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Vinita Silaparasetty
This guided project is for those who want to learn how to use Julia for data cleaning as well as exploratory analysis. This project covers the syntax of Julia from a data science perspective. So you will not build anything during the course of this project.
While you are watching me code, you will get a cloud desktop with all the required software pre-installed. This will allow you to code along with me. After all, we learn best with active, hands-on learning.
Special Features:
1) Work with 2 real-world datasets.
Read more
This guided project is for those who want to learn how to use Julia for data cleaning as well as exploratory analysis. This project covers the syntax of Julia from a data science perspective. So you will not build anything during the course of this project.
While you are watching me code, you will get a cloud desktop with all the required software pre-installed. This will allow you to code along with me. After all, we learn best with active, hands-on learning.
Special Features:
1) Work with 2 real-world datasets.
Read more
This guided project is for those who want to learn how to use Julia for data cleaning as well as exploratory analysis. This project covers the syntax of Julia from a data science perspective. So you will not build anything during the course of this project.
While you are watching me code, you will get a cloud desktop with all the required software pre-installed. This will allow you to code along with me. After all, we learn best with active, hands-on learning.
Special Features:
1) Work with 2 real-world datasets.
2) Detailed variable description booklets are provided in the github repository for this guided project.
3) This project provides challenges with solutions to encourage you to practice.
4) The real-world applications of each function are explained.
5) Best practices and tips are provided to ensure that you learn how to use pandas efficiently.
6) You get a copy of the jupyter notebook that you create which acts as a handy reference guide.
Please note that the version of Julia used is 1.0.4
Note: This project works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/fwv0zl/julia
What's inside
Syllabus
Project Overview
By the end of this project you will learn how to use Julia for data cleaning and exploratory data analysis.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
This course is suitable for beginners who seek knowledge in using Julia for data cleaning and exploratory analysis
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Reviews summary
Julia for beginners course
According to students, Julia for Beginners in Data Science is a highly rated course that is well received by learners. Many learners largely positive feedback about the course, especially its very engaging assignments and easy to follow content flow. Students that tried to use the online tool experienced technical issues that prevented them from using the tool. However, students that were able to use the tool found it very handson. Students also commented on how the course is very good for those who are new to Julia. Students also mentioned how instructors explain difficult concepts in a way that is easy for students to comprehend.
The content was very easy to follow.
"Good exposition, but I didn't understand how to use the tool. Thank you!!!"
"This is a very good introductory course to understand the Data Science capabilities of Julia packages."
The assignments are very engaging assignments.
"Very fun and helpful"
"Very good for beginners"
"es un gran project para aprende de manera rapida y sencilla sobre Julia, así mismo explica algunos conceptos basicos de la ciencia de datos y como se realizan con Julia"
This course is very good for beginners.
"Very good for beginners"
"es un gran project para aprende de manera rapida y sencilla sobre Julia, así mismo explica algunos conceptos basicos de la ciencia de datos y como se realizan con Julia"
The online tool had technical issues that prevented learners from using the tool.
"The online tool didn't work at first, then I ran out of the limited time alloted to me so I had to do the exercises on my own computer."
"Methods used are not explained. Deprecation warnings with a simple instruction are not followed. Code is hard to read (no spaces between args, etc.)."
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 Julia for Beginners in Data Science with these
activities:
Review Julia Tutorials
Show steps
Following a quick tutorial will help prepare you to learn how to use Julia during the course.
Browse courses on
Julia
Show steps
-
Search for a beginner-friendly Julia tutorial
-
Bookmarked the tutorial
-
Watch and take notes
Practice Julia Syntax
Show steps
Practice Julia code examples will reinforce your knowledge of the language and prepare you for the course.
Browse courses on
Julia
Show steps
-
Find a set of Julia syntax exercises
-
Solve the exercises
-
Check your answers
Refresh elementary statistics
Show steps
Reinforce statistical concepts and techniques that are fundamental to data cleaning and analysis.
Browse courses on
Probability and Statistics
Show steps
-
Review Statistical Concepts: mean, median, mode, standard deviation, variance, probability distributions
-
Practice solving probability and statistical problems
-
Learn about statistical software packages for data analysis
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Read 'Julia for Data Analysis' by Zacharias Voulgaris
Show steps
Provide a comprehensive overview of the Julia programming language for data cleaning and analysis.
View
Julia for Machine Learning
on Amazon
Show steps
-
Read chapters 1-3 to understand the basics of Julia
-
Read chapters 4-6 to learn about data cleaning and manipulation in Julia
-
Read chapters 7-9 to explore data analysis techniques in Julia
Practice data cleaning and manipulation tasks
Show steps
Improve proficiency in data cleaning and manipulation, which are essential for data analysis.
Browse courses on
Data Cleaning
Show steps
-
Complete online coding exercises on data cleaning and manipulation
-
Solve practice problems involving data cleaning and manipulation
Join a study group or participate in online discussions
Show steps
Enhance understanding through peer collaboration and discussions.
Show steps
-
Find a study group or online community related to data analysis
-
Participate in discussions, ask questions, and share knowledge
Create a Jupyter notebook showcasing data analysis project
Show steps
Reinforce understanding of data cleaning and analysis concepts by applying them to a real-world project.
Show steps
-
Define a data analysis problem
-
Load and clean the data
-
Explore and analyze the data
-
Build a predictive model (optional)
-
Write a report or presentation showcasing the results
Follow tutorials on advanced Julia topics
Show steps
Expand knowledge of Julia programming and data analysis techniques beyond the scope of the course.
Show steps
-
Identify areas for improvement in your Julia skills or data analysis knowledge
-
Find online tutorials or courses that cover those topics
-
Follow the tutorials and complete the exercises
Mentor a junior student or colleague in data analysis
Show steps
Deepen your understanding by teaching and assisting others in their learning journey.
Browse courses on
Mentoring
Show steps
-
Identify a mentee who is interested in learning data analysis
-
Provide guidance and support on data cleaning, manipulation, and analysis
Participate in a data analysis or data science competition
Show steps
Challenge yourself and apply data cleaning and analysis skills in a competitive setting.
Show steps
-
Identify a competition that aligns with your interests and skill level
-
Download the data and familiarize yourself with the problem statement
-
Build a model and submit your results
Review Julia Tutorials
Show steps
Following a quick tutorial will help prepare you to learn how to use Julia during the course.
Browse courses on
Julia
Show steps
Show steps
Show steps
- Search for a beginner-friendly Julia tutorial
- Bookmarked the tutorial
- Watch and take notes
Practice Julia Syntax
Show steps
Practice Julia code examples will reinforce your knowledge of the language and prepare you for the course.
Browse courses on
Julia
Show steps
Show steps
Show steps
- Find a set of Julia syntax exercises
- Solve the exercises
- Check your answers
Refresh elementary statistics
Show steps
Reinforce statistical concepts and techniques that are fundamental to data cleaning and analysis.
Browse courses on
Probability and Statistics
Show steps
Show steps
Show steps
- Review Statistical Concepts: mean, median, mode, standard deviation, variance, probability distributions
- Practice solving probability and statistical problems
- Learn about statistical software packages for data analysis
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Read 'Julia for Data Analysis' by Zacharias Voulgaris
Show steps
Provide a comprehensive overview of the Julia programming language for data cleaning and analysis.
View
Julia for Machine Learning
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-3 to understand the basics of Julia
- Read chapters 4-6 to learn about data cleaning and manipulation in Julia
- Read chapters 7-9 to explore data analysis techniques in Julia
Practice data cleaning and manipulation tasks
Show steps
Improve proficiency in data cleaning and manipulation, which are essential for data analysis.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Complete online coding exercises on data cleaning and manipulation
- Solve practice problems involving data cleaning and manipulation
Join a study group or participate in online discussions
Show steps
Enhance understanding through peer collaboration and discussions.
Show steps
Show steps
Show steps
- Find a study group or online community related to data analysis
- Participate in discussions, ask questions, and share knowledge
Create a Jupyter notebook showcasing data analysis project
Show steps
Reinforce understanding of data cleaning and analysis concepts by applying them to a real-world project.
Show steps
Show steps
Show steps
- Define a data analysis problem
- Load and clean the data
- Explore and analyze the data
- Build a predictive model (optional)
- Write a report or presentation showcasing the results
Follow tutorials on advanced Julia topics
Show steps
Expand knowledge of Julia programming and data analysis techniques beyond the scope of the course.
Show steps
Show steps
Show steps
- Identify areas for improvement in your Julia skills or data analysis knowledge
- Find online tutorials or courses that cover those topics
- Follow the tutorials and complete the exercises
Mentor a junior student or colleague in data analysis
Show steps
Deepen your understanding by teaching and assisting others in their learning journey.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify a mentee who is interested in learning data analysis
- Provide guidance and support on data cleaning, manipulation, and analysis
Participate in a data analysis or data science competition
Show steps
Challenge yourself and apply data cleaning and analysis skills in a competitive setting.
Show steps
Show steps
Show steps
- Identify a competition that aligns with your interests and skill level
- Download the data and familiarize yourself with the problem statement
- Build a model and submit your results
Career center
Learners who complete Julia for Beginners in Data Science will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models to solve business problems. This course may be useful for working on the Machine Learning Engineer career path as it will teach the basics of Julia programming language, which is widely used for machine learning.
Data Analyst
Data Analyst
A Data Analyst collects, cleans, and analyzes data to provide insights to businesses and organizations. This course may be useful for working on the Data Analyst career path as it will teach the basics of Julia programming language, which can be very helpful for data analysis.
Financial Analyst
Financial Analyst
A Financial Analyst analyzes financial data to make investment decisions. This course may be useful for working on the Financial Analyst career path as it will teach the basics of Julia programming language, which is widely used for financial analysis.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data pipelines and systems. This course may be useful for working on the Data Engineer career path as it will teach the basics of Julia programming language, which is widely used for big data processing.
Statistician
Statistician
A Statistician collects, analyzes, interprets, and presents data to provide insights to businesses and organizations. This course may be useful for working on the Statistician career path as it will teach the basics of Julia programming language, which is widely used for statistical analysis.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data and make investment decisions. This course may be useful for working on the Quantitative Analyst career path as it will teach the basics of Julia programming language, which is widely used for financial analysis.
Research Scientist
Research Scientist
A Research Scientist conducts research in a specific field of science or engineering. This course may be useful for working on the Research Scientist career path as it will teach the basics of Julia programming language, which is widely used for scientific computing.
Business Analyst
Business Analyst
A Business Analyst analyzes business data to identify problems and opportunities. This course may be useful for working on the Business Analyst career path as it will teach the basics of Julia programming language, which is a powerful tool for data analysis.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and statistical models to solve business problems. This course may be useful for working on the Operations Research Analyst career path as it will teach the basics of Julia programming language, which is widely used for operations research.
Risk Analyst
Risk Analyst
A Risk Analyst analyzes risk and develops strategies to mitigate risk. This course may be useful for working on the Risk Analyst career path as it will teach the basics of Julia programming language, which is widely used for risk analysis.
Actuary
Actuary
An Actuary uses mathematical and statistical models to assess risk and develop insurance products. This course may be useful for working on the Actuary career path as it will teach the basics of Julia programming language, which is widely used for actuarial science.
Data Scientist
Data Scientist
A Data Scientist uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. This course may be useful for working on the Data Scientist career path as it will teach the basics of Julia programming language, which can be very helpful for data analysis and visualization.
Technical Writer
Technical Writer
A Technical Writer writes technical documentation for software and hardware products. This course may be useful for working on the Technical Writer career path as it will teach you how to use the Julia programming language. Julia is a powerful language that is well-suited for writing technical documentation.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course may be useful for working on the Software Engineer career path as it will teach the basics of Julia programming language, which is a powerful language for developing high-performance applications.
Teacher
Teacher
A Teacher develops and delivers lesson plans to educate students. This course may be useful for working on the Teacher career path as it will help you learn how to use the Julia programming language. Julia is a powerful language that is well-suited for teaching programming concepts to students.
For more career information including salaries, visit:
OpenCourser.com/course/fwv0zl/julia
Reading list
We've selected six 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
Julia for Beginners in Data Science.
Provides a comprehensive overview of data science concepts and techniques, from data cleaning and wrangling to modeling and visualization. It is written in a clear and concise style, and it great resource for beginners who want to learn more about data science.
Save
Provides a comprehensive overview of the Julia programming language, including its syntax, data structures, and built-in functions. It great resource for beginners who want to learn more about Julia.
Provides a comprehensive overview of machine learning techniques in Julia, including supervised learning, unsupervised learning, and deep learning. It great resource for beginners who want to learn more about machine learning in Julia.
Provides a practical introduction to data science for business professionals. It covers topics such as data collection, analysis, and visualization. It good choice for anyone who wants to learn how to use data science to improve their business.
Save
Provides a broad overview of data science and its applications in various fields. It covers topics such as data mining, machine learning, and statistics. It good choice for anyone who wants to learn more about data science and its potential impact on society.
Save
Focuses on using Julia for financial modeling and analysis, including asset pricing, portfolio optimization, and risk management. It assumes some programming experience, but not necessarily in Julia.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/fwv0zl/julia
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Effort
1 hour 30 minutes
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Vinita Silaparasetty
Language
English
Good to know
This course is suitable for beginners who seek knowledge in using Julia for data cleaning and exploratory analysis
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