Digital literacy is the foundation upon which Coding skills are built. This programme develops Coding skills for solving real-life problems in the new digital world. The programme explores core programming skills and then develops proficiency in popular future-oriented Coding languages, paving the way to a career in software engineering.
This programme is for professionals involved in Coding. The programme is also well suited for the youth, students and graduates who wish to pursue a professional career in Coding; paving the way to a career in software engineering.
Duration: 8 weeks.
Mode: Course are facilitated by our expert faculty via our video conferencing platform.
- Introduction to Coding terminology, definitions, and concepts
- The evolution of Coding
- Introduction to problem solving, analytic logic, and information theory
- How computers work: introduction to computer science, algorithms, and the internet
- Introduction to the fundamentals of software engineering, Coding and some of the fastest growing Coding language
- Introduction to Data Science, data structures, accessing, filtering, comparing, managing data, and utilising popular software
- Coding a website using cutting-edge technologies and Coding languages
- Introduction to Artificial Intelligence, Robotics, Machine Learning, and Blockchain
- Critical success factors of Coding
What will you learn
- 15+ projects on Industrial applications of Python
- Python refresher with a recap of python structures
- Libraries for Data Analytics
- Structured Queries with Python.
- Data visualization with predictive analytics.
- Web Development Tools
- Web Development Tools
Session 1 – Python Refresher, Recap of Python Data Structures.
Session 2 – Statistics for Data Science and Libraries for data analytics and statistics
Session 3 – MySQL and SQLLite in databases
Session 4 – Tkinter Programming for GUI
Session 5 – Matplotlib for Data visualization, MatplotLib chart and customization
Session 6 – Seaborn for Data visualization, Seaborn chart and customization
Session 7 – Numpy, Scipy, Pandas for Data Analysis, Scikit-learn for machine learning
Session 8 – Data Wrangling for the transformation of raw data and Data Mugging for process analytics
Session 9 – Web Scraping for extracting data
Session 10 – Supervised and Unsupervised Learning Algorithms in Machine Learning
Session 11 – Django for web framework for web development
Session 12 – Flask API for web applications
Session 13 – Competitive Programming for Hackathons
Session 14 – GIT Hub for access control and storing projects and Kaggle
Session 15 – NLP with sentiment Analysis for Mining and classification and Capstone Project