Google Data Analytics Professional

Foundations: Data, Data, Everywhere

WEEK5 - Endless career possibilities

Businesses of all kinds value the work done by data analysts. In this part of the course, you’ll find out about these businesses and the specific jobs and tasks that analysts perform for them. You’ll also learn how your data analyst certificate will help you meet many of the requirements for a position with these businesses.

Learning Objectives

• Describe the role of a data analyst with specific reference to job roles
• Discuss how the Google Data Analytics Certificate can help a candidate meet the requirements of a given job
• Explain how a business task may be appropriate for a data analyst, with reference to fairness and the value of the data analyst
• Identify companies that would potentially hire data analysts
• Describe how one's prior experiences may be applied to a career as a data analyst
• Determine whether the use of data constitutes fair or unfair practices
• Understand the different ways organizations use data
• Explain the concept of data-driven decision-making including specific examples

Data analyst job opportunities

The job of a data analyst

Data gives advantage of data driven insights to improve their operations and make better decisions.

Coca-Cola, make all different kinds of flavor combinations people are coming up with and use them as inspirations for new products.

Google gives you the right answer to any questions in just seconds powered by data. Data determines a website's reliability and accuracy to get the most useful results for any search people make.

Weather pattern predictions in zoo, so that to know how to manage their staffing needs more accurately, and provide better experiences and resources to visitors.

Healthcare industry, clinic attendance data (predict when rush hours will hit/foot traffic) and prevent the complaints from long wait times.

Joey: Path to becoming a data analyst

"Hi, I'm Joey and I work as an analytics program manager within REWS. Now REWS stands for real estate and workplace services, and my job is to bring data and analytics into the decision-making here, especially with regards to creating a safe and fun work environment. My journey into analytics was a bit different in that I had no plan or really didn't see myself being where I am now. Now luckily, I started in a rotational program called the HRA program within people operations, which afforded me the ability to play three different roles essentially. I was in a generalist capacity in a specialist role and as an analyst, and I really found a love and a passion in the analytical work. I started on the business intelligence team, whose job was to provide SQL-based reporting back to the business. I realized the analytics is the right career path for me when I found myself enjoying coming to work and getting my work done. And I think I can connect that to two passions of mine. The first is problem-solving. I love taking a complex problem, a mystery, a riddle and being able to find the answers and come up with the solution. And then the second thing is being able to work with people and help people. In analytics I feel like the key to success is being able to blend the personal side with the technical side. At the beginning of my career, I focused a little more on the technical pieces, and I wanted to make sure I had the right technical knowledge to be able to answer questions. But what I found is over time I needed to grow that other side just as much. And I think that my career has allowed me those opportunities to kind of work each of those muscles, the human interaction part and the technical part to make sure that they're both growing at the end of the day."

Self-Reflection: Business use of data

my chitchat with chatgpt regarding this reflections

Consider the company, service, or product you chose in this reflection:

• How could it use data to improve customer experience?
• What kinds of data would it need to collect?
• How could insights from that data solve a problem?

my answer:

1. Analyze customer browsing and purchase history to provide personalized product recommendations. For instance, if a customer frequently buys electronics, the platform could suggest related accessories or new releases.
2. purchase history, browsing behavior, market trends, customer feedback
3. reducing cart abandonment(If data shows that customers frequently abandon their shopping carts at a particular stage, the platform can address potential friction points. This might involve streamlining the checkout process, offering discounts, or improving website navigation.), Enhancing Product Recommendations, Optimizing Delivery, Strategic Marketing Campaigns.

For example, consider a restaurant that delivers cold food to customers. More data about the delivery process, such as the average delivery time or the average number of daily deliveries, could help the restaurant streamline the process and deliver food on time. Data analytics helps businesses make better decisions, but getting there is a process. It begins with analyzing a business problem, identifying data about that problem, and then using data analysis to arrive at an answer. Sometimes you get an answer that solves your business problem, but it’s often just as likely that you discover other questions to investigate further.

Tony: Supporting careers in data analytics

"For any analyst, for any person that's honestly at the early stages of their career, understanding data, respecting data and knowing how to work with data is incredibly important because, my vision is that every role in some form or fashion will involve data and its use in learning how to extract insights from it will be at the core of any critical role across any company organization. Generally in those first two years, you are developing the core skill sets that make you a fantastic generalist, and then in the next 2-5 years, you're learning about something very specific as as it relates to your job. Whether it's the area that you're supporting or maybe a very technical component. Like, let's say you want to become a SQL expert so that you can manipulate large data sets for financial analysis purposes. Similarly, even if you come into finance as a data analyst, you can pop out of finance and go into what a lot of people like to call the business, which is typically your Operations Functions and become a business analyst or a data analysts. There's so many different paths that you can take from the starting point that you really can't predict your end. I'm just deeply passionate about working with and supporting young people and really giving them a jumpstart to their career. This stems from honestly my own personal experience, where in the first two years of my career, I had essentially zero support from my manager and my direct management chain. Having gone through that experience my first few years, I realize and I felt experience how that can slow you down, and especially when you are somebody that has a lot of potential and a lot of ability, you want to be in an environment that fosters that ability and really wants to see you grow. I think it's incredibly important to have programs like these that take away all the barriers that remove any of the constructs that prevent people from being able to find out what they need to be in an industry like this, to be successful in a role like a data analyst, so that they themselves can dream about where they can go in their career. My name is Tony. I'm a Finance program manager at Google."

Learning Log: Reflect on the data analysis process

(click the link above.pdf)

The data analysis process so far

• You asked an interesting question and defined a problem to solve through data analysis to answer that question.
• You thought deeply about what data you would need and how you would collect it in order to prepare for analysis.
• You processed your data by organizing and structuring it in a table and then moving it to a spreadsheet.
• You analyzed your data by inspecting and scanning it for patterns.
• You shared your first data visualization: a bar chart.
• Finally, after completing all the other steps, you acted: You reflected on your results, made decisions, and gained insight into your problem--even if that insight was that you didn't have enough data, or that there were no obvious patterns in your data.

Roles of a data analyst

• Technology: Use geographic data to power GPS technology in cars.

Technology relies on software and hardware to function.

• Marketing: Use demographic data to target advertisements for a new consumer product for youths.

Marketing uses audience insights to make decisions.

• Finance: Use stock market data to determine which portfolios to invest in.

Finance relies on daily market trends for insight.

• Healthcare: Use bed occupancy data to determine the number of nurses and orderlies to schedule on a given shift.

Healthcare involves reviewing hospital traffic to inform staff decisions.

• Hospitality: Use past booking data to accurately anticipate levels of demand for hotel rooms.

Hospitality looks at seasonal trends to predict demand.

• Government: Use population data to determine which communities need federal funding.

Government relies on demographic information in order to provide proper support.

The importance of fair business decisions

The power of data in business

Business using data analytics and their operation all have issues to explore, questions to answer, or problems to solve.

Coca-Cola had a question about new products. Data analysis gave them insights into new flavors customers already like. The City Zoo and Aquarium had a problem with staffing. Data, helped them figure out the best staffing strategy. These questions and problems become the foundation for all kinds of business tasks, that you'll help solve as a data analyst.

Let's stick with our zoo example and see if we can imagine what a business task for a zoo might look like. We know the problem, unpredictable weather was making it hard for the zoo to anticipate staffing needs. Maybe the business task could be something like, analyze weather data from the last decade to identify predictable patterns. The data analysts could then plan out the best way to gather, analyze, and present the data needed to solve this task and meet the zoos goals. Then, using data, the zoo would be able to make informed decisions about their daily staffing.

The data analysts could then plan out the best way to gather, analyze, and present the data needed to solve this task and meet the zoos goals. Then, using data, the zoo would be able to make informed decisions about their daily staffing.

The simplest way to think about decision-making is that it's a choice between consequences, good, bad, or a combination of both.

In our zoo example, the zoo had the data they needed to make an informed decision that solved their problem. But what if they had made this decision without data? Let's say they just relied on observation and memory to track the weather and make staffing schedules. Well, we already know that wouldn't have solve their problem long-term. Data analytics gave them the information they needed to find the best possible solution to their problem. That's the power of data. Observation and intuition are powerful tools in decision-making, but they can only take us so far when we make decisions based on just observation and gut feelings, we're only seeing part of the picture. Data helps us see the whole thing. With data, we have a complete picture of the problem and its causes, which lets us find new and surprising solutions we never would've been able to see before. Data analytics helps businesses make better decisions. It all starts with a business task and the question it's trying to answer. With the skills you'll learn throughout this program, you'll be able to ask the right questions, plan out the best way to gather and analyze data, and then present it visually to arm your team so they can make an informed, data-driven decision. That makes you critical to the success of any business you work for. Data is a powerful tool.

Rachel: Data detectives

"Hi, my name is Rachel, and I'm the Business systems and analytics lead at Verily. There are a lot of different types of problems that a data analyst can solve. I've been lucky enough over my career to have seen a lot of them and to take in a lot of very different types of data and help turn that into meaningful answers. I think one of the most important things to remember about data analytics is that data is data. I'm a finance data analyst and so my role at Verily is to take all of our financial information, all of the information of the money we're spending and the money we're making, and turn that into reports and insights so that our business leads can understand what we're doing. One of the most important things I've done at Verily recently was help create what's called a profit and loss statement for each of our business units. That means that in real time, our teams can see what their budget is and how they're spending against that budget. What that does is that helps our teams keep to that budget by either increasing their revenue streams so that they have more money to play with or pulling back their spending so that they can keep themselves within that budget. All of that really helps keep us on track as a company in making sure that we're hitting our goals. When you have a ton of data points, it can be overwhelming when you first sit down to make sense of it. You have tons of columns, tons of records, tons of different types of data, and finding a way to make sense of that is really hard and that's where the expertise of a data analyst comes in. It has been some of the most frustrating moments of my career, but also some of the most rewarding work I've ever done when it finally comes together. The best advice I have for any data analyst starting out is keep at it. If the angle you're taking doesn't work, try to find another one. Try to come at it in a different way, try to ask a different question, and eventually the data will yield and you'll get the insights you're looking for."

Understanding data and fairness

Data analysts have another important responsibility: making sure that their analyses are fair.

In other words, as a data analyst, you want to help create systems that are fair and inclusive to everyone.

In data analytics, fairness means ensuring that your analysis does not create or reinforce bias. This requires using processes and systems that are fair and inclusive. Ensuring that analysis does not create or reinforce bias requires using processes and systems that are fair and inclusive to everyone.

Let's say we have a company that's kind of notorious for being a boys club. There isn't much representation of other genders. This company wants to see which employees are doing well, so they start gathering data on employee performance and their own company culture. The data shows that men are the only people succeeding at this company. Their conclusion? That they should hire more men. After all, they're doing really well here, right? But that's not a fair conclusion for a couple of reasons.

First, it doesn't even consider all of the available data on company culture, so it paints an incomplete picture. Second, it doesn't think about the other surrounding factors that impact the data, or in other words, the conclusion doesn't consider the difficulties that people of different gender identities have trying to navigate a toxic work environment. If the company only looks at this conclusion, they won't acknowledge and address how harmful their culture is and they won't understand why certain people are set up to fail within it. That's why it's important to keep fairness in mind when analyzing data. The conclusion that only men are succeeding at this company is true, but it ignores other systematic factors that are contributing to this problem.

An ethical data analyst can look at the data gathered and conclude that the company culture is preventing some employees from succeeding, and the company needs to address those problems to boost performance. See how this conclusion paints a much more complete and fair picture. It recognizes the fact that some people aren't doing as well in this company and factors in why that could be, instead of discriminating against a huge number of applicants in the future.

As a data analyst it's your responsibility to make sure your analysis is fair and factors in the complicated social context that could create bias in your conclusions. It's important to think about fairness from the moment you start collecting data for a business task to the time you present your conclusions to your stakeholders.

For now, let's check out an example of a data analysis that does a good job of considering fairness in its conclusion. A team of Harvard data scientists were developing a mobile platform to track patients at risk of cardiovascular disease in an area of the United States called the Stroke Belt. It's important to call out that there were a variety of reasons people living in this area might be more at risk. With that in mind, these data scientists recognized that fairness needed to be a priority for this project, so they built fairness into their models. The team took several fairness measures to make sure they were being as fair as possible when examining sensitive and potentially biased data. First, they teamed analysts with social scientists who could provide insights on human bias and the social context that created them. They also collected self reported data in a separate system to avoid the potential for racial bias, which might skew the results of their study and unfairly represent patients. To make sure this sample population was representative, they oversampled non-dominant groups to ensure the model was including them. It's clear that the team made fairness a top priority every step of the way. This helped them collect data and create conclusions that didn't negatively impact the communities they were studying. Hopefully these examples have given you a better idea of what fairness means in data analysis.

Self-Reflection: Business cases

Case Study #1

To improve the effectiveness of its teaching staff, the administration of a high school offered the opportunity for all teachers to participate in a workshop. They were not required to attend; instead, the administration encouraged teachers to sign up. Of the 43 teachers on staff, 19 chose to take the workshop.

At the end of the academic year, the administration collected data on teacher performance for all teachers on staff. The data was collected via student survey. In the survey, students were asked to rank each teacher's effectiveness on a scale of 1 (very poor) to 6 (very good).

The administration compared data on teachers who attended the workshop to data on teachers who did not. The comparison revealed that teachers who attended the workshop had an average score of 4.95, while teachers who did not attend had an average score of 4.22. The administration concluded that the workshop was a success.

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Reflection #1 Consider this scenario:

• What are the examples of fair or unfair practices?
• How could a data analyst correct the unfair practices?

This is an example of unfair practice. It is tempting to conclude—as the administration did—that the workshop was a success. However, since the workshop was voluntary and not random, it is not appropriate to infer a causal relationship between attending the workshop and the higher rating. The workshop might have been effective, but other explanations for the differences in the ratings cannot be ruled out. For example, another explanation could be that the staff volunteering for the workshop were the better, more motivated teachers. This group of teachers would be rated higher on whether or not the workshop was effective. It’s also notable that there is no direct connection between student survey responses and workshop attendance. The data analyst could correct this by asking for the teachers to be selected randomly to participate in the workshop. They could also collect data that measures something more directly related to workshop attendance, such as the success of a technique the teachers learned in that workshop.

Case Study #2

An automotive company tests the driving capabilities of its self-driving car prototype. They carry out the tests on various types of roadways—specifically, a race track, trail track, and dirt road.

The researchers only test the prototype during the daytime. They collect two types of data: sensor data from the car during the drives and video data of the drives from cameras on the car.

They review the data after the initial tests. The results illustrate that the new self-driving car meets the performance standards across each of the roadways. As a result, the car can progress to the next phase of testing, which will include driving in various weather conditions.

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Reflection #2

• What are the examples of fair or unfair practices?
• How could a data analyst correct the unfair practices?

This case study shows an unfair practice. While the researchers test the prototype on three different tracks, they only conduct tests during the day. Conditions on each track may be very different during the day and night and this could change the results significantly. The data analyst should correct this by asking the test team to add in nighttime testing to get a full perspective of how the prototype performs at any time of the day on the tracks.

Case Study #3

An amusement park plans to add new rides to their property. First, they need to determine what kinds of new rides visitors want the park to build. In order to understand their visitors’ interests, the park develops a survey.

They decide to distribute the survey near the roller coasters because the lines are long enough that visitors will have time to answer all of the questions. After collecting this survey data, they find that most of the respondents want more roller coasters at the park. They conclude that they should add more roller coasters, as most of their visitors prefer them.

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Reflection #3

• What are the examples of fair or unfair practices?
• How could a data analyst correct the unfair practices?

This case study contains an unfair practice. While the decision to distribute surveys in places where visitors would have time to respond makes sense, it accidentally introduces sampling bias. The only respondents to the survey are people waiting in line for the roller coasters. This may unfairly bias survey results, because respondents might prefer roller coasters. A data analyst could reduce sampling bias by distributing the survey at the entrance and exit of the amusement park. This would avoid targeting roller coaster fans and provide results from the park’s general audience.

Case opinion

(Click above link)

Alex: Fair and ethical data decisions

"Hi, I'm Alex. I'm a research scientist at Google. My team is called the ethical AI team, we're a group of folks that really are concerned not only about how AI the technology operates, but how it interacts with society and how it might help or harm marginalized communities. When we talk about data ethics, we think about what is the good and right way of using data? What are going to be ways that uses of data are going to be beneficial to people? When it comes to data ethics, it's not just about minimizing harm but it's actually this concept of beneficence. When we think about data ethics we're thinking about, who's collecting the data? Why are they collecting it? How are they collecting it and for what purpose? Because of the way that organizations have imperatives to make money or to report to somebody or provide some analysis, we also have to keep strongly in mind how this is actually going to benefit people at the end of the day. Are the people represented in this data going to be benefited by this? I think that's the thing you never want to lose sight of as a data scientist or a data analyst. I think aspiring data analysts need to keep in mind that a lot of the data that you're going to encounter is data that comes from people so at the end of the day, data are people. You want to have a responsibility to those people that are represented in those data. Second, is thinking about how to keep aspects of their data protected and private. We don't want to go through our practice thinking about data instances as something we can just throw on the web. No, there needs to be considerations about how to keep that information, and likenesses like their images, or their voices, or their text. How do we keep that private? We also need to think about how we can have mechanisms of giving users and giving consumers more control over their data. It's not going to be sufficient just to say, we collect all this data and trust us with all these data. But we need to ensure that there's actionable ways in which people can consent to giving those data, and ways that they can ask for it to be revoked or removed. Data's growing and at the same time, we need to empower people to have control over their own data. The future is that data is always growing, we haven't seen any evidence that data is actually shrinking. With the knowledge that data's growing, these issues become more and more piqued, and more and more important to think about."

Exploring your next job

Data analysts in different industries

Data is already being used by countless industries in all kinds of different ways, tech, marketing, finance, health care, the list goes on. But one thing that's important to keep in mind, every industry has specific data needs that have to be addressed differently by their data analysts.

The same revenue data can be used in three different ways by data analysts in three different industries, financial services, Telecom, and tech.

For example, a finance analyst at a bank post public revenue data of Telecom company X to create a forecast that predicts where revenues will be in the future to recommend the stock price. The business analyst at Telecom company X uses that same data to advise the sales team. Then a data analyst at the company who created a customer management tool for Telecom company X will use that revenue data to determine how efficiently the software is performing.

"Finance, telecom, and tech, all use data differently, so they need analysts who have different skills. It all comes down to what the needs of the industry are. Those needs will determine what task you'll be given, the questions you'll be answering and even how you'll approach job searching. If you're just starting out, a great way to guide your search is to think first about what you're interested in. Does helping people get healthier sound meaningful to you? Maybe you want to focus on using data to improve hospital admissions. What about helping people save for a happy retirement? You might want a job that uses data to determine risk factors in financial investments. Or maybe you're interested in helping journalism grow in your city. A job using data to help find your local news website and find more subscribers could be the perfect role for you. The key is to think about your interests early in your job search. That'll lead you in the right direction, and it will help you in interviews too. Potential employers will want to know why you're interested in their company, and how you can address their needs, so if you can speak about your motivation to work in data analytics during interviews, you'll make yourself stand out in a great way. You'll have options when it comes to where you work and who you work for. But remember, you want to enjoy what you do, so it's a good idea to think about how you want to use your skills."

Location and travel.

When you start your job search, you need to make some decisions about where you want to live, so it helps to ask yourself some questions, does your preferred industry have opportunities in your area? Are you trying to stay local or would you be happy relocating? How long are you willing to commute to work every day? Will you drive to work, walk, take public transport? Is that possible year-round? How do you feel about working remotely? Does working from home excite you or bore you? Of course, you'll want to consider cost of living, and whether or not you want the convenience of city living or a quiet suburban home, and it's not just about where you'll be based, some jobs may ask you to travel, which could be an exciting chance to see the world or a deal-breaker. It's all about what you want out of this job, so start asking yourself some of these questions. Figuring out the answers can help you narrow down your search even further, so you're only looking at jobs you'd actually accept. Once you've answered enough questions, you'll be able to identify some specific companies that fit your needs.

At this point, it's a good time to think about your values and what company culture is a good fit for you. Ready, here comes some more questions, do you work best in a team or by yourself? Do you like to have a set routine or do you enjoy taking a new project and trying new things? Do your values match the company's values? You'll want to pay attention to these things during your job search and interview process, so you can be sure you fully invested in the company you work for. That's the best way to start building an exciting and fulfilling career. This program will help you learn the core skills for data analytics in any setting, it's up to you where you want to take them, whether that means starting in a completely new industry, or moving into an analyst position in an industry you already have experience in. Hopefully what we've covered here has helped you get on track for your future job search.

Data analyst roles and job descriptions

Decoding the job description The data analyst role is one of many job titles that contain the word “analyst.” To name a few others that sound similar but may not be the same role:

• Business analyst — analyzes data to help businesses improve processes, products, or services
• Data analytics consultant — analyzes the systems and models for using data
• Data engineer — prepares and integrates data from different sources for analytical use
• Data scientist — uses expert skills in technology and social science to find trends through data analysis
• Data specialist — organizes or converts data for use in databases or software systems
• Operations analyst — analyzes data to assess the performance of business operations and workflows

• Marketing analyst — analyzes market conditions to assess the potential sales of products and services
• HR/payroll analyst — analyzes payroll data for inefficiencies and errors
• Financial analyst — analyzes financial status by collecting, monitoring, and reviewing data
• Risk analyst — analyzes financial documents, economic conditions, and client data to help companies determine the level of risk involved in making a particular business decision
• Healthcare analyst — analyzes medical data to improve the business aspect of hospitals and medical facilities

Samah: Interview best practices

"My name is Samah Moid, and I'm a recruiter here at Google for the large customer sales team. Basically, I hire talent for the sales team here. Even within the sales recruiting space, I recruit specifically for the analytical lead roles here at Google. I want the candidate to be as comfortable as possible. As a recruiter I'm also their advocate. If they're a good fit for the team, I'd like to present them in the best light. As a recruiter some advice I would give for a data analyst that's starting to look for a job. Another tip, I would say for a data analyst that's looking for a new job is to increase your professional network. There are many ways to increase your professional network. One of them is to increase your online footprint, reach out to other analysts on LinkedIn, join local meet-ups with other data scientists. Sometimes when we're looking for a unique skill set, recruiters are going on websites like LinkedIn, and GitHub, and trying to find that talent themselves. It's really important to have your LinkedIn updated along with websites like GitHub, where you can showcase a lot of the data analysts projects you've done. Another tip I would say for an in-person interview is to prepare questions for the interviewer. Make sure they're not broad questions. They should be questions that will help you understand the team and the job better. If you're given a case study in an interview, you should expect to be given a business problem along with the sample data set. Then you'd be asked to take that sample data set, analyze it, and come up with a solution. One of the things you can do to help prepare yourself for this is to ensure you are analyzing the data and coming up with a solution that relates back to that data. Sometimes there is no right answer, and a lot of times interviewers are looking to see your thought process and the way you get to your solution. I highly encourage that if you find a role that you're interested in, not only apply to it, but go the next step. Look for the recruiter. Look for the hiring manager online. See if you can reach out to them and set up a coffee chat or send them your resume directly. Online applications could be a really big black hole where you never hear back from the recruiter or the team. When you reach out directly to a hiring manager or recruiter, it really shows your eagerness for the role and your interests for the role. Even if sometimes you don't get a response from reaching out, you never know, you try multiple different times. That one time you get a response back from a recruiter or hiring manager, could be the time you get the job that you really wanted."

Beyond the Numbers: A Data Analyst Journey

Rather than a reading, we invite you to watch Anna Leach's TEDx talk on YouTube or on the TED platform to learn about another interesting journey as a data analyst.

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Module 5 Glossary