Although data scientists often focus on data analysis, they must understand the research process to gain valid insights from this work. This course covers the fundamentals of data research - from developing a good question to designing good data collection strategies that will put your results in context.

Upon completion of the course, you will have a basic understanding of data analysis and inference, and data science research design. You’ll also explore experimental data analysis and modelling.

Understand the data research process

Data is only as good as its source - without this key information, it can be difficult to query the validity or context of your data, or make robust statements as a result of your analysis. This course will help you to understand the research process and grow your knowledge of the planning required for high quality analysis.

Build the confidence to query your data

You’ll build your understanding of the process so that you can properly query your own and external data for more robust analysis. That includes understanding how to partition data, test hypothesis, and use inference.

Apply Python programming knowledge in your labs

In this course, all of the labs are done with Python, while the videos are language-agnostic. By the end, you’ll understand the basics of the research process, you’ll be able to plan for analysis, and you’ll know the basics of correlational and experimental design.

Start a career in data science

Developing practical skills across research methods and Python programming will mean that you have some of the most in-demand skills within data science. Jobs in the industry have grown 37% in the last three years, while salaries have grown 11-14% year on year, making it an excellent area to start or change careers today.

This self-paced course is designed for learners with:

*a basic knowledge of maths *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