Python for Everyone: Coding, Data Science & ML Essentials syllabus

 

Week 1: Introduction to Coding and Python

Topic: Introduction to coding and Python
Details:

• Overview of programming concepts and Python's significance

• Installing Python and setting up the development environment

• Introduction to IDEs like PyCharm, VS Code, or Jupyter Notebooks

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Week 2: Variables and Data Types

Topic: Understanding variables and data types
Details:

• Variables: Naming conventions and assignment

• Data types: strings, integers, floats, and booleans

• Simple calculations and printing output

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Week 3: User Interaction

Topic: Using the input() function for user interaction
Details:

• Reading user input

• Converting input types

• Using input in simple programs

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Week 4: Decision Making with If-Else Statements

Topic: Basic if-else statements for decision-making
Details:

• Syntax and structure of if, elif, and else

• Nested if-else statements

• Practical examples and exercises

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Week 5: Introduction to Loops

Topic: Introduction to loops for repetitive tasks
Details:

• While loops: syntax and use cases

• For loops: syntax and use cases

• Loop control statements: break, continue, and pass

• Simple loop exercises

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Week 6: Functions and Code Organization

Topic: Introduction to functions
Details:

• Definition and syntax of functions

• Parameters and return values

• The importance of functions in organizing code

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Week 7: Built-in and User-Defined Functions

Topic: Exploring built-in functions and creating user-defined functions
Details:

• Common built-in functions in Python

• Creating and using user-defined functions

• Scope and lifetime of variables

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Week 8: Working with Lists

Topic: Understanding and using lists
Details:

• Creating and modifying lists

• List indexing and slicing

• Common list operations (append, remove, pop, etc.)

• List comprehensions

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Week 9: String Manipulation

Topic: Introduction to string manipulation
Details:

• String slicing and indexing

• String concatenation and formatting

• Common string methods (split, join, replace, etc.)

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Week 10: Recap and Practice

Topic: Recap and practice exercises
Details:

• Review of previous topics

• Practice exercises and mini-projects

• Q&A session for clarification of doubts

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Week 11: Introduction to Dictionaries

Topic: Working with dictionaries for key-value data storage
Details:

• Creating and accessing dictionaries

• Dictionary methods and operations (keys, values, items, etc.)

• Practical examples and exercises

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Week 12: Working with Files

Topic: Reading and writing data to files
Details:

• File handling modes (read, write, append)

• Reading from and writing to files

• Practical file handling exercises

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Week 13: Exceptions and Error Handling

Topic: Introduction to exceptions and error handling
Details:

• Understanding exceptions

• Try, except, else, and finally blocks

• Raising exceptions

• Practical error handling exercises

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Week 14: Introduction to Object-Oriented Programming

Topic: Basic introduction to OOP
Details:

• Understanding classes and objects

• Creating classes and objects

• Attributes and methods

• Practical examples of OOP concepts

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Week 15: Final Recap and Practice

Topic: Recap and practice exercises
Details:

• Comprehensive review of all topics

• Advanced practice exercises and projects

• Final Q&A and course completion

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📊 Data Science & Machine Learning Extension

Week 16: Introduction to Data Science & Jupyter Notebooks

Topic: Getting started with Data Science
Details:

• What is Data Science?

• Setting up Jupyter Notebooks

• Introduction to NumPy and Pandas

• Loading and inspecting data

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Week 17: Data Manipulation with Pandas

Topic: Data wrangling and cleaning
Details:

• DataFrames and Series

• Reading/writing CSV, Excel

• Handling missing data

• Filtering, sorting, grouping data

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Week 18: Data Visualization

Topic: Exploring data visually
Details:

• Plotting with Matplotlib

• Advanced visuals using Seaborn

• Histograms, scatter plots, box plots

• Customizing graphs for insights

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Week 19: Introduction to Machine Learning

Topic: Machine Learning fundamentals
Details:

• What is ML? Types of ML (Supervised, Unsupervised)

• Scikit-learn basics

• Splitting data into training/testing sets

• Evaluation metrics (accuracy, precision, recall)

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Week 20: Building Your First ML Model

Topic: Creating a classification model
Details:

• Logistic Regression or Decision Trees

• Model training and prediction

• Evaluating model performance

• Model improvement basics

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Week 21: Capstone Project & Course Wrap-up

Topic: Apply what you’ve learned
Details:

• Real-world data project (e.g., Titanic, Iris, or custom dataset)

• Full pipeline: load → clean → visualize → model → evaluate

• Presentation and peer review

• Final certification and next steps