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Python Data Wrangling Training

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

Include dates with afternoon start times

#1273

aug 9 - 11 10:00 - 17:30 CEST

AnyWare
aug 17 - 19 15:00 - 22:30 CEST

New York or AnyWare
nov 8 - 10 15:00 - 22:30 CET

Ottawa or AnyWare
nov 15 - 17 10:00 - 17:30 CET

AnyWare
feb 8 - 10 15:00 - 22:30 CET

New York or AnyWare
feb 14 - 16 10:00 - 17:30 CET

AnyWare
maj 2 - 4 10:00 - 17:30 CEST

AnyWare

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#1273

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

Module 1: Introduction to Data Structure Using Python

In this module, you will learn about:

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:

Advanced Data Structures
Basic File Operations in Python

Module 3: Introduction to NumPy, Pandas, and Matplotlib

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

Module 4: Deep Dive into Data Wrangling with Python

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

Module 5: Getting Comfortable with Different Data Sources

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

Module 6: Learning the Hidden Secrets of Data Wrangling

In this module, you will learn about:

Advanced List Comprehension and the zip function
Data Formatting

Module 7: Advanced Web Scraping and Data Gathering

In this module, you will learn about:

Basics of Web Scraping and BeautifulSoup libraries
Reading Data from XML

Module 8: RDBMS and SQL

In this module, you will learn about:

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:

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.