About this Course
This is the first course in the Google Data Analytics Certificate. These courses will equip you with the skills you need to apply to introductory-level data analyst jobs. Organizations of all kinds need data analysts to help them improve their processes, identify opportunities and trends, launch new products, and make thoughtful decisions. In this course, you’ll be introduced to the world of data analytics through hands-on curriculum developed by Google. The material shared covers plenty of key data analytics topics, and it’s designed to give you an overview of what’s to come in the Google Data Analytics Certificate. Current Google data analysts will instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.
Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary. By the end of this course, you will: - Gain an understanding of the practices and processes used by a junior or associate data analyst in their day-to-day job. - Learn about key analytical skills (data cleaning, data analysis, data visualization) and tools (spreadsheets, SQL, R programming, Tableau) that you can add to your professional toolbox. - Discover a wide variety of terms and concepts relevant to the role of a junior data analyst, such as the data life cycle and the data analysis process. - Evaluate the role of analytics in the data ecosystem. - Conduct an analytical thinking self-assessment. - Explore job opportunities available to you upon program completion, and learn about best practices in the job search.
Data helps us make decisions in everyday life and in business. In this first part of the course, you’ll learn how data analysts use data analytics and the tools of their trade to inform those decisions. You’ll also discover more about this course and the overall program expectations.
Learning Log: Think about data in daily life
Overview
By now, you've started to discover how powerful data can be. Throughout this course, you’ll be asked to make entries in a learning log. Your log will be a personal space where you can keep track of your thinking and reflections about the experiences you will have collecting and analyzing data. Reflections may include what you liked, what you would change, and questions that were raised. By the time you complete the entry for this activity, you will have a stronger understanding of data analytics.
Everyday data
Before you write an entry in your learning log, think about where and how you use data to make decisions. You will create a list of at least five questions that you might use data to answer. Here are a few examples to inspire you:
What’s the best time to go to the gym?
How does the length of your commute to work vary by day of the week?
How many cups of coffee do you drink each day?
What flavor of ice cream do customers buy?
How many hours of sleep do you get each day?
Then, you will select one of the five questions from your list to explore further and write down the types of data you might collect in order to make a decision. That’s data analysis in action!
Access your learning log
To use the learning log for this course item, click the link below and select Use Template.
Link to learning log template: Think about data in daily life
OR
If you don’t have a Google account, you can download the template directly from the attachment below.
Reflection
After you consider how you use data analysis in your own life, take a moment to reflect on what you discovered. Reflections may include what you liked, what you would change, and questions that were raised. In your new learning log entry, you will write 2-3 sentences (40-60 words) in response to each question below:
What are some considerations or preferences you want to keep in mind when making a decision?
What kind of information or data do you have access to that will influence your decision?
Are there any other things you might want to track associated with this decision?
When you’ve finished your entry in the learning log template, make sure to save the document so your response is somewhere accessible. This will help you continue applying data analysis to your everyday life. You will also be able to track your progress and growth as a data analyst.
Example
Since this is your first learning log, an example has been provided using one of the questions above to help you.
Link to learning log example: Think about data in daily life
OR
If you don’t have a Google account, you can download the example directly from the attachment below.
Case Study: New data perspectives
As you have been learning, you can find data pretty much everywhere. Any time you observe and evaluate something in the world, you’re collecting and analyzing data. Your analysis helps you find easier ways of doing things, identify patterns to save you time, and discover surprising new perspectives that can completely change the way you experience things.
Here is a real-life example of how one group of data analysts used the six steps of the data analysis process to improve their workplace and its business processes. Their story involves something called people analytics — also known as human resources analytics or workforce analytics. People analytics is the practice of collecting and analyzing data on the people who make up a company’s workforce in order to gain insights to improve how the company operates.
Being a people analyst involves using data analysis to gain insights about employees and how they experience their work lives. The insights are used to define and create a more productive and empowering workplace. This can unlock employee potential, motivate people to perform at their best, and ensure a fair and inclusive company culture.
The six steps of the data analysis process that you have been learning in this program are: ask, prepare, process, analyze, share, and act. These six steps apply to any data analysis. Continue reading to learn how a team of people analysts used these six steps to answer a business question.
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. The analysts used the data analysis process to answer the following question: how can the organization improve the retention rate for new employees?
Here is a break down of what this team did, step by step.
First up, the analysts needed to define what the project would look like and what would qualify as a successful result. So, to determine these things, they asked effective questions and collaborated with leaders and managers who were interested in the outcome of their people analysis. These were the kinds of questions they asked:
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?
It all started with solid preparation. The group built a timeline of three months and decided how they wanted to relay their progress to interested parties. Also during this step, the analysts identified what data they needed to achieve the successful result they identified in the previous step - in this case, the analysts chose to gather the data from an online survey of new employees. These were the things they did to 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.
The group sent the survey out. Great analysts know how to respect both their data and the people who provide it. Since employees provided the data, it was important to make sure all employees gave their consent to participate. 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.
Then, the analysts did what they do best: analyze! From the completed surveys, 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. To do otherwise would diminish trust in the survey process and reduce their ability to collect truthful data from employees in the future.
Just as they made sure the data was carefully protected, the analysts were also careful sharing the report. This is how they shared their findings:
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. As a result, they could have productive team conversations about next steps to improve employee engagement.
The last stage of the process for the team of analysts was to work with leaders within their company and decide how best to implement changes and take actions based on the findings. These were their recommendations:
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.
A year later, the same survey was distributed to employees. Analysts anticipated that a comparison between the two sets of results would indicate that the action plan worked. Turns out, the changes improved the retention rate for new employees and the actions taken by leaders were successful!
Is people analytics right for you?
One of the many things that makes data analytics so exciting is that the problems are always different, the solutions need creativity, and the impact on others can be great — even life-changing or life-saving. As a data analyst, you can be part of these efforts. Maybe you’re even inspired to learn more about the field of people analytics. If so, consider learning more about this field and adding that research to your data analytics journal. You never know: One day soon, you could be helping a company create an amazing work environment for you and your colleagues!
Additional Resource
To learn more about some recent applications of data analytics in the business world, check out the article “4 Examples of Business Analytics in Action” from Harvard Business School. The article reveals how corporations use data insights to optimize their decision-making process.
Learning Log: Consider how data analysts approach tasks
Overview
Earlier you learned about how data analysts at one organization used data to improve employee retention. Now, you’ll complete an entry in your learning log to track your thinking and reflections about those data analysts' process and how they approached this problem. By the time you complete this activity, you will have a stronger understanding of how the six phases of the data analysis process can be used to break down tasks and tackle big questions. This will help you apply these steps to future analysis tasks and start tackling big questions yourself.
Review the six phases of data analysis
Before you write your entry in your learning log, reflect on the case study from earlier. The data analysts wanted to use data to improve employee retention. In order to do that, they had to break this larger project into manageable tasks. The analysts organized those tasks and activities around the six phases of the data analysis process:
Ask
Prepare
Process
Analyze
Share
Act
The analysts asked questions to define both the issue to be solved and what would equal a successful result. Next, they prepared by building a timeline and collecting data with employee surveys that were designed to be inclusive. They processed the data by cleaning it to make sure it was complete, correct, relevant, and free of errors and outliers. They analyzed the clean employee survey data. Then the analysts shared their findings and recommendations with team leaders. Afterward, leadership acted on the results and focused on improving key areas.
Access your learning log
To use the template for this course item, click the link below and select “Use Template.”
Link to learning log template: Consider how data analysts approach tasks
OR
If you don’t have a Google account, you can download the template directly from the attachment below.
Reflection
In your learning log template, write 2-3 sentences (40-60 words) reflecting on what you’ve learned from the case study by answering each of the questions below:
Did the details of the case study help to change the way you think about data analysis? Why or why not?
Did you find anything surprising about the way the data analysts approached their task?
What else would you like to learn about data analysis?
When you’ve finished your entry in the learning log template, make sure to save the document so your response is somewhere accessible. This will help you continue applying data analysis to your everyday life. You will also be able to track your progress and growth as a data analyst.
Data and gut instinct
Detectives and data analysts have a lot in common. Both depend on facts and clues to make decisions. Both collect and look at the evidence. Both talk to people who know part of the story. And both might even follow some footprints to see where they lead. Whether you’re a detective or a data analyst, your job is all about following steps to collect and understand facts.
Analysts use data-driven decision-making and follow a step-by-step process. You have learned that there are six steps to this process:
Ask questions and define the problem.
Prepare data by collecting and storing the information.
Process data by cleaning and checking the information.
Analyze data to find patterns, relationships, and trends.
Share data with your audience.
Act on the data and use the analysis results.
But there are other factors that influence the decision-making process. You may have read mysteries where the detective used their gut instinct, and followed a hunch that helped them solve the case. Gut instinct is an intuitive understanding of something with little or no explanation. This isn’t always something conscious; we often pick up on signals without even realizing. You just have a “feeling” it’s right.
Why gut instinct can be a problem
At the heart of data-driven decision making is data. Therefore, it's essential that data analysts focus on the data to ensure they make informed decisions. If you ignore data by preferring to make decisions based on your own experience, your decisions may be biased. But even worse, decisions based on gut instinct without any data to back them up can cause mistakes.
Consider an example of a restaurant entrepreneur, partnering with a well known chef to develop a new restaurant in a bustling part of the city’s central shopping district. The well known chef has several restaurants across the city. Banking on their reputation, the restaurant entrepreneur and chef followed gut instinct and created another uniquely themed restaurant. However, fundraising efforts fell short to fund the opening of the restaurant after months of planning and preparation. The property will go back on the market to be sold at a loss. Had the entrepreneur done more research, they would've found data showing prospective customers in this new restaurant location were very different from the chef's other restaurants.
The more you understand the data related to a project, the easier it will be to figure out what is required. These efforts will also help you identify errors and gaps in your data so you can communicate your findings more effectively. Sometimes past experience helps you make a connection that no one else would notice. For example, a detective might be able to crack open a case because they remember an old case just like the one they’re solving today. It's not just gut instinct.
Data + business knowledge = mystery solved
Blending data with business knowledge, plus maybe a touch of gut instinct, will be a common part of your process as a junior data analyst. The key is figuring out the exact mix for each particular project. A lot of times, it will depend on the goals of your analysis. That is why analysts often ask, “How do I define success for this project?”
In addition, try asking yourself these questions about a project to help find the perfect balance:
What kind of results are needed?
Who will be informed?
Am I answering the question being asked?
How quickly does a decision need to be made?
For instance, if you are working on a rush project, you might need to rely on your own knowledge and experience more than usual. There just isn’t enough time to thoroughly analyze all of the available data. But if you get a project that involves plenty of time and resources, then the best strategy is to be more data-driven. It’s up to you, the data analyst, to make the best possible choice. You will probably blend data and knowledge a million different ways over the course of your data analytics career. And the more you practice, the better you will get at finding that perfect blend.
Origins of the data analysis process
When you decided to join this program, you proved that you are a curious person. So let’s tap into your curiosity and talk about the origins of data analysis. We don’t fully know when or why the first person decided to record data about people and things. But we do know it was useful because the idea is still around today!
We also know that data analysis is rooted in statistics, which has a pretty long history itself. Archaeologists mark the start of statistics in ancient Egypt with the building of the pyramids. The ancient Egyptians were masters of organizing data. They documented their calculations and theories on papyri (paper-like materials), which are now viewed as the earliest examples of spreadsheets and checklists. Today’s data analysts owe a lot to those brilliant scribes, who helped create a more technical and efficient process.
It is time to enter the data analysis life cycle—the process of going from data to decision. Data goes through several phases as it gets created, consumed, tested, processed, and reused. With a life cycle model, all key team members can drive success by planning work both up front and at the end of the data analysis process. While the data analysis life cycle is well known among experts, there isn't a single defined structure of those phases. There might not be one single architecture that’s uniformly followed by every data analysis expert, but there are some shared fundamentals in every data analysis process. This reading provides an overview of several, starting with the process that forms the foundation of the Google Data Analytics Certificate.
The process presented as part of the Google Data Analytics Certificate is one that will be valuable to you as you keep moving forward in your career:
Ask: Business Challenge/Objective/Question
Prepare: Data generation, collection, storage, and data management
Process: Data cleaning/data integrity
Analyze: Data exploration, visualization, and analysis
Share: Communicating and interpreting results
Act: Putting your insights to work to solve the problem
Understanding this process—and all of the iterations that helped make it popular—will be a big part of guiding your own analysis and your work in this program. Let’s go over a few other variations of the data analysis life cycle.
EMC's data analysis life cycle
EMC Corporation's data analytics life cycle is cyclical with six steps:
Discovery
Pre-processing data
Model planning
Model building
Communicate results
Operationalize
EMC Corporation is now Dell EMC. This model, created by David Dietrich, reflects the cyclical nature of real-world projects. The phases aren’t static milestones; each step connects and leads to the next, and eventually repeats. Key questions help analysts test whether they have accomplished enough to move forward and ensure that teams have spent enough time on each of the phases and don’t start modeling before the data is ready. It is a little different from the data analysis life cycle this program is based on, but it has some core ideas in common: the first phase is interested in discovering and asking questions; data has to be prepared before it can be analyzed and used; and then findings should be shared and acted on.
For more information, refer to this e-book, Data Science & Big Data Analytics.
SAS's iterative life cycle
An iterative life cycle was created by a company called SAS, a leading data analytics solutions provider. It can be used to produce repeatable, reliable, and predictive results:
Ask
Prepare
Explore
Model
Implement
Act
Evaluate
The SAS model emphasizes the cyclical nature of their model by visualizing it as an infinity symbol. Their life cycle has seven steps, many of which we have seen in the other models, like Ask, Prepare, Model, and Act. But this life cycle is also a little different; it includes a step after the act phase designed to help analysts evaluate their solutions and potentially return to the ask phase again.
For more information, refer to Managing the Analytics Life Cycle for Decisions at Scale.
Project-based data analytics life cycle
A project-based data analytics life cycle has five simple steps:
Identifying the problem
Designing data requirements
Pre-processing data
Performing data analysis
Visualizing data
This data analytics project life cycle was developed by Vignesh Prajapati. It doesn’t include the sixth phase, or what we have been referring to as the Act phase. However, it still covers a lot of the same steps as the life cycles we have already described. It begins with identifying the problem, preparing and processing data before analysis, and ends with data visualization.
For more information, refer to Understanding the data analytics project life cycle.
Big data analytics life cycle
Authors Thomas Erl, Wajid Khattak, and Paul Buhler proposed a big data analytics life cycle in their book, Big Data Fundamentals: Concepts, Drivers & Techniques. Their life cycle suggests phases divided into nine steps:
Business case evaluation
Data identification
Data acquisition and filtering
Data extraction
Data validation and cleaning
Data aggregation and representation
Data analysis
Data visualization
Utilization of analysis results
This life cycle appears to have three or four more steps than the previous life cycle models. But in reality, they have just broken down what we have been referring to as Prepare and Process into smaller steps. It emphasizes the individual tasks required for gathering, preparing, and cleaning data before the analysis phase.
For more information, refer to Big Data Adoption and Planning Considerations.
Key takeaway
From our journey to the pyramids and data in ancient Egypt to now, the way we analyze data has evolved (and continues to do so). The data analysis process is like real life architecture, there are different ways to do things but the same core ideas still appear in each model of the process. Whether you use the structure of this Google Data Analytics Certificate or one of the many other iterations you have learned about, we are here to help guide you as you continue on your data journey.
Glossary: Terms and definitions
We’ve covered a lot of terms—some of which you may have already known, and some of which are new. To make it easy to remember what a word means, we created this glossary of terms and definitions.
If you don’t have a Google account, you can download the glossary directly from the attachment below.
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