Course

Course Summary
Credit Type:
Course
ACE ID:
IBM-0017
Organization:
IBM
Location:
Online
Length:
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
Description

Objective:

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:

70%
Supplemental Materials