Google Data Analytics Capstone: Complete a Case Study

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• What is data analytics?

  Data is a group of facts that can take many different forms, such as numbers, pictures, words, videos, observations, and more. We use and create data everyday, like when we stream a show or song or post on social media.

  Data analytics is the collection, transformation, and organization of these facts to draw conclusions, make predictions, and drive informed decision-making.

• Why start a career in data analytics?

  The amount of data created each day is tremendous. Any time you use your phone, look up something online, stream music, shop with a credit card, post on social media, or use GPS to map a route, you’re creating data. Companies must continually adjust their products, services, tools, and business strategies to meet consumer demand and react to emerging trends. Because of this, data analyst roles are in demand and competitively paid.

  Data analysts make sense of data and numbers to help organizations make better business decisions. They prepare, process, analyze, and visualize data, discovering patterns and trends and answering key questions along the way. Their work empowers their wider team to make better business decisions.

• Why enroll in the Google Data Analytics Certificate?

  You will learn the skill set required for becoming a junior or associate data analyst in the Google Data Analytics Certificate. Data analysts know how to ask the right question; prepare, process, and analyze data for key insights; effectively share their findings with stakeholders; and provide data-driven recommendations for thoughtful action.

  You’ll learn these job-ready skills in our certificate program through interactive content (discussion prompts, quizzes, and activities) in under six months, with under 10 hours of flexible study a week. Along the way, you'll work through a curriculum designed with input from top employers and industry leaders, like Tableau, Accenture, and Deloitte. You’ll even have the opportunity to complete a case study that you can share with potential employers to showcase your new skill set.

  After you’ve graduated from the program, you’ll have access to career resources and be connected directly with employers hiring for open entry-level roles in data analytics.

• What background is required?

  No prior experience with spreadsheets or data analytics is required. All you need is high-school level math and a curiosity about how things work.

• Do you need to be strong at math to succeed in this certificate?

  You don't need to be a math all-star to succeed in this certificate. You need to be curious and open to learning with numbers (the language of data analysts). Being a strong data analyst is more than just math, it's about asking the right questions, finding the best sources to answer your questions effectively, and illustrating your findings clearly in visualizations.

• What tools and platforms are taught in the curriculum?

  You'll learn to use analysis tools and platforms such as spreadsheets (Google Sheets or Microsoft Excel), SQL, presentation tools (Powerpoint or Google Slides), Tableau, RStudio, and Kaggle.

• Which “spreadsheet” platform is being taught?

  Learners can self-select which platform they want to use throughout the program: Google Sheets or Microsoft Excel. It’s up to the learner’s preference, and all activities throughout the syllabus can be performed on either platform.

• Why would I choose to complete the optional capstone project in this certificate?

  In the data analyst job hunt, it’s important to demonstrate that you’re able to ask the right questions and that you have the right skills to find the answers. Hiring managers often want proof that you can apply concepts in a meaningful way. Because of this, during the job application process, many employers ask for a link to a portfolio. Our optional capstone project will help learners produce meaningful artifacts for employers to reference during the job interview process. Learners will be encouraged to post these on a public Kaggle portfolio or on GitHub.

• Do you need to take each course in course order?

  We highly recommend completing the courses in the order presented because the content in each course builds on information from earlier lessons.

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