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Introduction to Python for Data Science is a three-day, hands-on course that introduces data analysts and business analysts to the Python programming language, as it’s often used in Data Science in web notebooks.

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Last Updated

March 5, 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
    • Running standalone scripts under Unix and Windows 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
    • The sorted() function 00:00:00
    • Alternate keys 00:00:00
    • Lambda functions 00:00:00
    • Sorting collections 00:00:00
    • Using operator.itemgetter() 00:00:00
    • Reverse sorting 00:00:00
    • Syntax errors 00:00:00
    • Exceptions 00:00:00
    • Using try/catch/else/finally 00:00:00
    • Handling multiple exceptions 00:00:00
    • Ignoring 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
    • numpy basics 00:00:00
    • Creating arrays 00:00:00
    • Indexing and slicing 00:00:00
    • Large number sets 00:00:00
    • Transforming data 00:00:00
    • Advanced tricks 00:00:00
    • Data Science Essentials 00:00:00
    • Working with Python in Data Science 00:00:00
    • pandas overview 00:00:00
    • Dataframes 00:00:00
    • Reading and writing data 00:00:00
    • Data alignment and reshaping 00:00:00
    • Fancy indexing and slicing 00:00:00
    • Merging and joining data sets 00:00:00
    • Creating a basic plot 00:00:00
    • Commonly used plots 00:00:00
    • Ad hoc data visualization 00:00:00
    • Advanced usage 00:00:00
    • Exporting images 00:00:00

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