First step towards Data Science
What you will learn
Python Programming Language from Scratch
Gaining practical experience with real-time exercises
Python Datatypes – List, Tuple, Set, Dictionary
Understanding the concept of Programs in Python
Writing and using Python functions
Various Functions – Range, Input, Map, Filter, Split, Enumerate, Zip, Unzip, Def, Lambda
Loops in Python – For loop, While loop etc
Indexing, Slicing, Datatype Casting in Python
You can download each lecture video and source code files
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- Embark on your initial journey into the dynamic world of data science with a hands-on, code-centric approach.
- Master the foundational syntax and logic of Python, building a solid platform for all subsequent data-intensive tasks.
- Develop an intuitive understanding of how to transform raw data into actionable insights through live coding challenges.
- Cultivate the ability to structure and execute Python scripts, moving beyond simple commands to build robust solutions.
- Empower yourself to craft reusable code blocks and tailor functionalities to specific data manipulation needs.
- Discover and leverage powerful built-in tools that streamline data processing and analysis workflows.
- Navigate iterative processes efficiently, enabling you to work with large datasets and complex operations.
- Acquire the skill of precisely accessing and manipulating data segments, crucial for focused analysis.
- Learn to seamlessly convert data between different formats, ensuring compatibility and flexibility in your projects.
- Benefit from the convenience of offline access to all lecture materials and accompanying code samples.
- PROS:
- Direct Application: Immediate practice solidifies theoretical Python concepts for data science applications.
- Skill Acceleration: Rapidly build practical Python proficiency essential for entry-level data science roles.
- Problem-Solving Focus: Engages learners in tackling realistic data-related challenges from the outset.
- CONS:
- Breadth vs. Depth: May offer a broad introduction to Python for data science but might not delve deeply into advanced statistical modeling or machine learning algorithms.
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