Data Wrangling with Python
For data to be useful and meaningful, it must be curated and refined. This Data Wrangling with Python course teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain.
The course starts with the absolute basics of Python, focusing mainly on data structures. It then delves into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialised pre-built routines in Python.
This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables.
To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. This data wrangling course will further help you grasp concepts through real-world examples and datasets.
- Use a diverse array of sources to extract data.
- Clean, transform and format data efficiently.
- Use python tricks to transform data into useful and meaningful data sets.
Data Wrangling with Python takes a practical approach to equip beginners with the most essential data analysis tools in the shortest possible time. It contains multiple activities that use real-life business scenarios for you to practise and apply your new skills in a highly relevant context.
Introduction to Data Structure using Python
- Python for Data Wrangling
- Lists, Sets, Strings, Tuples, and Dictionaries
Advanced Operations on Built-In Data Structure
- Advanced Data Structures
- Basic File Operations in Python
Introduction to NumPy, Pandas, and Matplotlib
- NumPy Arrays
- Pandas DataFrames
- Statistics and Visualisation with NumPy and Pandas
- Using NumPy and Pandas to Calculate Basic Descriptive Statistics on the DataFrame
Deep Dive into Data Wrangling with Python
- Subsetting, Filtering, and Grouping
- Detecting Outliers and Handling Missing Values
- Concatenating, Merging, and Joining
- Useful Methods of Pandas
Get Comfortable with a Different Kind of Data Sources
- Reading Data from Different Text-Based (and Non-Text-Based) Sources
- Introduction to BeautifulSoup4 and Web Page Parsing
Learning the Hidden Secrets of Data Wrangling
- Advanced List Comprehension and the zip Function
- Data Formatting
Advanced Web Scraping and Data Gathering
- Basics of Web Scraping and BeautifulSoup libraries
- Reading Data from XML