What are some of the most popular data science tools, how do you use them, and what are their features? In this course, you’ll learn about Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will learn about what each tool is used for, what programming languages they can execute, their features and limitations. With the tools hosted in the cloud on Skills Network Labs, you will be able to test each tool and follow instructions to run simple code in Python, R or Scala. To end the course, you will create a final project with a Jupyter Notebook on IBM Watson Studio and demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.
Aije Egwaikhide is a Data Scientist at IBM who holds a degree in Economics and Statistics from the University of Manitoba and a Post-grad in Business Analytics from St. Lawrence College, Kingston. She is currently pursuing her Masters’s in Management Analytics at Queens University. She is a current employee of IBM where she started as a Junior Data Scientist at the Global Business Services (GBS) in 2018. Her main role was making meaning out of data for their Oil and Gas clients through basic statistics and advanced Machine Learning algorithms. The highlight of her time in GBS was creating a customized end-to-end Machine learning and Statistics solution on optimizing operations in the Oil and Gas wells. She is part of the IBM Developer Skills Network group where she brings her real-world experience to the courses she creates. Aije uses her voice through social media to empower young women and directly/indirectly mentoring them on career paths in the STEM field.
Romeo Kienzler holds a M. Sc. (ETH) in Information Systems, Bioinformatics & Applied Statistics (Swiss Federal Institute of Technology). He has nearly two decades of experience in Software Enineering, Database Administration and Information Integration. Since 2012 he works as a Data Scientist for IBM. He published several works in the field with international publishers and on conferences. His current research focus is on massive parallel data processing architectures. Romeo also contributes to various open source projects.
Senior Developer Advocate with IBM Center for Open Data and AI Technologies, Svetlana has been a software engineer and technical lead for SPSS for many years. She works on open standards for machine learning model deployment PMML and ONNX. She holds PhD in Applied Math and MS in CS from University of Maryland, College Park. Svetlana loves to learn new technologies, share her expertise, and to encourage women in STEM.
Week 1 – Data Scientist’s Toolkit
2 hours to complete
This week, you will get an overview of the programming languages commonly used, including Python, R, Scala, and SQL. You’ll be introduced to the open source and commercial data science tools available. You’ll also learn about the packages, APIs, data sets and models frequently used by data scientists.
17 videos (Total 84 min)
Week 2 – Open Source Tools
10 hours to complete
This week, you will learn about three popular tools used in data science: GitHub, Jupyter Notebooks, and RStudio IDE. You will become familiar with the features of each tool, and what makes these tools so popular among data scientists today.
12 videos (Total 54 min)
Week 3 – IBM Tools for Data Science
3 hours to complete
This week, you will learn about an enterprise-ready data science platform by IBM, called Watson Studio. You’ll learn about some of the features and capabilities of what data scientists use in the industry. You’ll also learn about other IBM tools used to support data science projects, such as IBM Watson Knowledge Catalog, Data Refinery, and the SPSS Modeler.
15 videos (Total 72 min)
Week 4 – Final Assignment: Create and Share Your Jupyter Notebook
1 hour to complete
This week, you will demonstrate your skills by creating and configuring a Jupyter Notebook. As part of your grade for this course, you will share your Jupyter Notebook with your peers for review.
Cost: FREE to Register
Duration: 17
