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Tools for Data Science
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Tools for Data Science

Master essential data science tools including Python, R, Jupyter Notebooks, and Git. Perfect for beginners starting their data science journey.

Course Cost

Free course

Beginner

Skill Level

17 Hours

Self-paced lessons

This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full IBM Data Science Professional Certificate program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.

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4.5

4,90,390 Enrolled

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Aije Egwaikhide

Aije Egwaikhide

Romeo Kienzler

Romeo Kienzler

English

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4.5

4,90,390 Enrolled

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English

Aije Egwaikhide

Aije Egwaikhide

Romeo Kienzler

Romeo Kienzler

What you'll learn

  • Work with Jupyter Notebooks and JupyterLab environments

  • Use RStudio for statistical programming and visualization

  • Manage code with Git and GitHub repositories

  • Understand different data science programming languages

  • Implement data science libraries and packages effectively

Skills you'll gain

Data Science Tools
Python
R
Git
GitHub
Jupyter Notebooks
RStudio
Watson Studio
SQL
Data Analysis
Machine Learning

This course includes:

1.85 Hours PreRecorded video

13 assignments

Access on Mobile, Tablet, Desktop

FullTime access

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Share your certificate with prospective employers and your professional network on LinkedIn.

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Top companies offer this course to their employees

Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.

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There are 7 modules in this course

This comprehensive course introduces learners to the essential tools used in data science. Students learn about various programming languages including Python, R, and SQL, along with popular platforms like Jupyter Notebooks, RStudio, and GitHub. The curriculum covers libraries, packages, data sets, machine learning models, and both open-source and cloud-based tools. Through hands-on labs and practical exercises, learners gain experience with version control, data visualization, and collaborative coding practices.

Overview of Data Science Tools

Module 1 · 2 Hours to complete

Languages of Data Science

Module 2 · 1 Hours to complete

Packages, APIs, Data Sets, and Models

Module 3 · 1 Hours to complete

Jupyter Notebooks and JupyterLab

Module 4 · 2 Hours to complete

RStudio & GitHub

Module 5 · 6 Hours to complete

Create and Share your Jupyter Notebook

Module 6 · 3 Hours to complete

[Optional] IBM Watson Studio

Module 7 · 2 Hours to complete

Fee Structure

Reviews

Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.

Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

Aije Egwaikhide
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4.3

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87

Reviews

6,31,843

Students

6

Courses

Aije Egwaikhide is a fantastic example of how dedication and passion can lead to a successful career in tech! With her background in Economics and Statistics, paired with advanced qualifications in Business and Management Analytics, she’s truly paving the way in the field of data science. Her work at IBM, particularly in creating innovative machine learning solutions for the Oil and Gas sector, is an inspiring achievement.
Romeo Kienzler
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3.7

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188

Reviews

7,03,752

Students

10

Courses

Romeo Kienzler is the Chief Data Scientist and Course Lead at IBM, where he leverages nearly two decades of experience in software engineering, database administration, and information integration. He holds a Master of Science from the Swiss Federal Institute of Technology (ETH) in Information Systems, Bioinformatics, and Applied Statistics. Since joining IBM in 2012, Romeo has focused his research on massive parallel data processing architectures and has published numerous works in the field through international publishers and conferences. In addition to his professional contributions, he is actively involved in various open-source projects. On Coursera, he teaches several courses, including Deep Learning with Keras and TensorFlow, Introduction to Big Data with Spark and Hadoop, Scalable Machine Learning on Big Data using Apache Spark, and Tools for Data Science, all designed to equip learners with essential skills in data science and machine learning
Svetlana Levitan
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4.5

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4,881

Reviews

5,06,734

Students

1

Course

Svetlana Levitan is a Senior Developer Advocate at IBM's Center for Open Data and AI Technologies, where she plays a crucial role in advancing open standards for machine learning model deployment, specifically PMML and ONNX. With extensive experience as a software engineer and technical lead for SPSS, she has been instrumental in implementing statistical and machine learning components for nearly two decades. Svetlana holds a PhD in Applied Mathematics and an MS in Computer Science from the University of Maryland, College Park. Passionate about sharing her knowledge, she actively engages in promoting STEM education, particularly encouraging women to pursue careers in technology. Her commitment to lifelong learning and community involvement is reflected in her efforts to mentor others and participate in various educational initiatives. Through her work, Svetlana continues to influence the landscape of data science and AI, making significant contributions to the development of accessible and effective machine learning solutions.
Tools for Data Science

Beginner

Skill Level

17 Hours

Self-paced lessons

Course Cost

Free course

Completion

CERTIFICATE

Frequently asked Questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.