If there is a shortcut to becoming a Data Scientist, then learning to think and work like a successful Data Scientist is it. In this course, you will learn and then apply this methodology that you can use to tackle any Data Science scenario. You’ll explore two notable data science methodologies, Foundational Data Science Methodology, and the six-stage CRISP-DM data science methodology, and learn how to apply these data science methodologies. Most established data scientists follow these or similar methodologies for solving data science problems. Begin by learning about forming the business/research problem Learn how data scientists obtain, prepare, and analyze data. Discover how applying data science methodology practices helps ensure that the data used for problem-solving is relevant and properly manipulated to address the question. Next, learn about building the data model, deploying that model, data storytelling, and obtaining feedback You’ll think like a data scientist and develop your data science methodology skills using a real-world inspired scenario through progressive labs hosted within Jupyter Notebooks and using Python.
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What is Data Science?
Introduction to Data Analytics
Excel Basics for Data Analysis
Google Data Analytics Capstone: Complete a Case Study
Databases and SQL for Data Science with Python
Ask Questions to Make Data-Driven Decisions
Data Analysis with R Programming
Process Data from Dirty to Clean
Foundations: Data, Data, Everywhere
Data Visualization and Dashboards with Excel and Cognos
Tools for Data Science
Analyze Data to Answer Questions
Prepare Data for Exploration
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Mga Komento
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Introduction to CRISP-DM
Introduction to CRISP-DM
Introduction to CRISP-DM
Introduction to CRISP-DM
Course Summary
Course Summary
Course Summary
Storytelling
Storytelling
Storytelling
