IBM Global University Programs

Data Science • Business Understanding • Collection • Wrangling • Modeling • Visualization • ML Optimization

Course Overview

Content

01. Overview 02. Objectives 03. Skills 04. Audience 05. Sample Lesson Plan

06. Instructor

9

01. Overview

Engage with your team on real-world challenges, where the scientific method meets real business, and data fueled systems can use machine learning models to find insights into the future.

What is Data Science?

In the domain of data science, solving problems and answering questions through data analysis is standard practice. Often, data scientists construct a model to predict outcomes or discover underlying patterns, with the goal of gaining insights.

Organizations can then use these insights to take actions that ideally improve future outcomes.

There are numerous rapidly evolving technologies for analyzing data and building models. In a remarkably short time, they have progressed from desktops to massively parallel warehouses with huge data volumes, and from in-database analytic functionality in relational databases to unstructured big data tools.

Analytics on unstructured or semi-structured data is becoming increasingly important as a way to incorporate sentiment and other useful information written in natural language into predictive models, often leading to significant improvements in model quality and accuracy.

Emerging analytics approaches seek to automate many of the steps in model building and application, making machine- learning (ML) technology a necessary evolution towards modern data science.

Successful ML projects require a combination of algorithms + data + team, and a very powerful compute infrastructure*.

Learn more at http://ibm.biz/watsonstudio2018

* Watson Studio accelerates the machine and deep learning workflows required to infuse AI into business to drive innovation. It provides a suite of tools for data scientists, application developers and subject matter experts, allowing them to collaboratively connect to data, wrangle that data and use it to build, train and deploy models at scale.

02. Objectives Data Science Course objectives:

• Understand the Data Science ecosystem today • Explore industry real world Data Science use cases • Explore the scientific method for data-driven projects • Acquire experience using popular data analytics tools such as

Jupyter Notebook (Python) and R Studio • Build and train Machine Learning and Deep Learning models

using predictive analytics and neural networks • Explore a wide range of data visualization techniques and tools

for data journalism such as PixieDust • Deploy models using streams, and optimize them through

models monitor, analysis and management • Understand basic concepts of Design Thinking • Propose solutions to real world scenarios leveraging data

science methodologies and technologies

03. Skills

This course covers the following skills, included in its digital badge offering.

METHODOLOGY LABORATORIES USE CASES

01. Data Science Evolution 01. Cloud Computing 01. Marketing Optimization

02. Data Science Ecosystem 02. Cloud Services 02. Facebook Analytics

03. Business Understanding 03. Watson Studio 03. Traffic Analytics

04. Data Collection 04. Jupyter Notebooks 04. DataSF Open Data

05. Data Wrangling 05. Python 05. Opioid Prescriptions

06. Data Engineering 06. R Studio 06. Kaggle datasets

07. Data Modeling 07. SPSS Modeler 07. Food Insecurity

08. Machine Learning 08. TensorFlow 08. Diet-related diseases

09. Deep Learning 09. Neural Network Modeler 09. Data Accuracy

10. Data Visualization 10. Apache SystemML 10. Fraudulent Reviews

11. Business Optimization 11. Keras 11. Storytelling

12. Data Monitoring 12. PixieDust 12. Data Journalism

13. Domain Adaptation 13. Watson Analytics 13. Design Thinking

04. Audience

Course target audience and pre-requisite skills requirements.

Who should take this course?

This course is intended for adults of all ages, with an active interest in acquiring entry-level skills needed to apply for entry level jobs in the Data Science domain

This course has no pre-requisites beyond basic IT literacy*

*Basic IT Literacy – Refers to skills required to operate at the user level a graphical operating system environment such as Microsoft Windows® or Linux Ubuntu®, performing basic operating commands such as launching an application, copying and pasting information, using menus, windows and peripheral devices such as mouse and keyboard. Additionally, users should be familiar with internet browsers, search engines, page navigation, and forms.

05. Lesson Plan* SAMPLE ®

*Lesson Plan – This lesson plan is a sample document that depicts the potential implementation of the course inside a university elective course over a 14 weeks period. This information is provided as a reference only; it does not constitute in any way an obligation or mandatory implementation requirement for our academic partners. The course material included in this offering can be offered through any academic offerings at the sole discretion of the academic institutions joining the program.

+25 % METHODOLOGY

+35 % LABORATORIES

+40 % CHALLENGES USE CASES

Curriculum Design Framework

06. Instructor

The instructor delivering this course should meet the following requirements

Who should deliver this course?

The facilitator delivering this course has taken the course previously and successfully passed the exam.

Recommended skills include:

• Avid speaker with good presentation skills • Pedagogical group management skills • Encourage critical thinking and domain exploration • Familiarity with scripting languages • Usage of entry-level data handling tools (spreadsheets) • Experience handling data sets and IP copyrights