Week 2: Data-driven decisions

Introduction

Data analysis is the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains.

Data and decisions

Data is essential for making decisions. It can help us to understand what is happening, identify trends, and make predictions. By analyzing data, we can make better decisions about our businesses, our lives, and our world.

There are many different ways to use data to make decisions. Some common methods include:

• Descriptive analysis: This type of analysis describes the data by summarizing it using statistics. For example, we can use descriptive analysis to find the average age of our customers or the most popular product in our store.
• Inferential analysis: This type of analysis uses data to make inferences about a population. For example, we can use inferential analysis to estimate the percentage of our customers who are likely to buy a new product.
• Predictive analysis: This type of analysis uses data to predict future events. For example, we can use predictive analysis to forecast sales for the next quarter or predict which customers are likely to churn.

The importance of data in decision-making

Data is becoming increasingly important in decision-making. In the past, decisions were often made based on gut instinct or experience. However, as the amount of data available has grown, businesses are increasingly turning to data-driven decision-making.

There are several reasons why data is so important in decision-making:

• Data can help us to make better decisions. By understanding the data, we can identify patterns and trends that can help us make informed choices.
• Data can help us to avoid making mistakes. By analyzing data, we can identify risks and potential problems before they occur.
• Data can help us to be more efficient. By using data to automate tasks, we can free up time to focus on other areas of our business.
• Data can help us to be more creative. By using data to explore new possibilities, we can find new ways to improve our business.

Conclusion

Data is an essential tool for making decisions. By understanding the data, we can make better, more informed choices. As the amount of data available continues to grow, the importance of data-driven decision-making will only increase.

Here are some additional tips for using data to make decisions:

• Start with a clear goal in mind. What do you want to achieve by analyzing the data?
• Choose the right data. Not all data is created equal. Make sure you are using the data that is most relevant to your decision.
• Use the right tools. There are many different tools available for data analysis. Choose the tools that are right for you and your data.
• Interpret the results carefully. Don’t just look at the numbers. Think about what the results mean and how they can help you make a decision.
• Iterate and improve. Data analysis is an iterative process. Don’t be afraid to make changes to your analysis as you learn more.

Welcome back. Now it’s time to go even further and
build on what you’ve learned about problem-solving in data analytics and crafting
effective questions. Coming up, we’ll cover
a wide range of topics. You’ll learn about
how data can empower our decisions, big and small; the difference between quantitative and
qualitative analysis and when to use them; the pros and cons of different
data visualization tools; what metrics are, and
how analysts use them; and how to use mathematical
thinking to connect the dots. To be honest, I’m still learning more about these
things every day, and so will you! Like how quantitative and qualitative
data can work together. In my role in finance, most of my work is quantitative, but recently I was working
on a project that focused a lot on empathy and trust and that was really new for me. But we took those more
qualitative things into account during analysis, and that really helped
me understand how quantitative and
qualitative data can come together to help us make
powerful decisions. Now you’re on your
way to building your own data analyst toolkit. Before you know it, you’ll be analyzing
all kinds of data yourself and learning new
things while you do it. But first, let’s start small with the power
of observation.

Introduction

Data is a powerful tool that can be used to make better decisions. By analyzing data, we can identify patterns and trends that can help us make informed choices. Data can be used to make decisions in all areas of life, from personal finance to business strategy.

How data empowers decisions

Data can empower decisions in a number of ways:

• By providing insights: Data can help us to understand what is happening, identify trends, and make predictions. This information can be used to make better decisions about our businesses, our lives, and our world.
• By reducing risk: By analyzing data, we can identify risks and potential problems before they occur. This can help us to make better decisions that avoid costly mistakes.
• By improving efficiency: Data can be used to automate tasks, freeing up time to focus on other areas of our business. This can lead to improved efficiency and productivity.
• By driving innovation: Data can be used to explore new possibilities and find new ways to improve our businesses. This can lead to innovation and growth.

The importance of data-driven decision-making

Data-driven decision-making is the process of making decisions based on the analysis of data. This is in contrast to making decisions based on gut instinct or experience. Data-driven decision-making is becoming increasingly important as the amount of data available has grown.

There are several reasons why data-driven decision-making is important:

• It can help us to make better decisions. By analyzing data, we can identify patterns and trends that can help us make informed choices.
• It can help us to avoid making mistakes. By analyzing data, we can identify risks and potential problems before they occur.
• It can help us to be more efficient. By using data to automate tasks, we can free up time to focus on other areas of our business.
• It can help us to be more creative. By using data to explore new possibilities, we can find new ways to improve our business.

The challenges of data-driven decision-making

There are some challenges associated with data-driven decision-making:

• Data quality: The quality of the data is important. If the data is inaccurate or incomplete, it can lead to bad decisions.
• Data bias: Data can be biased, which can lead to biased decisions. It is important to be aware of potential biases in the data and to take steps to mitigate them.
• Interpreting the data: It can be difficult to interpret the data and to draw meaningful conclusions from it. It is important to have expertise in data analysis to be able to do this effectively.

Conclusion

Data is a powerful tool that can be used to make better decisions. By understanding the challenges and limitations of data-driven decision-making, we can use data to make better choices in all areas of our lives.

Here are some additional tips for using data to make decisions:

• Start with a clear goal in mind. What do you want to achieve by analyzing the data?
• Choose the right data. Not all data is created equal. Make sure you are using the data that is most relevant to your decision.
• Use the right tools. There are many different tools available for data analysis. Choose the tools that are right for you and your data.
• Interpret the results carefully. Don’t just look at the numbers. Think about what the results mean and how they can help you make a decision.
• Iterate and improve. Data analysis is an iterative process. Don’t be afraid to make changes to your analysis as you learn more.

Fill in the blank: Data-inspired decision-making explores different data sources to find _____.

commonalities

Data-inspired decision-making explores different data sources to find commonalities.

We’ve talked a lot
about what data is and how it plays
into decision-making. What do we know already? Well, we know that data
is a collection of facts. We also know that
data analysis reveals important patterns and
insights about that data. Finally, we know
that data analysis can help us make more
informed decisions. Now, we’ll look at
how data plays into the decision-making
process and take a quick look at the
differences between data-driven and
data-inspired decisions. Let’s look at a
real-life example. Think about the last time you searched “restaurants
near me” and sorted the results by rating to help you decide which
one looks best. That was a decision
you made using data. Businesses and other
organizations use data to make better
decisions all the time. There’s two ways
they can do this, with data-driven or
data-inspired decision-making. We’ll talk more about data-inspired
decision-making later on, but here’s a quick
definition for now. Data-inspired decision-making explores different data sources to find out what
they have in common. Here at Google, we use
data every single day, in very surprising ways too. For example, we use data
to help cut back on the amount of energy spent
cooling your data centers. After analyzing years of data collected with
artificial intelligence, we were able to make decisions that help reduce the energy we use to cool our data
centers by over 40 percent. Google’s People Operations team also uses data to improve how we hire new Googlers and how we get them
started on the right foot. We wanted to make sure we weren’t passing over any
talented applicants and that we made their
transition into their new roles as
smooth as possible. After analyzing data on
applications, interviews, and new hire
orientation processes, we started using an algorithm. An algorithm is a
process or set of rules to be followed
for a specific task. With this algorithm, we
reviewed applicants that didn’t pass the initial
screening process to find great candidates. Data also helped us determine the ideal number of
interviews that lead to the best possible
hiring decisions. We’ve created new
onboarding agendas to help new employees get
started at their new jobs. Data is everywhere. Today, we create so much data
that scientists estimate 90 percent of the world’s data has been created in just
the last few years. Think of the potential here. The more data we have, the bigger the problems
we can solve and the more powerful our
solutions can be. But responsibly gathering data is only part of the process. We also have to turn data into knowledge that helps us
make better solutions. I’m going to let fellow Googler, Ed, talk more about that. Just having tons of
data isn’t enough. We have to do something
meaningful with it. Data in itself
provides little value. To quote Jack Dorsey, the founder of
Twitter and Square, “Every single action
that we do in this world is triggering
off some amount of data, and most of that data is
meaningless until someone adds some interpretation of it or someone adds a
narrative around it.” Data is straightforward,
facts collected together, values that describe something. Individual data
points become more useful when they’re
collected and structured, but they’re still somewhat
meaningless by themselves. We need to interpret data to
turn it into information. Look at Michael Phelps’ time in a 200-meter individual
medal swimming race, one minute, 54 seconds. Doesn’t tell us much. When we compare it to his competitor’s
times in the race, however, we can see
that Michael came in the first place and
won the gold medal. Our analysis took
data, in this case, a list of Michael’s races
and times and turned it into information by comparing
it with other data. Context is important. We needed to know that this
race was an Olympic final and not some other random race to determine that this was
a gold medal finish. But this still isn’t knowledge. When we consume
information, understand it, and apply it, that’s when
data is most useful. In other words, Michael
Phelps is a fast swimmer. It’s pretty cool how
we can turn data into knowledge that helps us
in all kinds of ways, whether it’s finding the
perfect restaurant or making environmentally
friendly changes. But keep in mind, there are limitations
to data analytics. Sometimes we don’t have access to all of
the data we need, or data is measured
differently across programs, which can make it difficult
to find concrete examples. We’ll cover these more
in detail later on, but it’s important that you start thinking about them now. Now that you know how data
drives decision-making, you know how key your role as a data analyst is
to the business. Data is a powerful tool
for decision-making, and you can help provide
businesses with the information they need to solve problems
and make new decisions, but before that, you will need to learn a little more about the kinds of data you’ll be working with and how
to deal with it.

Introduction

In data analysis, there are two main types of data: qualitative and quantitative. Qualitative data is non-numerical data that describes things in words. Quantitative data is numerical data that can be measured and counted.

Qualitative data

Qualitative data is often used to understand people’s thoughts, feelings, and experiences. It can be collected through interviews, focus groups, and surveys. Qualitative data can be analyzed using a variety of methods, such as thematic analysis and content analysis.

Quantitative data

Quantitative data is often used to measure things like sales, profits, and customer satisfaction. It can be collected through surveys, experiments, and observations. Quantitative data can be analyzed using a variety of statistical methods, such as regression analysis and hypothesis testing.

The difference between qualitative and quantitative data

The main difference between qualitative and quantitative data is that qualitative data is non-numerical while quantitative data is numerical. This means that qualitative data cannot be measured or counted, while quantitative data can.

When to use qualitative and quantitative data

The best type of data to use depends on the research question. If you are trying to understand people’s thoughts, feelings, and experiences, then qualitative data is a good choice. If you are trying to measure something like sales or profits, then quantitative data is a better choice.

Using both qualitative and quantitative data

In many cases, it is helpful to use both qualitative and quantitative data. This is because qualitative data can provide insights that quantitative data cannot, and vice versa. For example, you could use qualitative data to understand why customers are not buying your product, and then use quantitative data to measure the impact of a marketing campaign on sales.

Conclusion

Qualitative and quantitative data are both important in data analysis. The best type of data to use depends on the research question. In many cases, it is helpful to use both qualitative and quantitative data.

Here are some additional tips for using qualitative and quantitative data:

• Be aware of the limitations of each type of data. Qualitative data is not always reliable, and quantitative data can be misleading.
• Use the right tools for the job. There are many different tools available for analyzing data. Choose the tools that are right for you and your data.
• Interpret the results carefully. Don’t just look at the numbers. Think about what the results mean and how they can help you answer your research question.
• Be creative. There is no one right way to analyze data. Be open to new ideas and approaches.

Fill in the blank: Quantitative data is specific and _____.

objective

Quantitative data is a specific and objective measure, such as a number, quantity or range.

Which of the following examples would be determined using qualitative data?

The most well-liked make and model of car in Puerto Rico

The most well-liked make and model of car in Puerto Rico would be determined using qualitative data. Qualitative data is a subjective and explanatory measure of a quality or characteristic.

Hi again. When it comes to
decision-making, data is key. But we’ve also learned that there are a lot of
different kinds of questions that data
might help us answer, and these different questions make different kinds of data. There are two kinds of data
that we’ll talk about in this video, quantitative
and qualitative. Quantitative data is all about the specific and objective
measures of numerical facts. This can often be the what, how many, and how
often about a problem. In other words, things
you can measure, like how many commuters take the train to
work every week. As a financial analyst, I work with a lot
of quantitative data. I love the certainty
and accuracy of numbers. On the other hand, qualitative data describes subjective or
explanatory measures of qualities and characteristics or things that can’t be measured
with numerical data, like your hair color. Qualitative data is great for helping us answer
why questions. For example, why
people might like a certain celebrity or snack
food more than others. With quantitative data,
we can see numbers visualized as charts or graphs. Qualitative data can then give us a more high-level
understanding of why the numbers are
the way they are. This is important
because it helps us add context to a problem. As a data analyst, you’ll be using both quantitative and
qualitative analysis, depending on your business task. Reviews are a great
example of this. Think about a time you
used reviews to decide whether you wanted to buy
something or go somewhere. These reviews might have told you how many people dislike
that thing and why. Businesses read
these reviews too, but they use the data
in different ways. Let’s look at an example of
a business using data from customer reviews to see qualitative and quantitative
data in action. Now, say a local ice cream
shop has started using their online reviews
to engage with their customers and
build their brand. These reviews give
the ice cream shop insights into their
customers’ experiences, which they can use to inform
their decision-making. The owner notices that their
rating has been going down. He sees that lately his shop has been receiving
more negative reviews. He wants to know why, so he starts asking questions. First are measurable questions. How many negative
reviews are there? What’s the average rating? How many of these reviews
use the same keywords? These questions generate
quantitative data, numerical results that help confirm their customers
aren’t satisfied. This data might lead them
to ask different questions. Why are customers
unsatisfied? How can we improve
their experience? These are questions that
lead to qualitative data. After looking
through the reviews, the ice cream shop
owner sees a pattern, 17 of negative reviews use the word “frustrated.”
That’s quantitative data. Now we can start collecting
qualitative data by asking why this word
is being repeated? He finds that customers are frustrated because the shop is running out of popular flavors
before the end of the day. Knowing this, the ice
cream shop can change its weekly order to
make sure it has enough of what the
customers want. With both quantitative
and qualitative data, the ice cream shop owner
was able to figure out his customers were unhappy
and understand why. Having both types of data
made it possible for him to make the right changes
and improve his business. Now that you know the
difference between quantitative and
qualitative data, you know how to get
different types of data by asking
different questions. It’s your job as a data
detective to know which questions to ask to
find the right solution. Then you can start thinking about cool and creative ways to help stakeholders better
understand the data. For example,
interactive dashboards, which we’ll learn about soon.

Introduction

Once you have completed your data analysis, it is important to share your findings with the relevant stakeholders. This can be a daunting task, but it is essential to communicate your results effectively in order to have an impact.

The importance of sharing your findings

There are several reasons why it is important to share your findings. First, sharing your findings can help to improve decision-making. By providing stakeholders with insights into your data, you can help them to make better decisions about their business. Second, sharing your findings can help to build trust and credibility. When stakeholders see that you are able to collect and analyze data effectively, they are more likely to trust your insights. Third, sharing your findings can help to advance your career. By publishing your findings in journals or presenting them at conferences, you can demonstrate your expertise in data analysis and make yourself more marketable to potential employers.

How to share your findings

There are many different ways to share your findings. The best way to share your findings will depend on the audience you are targeting and the purpose of your analysis.

• Written reports: Written reports are a common way to share research findings. They can be formal or informal, depending on the audience.
• Presentations: Presentations are another way to share your findings. They can be given to a small group or a large audience.
• Data visualizations: Data visualizations can be a powerful way to communicate your findings. They can be used to create infographics, dashboards, or even interactive web applications.
• Publications: Publishing your findings in a journal or conference is a great way to reach a wider audience and get feedback on your work.

Tips for sharing your findings

When sharing your findings, it is important to keep the following tips in mind:

• Be clear and concise: Your presentation should be easy to understand. Avoid using jargon or technical terms that your audience may not understand.
• Be specific: Don’t just tell your audience what you found. Explain why your findings are important and how they can be used.
• Be persuasive: Your goal is to convince your audience that your findings are valid and that they should take action based on them.
• Be interactive: Get your audience involved in your presentation. Ask questions, encourage discussion, and provide opportunities for feedback.

Conclusion

Sharing your findings is an important part of the data analysis process. By following the tips in this tutorial, you can communicate your results effectively and have a positive impact on your stakeholders.

Data is great, but if we can’t communicate the
story data is telling, it isn’t useful to anyone. We need ways to
organize data that help us turn it
into information. There are all kinds of tools
out there to help you visualize and share your data analysis
with stakeholders. Here, we’ll talk about two data presentation tools,
reports and dashboards. Reports and dashboards are both useful for data visualization. But there are pros and
cons for each of them. A report is a static
collection of data given to stakeholders
periodically. A dashboard on the other hand, monitors live, incoming data. Let’s talk about reports first. Reports are great for
giving snapshots of high level historical
data for an organization. For example, a finance
firm’s monthly sales. Reports come with a
lot of benefits too. They can be designed and
sent out periodically, often on a weekly
or monthly basis, as organized and easy to
reference information. They’re quick to
design and easy to use as long as you
continually maintain them. Finally, because reports
use static data or data that doesn’t change
once it’s been recorded, they reflect data that’s already
been cleaned and sorted. There are some downsides
to keep in mind too. Reports need regular maintenance and aren’t very
visually appealing. Because they aren’t
automatic or dynamic, reports don’t show
live, evolving data. For a live reflection
of incoming data, you’ll want to
design a dashboard. Dashboards are great
for a lot of reasons, they give your team more access to information being recorded, you can interact through data
by playing with filters, and because they’re dynamic, they have long-term value. If stakeholders need to
continually access information, a dashboard can be
more efficient than having to pull reports
over and over, which is a big time
saver for you. Last but not least, they’re just nice to look at. But dashboards do
have some cons too. For one thing, they take a
lot of time to design and can actually be less
efficient than reports, if they’re not used very often. If the base table
breaks at any point, they need a lot of maintenance to get back up and running again. Dashboards can
sometimes overwhelm people with information too. If you aren’t used to looking through data
on a dashboard, you might get lost in it. As a data analyst, you need to decide
the best way to communicate information
to your stakeholders. For example, what if your
stakeholders are interested in the company’s
social media engagement? Would a monthly report
that tells them the number of new followers
for their page be useful? Or a dashboard that monitors live social media engagement
across multiple platforms? Later on, you’ll create your own reports and dashboards to practice
using these tools. But for now, I want
to show you what a report and a dashboard
might look like. We’ll start by using a tool we’re already familiar
with, spreadsheets. Let’s see one way spreadsheet data could be
visualized in a report. This spreadsheet
has a data set with order details from a
wholesale company. That’s a lot of information. From the headers, we can see different things recorded here, like the order date, the salesperson, the unit price, and revenue for each
transaction recorded. It’s all useful information, but a little hard to
wrap your head around. We want a report
that’s easier to read. Let’s say your stakeholders want a quick look at the
revenue by salesperson. Using the data, you
could make them a pivot table with a graph that
shows that information. A pivot table is a data summarization tool that is used in data processing. Pivot tables are used to
summarize, sort, re-organize, group, count, total, or average data
stored in a database. It allows its users to transform columns into rows and
rows into columns. We’ll actually learn more
about pivot tables later. But I’ll show you
one really quick. We’ll select the Data menu
and click Pivot table button. It can pull data from this table. We can just press create and it’ll pull
up a new worksheet. Over here, it gives us the pivot table fields
we can choose from. Click select, salesperson
and revenue. Just like that, it
made a chart for us. At this point, you can play around with how
the graph looks, but the information is all there. Let’s move on to dashboards. If you need a more dynamic way to share information with
your stakeholders, dashboards are your friend. You might create something
like this Tableau dashboard. With interactive graphs that showcase multiple
views of the data. With this, users can change
location, date range, or any other aspect of
the data they’re viewing by clicking through different
elements on the dashboard. Pretty cool, right? Later in this program, we’ll look into how you can make your own data visualizations. We have a lot to learn
before we get to that. But I hope this was an
exciting first peek at the different visualization tools you’ll be using as
a data analyst.

A dashboard would be most beneficial for which of the following scenarios?

A project manager needs to monitor data as it becomes available.

Data and metrics are both important concepts in data analysis, but they have different meanings.

Data is raw information that has not been processed or analyzed. It can be numbers, text, images, or any other form of information.

Metrics are measurements of data. They are used to track and analyze data to answer questions and make decisions.

For example, the number of website visitors is a piece of data. The average time spent on a website is a metric.

Data is the foundation of metrics. Without data, there would be no metrics. However, data is not enough. It needs to be organized, analyzed, and interpreted in order to be useful. This is where metrics come in.

Metrics help us to make sense of data. They provide us with insights into what the data is telling us. Metrics can be used to track trends, identify patterns, and make predictions.

Data and metrics are both important tools for data analysis. They work together to help us to understand and use data to make better decisions.

Here are some of the key differences between data and metrics:

• Data is raw information, while metrics are measurements of data.
• Data is the foundation of metrics, but metrics are more useful for making decisions.
• Data can be qualitative or quantitative, while metrics are typically quantitative.
• Data can be collected from a variety of sources, while metrics are typically calculated from data.
• Data is often used to describe a situation, while metrics are often used to compare different situations.

Here are some examples of how data and metrics can be used in data analysis:

• A company might collect data on the number of website visitors and the average time spent on the website. They could then use these metrics to track trends in website traffic and identify pages that are most popular with visitors.
• A hospital might collect data on the number of patients admitted to the emergency room and the average length of stay. They could then use these metrics to track the efficiency of the emergency room and identify areas where improvements could be made.
• A school district might collect data on the test scores of students. They could then use these metrics to track the progress of students and identify areas where additional support is needed.

By understanding the difference between data and metrics, you can use them more effectively in your data analysis projects.

In the last video, we learned how you can
visualize your data using reports and dashboards to show off your
findings in interesting ways. In one of our examples, the company wanted to see the sales
revenue of each salesperson. That specific measurement of
data is done using metrics. Now, I want to tell you a little bit more
about the difference between data and metrics. And how metrics can be used to
turn data into useful information. A metric is a single, quantifiable type
of data that can be used for measurement. Think of it this way. Data starts as a collection of raw facts,
until we organize them into individual metrics that
represent a single type of data. Metrics can also be combined
into formulas that you can plug your numerical data into. In our earlier sales revenue example
all that data doesn’t mean much unless we use a specific
metric to organize it. So let’s use revenue by individual
salesperson as our metric. Now we can see whose sales
brought in the highest revenue. Metrics usually involve simple math. Revenue, for example, is the number of
sales multiplied by the sales price. Choosing the right metric is key. Data contains a lot of raw details
about the problem we’re exploring. But we need the right metrics
to get the answers we’re looking for. Different industries will use all kinds of
metrics to measure things in a data set. Let’s look at some more ways businesses
in different industries use metrics. So you can see how you might apply
metrics to your collected data. Ever heard of ROI? Companies use this metric all the time. ROI, or Return on Investment is
essentially a formula designed using metrics that let a business know
how well an investment is doing. The ROI is made up of two metrics, the net profit over a period of time and
the cost of investment. By comparing these two metrics, profit and
cost of investment, the company can analyze the data they have to see
how well their investment is doing. This can then help them decide
how to invest in the future and which investments to prioritize. We see metrics used in marketing too. For example, metrics can be used to
help calculate customer retention rates, or a company’s ability to
keep its customers over time. Customer retention rates can help the
company compare the number of customers at the beginning and the end of a period
to see their retention rates. This way the company knows how successful
their marketing strategies are and if they need to research new
approaches to bring back more repeat customers. Different industries use all
kinds of different metrics. But there’s one thing
they all have in common: they’re all trying to meet
a specific goal by measuring data. This metric goal is a measurable goal set
by a company and evaluated using metrics. And just like there are a lot
of possible metrics, there are lots of possible goals too. Maybe an organization wants to meet
a certain number of monthly sales, or maybe a certain percentage
of repeat customers. By using metrics to focus on
individual aspects of your data, you can start to see the story
your data is telling. Metric goals and formulas are great
ways to measure and understand data. But they’re not the only ways. We’ll talk more about how to interpret and
understand data throughout this course.

Mathematical thinking is the ability to use mathematical concepts and reasoning to solve problems. It is an essential skill for data analysts, who need to be able to understand and interpret data.

There are many different aspects of mathematical thinking that are important for data analysis. These include:

• Problem solving: Data analysts need to be able to identify and solve problems using mathematical methods. This includes being able to formulate problems in mathematical terms, identify the relevant data, and apply the appropriate mathematical techniques.
• Critical thinking: Data analysts need to be able to think critically about the data they are working with. This includes being able to identify potential biases or errors in the data, and to evaluate the results of their analyses.
• Communication: Data analysts need to be able to communicate their findings to others. This includes being able to explain complex mathematical concepts in a clear and concise way.
• Creativity: Data analysts need to be able to think creatively and come up with new ways to use data. This includes being able to identify patterns in data that may not be immediately obvious, and to develop new models and algorithms.

There are many different ways to develop mathematical thinking skills. Some of these include:

• Taking math courses: This is the most obvious way to develop mathematical thinking skills. Taking courses in statistics, calculus, and linear algebra will give you the foundation you need to be a successful data analyst.
• Practicing problem solving: There are many resources available online and in libraries that can help you practice problem solving. These resources can include practice problems, tutorials, and interactive exercises.
• Reading about data analysis: There are many books and articles available that can teach you about data analysis. These resources can help you learn about the different techniques that are used in data analysis, and how to apply them to real-world problems.
• Working on data analysis projects: The best way to develop mathematical thinking skills is to work on real-world data analysis projects. This will give you the opportunity to apply the skills you have learned and to develop new ones.

Mathematical thinking is an essential skill for data analysts. By developing your mathematical thinking skills, you will be well on your way to becoming a successful data analyst.

Here are some additional tips for developing mathematical thinking skills:

• Be patient and persistent. Learning mathematical thinking takes time and effort. Don’t get discouraged if you don’t understand something right away. Keep practicing and you will eventually get it.
• Don’t be afraid to ask for help. If you are stuck on a problem, don’t be afraid to ask for help from a teacher, tutor, or friend.
• Be creative. There is often more than one way to solve a problem. Don’t be afraid to try different approaches.
• Have fun! Mathematical thinking can be challenging, but it can also be very rewarding. Enjoy the process of learning and problem solving.

So far, you’ve learned a lot about
how to think like a data analyst. We’ve explored a few
different ways of thinking. And now, I want to take that one step
further by using a mathematical approach to problem-solving. Mathematical thinking is a powerful skill
you can use to help you solve problems and see new solutions. So, let’s take some time to talk about
what mathematical thinking is, and how you can start using it. Using a mathematical approach doesn’t mean
you have to suddenly become a math whiz. It means looking at a problem and
logically breaking it down step-by-step, so you can see the relationship
of patterns in your data, and use that to analyze your problem. This kind of thinking can also help you
figure out the best tools for analysis because it lets us see the different
aspects of a problem and choose the best logical approach. There are a lot of factors to consider
when choosing the most helpful tool for your analysis. One way you could decide which tool to
use is by the size of your dataset. When working with data, you’ll find
that there’s big and small data. Small data can be really small. These kinds of data tend to be made up
of datasets concerned with specific metrics over a short,
well defined period of time. Like how much water you drink in a day. Small data can be useful for
making day-to-day decisions, like deciding to drink more water. But it doesn’t have a huge impact
on bigger frameworks like business operations. You might use spreadsheets to organize and analyze smaller datasets
when you first start out. Big data on the other hand has larger, less specific datasets covering
a longer period of time. They usually have to be
broken down to be analyzed. Big data is useful for looking at large-
scale questions and problems, and they help companies make big decisions. When you’re working with data on this
larger scale, you might switch to SQL. Let’s look at an example of how a data
analyst working in a hospital might use mathematical thinking to solve
a problem with the right tools. The hospital might find that they’re
having a problem with over or under use of their beds. Based on that, the hospital could
make bed optimization a goal. They want to make sure that beds are
available to patients who need them, but not waste hospital resources like space or
money on maintaining empty beds. Using mathematical thinking, you can break
this problem down into a step-by-step process to help you find
patterns in their data. There’s a lot of variables
in this scenario. But for now, let’s keep it simple and
focus on just a few key ones. There are metrics that are related to this
problem that might show us patterns in the data: for example,
maybe the number of beds open and the number of beds used
over a period of time. There’s actually already a formula for
this. It’s called the bed occupancy rate, and it’s calculated using the total
number of inpatient days, and the total number of available
beds over a given period of time. What we want to do now is take our key
variables and see how their relationship to each other might show us patterns that
can help the hospital make a decision. To do that, we have to choose the tool
that makes sense for this task. Hospitals generate a lot of patient
data over a long period of time. So logically, a tool that’s capable
of handling big datasets is a must. SQL is a great choice. In this case, you discover that
the hospital always has unused beds. Knowing that, they can choose to get rid
of some beds, which saves them space and money that they can use to buy and
store protective equipment. By considering all of the individual
parts of this problem logically, mathematical thinking helped us see new
perspectives that led us to a solution. Well, that’s it for now. Great job. You’ve covered a lot of material already. You’ve learned about how empowering
data can be in decision-making, the difference between quantitative and
qualitative analysis, using reports and dashboards for
data visualization, metrics, and using a mathematical
approach to problem-solving. Coming up next,
we’ll be tackling spreadsheet basics. You’ll get to put what you’ve
learned into action and learn a new tool to help you
along the data analysis process. See you soon.