Core Python + DSA for Data Science

Readme.md

Week 1-2: Python Fundamentals

1. Basic Syntax and Data Types

   • Variables, data types (integers, floats, strings)
   • Type casting and basic input/output functions (print, input)

2. Control Structures

   • Conditionals (if, elif, else)
   • Loops (for, while)
   • List comprehensions for concise loops

3. Data Structures in Python

   • Lists: Operations, indexing, slicing, and list methods
   • Tuples and Sets: When to use, basic operations
   • Dictionaries: Key-value pairs, dictionary methods, nested dictionaries

4. Functions

   • Defining functions, parameters, and return values
   • Scope (local vs. global variables)
   • Lambda functions and basic usage

5. Basic File Handling

   • Reading from and writing to files
   • Understanding file modes (read, write, append)

---

Week 3-4: Advanced Python and Libraries

1. Object-Oriented Programming (OOP)

   • Classes and objects, __init__ method
   • Class attributes, methods, inheritance, polymorphism

2. Exception Handling

   • try, except, finally
   • Handling multiple exceptions

3. Core Libraries for Data Science Preparation

   • Numpy: Arrays, basic operations, broadcasting, reshaping
   • Pandas: DataFrames, basic data manipulation, filtering, merging, groupby
   • Matplotlib/Seaborn: Basic plotting, data visualization concepts

4. Working with APIs and JSON Data

   • Introduction to API calls in Python (requests library)
   • Parsing JSON data

---

Week 5-6: Data Structures and Algorithms (DSA) in Python

1. Array and String Manipulation

   • Common operations, indexing, slicing
   • Basic string manipulations, pattern matching

2. Linked Lists

   • Single and doubly linked lists
   • Basic operations (insert, delete, traverse)

3. Stacks and Queues

   • Implementations using lists
   • Applications and problem-solving

4. Trees and Graphs

   • Binary trees: Traversal methods (inorder, preorder, postorder)
   • Binary search trees, graph basics (BFS, DFS)

5. Sorting and Searching Algorithms

   • Bubble sort, selection sort, merge sort, quicksort
   • Linear and binary search

---

Week 7-8: Data Science Foundations with Python

1. Data Wrangling with Pandas

   • Advanced filtering, aggregation, handling missing values
   • Merging datasets, reshaping (pivot, melt)

2. Data Visualization

   • Creating visualizations with Matplotlib and Seaborn
   • Basic understanding of histograms, scatter plots, heatmaps

3. Exploratory Data Analysis (EDA)

   • Understanding data distributions, statistical summary
   • Detecting outliers, handling categorical variables

4. Introduction to Machine Learning Concepts

   • Overview of supervised and unsupervised learning
   • Linear regression concept, simple implementation in Python