Kurskode: GK100668

varighet: 2 Dag(er)

Sted: Virtual, Instructor Led Training
Katergori: Applications Development

Course Overview

New - Learn core Python data science skills.

This course introduces data analysts, business analysts, and anyone interested in Data Science to the Python programming language as itโ€™s often used in Data Science in web notebooks. The goal is to provide you with a baseline understanding of core concepts that will serve as a platform of knowledge to follow-up with more in-depth training and real-world practice.

The course begins with a quick overview of Python with demonstrations of both script-based and web notebook-based Python and then youโ€™ll dive into the essentials of Python necessary for a data scientist. The end of the course explores a quick integration of these skills with key Data Science libraries including NumPy, Pandas, and Matplotlib.

This class is hands-on and includes light programming labs that introduce students to basic Python syntax and concepts applicable to using Python to work with data.

Join an engaging hands-on learning environment, where youโ€™ll learn:

  • โ€ข How to work with Python interactively in web notebooks
  • โ€ข The essentials of Python scripting
  • โ€ข Key concepts necessary to enter the world of Data Science via Python

This course has a 50% hands-on labs to 50% lecture ratio with engaging instruction, demos, group discussions, labs, and project work.

An Overview of Python

  • โ€ข Why Python?
  • โ€ข Python in the Shell
  • โ€ข Python in Web Notebooks (iPython, Jupyter, and Zeppelin)
  • โ€ข Demo: Python, Notebooks, and Data Science

Getting Started

  • โ€ข Using variables
  • โ€ข Built-in functions
  • โ€ข Strings
  • โ€ข Numbers
  • โ€ข Converting among types
  • โ€ข Writing to the screen
  • โ€ข Command line parameters

Flow Control

  • โ€ข About flow control
  • โ€ข White space
  • โ€ข Conditional expressions
  • โ€ข Relational and Boolean operators
  • โ€ข While loops
  • โ€ข Alternate loop exits

Sequences, Arrays, Dictionaries, and Sets

  • โ€ข About sequences
  • โ€ข Lists and list methods
  • โ€ข Tuples
  • โ€ข Indexing and slicing
  • โ€ข Iterating through a sequence
  • โ€ข Sequence functions, keywords, and operators
  • โ€ข List comprehensions
  • โ€ข Generator Expressions
  • โ€ข Nested sequences
  • โ€ข Working with Dictionaries
  • โ€ข Working with Sets

Working with files

  • โ€ข File overview
  • โ€ข Opening a text file
  • โ€ข Reading a text file
  • โ€ข Writing to a text file
  • โ€ข Reading and writing raw (binary) data

Functions

  • โ€ข Defining functions
  • โ€ข Parameters
  • โ€ข Global and local scope
  • โ€ข Nested functions
  • โ€ข Returning values

Essential Demos

  • โ€ข Sorting
  • โ€ข Exceptions
  • โ€ข Importing Modules
  • โ€ข Classes
  • โ€ข Regular Expressions

The standard library

  • โ€ข Math functions
  • โ€ข The string module

Dates and times

  • โ€ข Working with dates and times
  • โ€ข Translating timestamps
  • โ€ข Parsing dates from text
  • โ€ข Formatting dates
  • โ€ข Calendar data

Python and Data Science

  • โ€ข Data Science Essentials
  • โ€ข Pandas Overview
  • โ€ข NumPy Overview
  • โ€ข SciKit Overview
  • โ€ข MatPlotLib Overview
  • โ€ข Working with Python in Data Science

No prior programming experience is required.

  • โ€ข Data Science Overview

Business Analysts, Data Analysts, and anyone interested in data science who is comfortable working with numerical data in Excel or other spreadsheet environments.

Kontakt oss: Kurs@sgpartner.no

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