Getting started with enterprise data science

Introduction

Whether it is fighting fraud, detecting cancer, or predicting a hurricane, you need data and AI. Join a new wave of data-savvy professionals with access to millions of jobs available in the market.

IBM SkillsBuild for Academia
Self-paced course

Man studying technical charts in front of a digital screen

Get acquainted with the foundations of Data science roles and the use of technology applied to enterprise projects.

Looking for a job?

Gain a new set of data analytics skills, complement them with low-code AI-powered technologies, and your industry knowledge, to get on your way to join a data science team, as part of a new breed of data-savvy professionals with access to millions of jobs available in the market.

Looking for a better job?

If you already have a job and some experience with data analytics, use this course to select a specialization and advance your career.

Objectives

Play different roles within a data science team, solve real challenges within the enterprise and leverage AI-powered technologies.

Scope

  • Data science team roles
  • Data analysis tools
  • Real-world use cases

Learning outcomes:

  • Understand the relevance of Data science projects in supporting the digital transformation of business across multiple industries
  • Acquire Data science cross-disciplinary skillset found at the intersection of statistics, computer programming, and domain expertise
  • Get acquainted with the following roles of a Data Science team: Data scientist, Data Engineer, Data analyst, and AI developer
  • Access Data science collaboration platforms in the cloud, including IBM Watson Studio and Data Refinery
  • Experience with data ingestion and manipulation using a CSV dataset.

Course experience

About this course

This course is divided into two practice levels. Each level covers more advanced topics and builds up on top of the concepts, practice, and skills addressed in the previous practice levels.

Level 1 — Data science teams

Defining the data science domains and their alignment with project team roles and technologies.

  1. 1. Data science landscape
  2. 2. Data science on the cloud

Level 2 — Data science tools

Exploring the benefits of using cloud technologies to empower Data science project teams.

  1. 1. Watson Studio data refinery visualizations (interactive case study)

Prerequisites

Skills you will need to have before joining this course offering.

  • Basic IT Literacy skills*

*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.

Digital credential

Foundational

Bagde - Getting Started width Enterprise Data Science

Getting started with enterprise Data Science

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About this badge

This badge earner has completed all the learning activities included in this online learning experience, including hands-on experience, concepts, methods, and tools related to Data Science roles and their use of technology applied to enterprise projects. The individual has demonstrated knowledge and understanding of the foundations of Data Science including Data science team roles, Data analysis tools, and real-world use cases for the application of the Data science method.

Skills

Data analyst, Data Engineer, Data exploration, Data refinery, Data Science, Data scientist, Data visualization, Fraud analytics, Watson Studio.

Criteria

  • Complete the self-paced online course Getting Started with Enterprise Data Science, which is made available in the IBM Academic Initiative portal.
  • Pass the final course assessment.