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⇱ Data Processing and Exploration with NumPy & Pandas | Coursera

Data Processing and Exploration with NumPy & Pandas

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Data Processing and Exploration with NumPy & Pandas

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

9 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

9 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Master NumPy for efficient numerical operations and broadcasting.

  • Learn to manipulate, clean, and analyze data with Pandas.

  • Utilize advanced Pandas features like groupby, merge, and pivot tables.

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Assessments

4 assignments

Taught in English

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This course is part of the Data Understanding and Data Visualization with Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 2 modules in this course

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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Dive into the world of data manipulation and numerical analysis with NumPy and Pandas, two of the most essential tools in the data science toolkit. Learn how to manipulate, process, and analyze large datasets with ease and efficiency. Whether you're working with numerical data, handling missing values, or exploring data through visualization, this course will equip you with the skills you need to tackle a wide range of data tasks. The course is divided into two major sections—NumPy and Pandas—each offering focused lessons on core concepts. You’ll start by learning the fundamentals of NumPy for numerical computing, including array creation, reshaping, and advanced operations such as broadcasting and universal functions. Then, you will delve into Pandas, exploring Series and DataFrame objects, handling missing data, and mastering powerful functions like groupby, merge, and pivot tables. This course is perfect for anyone looking to enhance their data processing and analysis skills. Whether you're an aspiring data scientist, business analyst, or just someone who works with data regularly, this course will provide you with the tools to work efficiently and effectively. No prior programming experience is required, but basic familiarity with Python will be helpful. The course is suitable for beginners to intermediate learners, with a focus on practical applications and hands-on exercises. By the end of the course, you will be able to work with large datasets, perform complex data manipulations, use NumPy for numerical operations, and confidently analyze data with Pandas. You’ll also gain expertise in data cleaning, reshaping, merging, and creating advanced data visualizations to inform data-driven decisions.

In this module, we will explore NumPy, a powerful library for numerical data processing in Python. You will learn how to create and manipulate arrays, use advanced functions like slicing, masking, and broadcasting, and apply mathematical operations using universal functions (ufuncs). This module also includes practical exercises, such as playing with images and implementing KNN classifiers, to help you solidify your understanding.

What's included

28 videos2 readings1 assignment

28 videos•Total 222 minutes
  • Introduction to NumPy•7 minutes
  • NumPy Dimensions•14 minutes
  • NumPy Shape, Size, and Bytes•5 minutes
  • NumPy Arange and Random Package•9 minutes
  • NumPy Arange and Random Package Quiz•1 minute
  • NumPy Arange and Random Package Solution•2 minutes
  • NumPy Random and Reshape•10 minutes
  • NumPy Slicing Combined•14 minutes
  • NumPy Slicing Combined Quiz•2 minutes
  • NumPy Slicing Combined Solution•3 minutes
  • NumPy Masking•9 minutes
  • NumPy Masking Quiz•2 minutes
  • NumPy Masking Solution•3 minutes
  • NumPy Broadcasting and Concatenation•10 minutes
  • NumPy Ufuncs and SpeedTest•6 minutes
  • Ufuncs Add, Sum, and Plus Operators•17 minutes
  • Ufuncs Subtract Power Mod•11 minutes
  • Ufuncs Comparisons Logical Operators•15 minutes
  • Ufuncs Comparisons Logical Operators Quiz•2 minutes
  • Ufuncs Comparisons Logical Operators Solution•4 minutes
  • Ufuncs Output Argument•7 minutes
  • NumPy Playing with Images•20 minutes
  • NumPy Playing With Images Quiz•2 minutes
  • NumPy Playing With Images Solution•5 minutes
  • NumPy KNN Classifier from Scratch•28 minutes
  • NumPy Structured Arrays•9 minutes
  • NumPy Structured Arrays Quiz•1 minute
  • NumPy Structured Arrays Solution•6 minutes
2 readings•Total 20 minutes
  • Introduction to the Course 'Data Processing and Exploration with NumPy & Pandas'•10 minutes
  • Full Specialization Resources•10 minutes
1 assignment•Total 15 minutes
  • NumPy for Numerical Data Processing - Assessment•15 minutes

In this module, we will dive into Pandas, a powerful library for data manipulation and analysis. You’ll learn how to work with Series and DataFrames, handle missing data, and apply advanced data operations like group by, merging, and rolling windows. The module also includes real-world examples, such as working with COVID-19 data, to reinforce your learning through hands-on practice.

What's included

27 videos1 reading3 assignments

27 videos•Total 211 minutes
  • Introduction to Pandas•7 minutes
  • Pandas Series•6 minutes
  • Pandas DataFrame•9 minutes
  • Pandas DataFrame Quiz•2 minutes
  • Pandas DataFrame Solution•6 minutes
  • Pandas Missing Values•7 minutes
  • Pandas loc and Iloc•7 minutes
  • Pandas in Practice•24 minutes
  • Pandas Group By•14 minutes
  • Pandas Group By Quiz•3 minutes
  • Pandas Group By Solution•3 minutes
  • Hierarchical Indexing•9 minutes
  • Pandas Rolling•9 minutes
  • Pandas Rolling Quiz•1 minute
  • Pandas Rolling Solution•3 minutes
  • Pandas Where•9 minutes
  • Pandas Clip•6 minutes
  • Pandas Clip Quiz•1 minute
  • Pandas Clip Solution•4 minutes
  • Pandas Merge•13 minutes
  • Pandas Merge Quiz•1 minute
  • Pandas Merge Solution•3 minutes
  • Pandas Pivot Table•16 minutes
  • Pandas Strings•6 minutes
  • Pandas DateTime•7 minutes
  • Pandas Hands-On COVID-19 Data•31 minutes
  • Pandas Hands-On COVID-19 Data Bug Fixed•3 minutes
1 reading•Total 10 minutes
  • Conclusion to the Course 'Data Processing and Exploration with NumPy & Pandas'•10 minutes
3 assignments•Total 90 minutes
  • Full Course Practice Assessment•15 minutes
  • Pandas for Data Manipulation and Understanding - Assessment•15 minutes
  • Full Course Assessment•60 minutes

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Frequently asked questions

Data processing and exploration are fundamental steps in any data science workflow. This course teaches you to leverage two powerful libraries—NumPy and Pandas—for efficient numerical data processing and data analysis. NumPy is used for handling large, multidimensional arrays and matrices, while Pandas provides robust tools for handling and analyzing structured data in tabular form. Mastering these tools is essential for data science, as they form the backbone for data manipulation, cleaning, and exploratory data analysis, which are crucial for extracting insights from data.

This course is designed to introduce you to the essentials of data processing and exploration using NumPy and Pandas. Throughout the course, you’ll learn how to work with arrays, apply transformations, perform slicing, broadcasting, and use advanced techniques like masking and reshaping in NumPy. In Pandas, you will explore how to work with Series and DataFrames, handle missing data, group data, and merge datasets. The course also covers practical applications of these libraries, including manipulating COVID-19 data and understanding hierarchical indexing and rolling functions.

Upon completing this course, you will be proficient in using NumPy for numerical operations and Pandas for data manipulation and analysis. You will be able to handle large datasets, perform data cleaning and preprocessing tasks, create meaningful visualizations, and implement complex data transformation techniques. With these skills, you can explore datasets, perform feature engineering, and prepare data for machine learning models or business intelligence tasks.

To enroll in this course, you should have a basic understanding of programming, particularly in Python. Familiarity with basic Python syntax, variables, loops, and functions will be helpful. It is also beneficial, though not mandatory, to have a basic understanding of statistics or data analysis concepts. If you are new to data science, the course is designed to be beginner-friendly, but prior programming experience will certainly help you grasp the material faster.

This course is ideal for anyone interested in learning how to manipulate and analyze data using Python. Whether you're a beginner starting your data science journey or an intermediate learner looking to deepen your understanding of data manipulation, this course offers valuable insights and hands-on experience. It is also suitable for those looking to switch careers into data science or improve their data analysis skills in a professional setting.

The course is designed to be completed in approximately 9 hours. This duration allows for a deep dive into both NumPy and Pandas, with plenty of hands-on examples and quizzes to help reinforce the concepts learned. You can complete the course at your own pace, and additional time may be needed if you want to revisit the material or work through extra exercises beyond the core content.

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

This course is currently available only to learners who have paid or received financial aid, when available.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,