Fundamentals of Python For Data Science
Introduction to Python for Data Science
Course Overview:
This beginner-friendly course introduces Python programming fundamentals tailored for data science. No prior experience required! Learn to write code, handle numerical and text data, use variables, and work with lists.
Key Skills Covered:
- Data types and basic operations
- Variables and lists manipulation
- Simple data processing and visualization
- Hands-on projects to reinforce learning
Start learning today to build a solid foundation in Python and set yourself up for a career in data science.
PREREQUISITES:
- No prior programming experience needed
- Basic math knowledge helpful
- Curiosity about data science
Master Data Science Skills Faster with Innomatics
From Beginner to Job-Ready
Gain the exact skills you need for a data science career, without extra fluff.
Build Your Project Portfolio
Work on real projects to showcase your data skills with confidence.
Challenge Yourself with Exercises
Dive into hands-on exercises with real-world data from day one.
Earn a Recognized Certification
Show your expertise with a certification that boosts your career.
NASSCOM Fundamentals of Python For Data Science (Syllabus)
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Day 1: Python Basics
- Introduction to Python
- Installation and features of Python
- Variables & Data types
- Operators (Arithmetic, Assignment, Comparison, Logical)
Day 2: Python Data Structures
- What are Data Structures?
- Primitive Data Types
- Lists and Tuples
- Sets and Dictionaries
Day 3: Python Control Flow
- Conditional Statements
- if-else and elif ladder
- Syntax of if else statements
- Problem Solving (Voting Age, BMI problem)
Day 4: Python Loops and Problem solving
- Loops (for, while)
- Syntax of for loops
- Syntax of while loops
- Problem Solving (Star pattern, Factorial)
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Day 5: Python Problem Solving
- Problem Solving
- Palindrome, Armstrong Numbers, Prime Number problem
Day 6: Introduction to Functions and Modules
- Creating functions using def keyword
- Intro to docstring
- Lambda Functions
- Filter, Map and Reduce Functions
- Introduction to Modules
- Working with DateTime
Day 7: File and Exception Handling
- Storing data in files
- Opening a file
- read and write on files
- Errors vs Exceptions
- Handling Exceptions with try, except and finally syntax
Day 8: Basics of Numpy
- Introduction to Numpy
- Handling Arrays with Numpy vs List
- Mathematical and Statistical operations