Julia for Beginners in Data Science from Coursera

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.

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

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

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

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Following a quick tutorial will help prepare you to learn how to use Julia during the course.

Browse courses on Julia

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Search for a beginner-friendly Julia tutorial
Bookmarked the tutorial
Watch and take notes

Practice Julia Syntax

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Practice Julia code examples will reinforce your knowledge of the language and prepare you for the course.

Browse courses on Julia

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Find a set of Julia syntax exercises
Solve the exercises
Check your answers

Refresh elementary statistics

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Reinforce statistical concepts and techniques that are fundamental to data cleaning and analysis.

Browse courses on Probability and Statistics

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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

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Provide a comprehensive overview of the Julia programming language for data cleaning and analysis.

View Julia for Machine Learning on Amazon

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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

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Improve proficiency in data cleaning and manipulation, which are essential for data analysis.

Browse courses on Data Cleaning

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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

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Enhance understanding through peer collaboration and discussions.

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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

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Reinforce understanding of data cleaning and analysis concepts by applying them to a real-world project.

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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

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Expand knowledge of Julia programming and data analysis techniques beyond the scope of the course.

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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

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Deepen your understanding by teaching and assisting others in their learning journey.

Browse courses on Mentoring

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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

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Challenge yourself and apply data cleaning and analysis skills in a competitive setting.

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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

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data 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.

See salaries and explore the career path for Financial Analyst

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

See salaries and explore the career path for Data Scientist

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.

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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.

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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.

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