Welcome to datascience.university
Thank you for taking the time to visit datascience.university. If you are here it means that you are interested in data analysis, or data science more generally. You may be a beginner – a newbie at data analysis – or an experienced data scientist. We hope we will have something here of value for all visitors – eventually , if not right now. We hope you will be patient as we build this into a resource that will help you become an awesome data analyst.
What is datascience.university?
At datascience.university we want to give you the benefits of a site that deals with many aspects of data science, from beginning topics to advanced practices. We are growing datascience.university so it has something for everyone. At datascience.university we aim to:
- Focus on fundamentals. Whether you are just beginning to take your first steps in data analysis, or you are a seasoned professional, focusing on the basics is … basic! Continually re-visiting, practicing, and re-examining the basics is the way to accelerate your learning.
- Develop skills. Skills development is what counts in productive employment. Some skills are doing skills and some are thinking skills – both are important, and having a realistic appreciation of your skill level is critical to success as a data analyst.
- Learn to work productively in teams. No-one knows everything. Learning to work with other data analysts on specific projects will assist you to be successful and valuable to potential employers.
- Build an evidence base for data analysis. A key issue when we see the results of any business or scientific report is whether we can trust the data analysis. As more people are trained and educated in data analysis we need evidence for what data analysis tools and procedures lead to data analysis that is replicable and reproducible.
How can datascience.university benefit you?
We do this by giving micro lessons, information, news, tips, and skill sets in demand by employers, in the following, and other, areas:
|• Programming languages||• Visualization|
|• Data storage and retrieval||• Visual & oral presentations|
|• Databases||• Skills audit|
|• Data cleaning||• Asking questions|
|• Exploratory data analysis||• Teamwork|
|• Regression||• How to listen|
|• Machine learning||• How to interpret results|
|• Building & testing models||• Communicating results|
|• Understand application field||• Conduct meaningful surveys|