Microsoft Future Ready: Data Science Research Methods Using Python Programming

This course is part of the Ethics Laws and Implementing an AI Solution on Microsoft Azure ExpertTrack, helping you understand and apply ethical and legal frameworks in data research, analytics and AI.

During this course, you’ll learn the fundamentals of data research including developing good research questions, designing data collection strategies, analysing data and putting results in context. You’ll also understand how data scientists play a key role in the research phase, not just the analysis, and how this will help extract valid insights from your data.

Understand data collection methods and the research process

To help you build the confidence to accurately analyse your data, this course will take you through the full research process. You’ll grow your knowledge of the planning needed for high quality data analysis and learn how to properly query your data for a robust analysis. This includes understanding how to test hypotheses, how to partition data and use inference in order to extra valid results.

Learn how to use and apply Python programming knowledge

During the course, you’ll use Python programming, an essential tool for data science and machine learning. All labs are done with Python, while the videos are language-agnostic, giving you plenty of opportunity to hone your skills with this powerful tool.

Advance your skills in data science

By the end of the course, you’ll understand the research process and develop practical skills across research methods and Python programming, a flexible language used in everything from data science to cutting-edge and scalable AI solutions.

This self-paced course is designed for learners with an interest in using data for research, and methods of wrangling, compiling and presenting data for research.

Learners will benefit from:

• A basic knowledge of math.
• Some programming experience – Python is preferred.
• A willingness to learn through exploration and perseverance.

• Course Introduction
• About this Course
• The Research Process
• The Psychology of Providing Data
• CloudSwyft Hands-On Lab 1
• Planning for Analysis
• Wrapping up the Week
• Research Claims, Measurement and Correlation and Experimental Design
• Power and Sample Size Planning
• Research Practices
• CloudSwyft Hands-On Lab 2
• Frequency Claims
• Association Claims
• Causal Claims
• CloudSwyft Hands-On Lab 3
• Wrapping Up the Week
• Measurement, Correlational and Experimental Design
• Survey Design and Measurement
• Reliability and Validity
• CloudSwyft Hands-On Lab 4
• Bivariate and Multivariate Designs
• Between and Within Groups Experimental Designs
• Factorial Designs
• CloudSwyft Hands-On Lab 5
• Wrapping Up the Course