We may earn an affiliate commission when you visit our partners.
Alper Tellioglu

This course covers the use of pandas arrays for data analysis and manipulation, including handling time data, categorical data, and sparse data. Hands-on exercises provide practical experience using pandas arrays to solve real-world problems.

Read more

This course covers the use of pandas arrays for data analysis and manipulation, including handling time data, categorical data, and sparse data. Hands-on exercises provide practical experience using pandas arrays to solve real-world problems.

Pandas is one of the most powerful libraries for data science and it provides powerful and easy to use data structures for handling different kinds of data. In this course, Pandas Arrays and Data Structures, you’ll learn a range of topics, including handling time delta and time interval data, working with categorical and sparse data, and using pandas arrays to manipulate and analyze string data. First, you'll explore through hands-on exercises and case studies that are practical experiences using pandas arrays to solve real-world problems in data analysis and manipulation. Next, you'll discover pandas Aries, handling date and time operations with timestamp time delta and in turbo handling messy data using concepts like sparse and categorical data. Finally, you'll learn and become familiar with pandas function to analyze large datasets efficiently. By the end of this course, you'll have a strong understanding of the pandas library, including its data structures and tools for data manipulation and will be able to apply this knowledge to real world datasets to gain insights and make data driving decisions.

Enroll now

Here's a deal for you

We found an offer that may be relevant to this course.
Save money when you learn. All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.

What's inside

Syllabus

Course Overview
Pandas Arrays and Data Types
Manipulating Data with Pandas

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Explores concepts of pandas arrays, data structures, and hands-on exercises, building a foundation for data analysis and manipulation
Taught by industry experts in data science, Alper Tellioglu, offering real-world insights and practical knowledge
Provides comprehensive coverage of pandas arrays, including time data, categorical data, and sparse data, addressing various data types and their applications in data analysis
Offers hands-on exercises and case studies, enabling learners to apply concepts directly and gain practical experience in solving data analysis problems
Delves into pandas functions for efficient analysis of large datasets, empowering learners to handle complex data scenarios effectively

Save this course

Save Pandas Arrays and Data Structures to your list so you can find it easily later:
Save

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Pandas Arrays and Data Structures with these activities:
Explore Pandas Arrays Data Structures
Refresh your understanding of Pandas arrays data structures to lay a strong foundation for this course.
Browse courses on Pandas
Show steps
  • Review the official Pandas documentation on arrays.
  • Complete a few hands-on exercises using Pandas arrays.
Compile notes and assignments
Strengthen your understanding by organizing and reviewing your notes and assignments.
Show steps
  • Gather all your notes, assignments, and any other relevant materials from this course.
  • Organize the materials into a logical structure.
Follow a Tutorial on Time Delta and Time Interval
Enhance your understanding of handling time-related data in Pandas by following a guided tutorial.
Show steps
  • Find a comprehensive tutorial on handling time data in Pandas.
  • Follow the tutorial step-by-step, completing all the exercises.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Answer questions on discussion forums
Enhance your understanding by helping others on discussion forums related to Pandas arrays.
Show steps
  • Join discussion forums or communities focused on Pandas Arrays.
  • Monitor the forums for questions related to topics covered in this course.
  • Provide helpful and informative answers to questions, citing specific examples and resources from the course.
Practice exercises on categorical data
Reinforce your skills in working with categorical data in Pandas through dedicated practice exercises.
Browse courses on Categorical Data
Show steps
  • Find a collection of practice exercises on manipulating categorical data.
  • Solve the exercises, referring to the Pandas documentation when needed.
Develop a Pandas cheat sheet
Solidify your knowledge by creating a comprehensive cheat sheet that summarizes key concepts and functions in Pandas.
Browse courses on Data Manipulation
Show steps
  • Gather relevant information from the course materials.
  • Organize the information into a structured and easy-to-read format.
  • Review and refine the cheat sheet to ensure clarity and accuracy.
Build a data analysis project using Pandas
Apply your knowledge by working on a practical data analysis project using Pandas.
Browse courses on Pandas
Show steps
  • Identify a real-world dataset that aligns with your interests.
  • Clean and prepare the dataset using Pandas.
  • Analyze the dataset using Pandas functions and techniques.
  • Visualize the results of your analysis.
  • Present your findings in a clear and concise manner.

Career center

Learners who complete Pandas Arrays and Data Structures will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data scientists are responsible for collecting, analyzing, and interpreting data to help businesses make better decisions. Pandas is a powerful tool for data science, and this course will give you a strong foundation in using Pandas for data exploration, analysis, and visualization. You'll learn how to handle different data types, including time data, categorical data, and sparse data. You'll also learn how to use Pandas to efficiently analyze large datasets. This course will help you build the skills you need to succeed as a data scientist.
Data Analyst
Data analysts are in increasingly high demand across a range of industries. They play a key role in transforming raw data into meaningful insights that can help businesses make better decisions. Pandas is one of the most popular Python libraries for data analysis, and this course will give you a solid foundation in using Pandas for data manipulation and analysis. You'll learn how to handle different data types, including time data, categorical data, and sparse data. You'll also learn how to use Pandas to efficiently analyze large datasets. This course will help you build the skills you need to succeed as a data analyst.
Quantitative Analyst
Quantitative analysts (quants) use mathematical and statistical modeling to analyze and predict financial data. Pandas is a powerful tool for quantitative analysis, and this course will give you a solid foundation in using Pandas for data manipulation and analysis. You'll learn how to handle different data types, including time data, categorical data, and sparse data. You'll also learn how to use Pandas to efficiently analyze large datasets. This course will help you build the skills you need to succeed as a quantitative analyst.
Machine Learning Engineer
Machine learning engineers are responsible for designing, developing, and deploying machine learning models. Pandas is a powerful tool for machine learning, and this course will give you a strong foundation in using Pandas for data preparation and feature engineering. You'll learn how to handle different data types, including time data, categorical data, and sparse data. You'll also learn how to use Pandas to efficiently analyze large datasets. This course will help you build the skills you need to succeed as a machine learning engineer.
Statistician
Statisticians use mathematical and statistical methods to collect, analyze, and interpret data. Pandas is a powerful tool for statistics, and this course will give you a solid foundation in using Pandas for data manipulation and analysis. You'll learn how to handle different data types, including time data, categorical data, and sparse data. You'll also learn how to use Pandas to efficiently analyze large datasets. This course will help you build the skills you need to succeed as a statistician.
Data Engineer
Data engineers are responsible for designing, building, and maintaining data infrastructure. Pandas is a powerful tool for data engineering, and this course will give you a solid foundation in using Pandas for data cleaning and transformation. You'll learn how to handle different data types, including time data, categorical data, and sparse data. You'll also learn how to use Pandas to efficiently analyze large datasets. This course will help you build the skills you need to succeed as a data engineer.
Business Analyst
Business analysts are responsible for analyzing business data to help companies make better decisions. Pandas is a powerful tool for business analysis, and this course will give you a solid foundation in using Pandas for data manipulation and analysis. You'll learn how to handle different data types, including time data, categorical data, and sparse data. You'll also learn how to use Pandas to efficiently analyze large datasets. This course will help you build the skills you need to succeed as a business analyst.
Operation Research Analyst
Operations research analysts use mathematical and statistical methods to improve the efficiency of business operations. Pandas is a powerful tool for operations research, and this course will give you a solid foundation in using Pandas for data manipulation and analysis. You'll learn how to handle different data types, including time data, categorical data, and sparse data. You'll also learn how to use Pandas to efficiently analyze large datasets. This course will help you build the skills you need to succeed as an operations research analyst.
Financial Analyst
Financial analysts use financial data to make investment recommendations. Pandas is a powerful tool for financial analysis, and this course will give you a solid foundation in using Pandas for data manipulation and analysis. You'll learn how to handle different data types, including time data, categorical data, and sparse data. You'll also learn how to use Pandas to efficiently analyze large datasets. This course will help you build the skills you need to succeed as a financial analyst.
Actuary
Actuaries use mathematical and statistical methods to assess risk. Pandas is a powerful tool for actuarial science, and this course will give you a solid foundation in using Pandas for data manipulation and analysis. You'll learn how to handle different data types, including time data, categorical data, and sparse data. You'll also learn how to use Pandas to efficiently analyze large datasets. This course will help you build the skills you need to succeed as an actuary.
Software Engineer
Software engineers design, develop, and test software. Pandas is a powerful tool for software engineering, and this course will give you a solid foundation in using Pandas for data manipulation and analysis. You'll learn how to handle different data types, including time data, categorical data, and sparse data. You'll also learn how to use Pandas to efficiently analyze large datasets. This course will help you build the skills you need to succeed as a software engineer.
Market Researcher
Market researchers use data to understand consumer behavior and trends. Pandas is a powerful tool for market research, and this course will give you a solid foundation in using Pandas for data manipulation and analysis. You'll learn how to handle different data types, including time data, categorical data, and sparse data. You'll also learn how to use Pandas to efficiently analyze large datasets. This course will help you build the skills you need to succeed as a market researcher.
Data Journalist
Data journalists use data to tell stories and inform the public. Pandas is a powerful tool for data journalism, and this course will give you a solid foundation in using Pandas for data manipulation and analysis. You'll learn how to handle different data types, including time data, categorical data, and sparse data. You'll also learn how to use Pandas to efficiently analyze large datasets. This course will help you build the skills you need to succeed as a data journalist.
Web Developer
Web developers design and develop websites. Pandas is a powerful tool for web development, and this course will give you a solid foundation in using Pandas for data manipulation and analysis. You'll learn how to handle different data types, including time data, categorical data, and sparse data. You'll also learn how to use Pandas to efficiently analyze large datasets. This course will help you build the skills you need to succeed as a web developer.

Reading list

We've selected nine books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Pandas Arrays and Data Structures.
Provides a comprehensive introduction to Keras for deep learning, including pandas, and is especially useful for learners who want to apply Keras to real-world problems.
Provides a comprehensive introduction to TensorFlow for deep learning, including pandas, and is especially useful for learners who want to apply TensorFlow to real-world problems.
Provides a comprehensive introduction to PyTorch for deep learning, including pandas, and is especially useful for learners who want to apply PyTorch to real-world problems.
Provides a comprehensive introduction to data science from the ground up, including pandas, and is especially useful for learners who want to build a deep understanding of the fundamentals.
Provides a comprehensive introduction to machine learning with Python, including pandas, and is especially useful for learners who want to apply machine learning to real-world problems.
Provides a comprehensive overview of Python libraries for data science, including pandas, and is particularly useful as a reference.
Provides a comprehensive introduction to Python for data analysis, including pandas, and is particularly useful as a reference.

Share

Help others find this course page by sharing it with your friends and followers:

Similar courses

Here are nine courses similar to Pandas Arrays and Data Structures.
Index Objects with Pandas
Most relevant
The Ultimate Beginners Guide to Data Analysis with Pandas
Most relevant
Fundamental Tools of Data Wrangling
Most relevant
Python and Pandas for Data Engineering
Most relevant
Python - Complete Python, Django, Data Science and ML...
Most relevant
Data Structures & Backend with Java
Most relevant
Python Data Analysis: NumPy & Pandas Masterclass
Most relevant
JavaScript Programming Essentials
Most relevant
Python Programming - Multithreading, OOP, NumPy and Pandas
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2024 OpenCourser