IBM: Data Science Tools: Practical Usage
This course is part of IBM: Fundamentos de ciencia de datos.
In this practical course, you'll explore essential data science tools including Jupyter Notebooks, RStudio IDE, and IBM Watson Studio. You'll learn each tool's specific purpose, compatible programming languages, key features, limitations, and how data scientists utilize them in professional settings. Through cloud-hosted environments, you'll gain hands-on experience running simple Python and R code. The course culminates in a final project where you'll create a Jupyter Notebook in IBM Watson Studio, demonstrating your ability to prepare notebooks, write Markdown, and share your work with peers. This course provides valuable practical knowledge of cutting-edge data science tools that you can immediately apply in real-world scenarios. Participants who successfully complete this IBM course can earn a verified digital skills badge, providing detailed credential verification of the knowledge and skills acquired.
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What you'll learn
Learn how to use various data science and data visualization tools hosted in Skills Network Labs
Understand Jupyter Notebook features and why it's popular among data scientists
Explore popular tools used by R programmers including RStudio IDE
Discover IBM Watson Studio's features and capabilities
Learn to create and share Jupyter Notebooks effectively
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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Module Description
This course provides comprehensive training in essential data science tools. You'll learn how to use Jupyter Notebooks, understanding its features and popularity among data scientists. The course covers RStudio IDE and tools frequently used by R programmers. You'll explore IBM Watson Studio's capabilities and features for data analysis and visualization. Through hands-on practice in cloud-hosted environments, you'll gain experience running code in both Python and R. The course emphasizes practical skills like creating and sharing Jupyter Notebooks, writing Markdown, and collaborating with peers. By the end of the course, you'll have completed a final project demonstrating your proficiency with these tools, preparing you for real-world data science challenges.
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
Championing Open Standards in AI and Machine Learning
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.
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