Course

Course Summary
Credit Type:
Course
ACE ID:
IBM-0024
Organization:
IBM
Location:
Online
Length:
24 weeks (72 hours)
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Lower-Division Baccalaureate 3 data analytics
Lower-Division Baccalaureate 3 data visualization
Lower-Division Baccalaureate 3 data mining
Lower-Division Baccalaureate 3 python for data science
The course is being recommended for a total of 12 semester hours in the lower-division
Description

Objective:

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

The course objective is to give learners the tools needed to analyze data and make data driven business decisions leveraging computer science and statistical analysis. Students learn Python- no prior programming knowledge necessary- and discover methods of data analysis and data visualization. Students utilize tools used by real data scientists like Numpy and Pandas, practice predictive modeling and model selection, and learn how to tell a compelling story with data to drive decision making.

Through guided lectures, labs, and projects in the IBM Cloud, learners gain hands-on experience tackling interesting data problems from start to finish.

In addition to earning a Specialization completion certificate from Coursera, learners also receive a digital badge from IBM, recognition as a specialist in applied data science.

This Specialization can also be applied toward the IBM Data Science Professional Certificate, already reviewed and awarded credit recommendation by ACE.

Learning Outcomes:

  • develop an understanding of python fundamentals
  • communicate data insights effectively through data visualizations
  • gain practical python skills and apply them to data analysis
  • create a project demonstrating your understanding of applied data science techniques and tools

General Topics:

  • Python for data science, AI & development
  • Python project for data science
  • Data analysis with python
  • Convolutional neural networks
  • Data visualization with python
  • Applied data science capstone
  • Sequence models
Instruction & Assessment

Instructional Strategies:

  • Audio Visual Materials
  • Case Studies
  • Lectures
  • Practical Exercises

Methods of Assessment:

  • Other
  • Quizzes
  • Peer review graded projects with rubrics

Minimum Passing Score:

70%
Supplemental Materials