What you'll learn:
- Build your first AI coding agent — a Personal Coding Assistant that audits, refactors, and tests code autonomously
- Understand what OpenAI Codex is and how AI coding agents actually work in 2026
- Learn how the Codex agent loop thinks — Plan, Do, Observe, Next Step — and why it matters
- Master the GPT-5 Codex model family — GPT-5.5, GPT-5.3-Codex, and GPT-5.3-Codex-Spark
- Master Codex Approval Modes — Read-Only, Auto, and Full Access
- Apply the branded K.I.M. Prompt Framework to write prompts Codex can't misinterpret
- Use context engineering with @file and drag-drop to stop Codex hallucinations
- Write your first brain of Agent — your project's "constitution" that Codex remembers across sessions
- Master Codex slash commands — /approvals, /model, /status — for a 10x faster CLI workflow
- Get a clear 2026 roadmap from beginner to advanced — MCPs, Codex SDK, and agentic AI workflows
Data Science Real World Use-Cases: Hands-On Python
Learn how Data Science is applied in real businesses by solving practical problems with Python. This course focuses on the complete data science workflow—from understanding business problems to collecting, cleaning, analyzing, visualizing, and modeling data using real-world datasets.
Instead of learning isolated concepts, you'll work through realistic use-cases that demonstrate how data scientists think, make decisions, and deliver business value. Whether you're an aspiring Data Scientist, Data Analyst, or Python enthusiast, this course will help you build job-ready skills through hands-on practice.
What you'll learn
Understand the complete Data Science project life cycle.
Solve real-world business problems using Python.
Collect, clean, and preprocess messy datasets.
Perform Exploratory Data Analysis (EDA) to uncover valuable insights.
Apply feature engineering techniques to improve model performance.
Build, evaluate, and optimize Machine Learning models.
Visualize data using professional charts and graphs.
Understand model deployment concepts and best practices.
Work with industry-style datasets and end-to-end case studies.
Gain practical experience that can be showcased in your portfolio.
This course is for
Beginners who want to learn Data Science through practical examples.
Students preparing for Data Science and Machine Learning careers.
Python programmers looking to apply their skills to real-world data.
Data Analysts who want to transition into Data Science.
Professionals interested in solving business problems with data.
Anyone who prefers learning by building real projects instead of watching theory.
By the end of this course, you'll have a strong understanding of how real Data Science projects are executed from start to finish and the confidence to tackle your own real-world datasets using Python.
