Google Data Analytics Professional Certificate - Course 1 : Foundations: Data, Data, Everywhere - week 2

Week 2 - Embrace your data analyst skills

• Curiosity
  • Curiosity is all about wanting to learn something. Curious people usually seek out new challenges and experiences. This leads to knowledge.
• Understanding Context
  • Context is the condition in which something exists or happens. This can be a structure or an environment.
• Having a technical mindset
  • A technical mindset involves the ability to break things down into smaller steps or pieces and work with them in an orderly and logical way.
• Data Design
  • Data design is how you organize information. As a data analyst, design typically has to do with an actual database.
• Data Strategy
  • Data strategy is the management of the people, processes, and tools used in data analysis.

Learning Log: Explore data from your daily life

• Create a list exploring an area of your daily life and include details, such as the date, time, cost, quantity, size, etc:
  • 토요일, 라떼와 치즈 스콘
  • 일요일, 커피집 오픈 안해서 편의점 라떼 구입
  • 월요일, 라떼와 초코 스콘

Reflection

• Are there any trends you noticed in your behavior?
  • 라떼만 마심
• Are there factors that influence your decision-making?
  • 치즈에서 초코 스콘으로 갈아탄 이유: 단게 커피랑 더 잘어울림
  • 커피집 문을 안열면 편의점 구매
• Is there anything you identified that might influence your future behavior?
  • 아직 없음

Data Analyst Skills

• Analytical skills: The qualities and characteristics associated with solving problems using facts
• A technical mindset: The analytical skill that involves breaking processes down into smaller steps and working with them in an orderly, logical way
• Data Design: A technical mindset
• Understanding context : The analytical skill that has to do with how you group things into categories
• Data Strategy: The analytical skill that involves managing the processes and tools used in data analysis

Week 2 - Thinking about Analytical thinking

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 of analytical thinking

1. visualization
2. strategy
3. problem-orientation
4. correlation,
5. big-picture and detail-oriented thinking.

Visualization

visualization is the graphical representation of information. Some examples include graphs, maps, or other design elements. Visualization is important because visuals can help data analysts understand and explain information more effectively.

Strategy

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.

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.

Correlation

A correlation is like a relationship. Correlation does not equal causation.

Big-picture and detail-oriented thinking

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. In other words, the pieces that make up your puzzle.

The questions data analysts ask when they’re on the hunt for a solution

What is the root cause of a problem? (Five Whys)

• Five Whys
  • In the Five Whys you ask “why” five times to reveal the root cause.

Where are the gaps in our process? (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.
• The general approach to gap analysis is understanding where you are now compared to where you want to be.

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.

Learning Log: Reflect on your skills and expectations

Analytical skill	Strength	Developing	Emerging	Comments/Plans/Goals
Curiosity	x
Context	x
Technical Mindset	x
Data Design		x
Data Strategy		x

Reflection

• What do you notice about the ratings you gave yourself in each area? How did you rate yourself in the areas that appeal to you most?
  • I assume I am very eager to know something that I don’t know or something that I wan’t to know. I can’t stand with not knowing something as I study with teammates or alone.
• If you are asked to rate your experience level in these areas again in a week, what do you think the ratings will be, and why do you think that?
  • Not much changes are expected. Since data design and data strategy are not the skills that quickly improve, those skills would remain as were.
• How do you plan on developing these skills from now on?
  • After I got the job, then these skills become naturally stronger than before, I assume.

Week 2 - Thinking about Outcomes

Data-driven decision-making involved using facts to guide business strategy.
It gives you greater confidence about your choice and your abilities to address business challenges. It helps you become more proactive when an opportunity presents itself, and it saves you time and effort when working towards a goal. Now let’s learn more about how these five skills help you tap into all the potential of data-driven decision-making.

Case Study

Appendix

Glossary: Terms and definitions

• Analytical skills: Qualities and characteristics associated with using facts to solve problems
• Analytical thinking: The process of identifying and defining a problem, then solving it by using data in an organized, step-by-step manner

Reference

Markdown table generator: https://www.tablesgenerator.com/markdown_tables#