Course Summary
Credit Type:
8-12 months (162 hours)
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Lower-Division Baccalaureate 3 introduction to data science
Lower-Division Baccalaureate 3 introduction to SQL programming
Lower-Division Baccalaureate 3 introduction to Python programming
Upper-Division Baccalaureate 3 advanced topics in data science


This course is offered through Coursera, which is an ACE Authorized Instructional Platform.

The course objective is to provide students with no prior experience or knowledge of computer science or programming languages with the latest job-ready tools and skills to pursue a job as an entry level data scientist. Students learn data science through hands-on practice in the IBM Cloud using real data science tools and real-world data sets while building a portfolio of data science projects.

Learning Outcomes:

  • develop hands-on skills using the tools, languages, and libraries used by professional data scientists
  • import and clean data sets, analyze and visualize data, and build and evaluate machine learning models and pipelines using Python
  • apply various data science skills, techniques, and tools to complete a project and publish a report.
  • learn what data science is, the various activities of a data scientist's job, and methodology to think and work like a data scientist

General Topics:

  • Defining data science and what data scientists do
  • Big data and data mining
  • Deep learning and machine learning
  • Data science in business
  • Data scientist's toolkit
  • Open source tools
  • IBM tools for data science
  • Creating and sharing a Jupyter notebook
  • From problem to approach and from requirements to collection
  • From understanding to preparation and from modeling to evaluation
  • From deployment to feedback
  • Python basics
  • Python data structures
  • Python programming fundamentals
  • Working with data in Python
  • Crowdsourcing short squeeze dashboard
  • Getting started with SQL
  • Introduction to relational databases and tables
  • Intermediate SQL
  • Accessing databases using Python
  • Course assignment
  • Bonus module: advanced SQL for data engineers
  • Importing datasets
  • Data wrangling
  • Exploratory data analysis
  • Model development
  • Model evaluation
  • Introduction to data visualization tools
  • Basic and specialized visualization tools
  • Advanced visualizations and geospatial tools
  • Creating dashboards with Plotly and Dash
  • Introduction to machine learning
  • Regression
  • Classification
  • Clustering
  • Recommender systems
  • Data wrangling and analysis on SpaceX dataset
  • Creating interactive dashboards
  • Performing predictive analysis
  • Presenting data science findings in a report
Instruction & Assessment

Instructional Strategies:

  • Audio Visual Materials
  • Case Studies
  • Computer Based Training
  • Discussion
  • Laboratory
  • Practical Exercises

Methods of Assessment:

  • Quizzes
  • peer-reviewed projects

Minimum Passing Score:

Supplemental Materials