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Data Science Real World Use-Cases Hands-On Python
Rating: 4.2 out of 5(475 ratings)
52,878 students

Data Science Real World Use-Cases Hands-On Python

Master Machine Learning , NLP , Time series Projects
Last updated 7/2026
English
English [Auto],

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

Course content

4 sections26 lectures4h 42m total length
  • Codex vs Claude vs Cursor vs GitHub Copilot - simple Camparison !6:03

Requirements

  • Basic knowledge of Python programming is recommended.

Description

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.

Who this course is for:

  • Software developers who want to integrate Codex into their daily workflow and ship 10x faster
  • Beginners curious about agentic AI and ready to build their first AI coding agent
  • Startup founders and solo builders racing to ship MVPs with limited engineering resources
  • Product managers who want to understand how AI coding agents change team velocity and roadmaps
  • Programming students and career switchers learning the modern, AI-native way to build software