Week 3: The wonderful world of data

What are the data phases?

The data phases are the different steps involved in the process of collecting, cleaning, analyzing, and sharing data. The six data phases are:

1. Ask: This is the first phase, where you define the problem or question that you want to answer with data.
2. Prepare: This phase involves collecting and cleaning the data. This may involve removing errors, correcting inconsistencies, and transforming the data into a format that is easy to analyze.
3. Process: This phase involves analyzing the data using statistical methods and machine learning algorithms.
4. Analyze: This phase involves interpreting the results of the analysis and drawing conclusions.
5. Share: This phase involves communicating the results of the analysis to stakeholders.
6. Act: This phase involves taking action based on the results of the analysis.

What are the data tools?

There are a variety of tools that can be used for each of the data phases. Some of the most common data tools include:

• Spreadsheets: Spreadsheets are a versatile tool that can be used for data collection, cleaning, and analysis.
• Databases: Databases are a more structured way to store data. They can be used for complex queries and analysis.
• Query languages: Query languages are used to extract data from databases.
• Visualization tools: Visualization tools are used to create charts and graphs that help to communicate data insights.

How to learn about data phases and tools?

There are a few different ways to learn about data phases and tools. You can:

• Take a data analytics course. This will give you a comprehensive overview of the data analysis process and the tools that are used.
• Read books and articles on data analytics. This is a great way to learn about specific data tools and techniques.
• Attend workshops and conferences. This is a great way to network with other data professionals and learn about the latest trends in data analytics.
• Practice using data tools. The best way to learn about data tools is to use them. There are many free and open-source data tools available online.

Conclusion

Learning about data phases and tools is an important first step in becoming a data analyst. By understanding the different phases of the data analysis process and the tools that are used, you will be well on your way to becoming a data analytics expert.Here are some additional resources that you may find helpful:

• Google Data Analytics Certificate: https://www.coursera.org/specializations/google-data-analytics
• DataCamp: https://www.datacamp.com/
• Kaggle: https://www.kaggle.com/
• Towards Data Science: https://towardsdatascience.com/
• Data Science Central: https://www.datasciencecentral.com/

Hey. It’s great to have you back. We’ve talked a little bit about the data analysis process. As a quick refresher, the data analysis process phases are ask, prepare, process, analyze, share, and act. You might remember
me saying earlier that this entire program is
modeled after these steps. Now, we’re going
to really dig in and explore how each of
these phases work together. But I’m getting a
little ahead of myself. First, let’s spend a little time understanding the data life cycle. No, data isn’t actually alive, but it does have a life cycle. How do data analysts bring data to life? Well, it starts with the
right data analysis tool. These include
spreadsheets, databases, query languages, and
visualization software. Don’t worry if you don’t
know how these work, or even what they are. At one point, every data analyst has been right where
you are right now, and they probably had a
lot of the same questions. I remember when I first started learning about spreadsheets. I was a young intern, and the company I
was working for was in the middle of
a big systems change. That meant, we had
to move tons of reports from the old
system to the new one. After a few weeks, I noticed that even the people who were further
in their careers were not as technically
minded as I was. That became a great opportunity
for me to add value. My aha spreadsheet moment came when I started
researching shortcuts that I could use to work with the spreadsheets
more efficiently. This would really
streamline the process of getting those reports moved
over to the new system. Once everything started flowing, I remember getting emails from other finance analysts
at the company. They were so grateful that someone had come in and fixed a problem
that no one else could. That inspired me to
go even further and learn how to
use spreadsheets in all sorts of incredible ways. As you continue
through this course, I bet you’ll be just
as impressed as I was. And before you know
it, you’ll bring data to life too. Let’s get started.

The data life cycle is the process of collecting, storing, analyzing, and disposing of data. It is an iterative process that can be divided into six main phases:

1. Planning: This phase involves determining the data needs of the organization, how the data will be collected and managed, and who will be responsible for it.
2. Collection: This phase involves collecting data from a variety of sources, such as surveys, sensors, and social media.
3. Management: This phase involves storing data in a secure and accessible location, and ensuring that it is backed up regularly.
4. Analysis: This phase involves using data to solve problems, make decisions, and improve business outcomes.
5. Archiving: This phase involves storing data that is no longer needed for active use, but may be needed for future reference.
6. Disposal: This phase involves disposing of data securely, so that it cannot be accessed by unauthorized individuals.

Here is a more detailed explanation of each phase:

Planning:

• Determine the data needs of the organization. What data do you need to collect? Why do you need it? How will you use it?
• Identify the data sources. Where will you get the data from?
• Select the data collection methods. How will you collect the data?
• Develop a data management plan. How will you store, secure, and back up the data?
• Assign data ownership and responsibilities. Who will be responsible for the data?

Collection:

• Collect the data from the identified sources.
• Clean and prepare the data for analysis. This may involve removing errors, correcting inconsistencies, and transforming the data into a format that is easy to analyze.

Management:

• Store the data in a secure and accessible location.
• Back up the data regularly to prevent data loss.
• Monitor the data quality and integrity.
• Protect the data from unauthorized access, use, or disclosure.

Analysis:

• Use the data to solve problems, make decisions, and improve business outcomes.
• Develop data-driven insights.
• Communicate the results of the analysis to stakeholders.

Archiving:

• Store data that is no longer needed for active use.
• Retain the data for a specified period of time, as required by law or regulation.
• Destroy the data securely when it is no longer needed.

Disposal:

• Dispose of the data securely, so that it cannot be accessed by unauthorized individuals.
• Follow all applicable laws and regulations for data disposal.

The data life cycle is an important process for managing data throughout its lifespan. By following the data life cycle, organizations can ensure that their data is secure, accessible, and used effectively.

Here are some additional tips for managing the data life cycle:

• Document the data life cycle process. This will help to ensure that everyone in the organization understands their roles and responsibilities.
• Use a data management tool to help automate and manage the data life cycle.
• Regularly review the data life cycle to ensure that it is meeting the needs of the organization.
• Stay up-to-date on the latest data privacy and security regulations.

By following these tips, organizations can effectively manage their data and protect it from unauthorized access, use, or disclosure.

In the data life cycle, which phase involves using data to solve problems, make good decisions, and support business goals?

Analyze

The analyze phase involves using data to solve problems, make great decisions, and support business goals.

Here’s a question for you. When you think about a life cycle,
what’s the first thing that comes to mind? Now I’m not a mind reader, but
I know whatever you’re thinking is right. There’s actually no wrong answer
because everything has a life cycle. One of the most well known examples
of a life cycle is a butterfly. Butterflies begin as eggs, hatch into
caterpillars and then become a chrysalis. That’s where the real magic happens. Data has a life cycle of its own too. In this video, we’re going to talk about
each of the stages in that life cycle to help you understand the individual
phases data goes through. The life cycle of data is plan,
capture, manage, analyze, archive and destroy. Let’s start with the first phase,
planning. This actually happens well before
starting an analysis project. During planning, a business decides
what kind of data it needs, how it will be managed
throughout its life cycle, who will be responsible for
it, and the optimal outcomes. For example, let’s say an electricity
provider wanted to gain insights into how to save people energy. In the planning phase, they might decide
to capture information on how much electricity its customers use each year, what types of buildings are being powered,
and what types of devices are being powered
inside of them. The electricity company would also decide which team members will
be responsible for collecting, storing, and sharing that data.
All of this happens during planning, and it helps set up the rest of the project.
The next phase is when you capture data. This is where data is collected from
a variety of different sources and brought into the organization. With so much data being created everyday, the ways to collect
it are truly endless. One common method is getting data
from outside resources. For example, if you were doing data analysis on weather
patterns, you’d probably get data from a publicly available dataset like
the National Climatic Data Center. Another way to get data is from
a company’s own documents and files, which are usually stored inside a
database. While we’ve mentioned databases before, we haven’t gone into too
much detail about what they are. A database is a collection of
data stored in a computer system. In the case of our electricity provider,
the business would probably measure data usage among its customers within
a database that it owns. As a quick note, when you maintain a database of customer
information, ensuring data integrity, credibility, and
privacy are all important concerns. You’ll learn a lot more
about that later on. Now that we’ve captured our data, we’ll move on to the next phase of
the data life cycle, manage. Here we’re talking about how we care for
our data, how and where it’s stored, the tools used to keep it safe and
secure, and the actions taken to make sure
that it’s maintained properly. This phase is very important to data
cleansing, which we’ll cover later on. Next it’s time to analyze your data.
This is where data analysts really shine. In this phase, the data is used to solve
problems, make great decisions, and support business goals. For example, one of our electricity
company’s goals might be to find ways to help customers save energy. Moving along the data life cycle
now evolves to the archive phase. Archiving means storing data in a place
where it’s still available, but may not be used again. During analysis,
analysts handle huge amounts of data. Can you imagine if we had to sort through
all of the available data that’s out there, even if it was no longer useful and
relevant to our work? It makes way more sense to archive
it than to keep it around. And finally, the last step of the data
life cycle, the destroy phase. Yes, it sounds sad, but when you
destroy data, it won’t hurt a bit. So let’s get back to our
electricity provider example. They would have data stored
on multiple hard drives. To destroy it, the company would
use a secure data erasure software. If there were any paper files,
they would be shredded too. This is important for protecting
a company’s private information, as well as private data about its customers. And there you have it,
the data life cycle. And now that you understand the different
phases data goes through during its life cycle, you can better understand how
to approach the data analysis process, which we’ll talk about soon.

1. Ask

The first step in the data analysis process is to ask a question. This question should be specific and measurable, and it should be something that the data can answer. For example, you might ask “What is the average customer satisfaction score for our product?” or “What are the top three reasons why customers are churning?”

2. Prepare

Once you have a question, you need to collect the data that you need to answer it. This data can come from a variety of sources, such as surveys, customer reviews, or sales data. You need to make sure that the data is accurate, complete, and relevant to your question.

3. Process

The next step is to process the data. This includes cleaning the data, removing any errors or inconsistencies, and transforming the data into a format that is easy to analyze. You may also need to combine different datasets to get the full picture.

4. Analyze

Now it’s time to analyze the data. This is where you use statistical and machine learning techniques to extract insights from the data. You can use charts, graphs, and other visualizations to help you understand the data.

5. Share

Once you have analyzed the data, you need to share your findings with the stakeholders. This includes communicating the insights in a way that is easy to understand and actionable. You may need to create reports, presentations, or other materials to share your findings.

6. Act

The final step is to act on the insights that you have found. This means using the insights to make decisions and take action. For example, you might use the insights to improve your product, launch a new marketing campaign, or optimize your customer service.The six phases of data analysis are a cyclical process. Once you have completed one cycle, you can start the process again with a new question. This process can be used to answer a variety of questions and solve a variety of problems.Here are some additional tips for conducting data analysis:

• Be clear about your goals. What do you want to achieve with your data analysis?
• Use the right tools. There are a variety of tools available for data analysis, so choose the ones that are right for your needs.
• Be patient. Data analysis can be a time-consuming process, so be patient and don’t expect to get results overnight.
• Be creative. There is no one right way to do data analysis, so be creative and try different approaches.
• Be open to feedback. Get feedback from others on your data analysis and use it to improve your results.

Now that you understand all the phases of
the data life cycle, it’s time to move on to the
phases of data analysis. They sound similar, but
are two different things. Data analysis isn’t a life cycle. It’s the process
of analyzing data. Coming up, we’ll
look at each step of the data analysis process and how it will relate to your
work as a data analyst. Even this program is designed
to follow these steps. Understanding these
connections will help guide your own analysis and your
work in this program. You’ve already learned that
this program is modeled after the stages of the
data analysis process. This program is
split into courses, six of which are based upon
the steps of data analysis: ask, prepare, process,
analyze, share, and act. Let’s start with
the first step in data analysis, the ask phase. In this phase, we do two things. We define the problem to
be solved and we make sure that we fully understand
stakeholder expectations. Stakeholders hold a
stake in the project. They are people who have invested time and resources into a project and are interested in the outcome. Let’s
break that down. First, defining a problem
means you look at the current state and identify how it’s different
from the ideal state. Usually there’s an obstacle
we need to get rid of or something wrong
that needs to be fixed. For instance, a sports
arena might want to reduce the time fans spend
waiting in the ticket line. The obstacle is
figuring out how to get the customers to their
seats more quickly. Another important
part of the ask phase is understanding
stakeholder expectations. The first step here is to determine who the
stakeholders are. That may include your manager, an executive sponsor,
or your sales partners. There can be lots
of stakeholders. But what they all have in common is that they
help make decisions, influence actions and strategies, and have specific goals
they want to meet. They also care about the
project and that’s why it’s so important to
understand their expectations. For instance, if your
manager assigns you a data analysis project
related to business risk, it would be smart to confirm
whether they want to include all types of risks
that could affect the company, or just risks related to weather such as
hurricanes and tornadoes. Communicating with your
stakeholders is key in making sure you stay engaged and on track throughout
the project. So as a data analyst, developing strong
communication strategies is very important. This part of the ask phase helps you keep focused on
the problem itself, not just its symptoms. As you learned earlier, the five whys are
extremely helpful here. In an upcoming course, you’ll learn how to ask
effective questions and define the problem by
working with stakeholders. You’ll also cover strategies
that can help you share what you discover in a way that keeps
people interested. After that, we’ll move on to the prepare step of the
data analysis process. This is where data analysts
collect and store data they’ll use for the
upcoming analysis process. You’ll learn more about
the different types of data and how to identify which kinds of data are most useful for solving a
particular problem. You’ll also discover why
it’s so important that your data and results are
objective and unbiased. In other words, any decisions
made from your analysis should always be based on facts and be fair and impartial. Next is the process step. Here, data analysts
find and eliminate any errors and inaccuracies that can get in the
way of results. This usually means cleaning data, transforming it into a
more useful format, combining two or more
datasets to make information more complete
and removing outliers, which are any data points that could skew the information. After that, you’ll learn
how to check the data you prepare to make sure it’s
complete and correct. This phase is all about
getting the details right. So you’ll also fix typos, inconsistencies, or missing
and inaccurate data. To top it off, you’ll gain strategies for verifying and sharing your data cleansing
with stakeholders. Then it’s time to analyze. Analyzing the data
you’ve collected involves using tools to transform and organize that information so that you can draw
useful conclusions, make predictions, and drive
informed decision-making. There are lots of powerful
tools data analysts use in their work and in this course you’ll learn
about two of them, spreadsheets and
structured query language, or SQL, which is often
pronounced “sequel.” The next course is based
on the share phase. Here you’ll learn how data analysts interpret
results and share them with others to help stakeholders make effective
data-driven decisions. In the share phase, visualization is a data
analyst’s best friend. So this course will highlight
why visualization is essential to getting others to understand what your
data is telling you. With the right visuals, facts and figures become
so much easier to see and complex concepts
become easier to understand. We’ll explore different
kinds of visuals and some great data
visualization tools. You’ll also practice your own presentation
skills by creating compelling slideshows
and learning how to be fully prepared
to answer questions. Then we’ll take a break from
the data analysis process to show you all of the really
cool things you can do with the programming language
R. You don’t need to be familiar with R or programming
languages in general. Just know that R is
a popular tool for data manipulation, calculation,
and visualization. For our final data analysis
phase, we have act. This is the exciting moment when the business takes all
of the insights you, the data analyst, have provided and puts them to
work in order to solve the original business
problem and will be acting on what you’ve learned
throughout this program. This is when you prepare
for your job search and have the chance to
complete a case study project. It’s a great opportunity
for you to bring together everything you’ve worked
on throughout this course. Plus adding a case study to your portfolio helps you stand out from the other
candidates when you interview for your
first data analyst job. Now you know the
different steps of the data analysis process and
how our course reflects it. You have everything you need to understand how this course works and my fellow Googlers and I will be here to guide
you every step of the way.

Regardless of what type of data analysis you’re conducting, the process is
generally the same. The example that I’ll
walk through is that of our employee engagement
survey, but you could imagine that this process
applies to just about any data analysis that you’re going to conduct as an analyst. The first thing you
want to do is ask. You want to ask all of the right questions at the beginning of the
engagement so that you better understand what your leaders and stakeholders
need from this analysis. The types of questions that
I generally ask are around, what is the problem that
we’re trying to solve? What is the purpose
of this analysis? What are we hoping
to learn from it? After you’ve asked all the right questions
and you’ve wrapped your arms around the scope of the analysis you need to conduct, the next step is to prepare. We need to be thinking
about what type of data we need to answer
those key questions. This could be anything from quantitative data or
qualitative data. It could be cross-sectional
or points in time versus longitudinal
over a long period of time. We need to be thinking
about the type of data we need in order to
answer the questions that we’ve set out
to answer based on what we learned when we
asked the right questions. We also need to be thinking about how we’re going to
collect that data or if we need to
collect that data. It may be the case that we need to collect
this data brand-new. So we need to think
about what type of data we’re going to be
collecting and how. For our employee engagement survey, we do that via survey of both quantitative and
qualitative questions. But it may actually be the
case that for many analyses, the data that you’re
looking for already exist. Then it’s a question of working with those data
owners to make sure that you are able to leverage that data and use it responsibly. After you’ve done all the hard
work to collect your data, now you need to
process that data. It begins with cleaning. This to me is the most fun part of the data analytics process. We can think of it as
the initial introduction or the handshake, hello, to your data. This is where you get a chance to understand its structure, its quirks, its nuances, and you really get a
chance to understand deeply what type of
data you’re going to be working with and understanding what potential that data has to answer all of your questions. This is such an important
part, too, where we’re running
through all of our quality assurance checks. For example, do we have all of the data that we
anticipated we would have? Are we missing data at
random or is it missing in a systematic way such that maybe something went wrong with our data collection effort? If needed, did we code all
of our data the right way? Are there any outliers that
we need to treat differently? This is the part
where we spend a lot of time really digging deeply into the structure and nuance of
the data to make sure that you’re able to analyze it appropriately
and responsibly. After cleaning our
data and running all of our quality
assurance checks, now is the point where
we analyze our data, making sure to do so in as objective and unbiased
a way as possible. To do this, the first
thing we do is run through a series of
analyses that we’ve already planned
ahead of time based on the questions that we know we want to answer from the very, very beginning of the process. One thing that’s probably the hardest about this
particular process, the hardest thing
about analyzing data, is that we as analysts are
trained to look for patterns. Over time as we become better
and better at our jobs, what we’ll often find
is that we can start to intuit what we might
see in the data. We might have a
sneaking suspicion as to what the data
are going to tell us. This is the point where
we have to take a step back and let the data
speak for itself. As data analysts, we
are storytellers, but we also have to
keep in mind that it is not our story to tell. That story belongs to the data, and it is our job as
analysts to amplify and tell that story in as unbiased and objective
a way as possible. The next step is to share
all of the data and insights that you’ve
generated from your analyses. Now typically for employee
engagement survey, we start by sharing the
high-level findings with our executive team. We want them to have
a landscape view of how the organization is feeling, and we want to make sure
that there aren’t any surprises as they dig deeper and deeper
into the data to understand how teams are feeling and how individual
employees are feeling. All of this work from asking the right questions
to collecting your data, to analyzing and sharing, doesn’t mean much of anything if we aren’t taking action
on what we’ve just learned. This to me is the
most critical part, especially of our employee
engagement survey. I like to say that the survey
is actually the easy part, and acting on the results is really where the
real work begins. This is where we use all of
those data-driven insights to decide what types of interventions
we want to introduce, not only at the
organizational level, but also at the
team level as well. We might find, for example, that the organization is
working on a series of interventions to help improve part of the employee experience, whereas individual teams have additional roles,
responsibilities to play, to either bolster some of
those efforts or to introduce new ones to better
meet their team where their strengths and
opportunity areas are. The data analysis
process is rigorous, but it is lengthy. I can completely appreciate
that we as data analysts, get so excited about just diving right into the data
and doing what we do best. The challenge is that if we don’t work through the process
in its entirety, if we try to skip steps, we’re not going to
be able to elicit the insights that
we’re looking for. I absolutely love my job. I have such a deep
appreciation for data and what it can do and what type of insight
we can derive from it.

Introduction

Data analysts use a variety of tools to help them collect, clean, store, analyze, and visualize data. The most common data analysis tools include:

• Spreadsheets: Spreadsheets are digital worksheets that can be used to store, organize, and sort data. They can also be used to perform calculations, find patterns, and group information.
• Query languages: Query languages are computer programming languages that allow data analysts to retrieve and manipulate data from databases. The most common query language is SQL.
• Data visualization tools: Data visualization tools are used to create graphical representations of data. This can help data analysts communicate their insights to others in an effective and compelling way.

Spreadsheets

Spreadsheets are one of the most popular data analysis tools. They are easy to use and can be used to perform a variety of tasks, such as:

• Storing data: Spreadsheets can be used to store large amounts of data in a structured format. This makes it easy to find and access the data when you need it.
• Organizing data: Spreadsheets can be used to organize data in a variety of ways, such as by category, date, or location. This can help you to make sense of the data and identify patterns.
• Performing calculations: Spreadsheets can be used to perform calculations on data, such as adding, subtracting, multiplying, and dividing. This can help you to summarize the data and identify trends.
• Finding patterns: Spreadsheets can be used to find patterns in data. This can help you to identify relationships between different variables.
• Grouping information: Spreadsheets can be used to group information together. This can help you to make sense of the data and identify trends.

Query languages

Query languages are used to retrieve and manipulate data from databases. The most common query language is SQL. SQL stands for Structured Query Language.SQL is a powerful tool that can be used to do a variety of tasks, such as:

• Retrieve data from a database: SQL can be used to retrieve data from a database based on certain criteria. For example, you could use SQL to retrieve all of the customers who have purchased a product in the past month.
• Manipulate data in a database: SQL can be used to manipulate data in a database, such as updating, deleting, or inserting data.
• Create and manage databases: SQL can be used to create and manage databases. This includes creating tables, adding columns, and defining relationships between tables.

Data visualization tools

Data visualization tools are used to create graphical representations of data. This can help data analysts communicate their insights to others in an effective and compelling way.There are many different data visualization tools available, each with its own strengths and weaknesses. Some popular data visualization tools include:

• Tableau
• QlikView
• Power BI
• Spotfire
• Matplotlib
• Seaborn

Choosing the right tool

The right data analysis tool for you will depend on your specific needs and requirements. If you are just starting out, it is a good idea to start with a simple tool like a spreadsheet. Once you have more experience, you can then choose a more advanced tool, such as a query language or a data visualization tool.

Conclusion

Data analysis tools are an essential part of the data analyst’s toolkit. By understanding the different types of tools available and how to use them, data analysts can be more effective in their work.I hope this tutorial has been helpful. Please let me know if you have any other questions.

Fill in the blank: A query language is a computer programming language that enables data analysts to retrieve and manipulate data from a _____.

database

A query language is a computer programming language that enables data analysts to retrieve and manipulate data from a database. SkipContinue

I’m looking forward to introducing you to
some of the tools data analyst use each and every day. There are tons of options out there. But the most common ones you’ll
see analyst use are spreadsheets, query languages and visualization tools. And this video is going to give you
a quick look at how these tools are being used by data analysts everyday. Believe it or not,
I was several years into my accounting and finance career before I saw all
of these tools working together. At that point I was very
experienced with spreadsheets, and had worked in large data sets with some
of the traditional database programs. I had the foundational skill
set to use query languages, and I had dabbled in visualizations, but
I had never brought them all together. Then I got hired here at Google. And it was so
eye-opening to come into a place like this with an abundance of
information everywhere you look. As an analyst at Google, the true power of
these tools became so much clearer to me. I became more focused on really maximizing
everything these tools could do, streamlining my reporting and
just making my work simpler. All of the sudden, I had a lot more time
and space to dedicate to identifying new problems to solve and
driving decision-making. Without a doubt, once you’ve
learned the power of these tools, you will be well on your way to
becoming the best data analyst you can possibly be. All right, I hope that story has you
even more motivated for this course. Let’s get started with spreadsheets. Again, there are lots of different
spreadsheet solutions, but two popular options
are Microsoft Excel and Google Sheets. To put it simply, a spreadsheet is
a digital worksheet. It stores, organizes, and sorts data. This is important because the usefulness
of your data depends on how well it’s structured. When you put your data into a spreadsheet,
you can see patterns, group information and
easily find the information you need. Spreadsheets also have some really useful
features called formulas and functions. A formula is a set of instructions
that performs a specific calculation using the data
in a spreadsheet. Formulas can do basic things like add,
subtract, multiply and divide, but they don’t stop there. You can also use formulas to find
the average of a number set. Look up a particular value, return the sum of a set of values that
meets a particular rule, and so much more. A function is a preset command that
automatically performs a specific process or
task using the data in a spreadsheet. That sounds pretty technical,
I know, so let’s break it down. Just think of a function as a simpler, more efficient way of doing something
that would normally take a lot of time. In other words, functions can
help make you more efficient. Those are the spreadsheet basics for now. Later on, you’ll see them in action and
start working with spreadsheets yourself. The next data analysis tool
is called query language. A query language is a computer programming
language that allows you to retrieve and manipulate data from a database. You’ll learn something called
structured query language, more commonly known as SQL. SQL is a language that lets data
analysts communicate with a database. A database is a collection of
data stored in a computer system. SQL is the most widely used structured
query language for a couple of reasons. It’s easy to understand and works very
well with all kinds of databases. With SQL, data analysts can access
the data they need by making a query. Although query means question, I like
to think of it as more of a request. So you’re requesting that
the database do something for you. You can ask it to do a lot of different
things such as insert, delete, select or update data. Okay, that’s a top level look at SQL. In a later video,
we’ll explore it further and use SQL to do some really
cool things with data. Lastly, let’s talk about
data visualization. You’ve learned that data visualization
is the graphical representation of information. Some examples include graphs,
maps, and tables. Most people process visuals
more easily than words alone. That’s why visualizations are so
important. They help data analysts communicate their
insights to others, in an effective and compelling way. When you think about the data analysis
process, after data is prepared, processed and analyzed, the insights are visualized so
it can be understood and shared. This makes it easier for
stakeholders to draw conclusions, make decisions, and
come up with strategies. Some popular visualization
tools are Tableau and Looker. Data analysts like using Tableau because
it helps them create visuals that are very easy to understand. This means that even non-technical users
can get the information they need. Looker is also popular with data
analysts because it gives them an easy way to create visuals
based on the results of a query. With Looker, you can give stakeholders
a complete picture of your work by showing them visualization data and
the actual data related to it. All visualization tools have great
features that are useful in different situations. Soon you will learn how to decide which
tool to use for a particular job. And that’s everything you need to
know about the data life cycle and the data analysis process. You’ll get a chance to
test out what you know, so you can feel confident moving
forward in this course. Feel free to take some time to
re-familiarize yourself with the concepts and when you’re ready,
give it your best shot. If you’re ever unsure of an answer,
you can always go back and review the videos and readings. Then you’ll be ready to move
on to the next set of videos, where we’ll continue exploring the data
analytics tools you’ve already covered. And you’ll get some really fascinating
insights into exactly how they work. Before long, you’ll have the knowledge and
confidence to start using them yourself. Stay tuned.