Data Wrangling with Python
PYTHON
Time:
3 Days
VILT
£270.00
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DOWNLOAD NOWWhat you’ll Learn
− Understand the fundamentals of data wrangling and its importance in data analysis.
− Grasp core Python concepts, particularly data structures (lists, sets, strings, tuples, dictionaries).
− Learn and utilize popular data-wrangling libraries like NumPy and Pandas.
− Gain practical knowledge in data extraction, cleaning, transformation, and formatting from various sources (internet, databases, spreadsheets).
− Develop proficiency in handling missing or incorrect data and reformatting based on analytical needs.
− Apply data wrangling techniques through real-world examples and datasets.
− Gain confidence in using Python for efficient data manipulation across diverse data sources.
Description
Data is the new oil, but it comes crude. To do anything meaningful – modeling, visualization, machine learning, for predictive analysis – you first need to wrestle and wrangle with data. Data Wrangling with Python teaches you the essentials that will get you up and running with data wrangling in no time. For data to be useful and meaningful, it must be curated and refined. Data Wrangling with Python teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain.
Requirements
Basic computer literacy: Familiarity with using a computer and navigating operating systems (Windows, macOS, Ubuntu) is assumed. The course starts with the basics of Python, focusing on data structures. This suggests the course is suitable for beginners with no prior Python experience.
Modules in this Course
− Python for Data Wrangling
− Lists, Sets, Strings, Tuples, and Dictionaries
− Advanced Data Structures
− Basic File Operations in Python
− NumPy Arrays
− Pandas DataFrames
− Statistics and Visualization with NumPy and Pandas
− Subsetting, Filtering, and Grouping
− Detecting Outliers and Handling Missing Values
- Concatenating, Merging, and Joining
- Useful Methods of Pandas
- Using NumPy and Pandas to Calculate Basic Descriptive
- Statistics on the DataFrame
− Reading Data from Different Text-Based (and Non-Text-Based) Sources
− Introduction to BeautifulSoup4 and Web Page Parsing
− Advanced List Comprehension and the zip Function
− Data Formatting
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