1.2.2.Thinking about Analytical Thinking - sj50179/Google-Data-Analytics-Professional-Certificate GitHub Wiki

All about thinking analytically

Analytical thinking involves identifying and defining a problem and then solving it by using data in an organized, step-by-step manner.

5 key aspects to analytical thinking

  1. Visualization : Visualization is the graphical representation of information. Visuals can help data analysts understand and explain information more effectively.
  2. Strategy : Having a strategic mindset is key to staying focused and on track. Strategizing helps data analysts see what they want to achieve with the data and how they can get there. Strategy also helps improve the quality and usefulness of the data we collect. By strategizing, we know all our data is valuable and can help us accomplish our goals.
  3. Problem-orientation : Data analysts use a problem-oriented approach in order to identify, describe, and solve problems. It's all about keeping the problem top of mind throughout the entire project.
  4. Correlation : Being able to identify a correlation between two or more pieces of data. A correlation is like a relationship. Correlation does not equal causation. In other words, just because two pieces of data are both trending in the same direction, that doesn't necessarily mean they are all related.
  5. Big-picture and Detail-oriented thinking : This means being able to see the big picture as well as the details. It helps you zoom out and see possibilities and opportunities. This leads to exciting new ideas or innovations. On the flip side, detail-oriented thinking is all about figuring out all of the aspects that will help you execute a plan.

Complete the table

Recall the analytical skills discussed in the last lesson:

  • Curiosity - a desire to know more about something, asking the right questions
  • Understanding context - understanding where information fits into the big picture
  • Having a technical mindset - breaking big things into smaller steps
  • Data design - thinking about how to organize data and information
  • Data strategy - thinking about the people, processes, and tools used in data analysis

In the Analytical Skills Table, each row contains one of the analytical skills above. Put an X in the column that you think best describes your current level with each area. The three ratings are:

  • Strength - an area you feel is one of your strengths
  • Developing - you have some experience with this area, but there’s still significant room for growth
  • Emerging - this is new to you, and will be gaining experience in this area from this course

Update the Comments/Plans/Goals column with a quick note on why you chose the rating for each area.

Reflection

Consider the ratings you gave yourself in the Analytical Skills Table. How many times did you rate a skill as a strength? What about developing or emerging? Reflect on why you chose that rating for those categories. Think about your past growth in each category and how you can use analytical thinking to foster growth in a weaker area. Write 5-7 sentences (100-150 words) reflecting on these questions and the ratings you gave yourself.

  • I think I'm good at the skill of Curiosity. Many time I want to know and learn more about something, but at the same time, I think I need to develop the ability to ask right and good questions. Understanding Context and Having a technical mindset are skills that I need to develop further than now. I like to find and collect information, but that doesn't always mean that I'm very good at using the information to fit into the big picture and break the big one into smaller steps. The skills of Data design and Data strategy are new to me. That's why I decide to study this course.

Exploring core analytical skills

The more ways you can think, the easier it is to think outside the box and come up with fresh ideas. But why is it important to think in different ways? Well because in data analysis, solutions are almost never right in front of you.

You need to think critically to find out the right questions to ask. But you also need to think creatively to get new and unexpected answers.

What is the root cause of a problem? A root cause is the reason why a problem occurs. If we can identify and get rid of a root cause, we can prevent that problem from happening again. A simple way to wrap your head around root causes is with the process called the Five Whys. In the Five Whys you ask "why" five times to reveal the root cause. The fifth and final answer should give you some useful and sometimes surprising insights.

Another question commonly asked by data analysts is, where are the gaps in our process? For this, many people will use something called gap analysis. Gap analysis lets you examine and evaluate how a process works currently in order to get where you want to be in the future. Businesses conduct gap analysis to do all kinds of things, such as improve a product or become more efficient. The general approach to gap analysis is understanding where you are now compared to where you want to be. Then you can identify the gaps that exist between the current and future state and determine how to bridge them.

A third question that data analysts ask a lot is, what did we not consider before? This is a great way to think about what information or procedure might be missing from a process, so you can identify ways to make better decisions and strategies moving forward.

The way data analysts think and ask questions plays a big part in how businesses make decisions. That's why analytical thinking and understanding how to ask the right questions can have such a huge impact on the overall success of a business.

Test your knowledge on analytical thinking

TOTAL POINTS 4

Question 1

What practice involves identifying, defining, and solving a problem by using data in an organized, step-by-step manner?

Analytical thinking

Context

Visualization

Data design

Correct. Analytical thinking involves identifying and defining a problem, then solving it by using data in an organized, step-by-step manner.

Question 2

Which of the following are examples of data visualizations? Select all that apply.

Reports

Graphs

Charts

Maps

Correct. Graphs, maps, and charts are used in data visualization.

Question 3

Gap analysis is used to examine and evaluate how a process currently works with the goal of getting to where you want to be in the future.

True

False

Correct. Gap analysis is used to examine and evaluate how a process currently works with the goal of getting to where you want to be in the future.

Question 4

Which aspect of analytical thinking involves being able to identify a relationship between two or more pieces of data?

Visualization

Correlation

Context

Data design

Correct. Correlation involves being able to identify a relationship between two or more pieces of data. A correlation is like a relationship.