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Python for Data Science Primer is a two-day course that introduces data analysts and business analysts (as well as anyone interested in Data Science) to the Python programming language, as it’s often used in Data Science in web notebooks.

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Unlimited Duration

Last Updated

March 3, 2021

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This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. In this course you will learn about:

· How to work with Python interactively in web notebooks

· The essentials of Python scripting

· Key concepts necessary to enter the world of Data Science via Python

Course Curriculum

    • Why Python? 00:00:00
    • Python in the Shell 00:00:00
    • Python in Web Notebooks (iPython, Jupyter, Zeppelin) 00:00:00
    • Demo: Python, Notebooks, and Data Science 00:00:00
    • Using variables 00:00:00
    • Builtin functions 00:00:00
    • Strings 00:00:00
    • Numbers 00:00:00
    • Converting among types 00:00:00
    • Writing to the screen 00:00:00
    • Command line parameters 00:00:00
    • About flow control 00:00:00
    • White space 00:00:00
    • Conditional expressions 00:00:00
    • Relational and Boolean operators 00:00:00
    • While loops 00:00:00
    • Alternate loop exits 00:00:00
    • About sequences 00:00:00
    • Lists and list methods 00:00:00
    • Tuples 00:00:00
    • Indexing and slicing 00:00:00
    • Iterating through a sequence 00:00:00
    • Sequence functions, keywords, and operators 00:00:00
    • List comprehensions 00:00:00
    • Generator Expressions 00:00:00
    • Nested sequences 00:00:00
    • Working with Dictionaries 00:00:00
    • Working with Sets 00:00:00
    • File overview 00:00:00
    • Opening a text file 00:00:00
    • Reading a text file 00:00:00
    • Writing to a text file 00:00:00
    • Reading and writing raw (binary) data 00:00:00
    • Defining functions 00:00:00
    • Parameters 00:00:00
    • Global and local scope 00:00:00
    • Nested functions 00:00:00
    • Returning values 00:00:00
    • Sorting 00:00:00
    • Exceptions 00:00:00
    • Importing Modules 00:00:00
    • Classes 00:00:00
    • Regular Expressions 00:00:00
    • Math functions 00:00:00
    • The string module 00:00:00
    • Working with dates and times 00:00:00
    • Translating timestamps 00:00:00
    • Parsing dates from text 00:00:00
    • Formatting dates 00:00:00
    • Calendar data 00:00:00
    • Data Science Essentials 00:00:00
    • Pandas Overview 00:00:00
    • NumPy Overview 00:00:00
    • SciKit Overview 00:00:00
    • MatPlotLib Overview 00:00:00
    • Working with Python in Data Science 00:00:00

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