Python Data Wrangling Training

Course 1273

  • Duration: 3 days
  • Language: English
  • Level: Intermediate
Get This Course 26.500 kr
  • 3-day instructor-led training course
  • After-course coaching available

  • Guaranteed to Run - you can rest assured that the class will not be cancelled.
    nov 8 - 10 15:00 - 22:30 CET
    Ottawa or Virtual
  • nov 15 - 17 10:00 - 17:30 CET
    London or Virtual
  • dec 7 - 9 15:00 - 22:30 CET
    Herndon, VA or Virtual
  • feb 8 - 10 15:00 - 22:30 CET
    New York or Virtual
  • feb 14 - 16 10:00 - 17:30 CET
    London or Virtual
  • maj 2 - 4 10:00 - 17:30 CEST
    London or Virtual
  • maj 3 - 5 15:00 - 22:30 CEST
    Ottawa or Virtual
  • jun 7 - 9 15:00 - 22:30 CEST
    Herndon, VA or Virtual
  • aug 9 - 11 15:00 - 22:30 CEST
    New York or Virtual

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In this Python Data Wrangling course, you will learn how to use Python to extract/transform data from various sources, including large database vaults and Excel financial tables.

You will also explore insights into why you should avoid traditional methods of data cleaning, as done in other languages, and take advantage of the specialised functions from NumPy and Pandas.

Python Data Wrangling Training Delivery Methods

  • In-Person

  • Online

Python Data Wrangling Training Benefits

  • Extract and parse data from various sources
  • Transform and clean data using Numpy and Pandas
  • Summarise and visualise data with Matplotlib

  • Read HTML, XML, and JSON data from internet resources

  • Search and filter data sets
  • Apply Python tools and techniques to process data sets efficiently
  • Continue learning and face new challenges with after-course one-on-one instructor coaching

Python Data Wrangling Training Outline

In this module, you will learn about:

  • Python for Data Wrangling
  • Lists, Sets, Strings, Tuples, and Dictionaries

In this module, you will learn about:

  • Advanced Data Structures
  • Basic File Operations in Python

In this module, you will learn about: 

  • NumPy Arrays 
  • Pandas DataFrames 
  • Statistics and Visualisation with NumPy and Pandas 
  • Using NumPy and Pandas to Calculate Basic Descriptive Statistics on the DataFrame 

In this module, you will learn about: 

  • Subsetting, Filtering, and Grouping 
  • Detecting Outliers and Handling Missing Values 
  • Concatenating, Merging, and Joining 
  • Useful Methods of Pandas 

In this module, you will learn about: 

  • Reading Data from Different Text-Based (and Non-Text-Based) Sources 
  • Introduction to BeautifulSoup4 and Web Page Parsing 

In this module, you will learn about: 

  • Advanced List Comprehension and the zip function 
  • Data Formatting 

In this module, you will learn about: 

  • Basics of Web Scraping and BeautifulSoup libraries 
  • Reading Data from XML 

In this module, you will learn about:

  • Refresher of RDBMS and SQL
  • Using an RDBMS (MySQL/PostgreSQL/SQLite)

In this module, you will learn about:

  • Applying Your Knowledge to a Real-life Data Wrangling Task
  • An Extension to Data Wrangling

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Python Data Wrangling Training Course FAQs

To succeed in this course, you should have a working knowledge of Python basics, including data structures, importing and using modules, creating functions, and using the Jupyter Notebook platform.

Data wrangling is the process of ingesting, cleaning, and unifying raw data sources into a format for easier analysis.

No. This is not a programming class, but rather an instruction on data management and processing. The Jupyter Notebook/Lab applications are used for their interactive features to speed development.

Yes, these skills are fundamental to creating a data analytics pipeline. Additional training may be required to perform visualisation, modeling, and prediction.

The software is based on the Anaconda distribution, a combination of Python, Jupyter, and many data analytics libraries. All software tools used are platform-independent and would work using Windows, Linux, or OS/X. The class runs in a Linux environment, but the skills and tools used would apply to any platform.

No, though we strive to keep our software up to date, for interoperability we often use older versions of packages. All the software packages used are available for any of the major operating systems.

No, we will read and write Excel spreadsheets, but not use the Excel product.