Week 1: Introducing data analytics

What do companies in e-commerce, entertainment, healthcare,
manufacturing, marketing, finance, tech, and hundreds of other
industries all have in common? You guessed it,
they all use data. Organizations of all kinds need data analysts to help them
improve their processes, identify opportunities
and trends, launch new products, provide
great customer service, and make thoughtful decisions. Hi, I’m Tony, a program manager at Google and a data
analyst myself. I like to welcome you to the Google Data
Analytics Certificate. Now, there are lots of great reasons to
earn this certificate. Maybe you’re thinking
about starting a career in the exciting
world of data analytics, or maybe you’re just fascinated by the power of data as I am. No matter what brought you here, you’re in the right place
to kick-start a career and learn industry-relevant
skills in data analytics. But first, what exactly is data? Well, I’ll like to say that data is a collection of facts. This collection can
include numbers, pictures, videos, words, measurements,
observations, and more. Once you have data, analytics puts it to work through analysis. Data analysis is the
collection, transformation, and organization of data in
order to draw conclusions, make predictions, and drive
informed decision-making. And it doesn’t stop there. Data evolves over time which means this analysis or analytics, as we call it, can give us new information throughout
data’s entire life cycle. Data is everywhere. You use and create data everyday. Have you ever read
reviews of a product before deciding whether
or not to buy it? That’s data analysis. Or maybe you wear a
fitness tracker to count your steps so you can stay
active throughout the day. That’s data analysis. But you don’t just use data. You also create huge amounts
of it every single day. Any time you use your phone, look up something
online, stream music, shop with a credit card, post on social media or use GPS to map a route,
you’re creating data. Our digital world and the millions of smart
devices inside of it have made the amount of data available truly mind-blowing. Here at Google we
process more than 40,000 searches every second. That’s 3.5 billion searches a day and 1.2 trillion
searches every year. Here’s another way
to think about it. YouTube has almost
two billion users. If YouTube users
made up a country, it would be the
largest in the world. All of that data is transforming
the world around us. The publication The
Economist recently called data the world’s
most valuable resource. It’s easy to see why data analysts are so valued
by their organizations. What exactly does
a data analyst do? Put simply, a data analyst
is someone who collects, transforms, and organizes data in order to help make
informed decisions. Besides the role itself, one of the most
exciting parts of being a data analyst is the number
of opportunities available. The demand for data
analysts is greater than the number of qualified people to fill these job openings. This certificate program is a great first step in your journey to finding
a job you love. Data analysts come from many different
backgrounds and have all kinds of life experiences. You don’t need decades
of work experience or an expensive education
to get started. Many data analysts
taught themselves the skills they needed
to land their first job, just like you’re doing right now. Now let’s talk more about
what you’re going to learn. The Google Data Analytics
Certificate is split into courses based on different
processes for data analysis. Those are ask, prepare, process, analyze, share, and act. Plan to watch these
videos in order. Each one covers a new topic and every topic builds on what
you’ve learned before, making it easy to
track your progress. You’re in the driver’s seat. Even though you might see
things organized by weeks, everything can be completed
at your own pace. So you decide how much
you want to do each day. By the end of the program, you’ll take everything you’ve
learned and turn it into a project that you can use
to show off your skills, and wow hiring managers
at your job interviews. Now along the way you’ll
also hear from Googlers. That’s what we call people
who work here at Google. They’ll give you an inside look at what it’s like to work in our industry and share personal stories of how
they got into the field. They’ll also give you
some excellent tips on how to land your dream job. Stay tuned. Some
of them are going to introduce themselves
in just a sec. I’m Angie. I’m a program manager of engineering at Google. I truly believe that cleaning data is the heart
and soul of data. It’s how you get
to know your data, its quirks, its
flaws, its mysteries. I love a good mystery. It felt like a superpower almost. I was a detective and I had gone in there and I’d really
solved something. Hi, I’m Alex. I’m a research
scientist at Google. I research the
different impacts of artificial intelligence
on society and our users. My name is Lilah Jones. I’m a part of our cloud team. I get a chance to lead a team of amazing individuals that are focused on helping
customers get to the cloud. Hi, I’m Evan. I’m a learning portfolio
manager here at Google. I have one of the coolest jobs in the world where I get to look at all the different
technologies that affect big data and then work them into training courses like this
one for students to take. I’ll be your instructor
for the first course. I’ll take you through
each module that will cover a specific topic
in a few different ways. You’ll have videos, reading
materials, quizzes, hands-on activities, and
discussion prompts for you to chat about with other
students in an online forum. I’m really excited to be guiding
you through this course, but I’m especially excited that you’ve chosen
this adventure. Lifelong learning is something that I’m very passionate about. Growing up, when I looked around, I often didn’t see many
options available to me. It wasn’t until I started getting serious about my education that I realized I had the control to make
my own opportunities with education being the key
that would open those doors. The more I learned and
the harder I worked, the more possibilities opened up. Had I not gone after that knowledge and continued
challenging myself, I may not be where I am today. Learning allowed me to grow
personally, be successful, visit places I would
never have seen, and meet people I would
never have known. Now I’m going to introduce
some of those great people. Hello, I’m Ximena,
financial analyst. I’ll be helping you learn how to ask the right
questions about data, the project you work on and the problems you’re
trying to solve. Hey, my name is Hallie,
analytical lead. I’m so excited to show you how to prepare your data so
it’s ready for analysis. Hello, I’m Sally, measurement
and analytical lead. Together we’ll cover how to
process and clean your data. Cleaning data doesn’t
require soap and water. I’m talking about making
sure your data is complete, correct, and relevant to the problem you’re
trying to solve. Hey, I’m Ayanna, global
insights manager. We’ll be digging into analysis. You’ll learn how to
collect, transform, and organize data so that you can use it to discover
useful information, draw conclusions, and
make great decisions. My name’s Kevin and with my experience as Director
of analytics at Google, I’ll guide you through
what I think is the most exciting part of the data analysis process: plan, create, and present effective and compelling
data visualizations. Hello, my name is Carrie. I can’t wait to tell you about all the exciting things you can do with the programming
language R. Are you ready? Hi, I’m Rishie, Global analytics skills
curriculum manager. I’m going to help you bring together everything
you learned during this program by creating a case study that will
dazzle any hiring manager. Just like the capstone of a great building shows
everyone that it’s complete, your case study will signify your own great
achievement of earning a Google certificate
in data analytics. Okay, are you getting excited about the potential of
becoming a data analyst? So much is possible with data. You’re about to enter
a whole new world. Ready? Let’s go.

“Data! Data! Data! I can’t
make bricks without clay.” Any guesses who said this?
I’ll give you a hint. It wasn’t a famous tech CEO, or a data analyst. The person who said this lived long before the tech
companies even existed. But I bet you’ve
still heard of him. This line was said
by Sherlock Holmes, the famous detective created
by Sir Arthur Conan Doyle. What Doyle meant was that Holmes couldn’t draw
any conclusions, which would be the
bricks he mentioned without data, or the clay. You’re probably
not here to become a world famous detective, but data is still the building
block that you’ll use for everything you do in your
new data analyst career, Sherlock Holmes would agree. By starting this program, you’ve shown that you and Sherlock Holmes have
something in common, you both have an interest
in learning more. That’s one of the most
important qualities that data analysts can have. Now, there are a bunch of
different ways to explore data, but one of the great things
about data analytics is that you can often learn how
you want, when you want. That might mean doing
your own research, talking with people
in the industry, or taking online courses. With that said, welcome
to your first course. This is your introduction to the wonderful world
of data analytics. Since data analytics is
the science of data, you’ll use this course to
begin to learn all about data. Data is basically a
collection of facts or information, and
through analysis, you’ll learn how to use the
data to draw conclusions, and make predictions,
and decisions. Personally, I didn’t jump right into the data
analytics field. I thought data analysis was
for computer engineers. Instead, I started off with
dreams of working in finance. Once I got through an
internship though, I realized it wasn’t the
career path I wanted to take. I started to learn about
financial planning and analysis, and all of the work finance analysts were
doing with data. I realized that finance
analysts are really just data analysts working
in a finance department. These analysts were
helping to guide business decisions by
knowing how to use data. It was then I realized
how powerful data is, and I started to embrace it. Soon enough, I realized I could do this data analysis myself. Data analytics is a big
open world of opportunity. There are so many areas that
your analysis skills can be applied and in all
different ways. If you’re new to this world, you’ll learn how to
identify which path and industry might suit your skills, and your interests the best. For those of you who already
have some experience, we’ll help you open doors to new and exciting
opportunities. One of the skills
you’ll gain from the program is how to follow the best practices that analysts use to help make
data-driven decisions. Computers are one
part of the process, but analysts rely on so much
more to make decisions. That’s why learning how
to think analytically, and using your other
skills and traits on the job will make
your work easier. I know you already know how
to make good decisions, you chose to be here after all. In this first course, you’ll learn more
about each phase of the data analysis process. Ask, prepare, process,
analyze, share, and act. As a data analyst, you’ll go through
these steps as you use data to inform
your decisions. Eventually, you’ll see how this program itself is in a way, its own version of this process. While I know you’ll enjoy
watching these videos, your trip to the first course will include a whole lot more. Other videos will take
the form of vignettes, where you’ll learn from data
analytics professionals, who are already established
in their careers. They’ll offer words of
wisdom as well as tales of their own experiences starting
off on their career path. You’ll start your own data
journal that will help you keep track of what you’ve learned throughout the course. You’ll also add your own
thoughts about what you’re learning as well,
throughout the program. You’ll read up on how to navigate this program in the
world of data analytics. You’ll complete
activities, including some that will help you get in the
mindset of a data analyst. Along the way, you’ll also have the chance to connect with
your fellow learners. Discussion prompts will give you a chance to share your thoughts, and at the same time see what your peers think about
all that you’re learning. These prompts will help you build a community support system to
use throughout the program. Enough talking, let’s get
started on this exciting path. Your next step awaits.

Question: Fill in the blank: Data is a collection of _____ that can be used to draw conclusions, make predictions, and assist in decision-making.

Answer: facts

Data is a collection of facts that can be used to draw conclusions, make predictions, and assist in decision-making.

Data analytics in everyday life

Data analytics is the process of collecting, cleaning, and analyzing data to extract meaningful insights. It is used in a wide range of industries, from businesses to government agencies to non-profit organizations. Data analytics is also becoming increasingly common in everyday life, as people use it to make better decisions about their health, finances, and personal relationships.

Here are some examples of how data analytics is used in everyday life:

• Personal finance: Data analytics can be used to track spending habits, budget effectively, and save money. For example, you can use a budgeting app to track your income and expenses, and then use data analytics to identify areas where you can cut back.
• Health and fitness: Data analytics can be used to track fitness goals, monitor health trends, and identify potential health problems. For example, you can use a fitness tracker to track your steps, heart rate, and sleep patterns. You can then use data analytics to identify trends and set goals for yourself.
• Shopping: Data analytics is used by retailers to personalize recommendations, target ads, and optimize their supply chains. For example, Amazon uses data analytics to recommend products to you based on your past purchase history and browsing behavior.
• Transportation: Data analytics is used to improve traffic flow, reduce congestion, and make travel safer. For example, Google Maps uses data analytics to predict traffic conditions and provide you with the fastest route to your destination.
• Social media: Social media platforms use data analytics to personalize your feed, show you relevant ads, and measure the effectiveness of their marketing campaigns. For example, Facebook uses data analytics to show you ads based on your interests and demographics.

How to use data analytics in your own life

There are a number of ways to use data analytics in your own life. Here are a few tips:

• Identify your data needs and goals. What do you want to learn from your data? Once you know your goals, you can start to collect the data that you need.
• Choose the right data analytics tools. There are a number of data analytics tools available, both free and paid. Choose the tools that are right for your needs and skill level.
• Clean and analyze your data. Once you have collected your data, you need to clean it and remove any errors. Then, you can start to analyze your data using the tools that you have chosen.
• Interpret your results. Once you have analyzed your data, you need to interpret the results. What do the results mean for you? What actions can you take based on the results?

Data analytics can be a powerful tool for making better decisions in all areas of your life. By following the tips above, you can start to use data analytics to improve your personal finance, health, fitness, shopping, transportation, and social media experiences.

Here are some additional tips for using data analytics in your everyday life:

• Be mindful of your privacy. When you collect and share data, be mindful of your privacy and security. Only share data with trusted organizations and use strong passwords to protect your accounts.
• Use data to make informed decisions. Don’t just blindly follow the recommendations of data analytics tools. Use your own judgment and common sense to make decisions that are right for you.
• Be skeptical of data. Not all data is accurate or reliable. Be skeptical of data that seems too good to be true or that comes from questionable sources.

Data analytics is a powerful tool, but it is important to use it responsibly and intelligently. By following the tips above, you can use data analytics to improve your life in many ways.

Welcome back. At this point, you’ve been introduced
to the world of data analytics and what data analysts do. You’ve also learned how this
course will prepare you for a successful career as an analyst. Coming up, you’ll learn all
the ways data can be used, and you’ll discover why data
analysts are in such high demand. I’m not exaggerating when I say every
goal and success that my team and I have achieved couldn’t
have been done without data. Here at Google,
all of our products are built on data and data-driven decision making. From concept to development to launch, we’re using data to figure out the best
way forward. And we’re not alone. Countless other organizations also
see the incredible value in data and, of course, the data analysts
who help them make use of it. So we know data opens up
a lot of opportunities. But to help you wrap your head around
all the ways you can actually use data, let’s go over a few examples
from everyday life. You might not realize it, but
people analyze data all the time. For instance, I’m a morning person. A long time ago,
I realized that I’m happier and more productive if I get to bed
early and wake up early. I came to this conclusion after noticing
a pattern in my day-to-day experiences. When I got seven hours of sleep and woke
up at 6:30, I was the most successful. So I thought about the relationship
between this pattern and my daily life, and I predicted that early to bed early
to rise would be the right choice for me. And I’m definitely my best self
when I wake up bright and early. I bet you’ve identified patterns and
relationships in your life, too. Maybe about your own sleep cycle or
how you feel after eating certain foods, or what time of day you like to workout. All of these are great examples of
real life patterns and relationships that you can use to make predictions
about the right actions to take, and that is a huge part of
data analysis right there. Now, let’s put this process
into a business setting. You may remember from an earlier video
that there’s a ton of data out there. And every minute of every hour of
every day, more data is being created. Businesses need a way to
control all that data so they can use it to improve processes,
identify opportunities and trends, launch new products, serve customers,
and make thoughtful decisions. For businesses to be on
top of the competition, they need to be on top of their data. That’s why these companies hire data
analysts to control the waves of data they collect every day, makes sense of it, and
then draw conclusions or make predictions. This is the process of turning
data into insights, and it’s how analysts help businesses
put all their data to good use. This is actually a good way to think about
analysis: turning data into insights. As a reminder, the more detailed
definition you learned earlier is that data analysis is the collection,
transformation, and organization of data in
order to draw conclusions, make predictions, and
drive informed decision-making. So after analysts have created
insights from data, what happens? Well, a lot. Those insights are shared with others,
decisions are made, and businesses take action. And here’s where it can
get really exciting. Data analytics can help organizations
completely rethink something they do or point them in a totally new direction. For example, maybe data leads them to
a new product or unique service, or maybe it helps them find a new way to
deliver an incredible customer experience. It’s these kinds of aha moments that
can help businesses reach another level, and that makes data analysts
vital to any business. Now that you know more of the amazing
ways data is being used every day, you can see why data analysts
are in such high demand. We’ll continue exploring how analysts can
transform data into insights that lead to action. And before you know it, you’ll be ready
to help any organization find new and exciting ways to transform their data.

Hi. I’m Cassie, and I lead Decision Intelligence
for Google Cloud. Decision Intelligence
is a combination of applied data science and the social and
managerial sciences. It is all about harnessing
the power and beauty of data. I help Google Cloud
and its customers turn their data into impact and make their businesses
and the world better. A data analyst is an explorer, a detective, and an artist
all rolled into one. Analytics is the quest
for inspiration. You don’t know what’s going to inspire you before you explore, before you take a look around. When you begin, you
have no idea what you’re going to find and whether you’re even
going to find anything. You have to bravely dive into the unknown and discover
what lies in your data. There is a pervasive myth that someone who works in data should know the
everything of data. I think that that’s
unhelpful because the universe of
data has expanded. It’s expanded so much that specialization
becomes important. It’s very, very difficult for one person to know and be
the everything of data. That’s why we need
these different roles. The advice that I give folks
who are entering the space is to pick their specialization
based on which flavor, which type of impact best
suits their personality. Now, data science, the discipline
of making data useful, is an umbrella term that
encompasses three disciplines: machine learning,
statistics, and analytics. These are separated by how many decisions you know you want to make before
you begin with them. If you want to make a few
important decisions under uncertainty, that is statistics. If you want to automate, in
other words, make many, many, many decisions under uncertainty, that is machine learning and AI. But what if you don’t know how many decisions you want
to make before you begin? What if what you’re looking
for is inspiration? You want to encounter
your unknown unknowns. You want to understand
your world. That is analytics. When you’re considering
data science and you’re choosing which
area to specialize in, I recommend going with
your personality. Which of the three excellences in data science feels like
a better fit for you? The excellence of
statistics is rigor. Statisticians are
essentially philosophers, epistemologists. They are very, very careful about protecting decision-makers
from coming to the wrong conclusion. If that care and rigor is what
you are passionate about, I would recommend statistics. Performance is the excellence of the machine learning
and AI engineer. You know that’s the one for
you if someone says to you, “I bet that you couldn’t build an automation system that
performs this task with 99.99999 percent accuracy,” and your response to that
is, “Watch me.” How about analytics? The excellence of an
analyst is speed. How quickly can you surf
through vast amounts of data to explore it and
discover the gems, the beautiful potential
insights that are worth knowing about and bringing
to your decision-makers? Are you excited by the
ambiguity of exploration? Are you excited by the idea of working on a lot
of different things, looking at a lot of
different data sources, and thinking through vast
amounts of information, while promising not to snooze past the important
potential insights? Are you okay being told, “Here is a whole lot of data. No one has looked at it before. Go find something interesting”? Do you thrive on creative,
open-ended projects? If that’s you, then analytics is probably
the best fit for you. A piece of advice that I have for analysts getting started on this journey is it can be pretty scary to
explore the unknown. But I suggest letting
go a little bit of any temptations towards
perfectionism and instead, enjoying the fun, the
thrill of exploration. Don’t worry about right answers. See how quickly you
can unwrap this gift and find out if there is
anything fun in there. It’s like your birthday,
unwrapping a bunch of things. Some of them you like.
Some of them you won’t. But isn’t it fun to know
what’s actually in there?

What is the data ecosystem?

A data ecosystem is a complex environment of co-dependent networks and actors that contribute to data collection, transfer, and use. It can span across sectors, such as healthcare, finance, and retail, to inform one another’s practices. A data ecosystem often consists of numerous data assemblages, as each actor within the system has their own sets of tangible and non-tangible elements for their operation.

Key components of a data ecosystem

A data ecosystem typically consists of the following components:

• Data providers: These are the organizations that collect and store data. They can be public or private entities, such as government agencies, businesses, and individuals.
• Data consumers: These are the organizations that use data to make decisions, improve products and services, and create new opportunities.
• Data intermediaries: These are the organizations that help data providers and consumers connect and exchange data. They can include data brokers, data exchanges, and cloud computing providers.
• Data technologies: These are the tools and software used to collect, store, manage, and analyze data. They can include databases, data warehouses, data lakes, and machine learning algorithms.
• Data governance: This refers to the policies and procedures that govern the use of data within an ecosystem. It is important to ensure that data is used in a responsible and ethical manner.

Benefits of data ecosystems

Data ecosystems can provide a number of benefits to participants, including:

• Increased efficiency and productivity: Data can be used to automate tasks, improve decision-making, and optimize processes.
• New products and services: Data can be used to develop new products and services that meet the needs of consumers.
• Improved customer experience: Data can be used to better understand customers and provide them with personalized experiences.
• Reduced costs: Data can be used to identify and eliminate waste, reduce costs, and improve profitability.
• Competitive advantage: Data can be used to gain a competitive advantage over other businesses.

Examples of data ecosystems

Data ecosystems can be found in a variety of industries, including:

• Healthcare: Healthcare data ecosystems can be used to improve patient care, develop new treatments, and conduct research.
• Finance: Financial data ecosystems can be used to detect fraud, manage risk, and make better investment decisions.
• Retail: Retail data ecosystems can be used to understand customer behavior, improve product recommendations, and optimize supply chains.
• Transportation: Transportation data ecosystems can be used to improve traffic flow, reduce congestion, and make travel safer.
• Manufacturing: Manufacturing data ecosystems can be used to improve quality control, reduce waste, and optimize production processes.

How to create a data ecosystem

To create a data ecosystem, organizations need to:

1. Identify their data needs and goals.
2. Identify the data providers and consumers that they need to partner with.
3. Develop a data governance framework to ensure that data is used in a responsible and ethical manner.
4. Choose the right data technologies to support their data ecosystem.
5. Implement and manage their data ecosystem.

Creating a data ecosystem can be a complex undertaking, but it can provide significant benefits to participants. By working together, organizations can create data ecosystems that unlock the value of data and drive innovation.

In data analytics, what is the term for elements that interact with one another in order to produce, manage, store, organize, analyze, and share data? (reminder: be sure to scroll down to see all options!)

Data ecosystems

Elements that interact with one another in order to produce, manage, store, organize, analyze, and share data are data ecosystems. These elements include hardware and software tools, as well as the people who use them.

Hello again. You’ve already learned
about being a data analyst and how this program will help
prepare you for your future career. Now, it’s time to explore
the data ecosystem, find out where data analytics fits into
that system, and go over some common misconceptions you might run into
in the field of data analytics. To put it simply, an ecosystem is a group
of elements that interact with one another. Ecosystems can be large,
like the jungle in a tropical rainforest or the Australian outback. Or, tiny, like tadpoles in a puddle,
or bacteria on your skin. And just like the kangaroos and
koala bears in the Australian outback, data lives inside its own ecosystem too. Data ecosystems are made up of various
elements that interact with one another in order to produce, manage, store,
organize, analyze, and share data. These elements include hardware and
software tools, and the people who use them. People like you. Data can also be found in
something called the cloud. The cloud is a place to keep data online,
rather than on a computer hard drive. So instead of storing data somewhere
inside your organization’s network, that data is accessed over the internet. So the cloud is just a term we use
to describe the virtual location. The cloud plays a big part in the data
ecosystem, and as a data analyst, it’s your job to harness the power of that data
ecosystem, find the right information, and provide the team with analysis
that helps them make smart decisions. For example, you could tap into
your retail store’s database, which is an ecosystem filled
with customer names, addresses, previous purchases, and customer reviews. As a data analyst, you could use this
information to predict what these customers will buy in the future, and make sure the store has the products
and stock when they’re needed. As another example, let’s think about a data ecosystem
used by a human resources department. This ecosystem would include information
like postings from job websites, stats on the current labor market, employment rates, and social media
data on prospective employees. A data analyst could use this information
to help their team recruit new workers and improve employee engagement and
retention rates. But data ecosystems aren’t just for stores
and offices. They work on farms, too. Agricultural companies regularly
use data ecosystems that include information including geological
patterns in weather movements. Data analysts can use this data to
help farmers predict crop yields. Some data analysts are even using
data ecosystems to save real environmental ecosystems. At the Scripps Institution of
Oceanography, coral reefs all over the world are monitored digitally, so they
can see how organisms change over time, track their growth, and
measure any increases or declines in individual colonies. The possibilities are endless. Okay, now let’s talk about some common
misconceptions you might come across. First is the difference between
data scientists and data analysts. It’s easy to confuse the two, but
what they do is actually very different. Data science is defined as
creating new ways of modeling and understanding the unknown
by using raw data. Here’s a good way to think about it. Data scientists create new questions
using data, while analysts find answers to existing questions by
creating insights from data sources. There are also many words and phrases you’ll hear throughout this course,
that are easy to get mixed up. For example, data analysis and
data analytics sound the same, but they’re actually very different
things. Let’s start with analysis. You’ve already learned that data analysis
is the collection, transformation, and organization of data in
order to draw conclusions, make predictions, and
drive informed decision-making. Data analytics in the simplest
terms is the science of data. It’s a very broad concept that encompasses
everything from the job of managing and using data to the tools and methods that
data workers use each and every day. So when you think about data,
data analysis and the data ecosystem, it’s important
to understand that all of these things fit under the data
analytics umbrella. All right, now that you know a little
more about the data ecosystem and the differences between data analysis and
data analytics, you’re ready to explore how data is
used to make effective decisions. You’ll get to see data-driven
decision-making, in action.

How data informs better decisions

Data is the lifeblood of good decision-making. By understanding the trends, patterns, and relationships in data, we can make better decisions about everything from our personal lives to our businesses to the world around us.

Here are some tips on how to use data to inform better decisions:

1. Identify your goals. What do you want to achieve with your decision? Once you know your goals, you can start to identify the data that you need to collect and analyze.
2. Collect the right data. Not all data is created equal. Make sure that you are collecting data that is relevant to your goals and that is accurate and reliable.
3. Clean and analyze your data. Once you have collected your data, you need to clean it to remove any errors or inconsistencies. Then, you can analyze your data using the right tools and techniques.
4. Interpret your results. Once you have analyzed your data, you need to interpret the results. What do the results mean for you? What actions can you take based on the results?
5. Communicate your findings. If you are making a decision that affects others, it is important to communicate your findings and the reasoning behind your decision. This will help to build trust and consensus.

Here are some examples of how data can be used to inform better decisions:

• A business can use data to make decisions about product development, marketing, and pricing. For example, a clothing company could use data to identify the most popular styles and colors, and then use that information to develop new products.
• A government agency can use data to make decisions about infrastructure, education, and healthcare. For example, a city could use data to identify the areas with the highest traffic congestion and then plan new roads and bridges.
• A non-profit organization can use data to make decisions about how to allocate resources and measure the impact of their programs. For example, a food bank could use data to identify the neighborhoods with the highest rates of hunger and then allocate more resources to those neighborhoods.

Data can be a powerful tool for making better decisions in all areas of our lives. By following the tips above, we can all learn to use data more effectively to make better choices.

Here are some additional tips for using data to inform better decisions:

• Be mindful of your biases. We all have biases, and it is important to be aware of them when making decisions based on data. Make sure that you are not filtering the data in a way that supports your preconceived notions.
• Use multiple sources of data. Don’t rely on just one source of data to make a decision. Try to collect data from multiple sources to get a more complete picture.
• Don’t be afraid to ask for help. If you are not sure how to analyze data or interpret the results, don’t be afraid to ask for help from a data scientist or other expert.

Data can be a complex tool, but it is important to learn how to use it effectively in order to make better decisions. By following the tips above, you can start to use data to improve your life in many ways.

So far, you’ve discovered that there
are many different ways data can be used. In our everyday lives, we use data when we wear
a fitness tracker or read product reviews to
make a purchase decision. And in business, we use data to
learn more about our customers, improve processes, and help employees
do their jobs more effectively. But this is just the tip of the iceberg. One of the most powerful ways you can put
data to work is with data-driven decision-making. Data-driven decision-making is defined as
using facts to guide business strategy. Organizations in many different
industries are empowered to make better, data-driven decisions by
data analysts all the time. The first step in data-driven decision-making
is figuring out the business need. Usually, this is a problem
that needs to be solved. For example, a problem could be a new
company needing to establish better brand recognition, so it can compete with
bigger, more well-known competitors. Or maybe an organization wants to improve
a product and needs to figure out how to source parts from a more sustainable
or ethically responsible supplier. Or, it could be a business trying to
solve the problem of unhappy employees, low levels of engagement,
satisfaction and retention. Whatever the problem is, once it’s
defined, a data analyst finds data, analyzes it and uses it to uncover trends,
patterns and relationships. Sometimes the data-driven strategy will
build on what’s worked in the past. Other times, it can guide a business
to branch out in a whole new direction. Let’s look at a real-world example. Think about a music or
movie streaming service. How do these companies know what
people want to watch or listen to, and how do they provide it? Well using data-driven decision-making, they gather information about what their
customers are currently listening to, analyze it, then use the insights
they’ve gained to make suggestions for things people will most
likely enjoy in the future. This keeps customers happy and coming back for more, which in turn
means more revenue for the company. Another example of data-driven
decision-making can be seen in the rise of e-commerce. It wasn’t long ago that most purchases
were made in a physical store, but the data showed people’s
preferences were changing. So a lot of companies created entirely
new business models that remove the physical store, and let people
shop right from their computers or mobile phones with products
delivered right to their doorstep. In fact, data-driven decision-making
can be so powerful, it can make entire
business methods obsolete. For example, data helped companies
completely move away from corded phones and
replace them with mobile phones. By ensuring that data is built
into every business strategy, data analysts play a critical role
in their companies’ success, but it’s important to note that no matter how
valuable data-driven decision-making is, data alone will never be as powerful
as data combined with human experience, observation, and sometimes even intuition. To get the most out of data-driven
decision-making, it’s important to include insights from people who are familiar
with the business problem. These people are called subject matter
experts, and they have the ability to look at the results of data analysis and
identify any inconsistencies, make sense of gray areas, and
eventually validate choices being made. Organizations that work this way put data
at the heart of every business strategy, but also benefit from
the insights of their people. It’s a win-win. As a data analyst, you play a key role in
empowering these organizations to make data-driven decisions,
which is why it’s so important for you to understand how data plays
a part in the decision-making process.

Identify the real-world examples of how a company might make data-driven decisions. Select all that apply.

• Scheduling a certain number of restaurant employees to work based on the average number of lunch-goers per day
• Choosing e-commerce solutions based on customer shopping preferences
• Suggesting new music to a customer based on their listening history

We’ve covered a lot.
I’m sure you have so much to think about already.
That’s a good thing. It means you’ve started
collecting data and you’re doing your own
personal analysis. That’s what it’s all about. You’ve built a
great base already. As this course continues, your knowledge and
data analysis skills will continue to grow. Once you’ve established
a solid foundation, you’ll apply what you’ve learned to the rest of the program. The data analysis
process will help provide a framework
for everything you do. Soon, you’ll take your
first graded assessment. It’s a great way to check
your understanding of the concepts and build
confidence in your knowledge. Everyone learns at
different speeds. So take your time. Get familiar with the concepts. As soon as you feel ready, you can go ahead and get started. Keep in mind, if at any point, you’re not sure about a question, you can always review
the videos and readings to remind
yourself of the answer. We’re all about open-
book tests here. Once you’ve passed, you’ll
be all set to move on. You’ve got this.
Before you know it, you’ll be done with all
of the courses, and you’ll be ready to create
your own case study. Then, if it’s what
you want to do, you’ll start your job search, equipped with the
tools and skills that will wow any
company you talk to. I can’t wait to see where
you go with data analytics. For now though, give
yourself a pat on the back for a job well
done. See you soon.