Python Fundamentals for Data Science

Data is the most valuable commodity for any business so being able to master your data and make it valuable is important. Python is one of the best programming languages for data science. This course is meant to take you from zero to hero in Python for data science. Gain the career-building Python skills you need to succeed as a data scientist. No prior coding experience required. In this track, you'll learn how this versatile language allows you to import, clean, manipulate, and visualize data—all integral skills for any aspiring data professional or researcher. Through interactive exercises, you'll get hands-on with some of the most popular Python libraries, including pandas, NumPy, Matplotlib, and many more. You'll then work with real-world datasets to learn the statistical and machine learning techniques you need to train decision trees and use natural language processing (NLP). Start this track, grow your Python skills, and begin your journey to becoming a confident data scientist.

What you will learn

Construct and run a Python program
Learn best practices for coding in Python and building an application
Gain the knowledge you need to pass LinkedIn's Python Skill Assessment
Curriculum
Our Python curriculum can be broken down into 4 essential topics that include:
  1. Data types (int, float, strings)
  2. Compound data structures (lists, tuples, and dictionaries)
  3. Conditionals, loops, and functions
  4. Object-oriented programming and using external libraries

Keywords

Negative Index

Access sub-strings

Access start of sub-strings

Access end of sub-strings

Access sub-strings by step size

Accessing sub-strings stepping backwards

Iterate a string: for in statement

Iterate by sub-string

String Methods len()

String Methods count()

String Methods find()

String Methods len()

Creating Lists

Accessing Lists by using Index

Insert a new value for an index