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

TOC of 8 weeks course

1. Foundations: Data, Data, Everywhere (this course)
2. Ask Questions to Make Data-Driven Decisions
3. Prepare Data for Exploration
4. Process Data from Dirty to Clean
5. Analyze Data to Answer Questions
6. S​hare Data Through the Art of Visualization
7. Data Analysis with R Programming
8. Google D​ata Analytics Capstone: Complete a Case Study

Week 1 - Transforming data into insights

Gap Analysis?

A gap analysis is the process companies use to compare their current performance with their desired, expected performance.
https://www.investopedia.com/terms/g/gap-analysis.asp

Case Study: New data perspectives

ask, prepare, process, analyze, share, and act.

Situation: An organization was experiencing a high turnover rate among new hires. Many employees left the company before the end of their first year on the job.

Ask

The analysts needed to define what the project would look like and what would qualify as a successful result.

• What do you think new employees need to learn to be successful in their first year on the job?
• Have you gathered data from new employees before? If so, may we have access to the historical data?
• Do you believe managers with higher retention rates offer new employees something extra or unique?
• What do you suspect is a leading cause of dissatisfaction among new employees?
• By what percentage would you like employee retention to increase in the next fiscal year?

Prepare

• They developed specific questions to ask about employee satisfaction with different business processes, such as hiring and onboarding, and their overall compensation.
• They established rules for who would have access to the data collected - in this case, anyone outside the group wouldn’t have access to the raw data, but could view summarized or aggregated data. For example, an individual’s compensation wouldn’t be available, but salary ranges for groups of individuals would be viewable.
• They finalized what specific information would be gathered, and how best to present the data visually. The analysts brainstormed possible project- and data-related issues and how to avoid them.

Process

The data analysts also made sure employees understood how their data would be collected, stored, managed, and protected. Collecting and using data ethically is one of the responsibilities of data analysts. In order to maintain confidentiality and protect and store the data effectively, these were the steps they took:

• They restricted access to the data to a limited number of analysts.
• They cleaned the data to make sure it was complete, correct, and relevant. Certain data was aggregated and summarized without revealing individual responses.
• They uploaded raw data to an internal data warehouse for an additional layer of security.

Analyze

the data analysts discovered that an employee’s experience with certain processes was a key indicator of overall job satisfaction.

These were their findings:

• Employees who experienced a long and complicated hiring process were most likely to leave the company.
• Employees who experienced an efficient and transparent evaluation and feedback process were most likely to remain with the company.

The group knew it was important to document exactly what they found in the analysis, no matter what the results.

Share

The analysts were also careful sharing the report.

• They shared the report with managers who met or exceeded the minimum number of direct reports with submitted responses to the survey.
• They presented the results to the managers to make sure they had the full picture.
• They asked the managers to personally deliver the results to their teams.

This process gave managers an opportunity to communicate the results with the right context.

Act

how best to implement changes and take actions based on the findings.

• Standardize the hiring and evaluation process for employees based on the most efficient and transparent practices.
• Conduct the same survey annually and compare results with those from the previous year.

Reflection Week 1 - Transforming data into insights

• Did the details of the case study help to change the way you think about data analysis? Why or why not?
  • Not sure. One thing is clear that I assume this is one of the sugar-coated examples of data analysis case study. Because in real world this is very comlicated, we can’t say we find correlations and fixed those and it works!.
• Did you find anything surprising about the way the data analysts approached their task?
  • not yet.
• What else would you like to learn about data analysis?
  • I’d like to know how to find patterns in data.

Dimensions of data analytics

data science, the discipline of making data useful, is an umbrella term that encompasses three disciplines: machine learning, statistics, and analytics. These are separated by how many decisions you know you want to make before you begin with them. If you want to make a few important decisions under uncertainty, that is statistics. If you want to automate, in other words, make many, many, many decisions under uncertainty, that is machine learning and AI. But what if you don’t know how many decisions you want to make before you begin? What if what you’re looking for is inspiration? You want to encounter your unknown unknowns. You want to understand your world. That is analytics.

Week 1 - Understanding the data ecosystem

To put it simply, an ecosystem is a group of elements that interact with one another.
Data ecosystems are made up of various elements that interact with one another in order to produce, manage, store, organize, analyze, and share data.
These elements include hardware and software tools, and the people who use them.

Data Scientists Vs. Data Analysts

• Data science is defined as creating new ways of modeling and understanding the unknown by using raw data.
• Data scientists create new questions using data, while analysts find answers to existing questions by creating insights from data sources.

Data analysis and Data analytics

• data analysis is the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making.
• Data analytics in the simplest terms is the science of data. It’s a very broad concept that encompasses everything from the job of managing and using data to the tools and methods that data workers use each and every day.
• Data, data analysis and data ecosystem fit under the data analytics umbrella.

Data Driven Decision-Making

Appendix

Resources

https://online.hbs.edu/blog/post/business-analytics-examples