Tools for Data Science from Coursera

In order to be successful in Data Science, you need to be skilled with using tools that Data Science professionals employ as part of their jobs. This course teaches you about the popular tools in Data Science and how to use them.

You will become familiar with the Data Scientist’s tool kit which includes: Libraries & Packages, Data Sets, Machine Learning Models, Kernels, as well as the various Open source, commercial, Big Data and Cloud-based tools.

Work with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will understand what each tool is used for, what programming languages they can execute, their features and limitations.

This course gives plenty of hands-on experience in order to develop skills for working with these Data Science Tools. With the tools hosted in the cloud on Skills Network Labs, you will be able to test each tool and follow instructions to run simple code in Python, R, or Scala.

Towards the end the course, you will create a final project with a Jupyter Notebook. You will demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.

What's inside

Syllabus

Overview of Data Science Tools

In this module, you will learn about the different types and categories of tools that data scientists use and popular examples of each. You will also become familiar with Open Source, Cloud-based, and Commercial options for data science tools.

Languages of Data Science

For users who are just starting on their data science journey, the range of programming languages can be overwhelming. So, which language should you learn first? This module will bring awareness about the criteria that would determine which language you should learn. You will learn the benefits of Python, R, SQL, and other common languages such as Java, Scala, C++, JavaScript, and Julia. You will explore how you can use these languages in Data Science. You will also look at some sites to locate more information about the languages.

Packages, APIs, Data Sets, and Models

In this module, you will learn about the various libraries in data science. In addition, you will understand an API in relation to REST request and response. Further, in the module, you will explore open data sets on the Data Asset eXchange. Finally, you will learn how to use a machine learning model to solve a problem and navigate the Model Asset eXchange.

Jupyter Notebooks and JupyterLab

With the advancement of digital data, Jupyter Notebook allows a Data Scientist to record their data experiments and results that others can reuse. This module introduces the Jupyter Notebook and Jupyter Lab. You will learn how to work with different kernels in a Notebook session and about the basic Jupyter architecture. In addition, you will identify the tools in an Anaconda Jupyter environment. Finally, the module gives an overview of cloud based Jupyter environments and their data science features.

RStudio & GitHub

R is a statistical programming language and is a powerful tool for data processing and manipulation. This module will start with an introduction to R and RStudio. You will learn about the different R visualization packages and how to create visual charts using the plot function. In addition, Distributed Version Control Systems (DVCS) have become critical tools in software development and key enablers for social and collaborative coding. While there are many distributed versioning systems, Git is amongst the most popular ones. Further in the module, you will develop the essential conceptual and hands-on skills to work with Git and GitHub. You will start with an overview of Git and GitHub, followed by creation of a GitHub account and a project repository, adding files to it, and committing your changes using the web interface. Next, you will become familiar with Git workflows involving branches and pull requests (PRs) and merges. You will also complete a project at the end to apply and demonstrate your newly acquired skills.

Create and Share your Jupyter Notebook

In this module, you will work on a final project to demonstrate some of the skills learned in the course. You will also be tested on your knowledge of various components and tools in a Data Scientist's toolkit learned in the previous modules.

[Optional] IBM Watson Studio

Watson Studio is a collaborative platform for the data science community and is used by Data Analysts, Data Scientists, Data Engineers, Developers, and Data Stewards to analyze data and construct models. In this module, you will learn about Watson Studio and IBM Cloud Pak for data as a service. Then you will create an IBM Watson Studio service and a project in Watson Studio. After creating the project, you will create a Jupyter notebook and load a data file. You will also explore the different templates and kernels in a Jupyter notebook. Finally, you will connect your Watson Studio account to GitHub and publish the notebook in GitHub. Note: This part of the course is optional and is not a mandatory requirement to complete the lab provided in this week of the course.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops Python, R, SQL, Java, Scala, C++, JavaScript, and Julia skills, which are core skills for data science

Taught by Romeo Kienzler, Svetlana Levitan, Aije Egwaikhide, who are recognized for their work in data science

Explores data science tools, which is standard in industry

Builds a strong foundation for beginners

Requires hands-on labs and interactive materials

Advises students to take other courses first as prerequisites

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 Tools for Data Science with these activities:

Organize course materials

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Stay organized and improve your understanding by compiling and reviewing course materials.

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Gather and organize notes, assignments, quizzes, and exams
Review the compiled materials regularly

Explore a book on Data Science fundamentals

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Lay a solid foundation for your learning journey by reviewing key concepts and principles.

View Digital Image Processing Using MATLAB on Amazon

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Obtain the book and read through the chapters
Take notes and highlight important concepts
Discuss the concepts with peers or a mentor

Compile a list of useful Data Science tools

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Expand your knowledge of the available tools and resources for Data Science.

Browse courses on Data Science Tools

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Research and identify various Data Science tools
Categorize and organize the tools into a list or spreadsheet
Share the compiled list with others for their benefit

Three other activities

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Practice using `Git` and `GitHub`

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Reinforce your understanding of these essential tools for collaboration and version control.

Browse courses on Version Control

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Find practice exercises for `Git` and `GitHub`
Complete practice problems to enhance skills
Apply `Git` and `GitHub` in a personal project

Create a `Jupyter Notebook` tutorial

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Deepen your knowledge by teaching others about the use and functionality of `Jupyter Notebooks`.

Browse courses on Documentation

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Choose a specific aspect of `Jupyter Notebooks` to focus on
Develop a step-by-step tutorial
Create a `Jupyter Notebook` to demonstrate the concepts
Share the tutorial with others for feedback

Participate in a Data Science competition

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Challenge yourself and apply your skills in a competitive setting.

Browse courses on Hands-on Experience

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Identify a suitable Data Science competition
Prepare for the competition by reviewing concepts
Participate in the competition and submit your solution
Analyze the results and learn from the experience

Career center

Learners who complete Tools for Data Science will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data scientists are responsible for developing and implementing data science models that can solve complex business problems. They work closely with data analysts to identify the data that is needed to build these models, and they use a variety of statistical and machine learning techniques to develop and test them. This course can help you develop the skills you need to be a successful data scientist, including how to use popular data science tools like Jupyter Notebooks, RStudio, and Git. You will also learn about the different types of data sets and machine learning models that are used in data science.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine learning engineers are responsible for designing, developing, and deploying machine learning models that can solve complex business problems. They work closely with data scientists to identify the data that is needed to build these models, and they use a variety of statistical and machine learning techniques to develop and test them. This course can help you develop the skills you need to be a successful machine learning engineer, including how to use popular data science tools like Jupyter Notebooks, RStudio, and Git. You will also learn about the different types of data sets and machine learning models that are used in data science.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data analysts play a critical role in the success of any data-driven organization. They are responsible for collecting, cleaning, and analyzing data to identify trends, patterns, and insights that can help businesses make better decisions. This course can help you develop the skills you need to be a successful data analyst, including how to use popular data science tools like Jupyter Notebooks, RStudio, and Git. You will also learn about the different types of data sets and machine learning models that are used in data science.

See salaries and explore the career path for Data Analyst

Data Engineer

Data engineers are responsible for building and maintaining the data infrastructure that is used to support data science and machine learning projects. They work closely with data scientists and machine learning engineers to ensure that the data that is used to build models is accurate and reliable. This course can help you develop the skills you need to be a successful data engineer, including how to use popular data science tools like Jupyter Notebooks, RStudio, and Git. You will also learn about the different types of data sets and machine learning models that are used in data science.

See salaries and explore the career path for Data Engineer

Software Engineer

Software engineers design, develop, and maintain software systems. They work with businesses to develop software solutions that meet their specific needs. This course can help you develop the skills you need to be a successful software engineer, including how to use popular data science tools like Jupyter Notebooks, RStudio, and Git. You will also learn about the different types of data sets and machine learning models that are used in data science.

See salaries and explore the career path for Software Engineer

Database Administrator

Database administrators are responsible for the design, implementation, and maintenance of database systems. They work with businesses to ensure that their data is stored and managed efficiently. This course can help you develop the skills you need to be a successful database administrator, including how to use popular data science tools like Jupyter Notebooks, RStudio, and Git. You will also learn about the different types of data sets and machine learning models that are used in data science.

See salaries and explore the career path for Database Administrator

Information Security Analyst

Information security analysts are responsible for protecting the IT systems of organizations from unauthorized access, use, disclosure, disruption, modification, or destruction. This course can help you develop the skills you need to be a successful information security analyst, including how to use popular data science tools like Jupyter Notebooks, RStudio, and Git. You will also learn about the different types of data sets and machine learning models that are used in data science.

See salaries and explore the career path for Information Security Analyst

Financial Analyst

Financial analysts analyze financial data to make investment recommendations. They work with businesses and individuals to help them make informed financial decisions. This course can help you develop the skills you need to be a successful financial analyst, including how to use popular data science tools like Jupyter Notebooks, RStudio, and Git. You will also learn about the different types of data sets and machine learning models that are used in data science.

See salaries and explore the career path for Financial Analyst

Product Manager

Product managers are responsible for the development and marketing of products. They work with teams of engineers, designers, and marketers to bring new products to market. This course can help you develop the skills you need to be a successful product manager, including how to use popular data science tools like Jupyter Notebooks, RStudio, and Git. You will also learn about the different types of data sets and machine learning models that are used in data science.

See salaries and explore the career path for Product Manager

Data Visualization Specialist

Data visualization specialists are responsible for creating visual representations of data. They work with businesses to communicate data insights and trends in a clear and concise way. This course can help you develop the skills you need to be a successful data visualization specialist, including how to use popular data science tools like Jupyter Notebooks, RStudio, and Git. You will also learn about the different types of data sets and machine learning models that are used in data science.

See salaries and explore the career path for Data Visualization Specialist

Quantitative Analyst

Quantitative analysts use mathematical and statistical techniques to analyze financial data. They work with investment firms and hedge funds to develop trading strategies and make investment decisions. This course can help you develop the skills you need to be a successful quantitative analyst, including how to use popular data science tools like Jupyter Notebooks, RStudio, and Git. You will also learn about the different types of data sets and machine learning models that are used in data science.

See salaries and explore the career path for Quantitative Analyst

Market Research Analyst

Market research analysts collect and analyze data about consumer behavior. They work with businesses to identify trends and develop marketing strategies. This course can help you develop the skills you need to be a successful market research analyst, including how to use popular data science tools like Jupyter Notebooks, RStudio, and Git. You will also learn about the different types of data sets and machine learning models that are used in data science.

See salaries and explore the career path for Market Research Analyst

Business Analyst

Business analysts use data to help businesses make better decisions. They work with stakeholders to identify the business problems that need to be solved, and they use data analysis techniques to develop and implement solutions. This course can help you develop the skills you need to be a successful business analyst, including how to use popular data science tools like Jupyter Notebooks, RStudio, and Git. You will also learn about the different types of data sets and machine learning models that are used in data science.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

Operations research analysts use mathematical and statistical techniques to solve complex business problems. They work with businesses to improve efficiency, reduce costs, and increase profits. This course can help you develop the skills you need to be a successful operations research analyst, including how to use popular data science tools like Jupyter Notebooks, RStudio, and Git. You will also learn about the different types of data sets and machine learning models that are used in data science.

See salaries and explore the career path for Operations Research Analyst

Statistician

Statisticians collect, analyze, and interpret data. They work with businesses to provide insights into data and help them make better decisions. This course can help you develop the skills you need to be a successful statistician, including how to use popular data science tools like Jupyter Notebooks, RStudio, and Git. You will also learn about the different types of data sets and machine learning models that are used in data science.

See salaries and explore the career path for Statistician

Tools for Data Science

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