Your Learning Strategy

Completing the Google Data Analytics Certification requires you to spend time working through Coursera content outside of our weekly training sessions.

We want you to develop a learning strategy for completing the data analytics program. Ultimately it is your decision how you will manage your time, but we want you to start with a plan that makes sense for you. You can always adapt your plan later as you experience the content.

How will you schedule your time to complete the coursework on Coursera?

The coursework is a combination of short videos, reading, hands-on tasks, and quizzes. So it makes it pretty easy to break up your time in a way that works with your schedule or learning style. Describe where you plan to do this work as well -- whether at home, in a library, or somewhere else. It is recommended that you avoid really long periods of staring at your screen -- consider getting outside a little bit and moving around at intervals.

How will you "take notes"?

How you decide to note important concepts is up to you, but it is important that you have a system that works for you. You may choose to do traditional hand-written notes, draw concept maps, or use the note feature in Coursera to highlight important concepts. Having some type note-taking strategy will help you understand and retain the concepts presented before you complete quizzes.

Who will be your personal advocate(s)?

Of course the Industry Connect team is here to help and encourage you the whole way, but it will really help to have a family member, friend, or mentor encouraging you along the way providing some accountability. This person does not need to understand data analytics, just somebody to cheer you on and keep you moving forward.

What aspect of data analytics interests you most?

Begin to develop your unique perspective on data analytics. Your views will change as time goes on, but try to make the experience personal. Are you interested in how data is used in a particular domain -- like fitness, health, business, marketing, science, environment or gaming? Data analysts participate in a range of tasks that include working with people and with computers. Data analysts practice soft skills like interviewing, collaborating, and presenting; and of course they apply "hard" skills in programming and math as well. So all these skills are necessary, but some may be your primary interests that draw you in. Ultimately you want to slowly develop your "voice" and how you will contribute to a team's effort.