A proper data science and analytics project can involve many people, different resources, and a significant amount of time. Having a "big picture" perspective is necessary to keep the project on track. This course will discuss each step of a typical project, from defining the problem and gathering the data and resources, to putting the solution into practice. Various industry standard methodologies for analytics, data mining, and data science projects will be covered, to provide the participants with a framework to keep projects focused on strategy and business value.
What You Will Learn
Upon completion of this course, the learners are expected to:
- manage data science and analytics projects more effectively by applying practical, field-tested skills for project management and using valuable project management tools;
- plan end to end data science projects including activities involved, dependencies, external/internal resource needs and skills requirements;
- manage data science team and ensure alignment to larger project/program objectives;
- manage stakeholder expectations on the delivery of data science projects; and
- apply best practices of project control such as creating solid project baselines, timely monitoring and reporting of the project’s progress, and correctly interpreting project analytics to make needed adjustments to the project schedule.
You will need a computer or laptop with Microsoft Excel installed. Computer or laptop requirements are:
- For Windows: Core i3 or better, 4GB RAM or better, MS Excel 2007 or better
- For MacBook: ideally MS Excel 2013 or newer should be installed (some functions require this version on the Mac). If the version of MS Excel is 2011, download and install StatPlus.