Highlights
- Data Wrangling with Python Training:
- Introduction to Data Wrangling with Python
- Introduction to Python programming language
- Overview of the Pandas library for data manipulation & analysis
- Learn how to Read & writing various data formats (CSV, Excel, SQL)
- Exploratory Data Analysis (EDA): Descriptive statistics for understanding dataset characteristics.
- Introduction to Regular Expressions: Pattern matching for data cleaning.
- Learn to write scripts for automating repetitive data wrangling tasks.
- Applying learned skills to real-world projects
- Introduction to version control systems (e.g., Git),managing code changes.
- Learn to clean, transfer and scrap data using Python and its related tools
Course Details
Introduction to Data Wrangling:
- Understanding the importance of data wrangling in the data science workflow
- Common challenges & issues in raw datasets
Python Basics for Data Wrangling:
- Introduction to Python programming language.
- Data types, variables, and basic operations.
- Working with Pandas:
- Overview of the Pandas library for data manipulation and analysis.
- Reading and writing various data formats (CSV, Excel, SQL).
- DataFrame creation and manipulation.
Data Cleaning Techniques:
- Identifying and handling missing data.
- Removing duplicates.
- Data type conversion and normalization.
- Exploratory Data Analysis (EDA):
- Descriptive statistics for understanding dataset characteristics.
- Visualizations using libraries like Matplotlib and Seaborn.
Data Transformation:
- Reshaping and pivoting data.
- Merging and joining datasets.
Handling Time Series Data:
- Working with time-based data using Pandas.
- Resampling and frequency conversion.
Data Wrangling with NumPy:
- Introduction to NumPy for numerical operations.
- Working with arrays and matrices.
- Introduction to Regular Expressions:
- Pattern matching for text data cleaning.
- Using regular expressions for data extraction.
Data Wrangling Best Practices:
- Writing modular and reusable code.
- Handling large datasets efficiently.
- Error handling and debugging techniques.
- Real-world Case Studies:
- Applying data wrangling skills to real-world datasets.
- Solving practical challenges in diverse domains.
Integration with Other Tools:
- Integrating data wrangling into the broader data science ecosystem.
- Collaboration with databases and big data frameworks.
Version Control for Data Wrangling Scripts:
- Introduction to version control systems (e.g., Git) for managing code changes.
- Best practices for collaborative data wrangling projects.
- Automation and Scripting:
- Writing scripts for automating repetitive data wrangling tasks.
- Building data pipelines for efficient workflows.
Advanced Topics (Optional):
- Advanced Pandas features.
- Custom functions and transformations.
- Introduction to machine learning data preparation.
Hands-on Projects:
- Applying learned skills to real-world projects.
- Feedback and code review for improvement.
- Final Project and Certification:
- Capstone project to showcase comprehensive data wrangling skills.
- Certification upon successful completion of the course.
Who should attend
This Data Wrangling Training course is designed for aspiring data professionals, analysts, and scientists seeking to master the essential skill of data wrangling using Python. Whether you're a beginner looking to build a strong foundation or an experienced data practitioner, this course caters to a diverse audience. Specifically, it is ideal for:
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Data Enthusiasts: Individuals with a passion for working with data who want to learn how to effectively clean, transform, and analyze datasets.
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Data Analysts: Professionals involved in extracting insights from data who wish to streamline their data preparation processes.
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Data Scientists: Those working in or aspiring to enter the field of data science, looking to bolster their skills in handling diverse and complex datasets.
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Business Analysts: Professionals responsible for extracting meaningful business insights from data & wanting to optimize their data manipulation workflows.
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Researchers: Academics and researchers who deal with data for their studies and want to acquire practical skills in preparing and analyzing datasets.
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IT Professionals: Developers and IT specialists interested in expanding their Python programming skills to include data manipulation for a more comprehensive understanding of data workflows.
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Anyone Transitioning to Data Roles: Individuals transitioning from other fields to data-related roles, seeking a practical and hands-on introduction to data wrangling with Python.
Feedback
4.8 out of 5 average
"Our tailored course provided a well rounded introduction and also covered some intermediate level topics that we needed to know. Helps data analysts with task related to big data as well.
Brian Leek, Data Analyst, May 2023
“JBI did a great job of customizing their syllabus to suit our business needs and also bringing our team up to speed on the current best practices. very impressive”
Brian F, Team Lead, RBS, Data Analysis Course, 20 April 2022