Data Science Tools
Learn about the most popular data science tools, including how to use them and what their features are in this introductory course.
Learn about the most popular data science tools, including how to use them and what their features are in this introductory course.
This introductory course from IBM provides a hands-on overview of essential data science tools used by professionals in the field. You'll explore popular platforms like Jupyter Notebooks, RStudio IDE, and IBM Watson Studio, learning their functions, capabilities, limitations, and practical applications. The curriculum covers various programming languages used in data science including Python, R, Julia, and SQL, as well as important components of a data scientist's toolkit such as libraries, packages, datasets, and machine learning models. Through cloud-hosted environments, you'll get practical experience testing these tools and running simple code in Python or R. The course culminates in a final project where you'll create and share a Jupyter Notebook on IBM Watson Studio, demonstrating your proficiency in notebook preparation and Markdown. Successful completion earns you a verifiable skill badge that showcases your newly acquired knowledge to potential employers.
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What you'll learn
List and describe various tools used by data scientists and machine learning engineers
Explain different programming languages used in data science including Python, R, Julia and SQL
Navigate and utilize Jupyter Notebooks for data science projects
Work with RStudio IDE for R programming and analysis
Use GitHub for version control and collaboration in data science projects
Create, edit and share Jupyter Notebooks with proper documentation
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments and exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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Fee Structure
Payment options
Financial Aid
Instructors
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
Developer Advocate at IBM
Maureen McElaney is a Developer Advocate at IBM Center of Open Source Data and Ai Technologies. She is on the LF AI Trusted AI Committee underneath the Linux Foundation.
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