CCNA 200-301 Pearson uCertify Network Simulator
ISBN: 978-1-61691-837-8Cisco 200-301-SIMULATOR.AB1
Achieve proficiency in the data analysis process in no time!
(DATA-WRGLG-PYTHON.AJ1) / ISBN : 978-1-64459-302-8This Data Wrangling with Python course is your access point to polishing your data cleaning and manipulation skills. You’ll learn how to handle advanced data structures, perform file operations, and leverage powerful libraries such as NumPy, Pandas, and Matplotlib. In hands-on labs, you’ll transform raw data into valuable insights. Ideal for data scientists and analysts, this course covers everything from basic concepts to advanced web scraping and SQL.
Learn Python for data analysis data wrangling with Pandas & NumPy techniques to streamline your data analysis operations Implement data cleaning to prepare datasets for analysis Use Python Libraries like NumPy, Pandas, and Matplotlib Perform data manipulation with advanced data structures Conduct file operations for data handling and storage Leverage SQL for database interactions and data retrieval Apply web scraping methods to gather data from online sources Execute data analytic functions and create visualizations Develop problem-solving skills with real-life data-wrangling tasks Enhance data preprocessing capabilities for machine learning (ML)
10+ Interactive Lessons | 11+ Exercises | 72+ Quizzes | 84+ Flashcards | 84+ Glossary of terms
47+ Pre Assessment Questions | 53+ Post Assessment Questions |
45+ LiveLab | 6+ Video tutorials | 07+ Minutes
33+ Videos | 03:13+ Hours
Still have questions? Find out more about our data wrangling and analysis with the Python course.
Contact Us NowData cleaning and wrangling in Python involves removing or correcting data anomalies. This can be done using the Pandas library, which provides functions for handling missing values, correcting data types, and removing duplicates to prepare raw data for transformation into meaningful insights.
Yes, having prior experience, especially in Python, is beneficial for taking this data wrangling course.
The top Python libraries for data wrangling include:
Pandas: For data manipulation and analysis
NumPy: For numerical operations
Matplotlib and Seaborn: For data visualization
PyJanitor: For extended data cleaning functions
Data Cleaning is the process of identifying and correcting errors in the data.
Data Wrangling is a broader process that includes data cleaning, transforming, and mapping raw data into a more useful format for analysis.
Common data wrangling techniques in Python include:
Data Merging: Combining multiple data sources into one dataset.
Data Transformation: Changing the format or structure of the data.
Data Subsetting: Selecting specific rows or columns of interest.
Handling Outliers: Identifying and correcting outliers in the data.
Data Aggregation: Summarizing data by grouping and calculating statistics.
NumPy provides support for numerical operations on large, multi-dimensional arrays and matrices, which are essential for efficient data manipulation.
Pandas offers data structures and functions designed to make data manipulation and analysis easy, such as DataFrames for handling tabular data.
Career opportunities after completing our Python for data wrangling course include roles such as: