Google Data Analytics Professional Certificate — Why you should take it?
Back in March 2021, Google launched multiple courses associating with coursera aiming to provide alternative to a degree. Out of those courses, the one that really interested me was the “Google Data Analytics Professional Certificate”.
The course is designed for entry level data analyst position and even though I have experience in Analytics, I thought of taking this course just out of curiosity. The course did not disappoint me.! It is designed to be completed in 5 months, however, if you can spend at least 3-5 hrs a day, it is possible to complete the course under 2 months. For experienced professionals, this course would be a nice refresher.
In case if you can’t read the whole story, here are 5 important points about the course.
- It covers all basics of Spreadsheets, SQL, Tableau, R.
- The video lectures are well produced and focuses more on the process of analytics unlike other courses out there just focusing on techniques.
- You get 90 days access to Google’s Big Query and each module will have individual certificate.
- The capstone project is guided and it will be very useful for junior analyst portfolio.
- Design thinking is included in the course which is very important for any analytics professional.
Structure of the Course:
This course is divided into 8 modules which will build skills one after the other.
- Foundations: Data, Data, Everywhere : This is an introduction part to the course which covers most basics about data, datatypes, sample problems. Emphasis about setting ethical guidelines for data analysts.
- Ask Questions to Make Data-Driven Decisions: This module is all about the how to ask right and effective questions to the stakeholders about the data project that you may undertake.
- Prepare Data for Exploration: This module is about how to extract data from various sources, use filters, and how to avoid bias from your data extracts. The common biases that the module includes., Observational Bias: Tendency for different people to observe things differently Ex.Manual Blood Pressure reading. Interpretation Bias: Person interpreting positive or negative way because of his own thoughts Confirmation Bias: Expecting data to follow our own thought/decisions.
- Process Data from Dirty to Clean: Data Cleaning would 60–70% of day-to-day work of every data analytics professional. This module focuses on cleaning the data by working with basic statistical functions, conditional formatting, SQL queries etc.
- Analyze Data to Answer Questions: This module is covers various techniques and methods that are helpful in analysis data to arrive at meaningful insights. Some of methods and techniques shown are Data Transformation, Pivot Table, Merging Data — Vlookup, Table Joins, Formulas.
- Share Data Through the Art of Visualization: Data Visualization by itself a vast subject and developing visualization skillset is important for every data analyst. This module focuses on Visualization using Tableau. Kudos to google to using Tableau as it is an industry standard these days. Google could have used Google data studio or Looker. However, including tableau as part of this course is telling that Google is giving importance to industry standards instead of just promoting their products and ecosystem.
- Data Analysis with R Programming: Excel can be sometimes limited in terms of functionalities and once you have developed skills with Spreadsheets and SQL, it is natural to scale up by learning R or Python. Analytics professional these days require to know data cleaning using R or Python. The R programming module focuses on tidyverse package and most common data cleaning and visualization tasks are taught. This module also focuses on preparing Analysis Reports using the R-Markdown.
- Google Data Analytics Capstone: Complete a Case Study: Whenever, you apply for an interview, the most common question you’ll be asked is Can you share your portfolio and walk us through the process. It would be very helpful for anyone to complete a project and share as your portfolio. In this module, you can choose one of the two case studies given or you can choose your own case study and you are allowed to complete your project using the tool you are comfortable on. My suggestion is try completing the case study using Spreasheets, R and Tableau. Try to replicate same solution in all these three tools. This can help you gain confidence in approaching to a problem not worrying about a tool.
What tools are covered?
- Spreadsheets
- SQL
- Tableau
- R
In case if you have quite good experience with all these tools, here are some of my suggestions that you can learn to improve your skills and get certifications.
- Spreadsheets: Exam MO-201: Microsoft Excel Expert. Link
- Tableau: Tableau Desktop Specialist Certification. Link
- R: Data Analyst with R certification from Datacamp. Link
- Statistics: Become a Statistics and Probability Master. Link
Remember., Learning + Practice = Mastery!
