Python Data Wrangling Training

Course 1273 Advantage Plan Course

Duration: 3 days
Sandbox: Yes
Language: English
Level: Intermediate

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 data cleaning methods, 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 Information

In this Python Wrangling course, you will learn how to do the following:

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.

Prerequisites:

You should know Python basics, including data structures, importing and using modules, creating functions, and using the Jupyter Notebook platform.

Python Data Wrangling Training Outline

Module 1: Introduction to Data Structure Using Python

In this module, you will learn about the following:

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

Module 2: Advanced Operations on Built-In Data Structure

In this module, you will learn about the following:

Advanced Data Structures
Basic File Operations in Python

Module 3: Introduction to NumPy, Pandas, and Matplotlib

In this module, you will learn about the following:

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

Module 4: Deep Dive into Data Wrangling with Python

In this module, you will learn about the following:

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

Module 5: Getting Comfortable with Different Data Sources

In this module, you will learn about the following:

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

Module 6: Learning the Hidden Secrets of Data Wrangling

In this module, you will learn about the following:

Advanced List Comprehension and the zip function
Data Formatting

Module 7: Advanced Web Scraping and Data Gathering

In this module, you will learn about the following:

Basics of Web Scraping and BeautifulSoup libraries
Reading Data from XML

Module 8: RDBMS and SQL

In this module, you will learn about the following:

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

Module 9: Application in Real Life and Conclusion of Course

In this module, you will learn about the following:

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

Commercial Pricing

23 730 kr

3-day instructor-led training course
After-course coaching available

Would you like to take this course as a team?

Include dates with afternoon start times

jan 28 - 30 9:00 - 16:30 CET

Stockholm or Virtual
jan 28 - 30 10:00 - 17:30 CET

London or Virtual
feb 11 - 13 15:00 - 22:30 CET

Ottawa or Virtual
mar 11 - 13 14:00 - 21:30 CET

Herndon, VA or Virtual
jun 24 - 26 15:00 - 22:30 CEST

Ottawa or Virtual
jul 29 - 31 9:00 - 16:30 CEST

Stockholm or Virtual
jul 29 - 31 10:00 - 17:30 CEST

London or Virtual
jul 29 - 31 15:00 - 22:30 CEST

Herndon, VA or Virtual

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Python Data Wrangling Training Course FAQs

This course is for data analysts and data scientists looking to utilise Python to extract from various sources and prepare it for machine learning modelling.

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.

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

Data wrangling is ingesting, cleaning, and unifying raw data sources into a format for more accessible 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. However, additional training may be required to perform visualisation, modelling, and prediction.

The software is based on the Anaconda distribution, a combination of Python, Jupyter, and many data analytics libraries. All software tools 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, we often use older versions of packages for interoperability. All the software packages used are available for any of the major operating systems.