Tools for Data Science
Master essential data science tools including Python, R, Jupyter Notebooks, and Git. Perfect for beginners starting their data science journey.
Master essential data science tools including Python, R, Jupyter Notebooks, and Git. Perfect for beginners starting their data science journey.
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.
4.5
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Instructors:
English
پښتو, বাংলা, اردو, 2 more
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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
This course includes:
1.85 Hours PreRecorded video
13 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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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.
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
Instructors
Data Scientist Aije Egwaikhide: Empowering Women in STEM and Innovating AI Solutions at IBM
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.
Chief Data Scientist at IBM Specializing in Data Science and Parallel Processing Architectures
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
Testimonials
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4.5 course rating
28,855 ratings
Frequently asked questions
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