Week 5: Endless career possibilities

Hey, great to have you back. Now it’s time to get down to business. We’re going to start talking about
practical ways businesses are using data, and the opportunities that
it can create for you. So far, you’ve learned a lot of
practical data analysis skills. With these next couple of videos,
we’re going to switch gears a little and talk about why you’re
learning these skills. Hopefully this will give you more
perspective into what kinds of opportunities are out there for you. Coming up, we’re going to talk more
about the kinds of roles data analysts play in different industries. The task that these roles required,
and the importance of fairness and avoiding bias and
analyzing data for business tasks. We’ll also talk about
opportunities you can tap into, and how this program factors into your future
success and the data analyst role. So with all that in mind,
let’s get started.

Tutorial on the Job of a Data Analyst

What is a data analyst?

A data analyst is a professional who collects, cleans, analyzes, and interprets data to help businesses and organizations make better decisions. They use their skills in statistics, programming, and data visualization to turn raw data into meaningful insights.

What are the responsibilities of a data analyst?

The specific responsibilities of a data analyst can vary depending on their industry and employer, but some common tasks include:

• Collecting and cleaning data: Data analysts may collect data from a variety of sources, such as surveys, website analytics, and customer databases. They then clean the data to ensure that it is accurate and consistent.
• Analyzing data: Data analysts use a variety of statistical methods and tools to analyze data and identify trends and patterns.
• Interpreting data: Data analysts interpret the results of their analysis and draw conclusions that can be used to inform business decisions.
• Communicating findings: Data analysts communicate their findings to stakeholders in a clear and concise way, often using data visualizations such as charts and graphs.

What skills and qualifications do data analysts need?

Data analysts typically need a bachelor’s degree in a quantitative field such as statistics, mathematics, or computer science. Many data analysts also have a master’s degree in data science or a related field.

In addition to their formal education, data analysts need a number of technical skills, including:

• Programming languages: Data analysts typically use programming languages such as Python, R, or SQL to clean and analyze data.
• Data visualization tools: Data analysts use data visualization tools to create charts and graphs that communicate their findings to stakeholders.
• Statistical methods: Data analysts use statistical methods to identify trends and patterns in data.

Data analysts also need strong communication and problem-solving skills. They must be able to communicate their findings to stakeholders in a clear and concise way, and they must be able to solve complex problems using data.

How to become a data analyst

If you are interested in becoming a data analyst, there are a few things you can do:

• Get a degree in a quantitative field: A bachelor’s degree in a quantitative field such as statistics, mathematics, or computer science is a good foundation for a career in data analytics.
• Learn programming languages and data visualization tools: Data analysts need to know how to use programming languages and data visualization tools to clean, analyze, and visualize data.
• Gain experience working with data: You can gain experience working with data by taking on internships or volunteer projects. You can also participate in hackathons or data science competitions.
• Network with other data analysts: Networking with other data analysts is a great way to learn about the industry and find job opportunities.

Conclusion

Data analysts play a vital role in many businesses and organizations. They use their skills in statistics, programming, and data visualization to turn raw data into meaningful insights that can be used to inform business decisions.

If you are interested in a career in data analytics, there are a number of things you can do to prepare, such as getting a degree in a quantitative field, learning programming languages and data visualization tools, and gaining experience working with data.

Previously, we learned about
what a data analyst does and why that work is so valuable. Now, let’s look at where data
analysts actually do their work. You’ll learn much more about the industries
you could work in as a data analyst. And how companies in these fields are
already using data analytics to do some really cool things. There are so many businesses out
there that have a big need for the skills you’re learning right now. Across industries like technology,
marketing, finance, health care, and so many more. Real companies are already using data
analytics to stay ahead of the curve. And the more they use data in their
business, the more they understand just how important data analyst
like you are to their success. Let’s look at a real life example of
a brand you’ll probably recognize, Coca-Cola. Data is changing the way Coca-Cola
approaches its marketing strategies. Coca-Cola uses data gathered from
consumer feedback to create advertising that speaks directly to different
audiences with different interests. How does this work? You know those high tech Coca-Cola
vending machines you see at movie theater sometimes? It’s always fun getting
to make your own flavors. Well, those machines have built-in
artificial intelligence and data analysis tools. This helps Coca-Cola see all the different
kinds of flavor combinations people are coming up with, which they can then
use as inspiration for new products. How cool is that? Ever wonder how Google gives you the right
answer to any question in just seconds? That’s powered by data too. We use all kinds of data to
determine a website’s reliability and accuracy to make sure you get the most
useful results for any search you make. But it isn’t just big companies like
Coca-Cola and Google that use data. Small businesses everywhere are also
starting to take advantage of data driven insights to improve their
operations and make better decisions. Small businesses can use data
to do all kinds of things. They might use data analytics to better
understand their customers’ buying habits, create more effective
social media messaging, or, in the case of one city zoo and
aquarium, predict the number of daily visitors
based on local climate data. City zoo and aquarium realized that, on rainy days, they were seeing
huge drop offs in attendance, but they had no way to accurately
predict when those rainy days would hit. This made staffing a real challenge. Some days they found
themselves overstaffed, other days they were unprepared for
the rush of visitors. To deal with this, data analyst took
years of weather records from the zoo and use that data to accurately
predict future weather patterns. This made it easy for the zoo to know
how much staff they needed when. Because the zoo could predict and manage
their staffing needs more accurately, they were able to provide a better
experience for visitors and dedicate more resources to creating
a better experience for the animals too. We see a similar thing in
the healthcare industry. Their data analysts look at clinic
attendance data to help hospitals and doctors offices predict when rush hours
will hit so they can be ready for it. Your local city hospital
is a great example. Let’s say they’ve been getting
complaints about long wait times. Sometimes an hour or more,
which made it hard for some patients to get the care they needed. So data analyst use data about the
hospitals daily foot traffic to help them make more informed decisions about how
many doctors they need on staff at any given time. This helped reduce wait times,
improve their patients experience, and make better use of the health
care worker’s time too. Like I said, there are many ways that
companies in different industries put data to use, but they can only do that if
they have data analyst they can rely on. So you might be wondering,
how you fit into the equation? Well, you’ve got plenty of options, but you don’t have to decide what industry
you want to work in right away. There will be plenty of time to think about that
as you make your way through this program. By the time you finish this program, you
have the core skills that will make you valuable in any industry that
makes data driven decisions. Which, as it turns out,
is most industries, even zoos. Coming up, we’ll check out the business
task where data can be helpful. And, we’ll explore even more how data
analysts are empowering businesses through data. I’ll see you then.

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.

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.

Tutorial on the Power of Data in Business

Data is one of the most valuable assets that a business can have. It can be used to improve decision-making, increase efficiency, and boost profits.

Here are some of the ways that businesses can use data to their advantage:

• Understand customers: Data can help businesses to understand their customers better, including their needs, wants, and preferences. This information can be used to develop more targeted marketing campaigns, create products and services that customers are more likely to buy, and improve the overall customer experience.
• Improve products and services: Data can be used to identify areas where products and services can be improved. For example, businesses can use data to track customer feedback, identify product defects, and analyze sales data to see which products are most popular.
• Make better decisions: Data can help businesses to make better decisions about everything from pricing to staffing to marketing strategy. By analyzing data, businesses can identify trends and patterns that would not otherwise be visible. This information can then be used to make informed decisions that are more likely to be successful.
• Increase efficiency: Data can be used to identify areas where businesses can streamline their operations and become more efficient. For example, businesses can use data to track employee productivity, identify bottlenecks in their workflows, and analyze costs.
• Boost profits: By using data to improve their decision-making, increase efficiency, and improve their products and services, businesses can boost their profits.

Here are some examples of how businesses are using data to their advantage:

• Amazon: Amazon uses data to personalize its website and product recommendations for each customer. It also uses data to optimize its supply chain and reduce costs.
• Netflix: Netflix uses data to recommend new movies and TV shows to its customers. It also uses data to produce original content that is more likely to be popular with its subscribers.
• Walmart: Walmart uses data to track customer behavior and identify trends. It also uses data to optimize its pricing and marketing strategies.

These are just a few examples of the many ways that businesses can use data to their advantage. By using data effectively, businesses can improve their decision-making, increase efficiency, and boost profits.

How to get started with using data in your business

If you are new to using data in your business, there are a few things you can do to get started:

1. Identify your goals: What do you want to achieve by using data? Once you know your goals, you can start to collect the data that you need.
2. Collect the right data: The type of data that you need will depend on your specific goals. However, some common types of data that businesses collect include customer data, sales data, and website analytics data.
3. Clean and analyze the data: Once you have collected your data, you need to clean it and analyze it to identify trends and patterns. This can be done using a variety of tools and techniques.
4. Interpret the data: Once you have analyzed the data, you need to interpret it and draw conclusions. This is where your business knowledge and expertise will come in handy.
5. Take action: Once you have interpreted the data, you need to take action based on your findings. This may involve making changes to your products or services, adjusting your marketing strategy, or improving your operations.

Using data effectively can be complex, but it is worth the effort. By using data to make better decisions, businesses can increase their efficiency and boost their profits.

As a data analyst, you’ll be tackling
business tasks that help companies use data. Coming up, we’ll talk more about what a
business task actually is, and some examples of
what they might look like in real data analyst jobs. Let’s take a second and think back on the real examples of businesses using data analytics and their operation
we’ve seen before. You might have noticed
a common theme across every example. They all have issues to explore, questions to answer,
or problems to solve. It’s easy for these
things to get mixed up. Here’s a way to keep
them straight when we talk about them in
data analytics. An issue is a topic or
subject to investigate. A question is designed to
discover information and a problem is an obstacle or complication that needs
to be worked out. 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. A business task is
the question or problem data analysis
answers for business. This is where you focus a lot of your efforts in the work you’ll
do for future employers. 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. We talked a little
about data-driven decision making in
previous videos. But just in case you need
a refresher, here it is. Data-driven decision-making
is when facts that have been discovered through data analysis are used to guide
business strategy. 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. With great power comes, well, you know the rest.
And you’re doing a super job taking in
all of this information. Up next, we’ll talk about
your responsibility as a data analyst to make
sure you’re gathering, analyzing, and presenting
data in a way that’s fair to the people being
represented by that data.

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. I found that data acts like a
living and breathing thing. 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.

• Introduction

Data analytics is the process of collecting, cleaning, analyzing, and interpreting data to gain insights that can be used to make better decisions. In data analytics, it is important to consider the fairness of the data and the analysis.

• What is fairness in data analytics?

Fairness in data analytics refers to the idea that everyone should be treated equally, regardless of their race, gender, or other personal characteristics. This means that the data should not be biased against any particular group of people.

• Why is fairness important in data analytics?

Fairness is important in data analytics because it can have a real impact on people’s lives. For example, if a company uses data analytics to make decisions about who to hire or who to give a loan to, and the data is biased, then the company could end up discriminating against certain groups of people.

• How to ensure fairness in data analytics

There are a number of things that can be done to ensure fairness in data analytics. Some of these things include:

* **Identifying and addressing bias:** The first step is to identify any potential bias in the data. This can be done by looking at the data for patterns that suggest discrimination.
* **Using fair algorithms:** There are a number of fair algorithms that can be used to analyze data. These algorithms are designed to minimize bias in the results.
* **Monitoring the results:** It is important to monitor the results of the analysis to make sure that they are fair. This can be done by looking for patterns that suggest discrimination.

• Conclusion

Fairness is an important consideration in data analytics. By taking steps to ensure fairness, data analysts can help to create systems that are fair and inclusive to everyone.Here are some additional tips for ensuring fairness in data analytics:

* Be transparent about the data and the analysis. This will help to build trust and credibility with the people who are affected by the analysis.
* Get input from a diverse group of people. This can help to identify potential bias in the data and the analysis.
* Be open to feedback. If someone raises concerns about the fairness of the data or the analysis, be willing to listen and make changes.

By following these tips, data analysts can help to ensure that their work is fair and ethical.

Fill in the blank: 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.

So far, we’ve covered the different roles data
analysts play in business environments
and the kinds of tasks that come with those roles. But data analysts have another
important responsibility: making sure that their
analyses are fair. Now, I know what you’re
probably thinking, data is based on
collected facts, how can it be unfair? Well, that’s a good question. Let’s learn what fairness
means when we talk about data analysis and why
it’s important for you as an analyst
to keep in mind. Fairness means ensuring that your analysis doesn’t
create or reinforce bias. In other words, as
a data analyst, you want to help create systems that are fair and
inclusive to everyone. Sounds simple enough? Well, here’s the tough part about fairness in
data analytics. There isn’t one standard
definition of it, but hopefully the way we’ve
just described it can give you one way to think
about fairness for right now, but it’s about to
get a bit trickier. Sometimes conclusions based on data can be true and unfair. What can you do then? Well, let’s find out
with an example. 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. But don’t worry, there’s a way to make a fair
conclusion here. 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. We’ll learn more about bias in the data analysis process
later on in another course. 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. But we’re going to keep building on our
understanding of fairness throughout this program and you’ll get to practice
with some activities.

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. How do we actually
improve the lives of people by using data? 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.

Data analysts are in high demand across a variety of industries. Here are some of the most common industries where data analysts work:

• Finance: Data analysts in the finance industry use data to track market trends, manage risk, and make investment decisions.
• Healthcare: Data analysts in the healthcare industry use data to improve patient care, identify fraud, and manage costs.
• Retail: Data analysts in the retail industry use data to understand customer behavior, optimize inventory, and personalize marketing campaigns.
• Manufacturing: Data analysts in the manufacturing industry use data to improve production processes, reduce waste, and increase efficiency.
• Technology: Data analysts in the technology industry use data to develop new products and services, improve customer experience, and protect against cyber threats.

The specific tasks that data analysts perform in different industries vary, but they generally involve collecting, cleaning, analyzing, and interpreting data. Data analysts also use their skills to communicate the results of their analysis to stakeholders.Here are some of the specific tasks that data analysts might perform in different industries:

• Finance:
  • Track market trends
  • Manage risk
  • Make investment decisions
  • Analyze credit risk
  • Develop trading strategies
• Healthcare:
  • Identify fraud
  • Manage costs
  • Improve patient care
  • Develop new treatments
  • Optimize clinical trials
• Retail:
  • Understand customer behavior
  • Optimize inventory
  • Personalize marketing campaigns
  • Forecast demand
  • Identify product trends
• Manufacturing:
  • Improve production processes
  • Reduce waste
  • Increase efficiency
  • Develop new products
  • Improve quality control
• Technology:
  • Develop new products and services
  • Improve customer experience
  • Protect against cyber threats
  • Analyze social media data
  • Develop machine learning models

The skills that are required for a data analyst role vary depending on the industry, but some of the most common skills include:

• Data mining
• Data analysis
• Machine learning
• Statistics
• Programming
• Communication
• Problem-solving
• Critical thinking

Data analysts typically have a bachelor’s degree in a related field, such as statistics, computer science, or mathematics. Some employers may also require candidates to have experience with specific data analysis software or programming languages.The job outlook for data analysts is very positive. The Bureau of Labor Statistics projects that employment of data analysts will grow 22% from 2020 to 2030, much faster than the average for all occupations. This growth is being driven by the increasing demand for data analysis in a variety of industries.If you are interested in a career in data analysis, there are a few things you can do to prepare:

• Get a degree in a related field.
• Gain experience with data analysis software and programming languages.
• Develop your communication and problem-solving skills.
• Network with data analysts in your desired industry.

Data analysts play an important role in many different industries. By using their skills to collect, clean, analyze, and interpret data, data analysts can help businesses make better decisions, improve efficiency, and reduce risk.

By now, we know that
there are all kinds of jobs in different industries
available for data analysts. But now it’s time to think about something
just as important, how can you tell if a
job is a good fit for you and your career
goals? Tough one. Don’t worry, that’s exactly what we’ll cover in this video. There’s a lot of
important factors to think about when searching
for your dream job. Let’s talk about some of the
most common factors first, industry, tools, location,
travel, and culture. 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 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. Then search for jobs that
allow you to do that. Next on the list
of things to think about, 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. After this, you have a
few activities to do, and then you’ll be
able to move on to the next part of this course. We learned a lot so far, like what opportunities
are out there for data analysts in
different industries. How data analysts help businesses
make better decisions. The importance of fairness
and data analytics, and the potential
questions you can start asking yourself before
your future job search, and you can always look back at these lessons if
you want to review. In an upcoming course, we’ll look at the skills all
successful data analysts have and you’ll learn how you can start
practicing them too. But before that, you’ll
have an assessment. Good luck, and I’ll
see you later.

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. Think about a time where you’ve used data to solve a problem, whether it’s in your professional
or personal projects. 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.

We’re at the end of this course, which means it’s time to show
off what you’ve learned. We’ve covered the different
kinds of industries using data to drive decisions
and how you can help them. How to promote fairness
in your data work, and opportunities that are out there in the world
of data analytics. I know you’ve got this. Once you finish the
course challenge, I’ll be right here to introduce
you to the next course.

Congratulations on finishing
this first course. You’ve already learned a lot and you’re ready to
take what you’ve learned and move forward and if you ever need a refresher, just remember that
these videos will still be here whenever you need them. You might remember your
next instructor from our introduction at the
beginning of the intro course. Get ready to meet
my fellow Googler and your instructor for the
next course, Ximena. She’s ready to help you get started on your next step towards finishing this program and
becoming a data analyst. This next course will build directly on some of the
topics you’ve learned so far and give you insight into the things we’ve
already talked about. Like any good detective, you learn how to ask
the right questions and use data to find answers. Employees in every industry needs to become comfortable
asking questions, but this can especially be
true for data analysts. A lot of data analysts try
to make their work perfect the first time even though they might not have
all the information. Instead of asking questions, they make assumptions that
can lead to mistakes. It’s so much better to be humble and inquisitive and
to ask questions. I’ll show you what I mean. One of the analysts I supervise came into Google with
no coding experience. He was nervous about leaving
a great first impression, so he tried to study up on multiple languages by
himself before he started. When the work actually began, he didn’t ask us his team questions or ask us for help when he
ran into roadblocks. There are a lot of great
places to find answers, especially online, and his initiative helped them
find some of those places. But at the end of the day, he forgot to tap into
his best resource, us, his team, because he was nervous about how he would be perceived
if he asked us for help. He almost missed out on some great insights
from his team members. As roadblocks persisted, he realized he needed
to make a change. He stopped trying to guess
expectations, processes, and more all on his own and started asking us more questions. As soon as he embraced
this new approach, he skyrocketed on our team. His learning went straight up the curve like a hockey stick. His impact on the organization, the number of people who
reached out to him and his career path going for
it all did the same. The bottom line is, you don’t need to know it all. The saying is true, there are no bad questions. Being open to learning is one of the most important qualities
for a data analyst. Speaking of learning,
in the next course, we’ll go into more
depth learning about basic spreadsheet skills and
when you need to use them, you’ll discover how to apply structured thinking to data work, and your focus on
how to best meet stakeholder needs
and expectations by gathering all the clues. Great work, and good
luck on the next course.