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The Data Science Method
About this courseSkip About this course
Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!
Despite and influx in computing power and access to data over the last couple of decades, our ability to use data within the decision-making process is either lost or not maximized all too often. We do not have a strong grasp of the questions asked and how to apply the data correctly to resolve the issues at hand.
The purpose of this course is to share the methods, models and practices that can be applied within data science, to ensure that the data used in problem-solving is relevant and properly manipulated to address business and real-world challenges.
You will learn how to identify a problem, collect and analyze data, build a model, and understand the feedback after model deployment.
Advancing your ability to manage, decipher and analyze new and big data is vital to working in data science. By the end of this course, you will have a better understanding of the various stages and requirements of the data science method and be able to apply it to your own work.
At a glance
- Institution: IBM
- Subject: Data Analysis & Statistics
- Level: Introductory
- Prerequisites: None
- Language: English
- Video Transcript: English
- Associated programs:
- Professional Certificate in Data Science Foundations
- Professional Certificate in IBM Data Science
- Associated skills: Big Data, Data Science, Problem Solving
What you'll learnSkip What you'll learn
- The major steps involved in tackling a data science problem.
- Why data scientists need a methodology and an approach.
- What it means to understand data, and prepare or clean data
- How to practice data science, including forming a concrete business question or research.
- Through completing a peer-reviewed assignment, you will demonstrate your understanding of the data science methodology by applying it to a problem that you define.