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The Data Science Method
About this courseSkip About this course
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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
- Language: English
- Video Transcripts: اَلْعَرَبِيَّةُ, Deutsch, English, Español, Français, हिन्दी, Bahasa Indonesia, Português, Kiswahili, తెలుగు, Türkçe, 中文
- Associated programs:
- Associated skills:Big Data, Problem Solving, Data Science
What you'll learnSkip What you'll learn
- Explain why a methodology for approaching data science problems is needed
- List the major steps involved in tackling a data science problem
- Determine appropriate data sources for your data science analysis methodology
- Describe the six stages in the Cross-Industry Process for Data Mining (CRISP-DM) methodology to analyze a case study
- Demonstrate your understanding of the data science methodology by applying it to a problem that you define