Course

Course Summary
Credit Type:
Course
ACE ID:
IBM-0023
Organization:
IBM
Location:
Online
Length:
24 weeks (96 hours total)
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Lower-Division Baccalaureate 1 Data Science
Lower-Division Baccalaureate 3 Introduction to Python Programming
Lower-Division Baccalaureate 2 Introduction to SQL Programming
Lower-Division Baccalaureate 2 Introduction to Statistics in Python
Description

Objective:

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

The specialization consists of 5 self-paced online modules that provide learners with the foundational skills required for Data Science, including open source tools and libraries, Python, Statistical Analysis, SQL, and relational databases. Students learn these data science pre-requisites through hands-on practice using real data science tools and real-world data sets.

Upon successfully completing these courses, learners gain the practical knowledge and experience to delve deeper into Data Science and work on more advanced Data Science projects.

Learning Outcomes:

  • Demonstrate a working knowledge of Data Science Tools such as Jupyter Notebooks, R Studio, GitHub, Watson Studio
  • Understand the popular tools and statistical techniques used by data scientists including Descriptive Statistics, Data Visualization, Probability Distribution, Hypothesis Testing and Regression, including how to effectively choose the right chart type for the audience and data type
  • Demonstrate programming skills for working with data including data structures, logic, working with files, invoking APIs, and libraries such as Pandas and Numpy
  • Describe concepts related to accessing Databases using Python
  • Describe SQL and Databases and demonstrate understanding of Relational Database fundamentals including SQL query language, Select statements, sorting & filtering, database functions, accessing multiple tables

General Topics:

  • Data Scientist's Toolkit
  • Open Source Tools
  • IBM Tools for Data Science
  • Final Assignment: Create and Share Your Jupyter Notebook
  • Python Basics
  • Python Data Structures
  • Python Programming Fundamentals
  • Working with Data in Python
  • APIs, and Data Collection
  • Crowdsourcing Short squeeze Dashboard
  • Course Introduction and Python Basics
  • Introduction & Descriptive Statistics
  • Data Visualization
  • Introduction to Probability Distributions
  • Hypothesis testing
  • Regression Analysis
  • Project Case: Boston Housing Data
  • Other Resources
  • Getting Started with SQL
  • Introduction to Relational Databases and Tables
  • Intermediate SQL
  • Accessing Databases using Python
  • Course Assignment
  • Bonus Module: Advanced SQL for Data Engineering (Honors)
Instruction & Assessment

Instructional Strategies:

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

Methods of Assessment:

  • Other
  • Quizzes
  • five peer review graded projects with rubrics

Minimum Passing Score:

70%
Supplemental Materials