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Data Wrangling with Python

Standard Course
Fundamental
Fully Ready

About the Course

This course teaches essential data manipulation skills using Python libraries like Pandas and NumPy. You’ll learn to read, reshape, filter, group, and clean data — preparing it for analysis. By the end, you’ll be able to transform real-world datasets to solve practical business problems.

Learning Outcomes

By the end of this course, participants will be able to:

  • Work with Pandas Series, DataFrames, and Index objects
  • Read, write, filter, and transform structured data using Pandas
  • Clean data to ensure quality and integrity, including handling missing values and outliers
  • Reshape, pivot, and organize data for effective analysis
  • Merge, join, and group datasets using core data manipulation techniques
  • Apply data wrangling skills to real-world problems through hands-on projects

Curriculum

  • Chapter 1: Intro to Pandas Data Structures

    Overview:

    In this chapter, participants will be introduced to the Pandas library, the foundation of data wrangling in Python. They will learn how to create and manipulate Series and DataFrames, and gain an understanding of Pandas indexing.

    Topics to Cover:

    • Diving into Pandas Series
    • Creating and working with Pandas DataFrames
    • Understanding Pandas Index

  • Chapter 2: Essential Functionality

    Overview:

    In this chapter, participants will explore Pandas’ essential functionality for data handling. They will practice reindexing, dropping data, filtering with conditions, and applying functions to transform datasets.

    Topics to Cover:

    • Reindexing and dropping data
    • Selecting and filtering data by conditions
    • Applying functions and mapping

  • Chapter 3: Reading and Writing Data

    Overview:

    In this chapter, participants will learn how to read and write data in different formats. They will gain experience working with common file types and understand how to integrate external data into Pandas workflows.

    Topics to Cover:

    • Reading and writing data
    • Dealing with common types of data files (CSV, Excel, JSON, etc.)
    • Learning relevant I/O methods

  • Chapter 4: Reshaping and Pivoting Data

    Overview:

    In this chapter, participants will reshape datasets for better analysis and visualization. They will work with hierarchical indexing and practice converting data between long and wide formats.

    Topics to Cover:

    • Reshaping with hierarchical indexing
    • Changing long format to wide format

  • Chapter 5: Data Transformation

    Overview:

    In this chapter, participants will clean and transform data using Pandas functions. They will handle duplicates, replace values, categorize data, and address outliers to prepare datasets for analysis.

    Topics to Cover:

    • Removing duplicate values
    • Transforming data using functions and mapping
    • Replacing values and categorizing data
    • Dealing with outliers

  • Chapter 6: String Manipulation

    Overview:

    In this chapter, participants will learn how to work with text data in Pandas. They will explore common string operations, use vectorized string functions, and get introduced to regular expressions for advanced string processing.

    Topics to Cover:

    • Common string operations and object methods
    • Introduction to regular expressions
    • Vectorized string functions in Pandas

  • Chapter 7: Combining & Merging Datasets

    Overview:

    In this chapter, participants will combine datasets using Pandas’ merging and concatenation features. They will practice joining DataFrames based on indexes and working across multiple axes.

    Topics to Cover:

    • Merging DataFrames
    • Combining datasets using indexes
    • Concatenation along different axes

  • Chapters 8–10: GroupBy Mechanics, Operations, and Transformations

    Overview:

    In these chapters, participants will master the GroupBy process in Pandas. They will learn how to split, apply, and combine data, perform group-wise operations, and apply advanced techniques such as binning and quartile analysis.

    Topics to Cover:

    • Understanding the GroupBy process (split-apply-combine)
    • Performing group-wise operations and transformations
    • Applying binning, quartiles, and bucket analysis

  • Chapter 11: Web Scraping & API

    Overview:

    In this chapter, participants will go beyond static datasets by learning how to collect data from the web. They will be introduced to web scraping with BeautifulSoup and Selenium, and learn how to connect to APIs for data access.

    Topics to Cover:

    • Introduction to web scraping concepts
    • Using BeautifulSoup and Selenium for scraping
    • Accessing and working with APIs

  • Chapter 12: Python Mini Projects

    Overview:

    In this chapter, participants will apply their data wrangling skills to real-life datasets. They will work on mini projects to solve business problems across different domains, such as finance and operations.

    Topics to Cover:

    • Applying skills to real-world datasets
    • Solving practical business problems (e.g., finance, operations)
    • Building end-to-end data wrangling workflows

Tools

Python
Anaconda
Jupyter
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STUDENT REVIEWS

What our graduates are saying

I had an incredible experience at this data science bootcamp! Coming in with no prior knowledge, I was amazed at how quickly I gained a solid understanding of the field. The curriculum is intensive and hands-on, allowing me to learn real, applicable skills through working on real client projects. The staff are exceptionally supportive and always ready to help. This bootcamp truly transformed my career path, and I couldn’t be more grateful for the knowledge and confidence I’ve gained. Highly recommended for anyone looking to break into data science!

My experience was awesome. I was taught by professional data engineers who shared their insights based on their professional experience. The topics covered are relevant to what employers are looking for and I enjoyed hands-on projects. I learned all the skills I needed to be a data engineer and I got a full-time offer as a data engineer, thanks to the mentors from WecloudData. I highly recommend this program. I was able to land interviews at Amazon, Yelp, CI Financial e.t.c

Deciding to go with WeCloudData was one of the best decisions I made during my career switch. Their Bootcamps are intense but they get you where you need to go. The instructors are amazing and the TAs very knowledgeable. Their client projects taught me a lot even after the bootcamp was done. I was able to land my current job thanks to the skills and training from the course.

I had the pleasure of attending WeCloudData’s Data Fundamentals bootcamp, which was fantastic. The highly qualified instructors used cutting-edge teaching techniques that greatly facilitated understanding the logic of coding and programming. They carefully progressed us from fundamental to more complex subjects, avoiding any feeling of overload. I wholeheartedly endorse this course for anyone hoping to become a data analyst—it’stheonly one you’ll need. The teachers are very outstanding!

This bootcamp provided me with the necessary technical skills and industry knowledge to secure a data job. The instructors are knowledgeable and shared everything they know to help me achieve my goals. The TAs are resourceful and patient answering my questions. The projects in the bootcamp enhanced my knowledge and impressed interviews. This is a wonderful bootcamp if you are looking to either imporve your skills or switch career!

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Common Questions

Find answers to your questions about the Learning Track
  • Standard Courses: Focused, short courses that build foundational or intermediate skills through hands-on exercises, enabling you to apply what you learn immediately.
  • Track Courses: Structured learning paths that guide you from beginner to advanced levels. They include practical projects that integrate multiple tools and workflows, aligned with industry best practices, helping you gain the skills and confidence to tackle real-world challenges.

No. Track Courses are only accessible through the Professional or Unlimited+ subscription plans.

  • Standard Plan gives you access to all Standard Courses.
  • Professional Plan gives you access to both Standard and Track Courses within your chosen domain.
  • Unlimited+ Plan provides full access to all courses — both Standard and Track — across all domains.

 

Yes, all courses are designed to be self-paced. Learn when it fits your schedule.

Each course includes prerequisites if needed. Many Standard Courses are beginner-friendly.

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