with a discussion on the future of data science and addresses participant questions, wrapping with final presentations and discussions on group projects.