Course

Course Summary
Credit Type:
Course
ACE ID:
IBM-0017
Organization:
Location:
Online
Length:
12 weeks (170 hours)
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Lower-Division Baccalaureate 3 Introduction to Data Science
Lower-Division Baccalaureate 3 Introduction to Machine Learning
Lower-Division Baccalaureate 3 Introduction to Data Visualization
Upper-Division Baccalaureate 3 Advanced Topics in Data Science
Description

Objective:

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:

  • Master the most up-to-date practical skills and knowledge that data scientists use in their daily roles
  • Import and clean data sets, analyze and visualize data, and build machine learning models and pipelines
  • Learn the tools, languages, and libraries used by professional data scientists, including Python and SQL
  • Apply your new skills to real-world projects and build a portfolio of data projects that showcase your proficiency to employers

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
  • Computer Based Training
  • Discussion
  • Laboratory
  • Practical Exercises

Methods of Assessment:

  • Other
  • Quizzes
  • Peer-reviewed Assignments

Minimum Passing Score:

70%
Supplemental Materials