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.
Tools for Data Science
Tools for Data Science
This course is part of multiple programs.
Instructors: Aije Egwaikhide
Access provided by Indian Institute of Technology Delhi
558,095 already enrolled
Gain insight into a topic and learn the fundamentals.
4.5
(30,063 reviews)
Beginner level
Recommended experience
Flexible schedule
6 hours to complete
Learn at your own pace
89%
Most learners liked this course
What you'll learn
Describe the Data Scientist’s tool kit which includes: Libraries & Packages, Data sets, Machine learning models, and Big Data tools
Utilize languages commonly used by data scientists like Python, R, and SQL
Demonstrate working knowledge of tools such as Jupyter notebooks and RStudio and utilize their various features
Create and manage source code for data science using Git repositories and GitHub.
Skills you'll gain
- Cloud Computing
- Cloud Services
- Version Control
- Application Programming Interface (API)
- R Programming
- Statistical Programming
- Open Source Technology
- Development Environment
- Other Programming Languages
- Computer Programming Tools
- Jupyter
- GitHub
- Query Languages
- Big Data
- Data Science
- Python Programming
- Data Analysis Software
- Software Development Tools
- Machine Learning
- Git (Version Control System)
Details to know
Shareable certificate
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Assessments
8 assignments
Taught in English
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This course is available as part of
When you enroll in this course, you'll also be asked to select a specific program.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
There are 4 modules in this course
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.
What's included
6 videos4 readings2 assignments2 plugins
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.
What's included
5 videos1 reading2 assignments
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.
What's included
5 videos1 reading2 assignments2 plugins
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.
What's included
6 videos1 reading2 assignments3 app items2 plugins
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Learner reviews
4.5
30,063 reviews
- 5 stars
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21.67%
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6.41%
- 2 stars
2.06%
- 1 star
1.94%
Showing 3 of 30063
AA
4
Reviewed on Oct 15, 2024
Good Course overall focus in basic tools. the course could be shorter as someone who know which language who want to use and familir with the tools already shouldn't learn all the course materials
MA
4
Reviewed on May 19, 2023
The course is overwhelming for a beginner with no experiecne of programming. The examples given in the class seem difficult and should have been of a lower difficulty level to keep the hopes high.
GC
5
Reviewed on Apr 12, 2020
It serves perfecty its aim that is giving a first glance of the open course tools for data science. Of course each tool is briefly touched and it hands over the student the duty to deepen each tool.
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