Week 2: All about analytical thinking

What are Data Skills?

Data skills are the abilities to collect, analyze, and interpret data. They are essential for a variety of careers, including data science, data analysis, and business intelligence.There are many different data skills, but some of the most important ones include:

• Data collection: The ability to gather data from a variety of sources, such as databases, surveys, and experiments.
• Data cleaning: The ability to remove errors and inconsistencies from data.
• Data analysis: The ability to use statistical and machine learning techniques to extract insights from data.
• Data visualization: The ability to communicate the results of data analysis in a clear and concise way.
• Data storytelling: The ability to tell a compelling story using data.

How to Discover Your Data Skills

There are many ways to discover your data skills. Here are a few ideas:

• Take a data science or data analysis course: This is a great way to learn the basics of data science and data analysis.
• Work on a data science or data analysis project: This is a great way to apply your knowledge and skills to a real-world problem.
• Read books and articles about data science and data analysis: This is a great way to learn about the latest trends and techniques in data science.
• Attend data science and data analysis meetups and conferences: This is a great way to network with other data professionals and learn about new opportunities.
• Contribute to open source data science projects: This is a great way to gain experience and build your portfolio.

Conclusion

Discovering your data skills is an important first step in a career in data science or data analysis. By taking the time to learn about data science and data analysis, you can develop the skills you need to succeed in this growing field.Here are some additional tips for discovering your data skills:

• Be honest with yourself about your strengths and weaknesses.
• Don’t be afraid to ask for help from others.
• Be persistent and keep learning.

The field of data science is constantly evolving, so it’s important to be adaptable and willing to learn new things. By following these tips, you can discover your data skills and set yourself up for success in this exciting field.

Welcome. Now that you have
a solid foundation on the basics of data, it’s time to focus on some
particular skills and characteristics that will be key to your future career
as a data analyst. We’ll begin with five key skills, move on to the characteristics of analytical thinking and then learn how data analysts balance their roles
and responsibilities. Along the way, you’ll
also discover how to tap into your own natural
abilities for strategy, technical expertise,
and data design. These are incredibly
helpful skills to have and you’ll learn how
to make them even stronger. Finally, you’ll be introduced to some fascinating
real-world examples of how data is influencing the lives of people all around the world. All right. Let’s get started.

What are Key Data Analyst Skills?

Data analyst skills are the abilities that data analysts need to be successful in their role. They include a variety of technical and soft skills, such as:

• Technical skills: These skills involve the ability to use data analysis tools and techniques, such as SQL, Python, and R.
• Soft skills: These skills involve the ability to communicate effectively, work well with others, and solve problems.

Here are some of the most important key data analyst skills:

• Problem-solving: Data analysts need to be able to identify problems, gather data, and develop solutions.
• Critical thinking: Data analysts need to be able to think critically about data and identify patterns and trends.
• Communication: Data analysts need to be able to communicate the results of their analysis to stakeholders in a clear and concise way.
• Teamwork: Data analysts often work as part of a team, so they need to be able to collaborate effectively with others.
• Data visualization: Data analysts need to be able to visualize data in a way that is easy to understand.
• Machine learning: Data analysts need to be able to use machine learning techniques to extract insights from data.
• SQL: SQL is a programming language that is used to query and manipulate data in databases.
• Python: Python is a programming language that is used for data analysis, machine learning, and artificial intelligence.
• R: R is a programming language that is used for statistical analysis and data visualization.

How to Develop Key Data Analyst Skills

There are many ways to develop key data analyst skills. Here are a few ideas:

• Take a data analysis course: This is a great way to learn the basics of data analysis.
• Work on a data analysis project: This is a great way to apply your knowledge and skills to a real-world problem.
• Read books and articles about data analysis: This is a great way to learn about the latest trends and techniques in data analysis.
• Attend data analysis meetups and conferences: This is a great way to network with other data professionals and learn about new opportunities.
• Contribute to open source data analysis projects: This is a great way to gain experience and build your portfolio.

Conclusion

Key data analyst skills are essential for success in this field. By developing these skills, you can become a valuable asset to any organization.

Here are some additional tips for developing key data analyst skills:

• Be persistent and keep learning. The field of data analysis is constantly evolving, so it’s important to be adaptable and willing to learn new things.
• Practice your skills regularly. The best way to improve your skills is to practice them regularly.
• Get feedback from others. Ask for feedback from your peers, mentors, and supervisors on your work. This will help you identify areas where you can improve.

By following these tips, you can develop the key data analyst skills you need to succeed in this growing field.

Thinking about the way you organize your contacts in a new phone is an example of which analytical skill?

Data design

This is an example of data design, which involves how information is organized.

You are planning a road trip. Your first step is to break down the planning into smaller pieces. You begin by calculating your budget. Then, you choose a destination and departure date. Next, you plan where to stay, what vehicle to take, and how long you want to be on the road. Which analytical skill does this scenario describe?

Having a technical mindset

This scenario describes having a technical mindset. A technical mindset is the ability to break things down into smaller steps or pieces and work with them in an orderly and logical way.

Earlier, I told you that you already have
analytical skills. You just might not know it yet. When learning new
things, sometimes people overlook their own skills, but it’s important you take the time to acknowledge them, especially since these skills
are going to help you as a data analyst. In fact, you’re probably more
prepared than you think. Don’t believe me? Well, let me prove it. Let’s start by defining what I’m talking about here. Analytical skills are
qualities and characteristics associated with solving
problems using facts. There are a lot of aspects
to analytical skills, but, we’ll focus on
five essential points. They are curiosity,
understanding context, having technical mindset, data
design, and data strategy. Now, you may be thinking, “I don’t have these
kinds of skills,” or “I only have a couple of them.” But stay with me, and I bet
you’ll change your mind. Let’s start with curiosity. Curiosity is all about
wanting to learn something. Curious people usually seek out new challenges and experiences. This leads to knowledge. The very fact that you’re
here with me right now demonstrates that
you have curiosity. That was an easy one. Now think about
understanding context. Context is the condition in which something
exists or happens. This can be a structure
or an environment. A simple way of
understanding context is by counting to 5. One, two, three, four, five. All of those numbers exist in the context of one through five. But what if a friend of yours said to you, one, two, four, five, three? Well, the three will
be out of context. Simple, right? But it can
be a little tricky. There’s a good chance that
you might not even notice the three being out of context if you aren’t paying
close attention. That’s why listening and trying to understand the full
picture is critical. In your own life, you put things into context all the time. For example, let’s think
about your grocery list. If you group together
items like flour, sugar, and yeast, that’s you adding
context to your groceries. This saves you time when you’re at the baking
aisle at the grocery store. Let’s look at another example. Have you ever shuffled a deck of cards and noticed the joker? If you’re playing a game
that doesn’t include jokers, identifying that card means you understand it’s
out of context. Remove it, and you’re much more likely to play
a successful game. Now we know you have
both curiosity and the ability to
understand context. Let’s move on to the third
skill, a technical mindset. A technical mindset
involves the ability to break things down into
smaller steps or pieces and work with them in an
orderly and logical way. For instance, when
paying your bills, you probably already break down the process into smaller steps. Maybe you start by sorting them by the date they’re due. Next, you might add them up and compare that amount to the
balance in your bank account. This would help you see if
you can pay your bills now, or if you should wait
until the next paycheck. Finally, you’d pay them. When you take something that
seems like a single task, like paying your bills, and break it into smaller steps with an orderly process, that’s using a technical mindset. Now let’s explore the fourth part of an analytical skill set, data design. Data design is
how you organize information. As a data analyst, design typically has to do
with an actual database. But, again, the same skills can easily
be applied to everyday life. For example, think about the way you organize the
contacts in your phone. That’s actually a
type of data design. Maybe you list them by
first name instead of last, or maybe you use email addresses
instead of their names. What you’re really doing
is designing a clear, logical list that
lets you call or text a contact in a
quick and simple way. The last, but
definitely not least, the fifth and final element of analytical skills
is data strategy. Data strategy is the
management of the people, processes, and tools
used in data analysis. Let’s break that down. You manage people
by making sure they know how to use the right data to find solutions to the
problem you’re working on. For processes, it’s
about making sure the path to that solution
is clear and accessible. For tools, you make sure the right technology is
being used for the job. Now, you may be
doubting my ability to give you an example from real life that demonstrates
data strategy. But check this out.
Imagine mowing a lawn. Step 1 would be reading the
owner’s manual for the mower. That’s making sure the
people involved, or you, in this example, know how
to use the data available. The manual would
instruct you to put on protective eyewear
and closed-toe shoes. Then, it’s on to step 2: making the process, the
path, clear and accessible. This will involve you
walking around the lawn, picking up large sticks or rocks that might get in your way. Finally, for step 3, you check the lawn
mower, your tool, to make sure it has
enough gas and oil, and is in working condition, so the lawn can be mowed safely. There you have it. Now you know the five essential skills
of a data analyst. Curiosity, understanding context, having a technical mindset, data design, and data strategy. I told you that you are
already an analytical thinker. Now, you can start actively
practicing these skills as you move through the
rest of this course. Curious about what’s next? Move on to the next video.

What is analytical thinking?

Analytical thinking is the ability to break down information into smaller parts and identify patterns and relationships. It is a critical skill for problem-solving, decision-making, and innovation.

Why is analytical thinking important?

Analytical thinking is important for a variety of reasons:

• It can help you solve problems more effectively. When you can break down a problem into smaller parts, you are better able to identify the cause of the problem and develop a solution.
• It can help you make better decisions. When you have a clear understanding of the information, you are better equipped to make informed decisions.
• It can help you be more creative. When you can think outside the box, you are more likely to come up with new and innovative solutions.
• It can help you be more successful in your career. Analytical thinkers are often in high demand in the workplace. They are able to solve problems, make decisions, and innovate.

How to develop analytical thinking skills

There are many ways to develop analytical thinking skills. Here are a few tips:

1. Ask questions: Analytical thinkers are always asking questions. They want to understand the why behind things. When you are faced with a problem or a decision, take some time to ask yourself questions about it. What are the different factors involved? What are the possible solutions? What are the pros and cons of each solution?
2. Look for patterns: Analytical thinkers look for patterns in data and information. They try to see the big picture and how the pieces fit together. When you are analyzing data, pay attention to any patterns that you see. Are there any trends? Are there any correlations between different variables?
3. Test hypotheses: Analytical thinkers form hypotheses and then test them. They are willing to challenge their own assumptions and beliefs. When you are trying to solve a problem, come up with a hypothesis about what the solution might be. Then, gather evidence to test your hypothesis.
4. Be objective: Analytical thinkers try to be objective in their thinking. They avoid letting their biases cloud their judgment. When you are analyzing information, try to be as objective as possible. Don’t let your personal opinions or beliefs influence your interpretation of the data.
5. Be open-minded: Analytical thinkers are open to new ideas and perspectives. They are willing to change their minds when presented with new evidence. When you are thinking about a problem, be open to new ideas and solutions. Don’t be afraid to challenge the status quo.

Conclusion

Analytical thinking is a skill that can be learned and improved with practice. There are many resources available to help you develop your analytical thinking skills, such as books, articles, and online courses. If you want to be a better thinker, analytical thinking is a skill that you should develop. There are many benefits to thinking analytically, and the skills are transferable to many different areas of life.

Fill in the blank: Data visualization involves using _____ to represent and present data. Select all that apply.

maps, charts, graphs

Data visualization involves using graphs, maps, and charts to represent and present data.

To execute a plan using detail-oriented thinking, what does a data analyst consider?

The specifics

To execute a plan using detail-oriented thinking, a data analyst considers the specifics.

Now that you know
the five essential skills of a data analyst, you’re ready to learn more about what it means to
think analytically. People don’t often
think about thinking. Thinking is second nature to us. It just happens automatically, but there are actually many
different ways to think. Some people think creatively, some think critically, and some people think
in abstract ways. Let’s talk about
analytical thinking. Analytical thinking
involves identifying and defining a problem and then solving it by using data in an organized, step-by-step manner. As data analysts, how do
we think analytically? Well, to answer that question, we will now talk about
a second set of five. The five key aspects to
analytical thinking. They are visualization,
strategy, problem-orientation, correlation, and finally, big-picture and
detail-oriented thinking. Let’s start with visualization. In data analytics, visualization is the graphical
representation of information. Some examples include graphs, maps, or other design elements. Visualization is important
because visuals can help data analysts understand and explain information
more effectively. Think about it like this. If you are trying to explain
the Grand Canyon to someone, using words would be much more challenging than
showing them a picture. A visualization of the
Grand Canyon would help you make your
point much quicker. Now let’s talk about
the second part of analytical thinking,
being strategic. With so much data available, having a strategic mindset is key to staying
focused and on track. Strategizing helps data
analysts see what they want to achieve with the data
and how they can get there. Strategy also helps improve the quality and usefulness
of the data we collect. By strategizing, we
know all our data is valuable and can help us
accomplish our goals. Next step on the analytical
thinking checklist: being problem-oriented. Data analysts use a problem- oriented approach in
order to identify, describe, and solve problems. It’s all about keeping
the problem top of mind throughout the
entire project. For example, say a data
analyst is told about the problem of a warehouse constantly running
out of supplies. They would move forward with different strategies
and processes. But the number one
goal would always be solving the problem of keeping
inventory on the shelves. Data analysts also ask
a lot of questions. This helps improve
communication and saves time while
working on a solution. An example of that would be surveying customers about
their experiences using a product and
building insights from those questions to
improve their product. This leads us to
the fourth quality of analytical thinking: being able to identify a correlation between two
or more pieces of data. A correlation is
like a relationship. You can find all kinds
of correlations in data. Maybe it’s the relationship
between the length of your hair and the amount
of shampoo you need. Or maybe you notice a
correlation between a rainier season leading to a high number of
umbrellas being sold. But as you start identifying
correlations in data, there’s one thing you always
want to keep in mind: Correlation does not
equal causation. In other words, just
because two pieces of data are both trending
in the same direction, that doesn’t necessarily
mean they are all related. We’ll learn more
about that later. Now the final piece of the analytical thinking
puzzle: big-picture thinking. This means being able to see the big picture as
well as the details. A jigsaw puzzle is a great
way to think about this. Big-picture thinking is like looking at a complete puzzle. You can enjoy the
whole picture without getting stuck on every tiny piece that went into making it. If you only focus on
individual pieces, you wouldn’t be able
to see past that, which is why big-picture
thinking is so important. It helps you zoom out and see possibilities
and opportunities. This leads to exciting
new ideas or innovations. On the flip side, detail-oriented
thinking is all about figuring out all of the aspects that will help you
execute a plan. In other words, the pieces
that make up your puzzle. There are all kinds
of problems in the business world
that can benefit from employees who have
both a big-picture and a detail-oriented
way of thinking. Most of us are naturally
better at one or the other. But you can always develop the skills to fit
both pieces together. Now that you know
the five aspects of analytical thinking, visualization, strategy,
problem-orientation, correlation, and big-picture and
detail-oriented thinking, you can put them to work for you when you’re
working with data. As you continue through this
course, you’ll learn how.

What are core analytical skills?

Core analytical skills are the abilities that are essential for data analysis. They include:

• Problem-solving: The ability to identify and solve problems using data.
• Critical thinking: The ability to think clearly and rationally about data.
• Data visualization: The ability to represent data in a way that is easy to understand and interpret.
• Communication: The ability to communicate the results of data analysis to others.
• Creativity: The ability to think outside the box and come up with new ideas.
• Attention to detail: The ability to focus on the details and ensure that data is accurate and complete.
• Collaboration: The ability to work effectively with others.

Why are core analytical skills important?

Core analytical skills are important for a variety of reasons:

• They can help you make better decisions. By using data to analyze problems, you can make more informed decisions that are more likely to be successful.
• They can help you identify trends and patterns. By visualizing data, you can identify trends and patterns that would not be visible otherwise.
• They can help you communicate your findings. By communicating the results of your data analysis to others, you can share your insights and help others make better decisions.
• They can help you be more creative. By thinking outside the box, you can come up with new ideas that can help you solve problems and improve processes.
• They can help you be more accurate and efficient. By paying attention to detail, you can ensure that your data is accurate and complete, which can lead to more accurate and efficient results.
• They can help you be more successful in your career. By developing core analytical skills, you can make yourself more valuable to employers and increase your chances of getting a job or promotion.

How to develop core analytical skills

There are many ways to develop core analytical skills. Here are a few tips:

• Take a data analysis course. This is a great way to learn the basics of data analysis and develop your problem-solving and critical thinking skills.
• Read books and articles about data analysis. There are many resources available that can help you learn more about data analysis and develop your skills.
• Practice data analysis. The best way to improve your analytical skills is to practice. Try to analyze data from different sources and use different analytical techniques.
• Get feedback from others. Ask for feedback from your peers, mentors, and supervisors on your data analysis work. This will help you identify areas where you can improve.
• Join a data analysis community. There are many online and offline communities where you can connect with other data analysts and learn from each other.

Conclusion

Core analytical skills are essential for data analysis. By developing these skills, you can make better decisions, identify trends and patterns, communicate your findings, and be more creative, accurate, efficient, and successful in your career.

In the example problem of not having enough blueberries to make a pie, what root cause was revealed through the Five Whys process?

Mulberry bushes were damaged by a late frost

This was the answer to the fifth “why” question: why didn’t the mulberry bushes produce any fruit?. The Five Whys process is used to reveal a root cause of a problem through the answer to the fifth question. Read how root causes in business settings have also been identified using the Five Whys process in an article by Eric Ries.

Let’s recap what we’ve learned about analytical thinking so far. The 5 key aspects are
visualization, strategy, problem-orientation, correlation, and
using big-picture and detail-oriented thinking. We’ve seen how you already use them in your everyday life. We also talked about
how different people naturally use certain
types of thinking, but that you can absolutely
grow and develop the skills that might not
come as easily to you. This means you can become
a versatile thinker, which is a very important
part of data analysis. You might naturally be
an analytical thinker, but you can learn to
think creatively and critically, and be
great at all three. The more ways you can think, the easier it is to think outside the box and come up
with fresh ideas. But why is it important to
think in different ways? Well because in data
analysis, solutions are almost never right
in front of you. You need to think critically to find out the right
questions to ask. But you also need
to think creatively to get new and
unexpected answers. Let’s talk about some
of the questions data analysts ask when they’re on
the hunt for a solution. Here’s one that
will come up a lot: What is the root
cause of a problem? A root cause is the reason
why a problem occurs. If we can identify and
get rid of a root cause, we can prevent that problem
from happening again. A simple way to wrap
your head around root causes is with the
process called the Five Whys. In the Five Whys you ask “why” five times to
reveal the root cause. The fifth and final
answer should give you some useful and sometimes
surprising insights. Here’s an example of the
Five Whys in action. Let’s say you wanted to make a blueberry pie but couldn’t
find any blueberries. You’ve been trying to
solve a problem by asking, why can’t I make a blueberry pie? The answer will be, there are no blueberries at the store. There’s Why Number 1. You then ask, why were there
no blueberries at the store? Then you discover that the
blueberry bushes don’t have enough
fruit this season. That’s Why Number 2. Next, you’d ask, why was
there not enough fruit? This would lead to the fact that birds were eating
all the berries. Why Number 3, asked and answered. Now we get to Why Number 4. Ask why a fourth time and
the answer would be that, although the birds
normally prefer mulberries and don’t
eat blueberries, the mulberry bush didn’t
produce fruit this season, so the birds are eating
blueberries instead. Finally, we get to Why Number 5, which should reveal
the root cause. A late frost damaged
the mulberry bushes, so it didn’t produce any fruit. You can’t make a blueberry pie because of the late
frost months ago. See how the Five Whys can reveal some very surprising root causes. This is a great trick
to know, and it can be a very helpful process
in data analysis. Another question commonly
asked by data analysts is, where are the gaps
in our process? For this, many people will use something called gap analysis. Gap analysis lets you examine
and evaluate how a process works currently in order to get where you want
to be in the future. Businesses conduct gap analysis to do all kinds of things, such as improve a product
or become more efficient. The general approach
to gap analysis is understanding where you are now compared to where you want to be. Then you can identify the
gaps that exist between the current and future state and determine how to bridge them. A third question that data
analysts ask a lot is, what did we not consider before? This is a great way to think
about what information or procedure might be
missing from a process, so you can identify ways to make better decisions and
strategies moving forward. These are just a few
examples of the kinds of questions data analysts
use at their jobs every day. As you begin your career, I’m sure you’ll think
of a whole lot more. The way data analysts
think and ask questions plays a big part in how
businesses make decisions. That’s why analytical thinking and understanding how to ask the right questions can have such a huge impact on the
overall success of a business. Later, we’ll talk more
about how data-driven decisions can lead to
successful outcomes.

Introduction

Data is all around us. It is generated by everything we do, from the websites we visit to the products we buy. This data can be used to drive successful outcomes in many ways.In this tutorial, we will discuss how to use data to:

• Identify new opportunities
• Make better decisions
• Improve efficiency
• Increase customer satisfaction
• Reduce costs

Identifying new opportunities

Data can be used to identify new opportunities by helping you to understand your customers, your competitors, and your industry. For example, you can use data to identify new customer segments, to find out what your customers are looking for, and to see how your competitors are doing.

Making better decisions

Data can be used to make better decisions by providing you with insights into the past, present, and future. For example, you can use data to track the performance of your products and services, to identify trends, and to make predictions about future demand.

Improving efficiency

Data can be used to improve efficiency by helping you to identify areas where you can streamline your operations. For example, you can use data to track the time it takes to complete tasks, to identify bottlenecks, and to make changes that will save time and money.

Increasing customer satisfaction

Data can be used to increase customer satisfaction by helping you to understand what your customers want and need. For example, you can use data to track customer feedback, to identify areas where you can improve your products and services, and to make sure that your customers are getting the best possible experience.

Reducing costs

Data can be used to reduce costs by helping you to identify areas where you can save money. For example, you can use data to track your spending, to identify unnecessary expenses, and to make changes that will save you money.

Conclusion

Data is a powerful tool that can be used to drive successful outcomes in many ways. By using data effectively, you can improve your understanding of your customers, make better decisions, improve efficiency, increase customer satisfaction, and reduce costs.Here are some additional tips for using data to drive successful outcomes:

• Start with a clear goal in mind. What do you want to achieve by using data?
• Collect the right data. The data you collect should be relevant to your goal and should be accurate and reliable.
• Analyze the data carefully. Use data visualization tools to help you identify patterns and trends.
• Take action based on the insights you gain from the data. Don’t just collect data for the sake of collecting data. Use it to make changes that will improve your business.

By following these tips, you can use data to drive successful outcomes for your business.

In an earlier video, you learned about
five essential analytical skills. As a reminder, they’re curiosity,
understanding context, having a technical mindset,
data design, and data strategy. In the next couple of videos,
we’ll explore how these abilities all become part of
data-driven decision-making. But first, let’s look at the concept
of data-driven decision-making and why it’s more likely to lead
to successful outcomes. You might remember that data-driven
decision-making involved using facts to guide business strategy. Data analysts can tap into the power of
data to do all kinds of amazing things. With data, they can gain valuable
insights, verify their theories or assumptions, better
understand opportunities and challenges, support an objective,
help make a plan, and much more. In business, data-driven decision-making can improve
the results in a lot of different ways. For example, say a dairy farmer wants
to start making and selling ice cream. They could guess what flavors
customers would like, but there’s a better way to
get the information. The farmer could survey people and
ask them what flavors they prefer. This gives the farmer the data they need
to pick ice cream flavors people will enjoy. Here’s another example. Let’s say the president of an organization
is curious about what perks employees value most. She asked the human resources director
who says people value casual dress code. It’s a gut feeling, but the HR director
backs it up with the fact that he sees a lot of people wearing jeans and
t-shirts. But what if this company were to use a
more structured employee feedback process, such as a survey? It might reveal that employees actually
enjoy free public transportation cards the most. The human resources director just didn’t
realize that because he drives to work. These are just some of the benefits
of data-driven decision-making. It gives you greater confidence
about your choice and your abilities to address
business challenges. It helps you become more proactive when
an opportunity presents itself, and it saves you time and
effort when working towards a goal. Now let’s learn more about how these five
skills help you tap into all the potential of data-driven decision-making. First, think about curiosity and context. The more you learn about
the power of data, the more curious you’re likely to become. You’ll start to see patterns and
relationships in everyday life, whether you’re reading the news,
watching a movie, or going to an appointment across town. The analysts take their thinking
a step further by using context to make predictions, research answers, and eventually draw conclusions
about what they’ve discovered. This natural process is a great first
step in becoming more data-driven. Having a technical mindset comes next. Everyone has instincts, or as in the case of our human resources
director example, gut feelings. Data analysts are no different. They have gut feelings too. But they’ve trained themselves
to build on those feelings and use a more technical
approach to explore them. They do this by always seeking out
the facts, putting them to work through analysis, and using the insights they
gain to make informed decisions. Next, we come to data design, which has a strong connection
to data-driven decision-making. To put it simply, designing your data so that it is organized in a logical way makes
it easy for data analysts to access, understand, and
make the most of available information. And it’s important to keep in mind
that data design doesn’t just apply to databases. This kind of thinking can work with
all sorts of real-life situations too. The basic idea is this. If you make decisions that
are informed by data, you are more likely to make more
informed and effective decisions. The final ability is data strategy, which
incorporates the people, processes, and tools used to solve a problem. This is a big one to remember because data
strategy gives you a high-level view of the path you need to take
to achieve your goals. Also, data-driven decision-making
isn’t a one-person job. It’s much more likely to be successful if
everyone is on board and on the same page, so it’s important to make sure
specific procedures are in place and that your technology being used is
aligned with your data-driven strategy. Now you know how these five essential
analytical skills work towards making better, data-driven decisions. So far, many of the examples
you’ve heard are hypothetical. That means they could be true in theory,
but aren’t specific real-world cases. Next, we’ll look at some real examples. I can’t wait to share how data analysts
put data to work for amazing results.

Introduction

Data is all around us. It is generated by everything we do, from the websites we visit to the products we buy. This data can be used to do amazing things, from predicting the weather to detecting fraud.In this tutorial, we will discuss some real-world examples of how data is used to create “data magic”.

Example 1: Predicting the weather

Weather forecasting is a classic example of how data can be used to make predictions. Meteorologists use data from weather stations, satellites, and other sources to create models of the atmosphere. These models are then used to predict the weather for the next few days.

Example 2: Detecting fraud

Financial institutions use data to detect fraud. For example, they might use data on credit card transactions to identify patterns that suggest fraud. They might also use data on customer behavior to identify people who are at risk of committing fraud.

Example 3: Personalizing marketing

Marketers use data to personalize their marketing campaigns. For example, they might use data on past purchases to target customers with ads for products they are likely to be interested in. They might also use data on customer demographics to tailor their marketing messages.

Example 4: Improving healthcare

Healthcare providers use data to improve patient care. For example, they might use data on patient records to identify diseases early on. They might also use data on patient behavior to develop interventions that can help patients improve their health.

Example 5: Making cities smarter

Cities are using data to become smarter. For example, they might use data on traffic patterns to optimize traffic flow. They might also use data on crime rates to target resources where they are most needed.These are just a few examples of how data is used to create “data magic” in the real world. As data becomes more abundant and accessible, we can expect to see even more amazing things being done with it.

Conclusion

Data is a powerful tool that can be used to make predictions, detect fraud, personalize marketing, improve healthcare, and make cities smarter. As data becomes more abundant and accessible, we can expect to see even more amazing things being done with it.

In this video, I’m going to share some case studies that highlight the incredible
work data analysts do. Each of these scenarios
shows off the power of data-driven decision-making
in unexpected ways. The first story is about Google. As I mentioned a little
while back, here at Google, our mission is to organize the world’s information
and make it universally accessible
and useful. All of our products, from idea to
development to launch, are built on data and
data-driven decision-making. There are tons of examples
here at Google of people using facts to
create business strategy. But one of the most famous ones has to do with Google’s
human resources. Here’s how it went.
The HR department wanted to know if there was
value in having managers. Were their contributions
worthwhile? Or should everyone just be
an individual contributor? To answer that question, Google’s people
analytics team looked at past performance reviews
and employee surveys. The data they found
was plotted on a graph because as
you’ve learned, visuals are extremely helpful when trying to understand
a problem or concept. The graph revealed that Googlers had positive feelings
about their managers, but the data was pretty general and the team wanted
to learn more. So they dug deeper and split
the data into quartiles. A quartile divides
data points into four equal parts or quarters. Here’s where the really cool
stuff started happening. The data analysts discovered that there was a big difference between the very top and
the very bottom quartiles. As it turned out, the teams with the best managers were
significantly happier, more productive, and more likely to want to keep
working at Google. This confirmed that managers were valued and make a big difference. Therefore, the idea of having only individual contributors
was not implemented. But there was still
more work to do. Just knowing that
great managers create great results doesn’t lead
to actionable insights. You have to identify what
exactly makes a great manager, so the team took two additional steps
to collect more data. First, they launched
an awards program where employees could nominate
their favorite managers. For every submission you
had to provide examples or data about what makes
that manager great. The second step involved interviewing managers
who were graphed on the top and bottom quartiles. This helped the analytics
team see the differences between successful and less successful management behaviors. The best behaviors
were identified as were the most common reasons for a manager needing improvement. The final step was sharing these insights and
putting a procedure in place for evaluating managers with these
qualities in mind. This data-driven decision
continues to create an exceptional
company culture for my colleagues and
me. Thanks, data. Another interesting example comes from the nonprofit sector. Nonprofits are organizations
dedicated to advancing a social cause or advocating
for a particular effort, such as food security, education or the arts. In this case, data analysts researched how journalists can make a more meaningful impact for the nonprofits
they would write about. Because journalists
write for newspapers, magazines, and
other news outlets, they can help nonprofits
reach readers like you and me, who then take action to help nonprofits
reach their goals. For instance, say you
read about the problem of climate change in
an online magazine. If the article is effective, you’ll learn more about the
cause and might even be compelled to make greener choices in your day-to-day life, volunteer for a nonprofit, or make a donation. That’s an example of the journalist’s work
bringing about awareness, understanding, and engagement.
So, back to the story. The data analysts used a tracker
to monitor story topics, clicks, web traffic,
comments, shares and more. Then they evaluated the
information to make recommendations for
how the journalists could do their jobs even better. In the end, they came up with some great ideas for
how nonprofits and journalists can motivate people everywhere to work together and make the world
a better place. There’s really no end to what you can do as a data analyst. As you progress
through this program, you’ll discover even
more possibilities. Great job following along with the topics of these
past few videos. You learned all about
analytical skills and the five key characteristics
of data analysts. You probably even
learned that you are a pro at most
of these already. Next, you discovered what it
means to think analytically and the specific
skills data analysts develop to help them do it. You explored tools and processes that enable
data analysts to pinpoint a problem and ask the right questions in
order to solve them. Finally, some real-world
stories helped illustrate why data-driven
decision-making is usually more successful
than other methods. You’re building a
wonderful foundation for your career as
a data analyst. With every video, your skills
will continue to expand, and your understanding
of key data analytics concepts will only get stronger. Soon, you’ll have a chance to test out everything
you’ve learned. This is a really useful
opportunity to check your understanding of all the
concepts we’ve discussed, and if you’re ever
unsure about a question, you can review the videos and readings to find the answer. This is another awesome way
to practice collecting data. Keep up the great work.