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Matt Maybeno
Take Udacity's Introduction to Data Analysis course and learn to acquire, analyze and interpret data using NumPy and pandas.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Basic Python
- Basic descriptive statistics
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/2foec9/introduction
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What's inside
Syllabus
Learn about the data analysis process and the Python packages used in this course
Jupyter Notebooks are a great tool for sharing insights and visualizations alongside your code. This lesson covers how to create them and utilize their various features.
Read more
Syllabus
Learn about the data analysis process and the Python packages used in this course
Jupyter Notebooks are a great tool for sharing insights and visualizations alongside your code. This lesson covers how to create them and utilize their various features.
Read more
Use the pandas library to load data, view its properties, and start asking data analysis questions
Use the pandas library to perform data cleaning, filtering, and reshaping tasks. This includes troubleshooting issues with data as well as optimizing for memory usage and speed.
Draw conclusions and communicate results to stakeholders by calculating statistics and creating basic data visualizations with the pandas library
Choose one of Udacity's curated datasets, perform an investigation, and share your findings.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by experienced instructor Matt Maybeno who the platform recognizes for their work in data analysis
Develops core skills for roles in data science and statistical analysis
Strong fit for learners who want to perform data cleaning, filtering, and reshaping tasks
Builds a strong foundational understanding of basic Python and basic descriptive statistics
May not be suitable for learners looking for a comprehensive study
Save this course
Save Introduction to Data Analysis with Pandas and NumPy to your list so you can find it easily later:
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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 Introduction to Data Analysis with Pandas and NumPy with these
activities:
Organize and Review Course Materials
Show steps
Enhance your understanding by organizing and reviewing course materials regularly.
Show steps
-
Create a dedicated folder or notebook for course materials
-
Organize materials by topic or week
-
Regularly review and summarize key concepts
Read 'Data Science for Business'
Show steps
Start the course with a solid understanding of business application of data science concepts.
View
Data Science for Business
on Amazon
Show steps
-
Read the introduction and first three chapters
-
Complete the exercises in the first three chapters
Codecademy: Python for Data Science
Show steps
Strengthen your Python skills and reinforce the core concepts of data science.
Browse courses on
Python
Show steps
-
Sign up for a Codecademy account
-
Enroll in the 'Python for Data Science' course
-
Complete the lessons and practice exercises
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a Data Science Study Group
Show steps
Enhance your learning experience by collaborating with peers and discussing course materials.
Browse courses on
Data Science
Show steps
-
Find or create a Data Science study group
-
Set regular meeting times
-
Discuss course materials, share insights, and work through problems together
Kaggle Tutorial: Data Preparation with Pandas
Show steps
Learn how to handle and prepare data using Pandas, the primary tool for data scientists.
Browse courses on
Data Preparation
Show steps
-
Sign up for a Kaggle account
-
Enroll in the 'Data Preparation with Pandas' tutorial
-
Complete the tutorial and practice exercises
Attend a Data Science Meetup
Show steps
Connect with professionals in the data science field and learn about industry trends.
Browse courses on
Data Science
Show steps
-
Find a local Data Science Meetup group
-
Attend a meetup event
-
Network with other attendees
Attend a Data Science Workshop
Show steps
Acquire new skills or delve deeper into specific areas of data science through hands-on workshops.
Browse courses on
Data Science
Show steps
-
Research and identify relevant Data Science workshops
-
Register and attend the workshop
-
Actively participate in the workshop exercises and discussions
Create a Data Analysis Project using NumPy and Pandas
Show steps
Develop a deeper understanding of data analysis techniques through hands-on application of NumPy and Pandas.
Browse courses on
NumPy
Show steps
-
Choose a dataset from the Udacity Data Science Resource Library
-
Clean and explore the data using NumPy and Pandas
-
Perform data analysis tasks such as calculating statistics, creating visualizations, and building models
-
Write a report summarizing your findings
Organize and Review Course Materials
Show steps
Enhance your understanding by organizing and reviewing course materials regularly.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for course materials
- Organize materials by topic or week
- Regularly review and summarize key concepts
Read 'Data Science for Business'
Show steps
Start the course with a solid understanding of business application of data science concepts.
View
Data Science for Business
on Amazon
Show steps
Show steps
Show steps
- Read the introduction and first three chapters
- Complete the exercises in the first three chapters
Codecademy: Python for Data Science
Show steps
Strengthen your Python skills and reinforce the core concepts of data science.
Browse courses on
Python
Show steps
Show steps
Show steps
- Sign up for a Codecademy account
- Enroll in the 'Python for Data Science' course
- Complete the lessons and practice exercises
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a Data Science Study Group
Show steps
Enhance your learning experience by collaborating with peers and discussing course materials.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Find or create a Data Science study group
- Set regular meeting times
- Discuss course materials, share insights, and work through problems together
Kaggle Tutorial: Data Preparation with Pandas
Show steps
Learn how to handle and prepare data using Pandas, the primary tool for data scientists.
Browse courses on
Data Preparation
Show steps
Show steps
Show steps
- Sign up for a Kaggle account
- Enroll in the 'Data Preparation with Pandas' tutorial
- Complete the tutorial and practice exercises
Attend a Data Science Meetup
Show steps
Connect with professionals in the data science field and learn about industry trends.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Find a local Data Science Meetup group
- Attend a meetup event
- Network with other attendees
Attend a Data Science Workshop
Show steps
Acquire new skills or delve deeper into specific areas of data science through hands-on workshops.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Research and identify relevant Data Science workshops
- Register and attend the workshop
- Actively participate in the workshop exercises and discussions
Create a Data Analysis Project using NumPy and Pandas
Show steps
Develop a deeper understanding of data analysis techniques through hands-on application of NumPy and Pandas.
Browse courses on
NumPy
Show steps
Show steps
Show steps
- Choose a dataset from the Udacity Data Science Resource Library
- Clean and explore the data using NumPy and Pandas
- Perform data analysis tasks such as calculating statistics, creating visualizations, and building models
- Write a report summarizing your findings
Career center
Learners who complete Introduction to Data Analysis with Pandas and NumPy will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist uses data to build models and make predictions. This course introduces the data analysis process and the Python packages used in this field, including NumPy and pandas. You'll learn how to load, clean, and analyze data, and create data visualizations. This course may be useful for aspiring Data Scientists who want to build a foundation in data analysis.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer builds and deploys machine learning models. This course introduces the data analysis process and the Python packages used in this field, including NumPy and pandas. You'll learn how to load, clean, and analyze data, and create data visualizations. This course may be useful for aspiring Machine Learning Engineers who want to build a foundation in data analysis.
Business Analyst
Business Analyst
A Business Analyst uses data to improve business processes. This course introduces the data analysis process and the Python packages used in this field, including NumPy and pandas. You'll learn how to load, clean, and analyze data, and create data visualizations. This course may be useful for aspiring Business Analysts who want to build a foundation in data analysis.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses data to improve operations. This course introduces the data analysis process and the Python packages used in this field, including NumPy and pandas. You'll learn how to load, clean, and analyze data, and create data visualizations. This course may be useful for aspiring Operations Research Analysts who want to build a foundation in data analysis.
Statistician
Statistician
A Statistician uses data to solve problems and make predictions. This course introduces the data analysis process and the Python packages used in this field, including NumPy and pandas. You'll learn how to load, clean, and analyze data, and create data visualizations. This course may be useful for aspiring Statisticians who want to build a foundation in data analysis.
Data Analyst
Data Analyst
A Data Analyst uses data to solve business problems and communicate insights to stakeholders. This course introduces the data analysis process and the Python packages used in this field, including NumPy and pandas. You'll learn how to load, clean, and analyze data, and create data visualizations. This course may be useful for aspiring Data Analysts who want to build a foundation in data analysis.
Financial Analyst
Financial Analyst
A Financial Analyst uses data to make investment decisions and manage risk. This course introduces the data analysis process and the Python packages used in this field, including NumPy and pandas. You'll learn how to load, clean, and analyze data, and create data visualizations. This course may be useful for aspiring Financial Analysts who want to build a foundation in data analysis.
Marketing Analyst
Marketing Analyst
A Marketing Analyst uses data to improve marketing campaigns. This course introduces the data analysis process and the Python packages used in this field, including NumPy and pandas. You'll learn how to load, clean, and analyze data, and create data visualizations. This course may be useful for aspiring Marketing Analysts who want to build a foundation in data analysis.
Data Engineer
Data Engineer
A Data Engineer builds and maintains data infrastructure. This course introduces the data analysis process and the Python packages used in this field, including NumPy and pandas. You'll learn how to load, clean, and analyze data, and create data visualizations. This course may be useful for aspiring Data Engineers who want to build a foundation in data analysis.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses data to make investment decisions. This course introduces the data analysis process and the Python packages used in this field, including NumPy and pandas. You'll learn how to load, clean, and analyze data, and create data visualizations. This course may be useful for aspiring Quantitative Analysts who want to build a foundation in data analysis.
Actuary
Actuary
An Actuary uses data to assess risk and manage uncertainty. This course introduces the data analysis process and the Python packages used in this field, including NumPy and pandas. You'll learn how to load, clean, and analyze data, and create data visualizations. This course may be useful for aspiring Actuaries who want to build a foundation in data analysis.
Software Engineer (Data Science)
Software Engineer (Data Science)
A Software Engineer - Data Science builds and deploys machine learning models. This course introduces the data analysis process and the Python packages used in this field, including NumPy and pandas. You'll learn how to load, clean, and analyze data, and create data visualizations. This course may be useful for aspiring Software Engineers who want to specialize in data science.
Research Analyst
Research Analyst
A Research Analyst uses data to conduct research and make recommendations. This course introduces the data analysis process and the Python packages used in this field, including NumPy and pandas. You'll learn how to load, clean, and analyze data, and create data visualizations. This course may be useful for aspiring Research Analysts who want to build a foundation in data analysis.
Data Journalist
Data Journalist
A Data Journalist uses data to tell stories and communicate insights. This course introduces the data analysis process and the Python packages used in this field, including NumPy and pandas. You'll learn how to load, clean, and analyze data, and create data visualizations. This course may be useful for aspiring Data Journalists who want to build a foundation in data analysis.
Product Analyst
Product Analyst
A Product Analyst uses data to improve products. This course introduces the data analysis process and the Python packages used in this field, including NumPy and pandas. You'll learn how to load, clean, and analyze data, and create data visualizations. This course may be useful for aspiring Product Analysts who want to build a foundation in data analysis.
For more career information including salaries, visit:
OpenCourser.com/course/2foec9/introduction
Reading list
We've selected ten 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
Introduction to Data Analysis with Pandas and NumPy.
Comprehensive guide to data science with Python. It covers the basics of data analysis, machine learning, and deep learning. It good choice for students who want to learn the fundamentals of data science.
Comprehensive introduction to data science. It covers the basics of data analysis, machine learning, and deep learning. It good choice for students who want to learn the fundamentals of data science.
Comprehensive introduction to statistical learning. It covers the basics of statistical learning, including supervised learning, unsupervised learning, and reinforcement learning. It good choice for students who want to learn the fundamentals of statistical learning.
Comprehensive introduction to machine learning with Python. It covers the basics of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. It good choice for students who want to learn the fundamentals of machine learning.
Comprehensive introduction to machine learning with R. It covers the basics of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. It good choice for students who want to learn the fundamentals of machine learning.
Comprehensive introduction to big data analytics. It covers the basics of big data analytics, including big data technologies, big data analytics methods, and big data analytics applications. It good choice for students who want to learn the fundamentals of big data analytics.
Comprehensive introduction to data science for business. It covers the basics of data science for business, including data science for marketing, data science for finance, and data science for operations. It good choice for students who want to learn the fundamentals of data science for business.
Save
Comprehensive introduction to data mining. It covers the basics of data mining, including data preprocessing, data mining algorithms, and data mining applications. It good choice for students who want to learn the fundamentals of data mining.
Save
Comprehensive introduction to data visualization. It covers the basics of data visualization, including data visualization techniques, data visualization tools, and data visualization best practices. It good choice for students who want to learn the fundamentals of data visualization.
Save
Comprehensive introduction to deep learning. It covers the basics of neural networks, convolutional neural networks, and recurrent neural networks. It good choice for students who want to learn the fundamentals of deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/2foec9/introduction
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Effort
3 weeks
Via
Udacity
Instructor
Matt Maybeno
Language
English
Good to know
Taught by experienced instructor Matt Maybeno who the platform recognizes for their work in data analysis
Develops core skills for roles in data science and statistical analysis
Strong fit for learners who want to perform data cleaning, filtering, and reshaping tasks
Builds a strong foundational understanding of basic Python and basic descriptive statistics
May not be suitable for learners looking for a comprehensive study
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