Week 4: Set up your toolbox

What are core data tools?

Core data tools are the essential tools that every data analyst needs to know how to use. They include:

• Spreadsheets: Spreadsheets are a versatile tool that can be used for data cleaning, analysis, and visualization.
• SQL: SQL is a language for querying and managing databases. It is essential for data analysts who need to access and manipulate large amounts of data.
• Data visualization tools: Data visualization tools help to communicate data insights in a clear and concise way. They are essential for presenting data to stakeholders and making data-driven decisions.

How to use core data tools

The specific way you use core data tools will vary depending on your specific needs and the data you are working with. However, there are some general principles that apply to all core data tools.

• Clean your data. Before you can analyze your data, you need to make sure it is clean and free of errors. This may involve removing duplicate data, correcting typos, and filling in missing values.
• Organize your data. Once your data is clean, you need to organize it in a way that makes it easy to access and analyze. This may involve creating spreadsheets, databases, or data warehouses.
• Query your data. Once your data is organized, you can start to query it to extract the insights you need. This can be done using SQL or other query languages.
• Visualize your data. Data visualization is a powerful way to communicate data insights. There are many different data visualization tools available, so you can choose the one that best suits your needs.

Core data tools for beginners

If you are new to data analytics, there are a few core data tools that I recommend you start with:

• Microsoft Excel: Excel is a powerful spreadsheet program that is easy to learn and use. It is a good choice for beginners who need to perform basic data cleaning and analysis.
• SQL: SQL is a foundational language for data analytics. It is a good idea to learn SQL even if you are not planning to use it as your primary data analysis tool.
• Tableau: Tableau is a popular data visualization tool that is easy to use and produces high-quality visualizations. It is a good choice for beginners who want to create data visualizations that are easy to understand and share.

Advanced core data tools

Once you have mastered the basics of core data tools, you can start to explore more advanced tools. Some of the most popular advanced core data tools include:

• Python: Python is a versatile programming language that is often used for data analysis. It is a good choice for beginners who want to learn a more powerful data analysis tool.
• R: R is another popular programming language for data analysis. It is known for its statistical capabilities.
• Hadoop: Hadoop is a distributed computing framework that can be used to process large datasets. It is a good choice for businesses that need to analyze large amounts of data.

Conclusion

Core data tools are essential for data analysts. By learning how to use these tools, you can gain the skills you need to analyze data, communicate insights, and make data-driven decisions.I hope this tutorial has been helpful. If you have any questions, please feel free to ask.

Welcome back. In the
next few videos, you’ll continue to explore the data analytics tools
we discussed earlier, and you’ll get the chance to see them in action a little bit. This will give you
a clearer picture of how to use these tools. The rest of the program will build on from what
you learn here. We’ll start with a closer
look at spreadsheets. We’ll break spreadsheets
down to their basics to better understand a few of
their features and functions. You’ll also learn how
you might want to use them in your work
as a data analyst. For example, how do you sort data to make
it easier to use? We’ll find out. Next, we’ll see SQL in action. Data analysts use SQL in their
work all the time. Like when they need a
large amount of data in seconds to help answer a
quick business question. Chances are, you’re not
familiar with SQL. That’s okay. You’ll learn how using
SQL is just like ordering food at a super
speedy restaurant. Your SQL query might not be as delicious but you won’t have to wait long
to get your order. Speaking of food, what
better topic than dessert? You can think of data
visualization as the dessert to the meal
of data analytics. It’s served at the end of your analysis after
you’ve done what you need to get the right data
for a question or task. We’ve already seen that visualizations come
in a lot of forms, like graphs or charts. Just like dessert, they’re
a treat to look at. You’ll learn more about
these visual representations and see other examples
of how they might look. Then you’ll get to talk
about visualizations with other future data
analysts just like yourself. We’ll wrap things up
with an assessment, but you’ll have time to review what you’ve
learned before then. Okay, let’s keep going. By the way, is anyone
else hungry now?

What are columns and rows?

In data analytics, columns and rows are the two main components of a data table. Columns represent the different attributes of a data set, while rows represent the individual observations or records in the data set.

For example, a data table of customer orders might have columns for the customer’s name, address, order date, and order items. Each row in the table would represent a single order.

What are cells?

Cells are the intersections of columns and rows. They contain the actual data values for each attribute and observation.

In the example above, the cell at the intersection of the “Customer Name” column and the first row would contain the name of the first customer in the data set.

Why are columns and rows important?

Columns and rows are important for organizing and structuring data. They make it easy to identify and access specific data values, and they can help to improve the readability and understandability of data tables.

For example, if you are looking for the customer’s address in the example above, you can easily find it by looking at the “Address” column.

How to use columns and rows in data analyticsColumns and rows are used in many different data analytics tasks, such as:

• Data cleaning: Columns and rows can be used to identify and remove duplicate data, correct typos, and fill in missing values.
• Data analysis: Columns and rows can be used to perform calculations, create visualizations, and identify trends in data.
• Data modeling: Columns and rows can be used to build predictive models that can be used to make predictions about future events.
• Data visualization: Columns and rows can be used to create data visualizations that can help to communicate data insights to others.

Conclusion

Columns and rows are essential concepts in data analytics. By understanding how they work, you can gain the skills you need to organize, analyze, and visualize data.

Here are some additional tips for using columns and rows in data analytics:

• Use descriptive column names to make your data tables easier to understand.
• Use consistent formatting for your data values to make them easier to read.
• Use meaningful row names to identify the individual observations in your data set.
• Use comments to explain your data cleaning and analysis procedures.

By following these tips, you can create data tables that are clear, concise, and easy to use.

Spreadsheets are a big
part of data analytics. The sooner you make friends with spreadsheets, the better. Trust me, they’ll save
you a lot of time as a data analyst and make
your whole job easier. This spreadsheet is one example of how an organized
spreadsheet might look. In this video, we’ll
demonstrate some of the basic spreadsheet
concepts for all of you who are
new to this world. This might be a review
for some of you more experienced
folks out there, but it never hurts to
practice what you know. Plus, you might still
learn a trick or two. I showed you this image earlier. Let’s explore it further
because it’s a great example of the three main features
of a spreadsheet: cells, rows, and columns. They’ll be part of
almost everything you do in a
spreadsheet for making a simple grocery list to
analyzing a complex dataset. I use spreadsheets to
manage everything from my own personal finances to the annual homecoming party my friends and I
have every year. I’m the planner, so I use a spreadsheet to keep
things in order, making sure we have
everything we need. Speaking of keeping
things in order, columns are organized vertically in a spreadsheet and
are ordered by letter. And the rows are organized horizontally and are
ordered by number. So when you talk about
a specific cell, you name it by combining
the column letter and the row number where
the cell is located. For example, in
this spreadsheet, the word row is in cell D3. Let’s get started in
an actual spreadsheet. You can complete
all of the steps in just about any
spreadsheet program. Let’s get to know your
spreadsheet a little better now. We’ll start with some
basic operations. Keep in mind, as an analyst, you won’t always create
your own dataset. But for now, let’s do just that. I’ll click in cell A2 and
type my first name like this. Then I’ll click in cell
B2 and type my last name. Don’t worry if your name
doesn’t fit in the cell, you can always make columns
wider if you need to. All you have to do
is click and drag the right edge of the column
until your name fits. Or you can use the
text wrapping feature, which will set cells to
automatically change their height to allow the
text in the cell to fit. To use this feature, select the cells, columns, or rows with text, then use the format menu to look at the text
wrapping options. It is automatically set to allow the text to overflow
out of the cell. But you can wrap the text instead so all of
the text is visible. The clip option will
cut off the text in the cell so only the text
that fits is visible. There it is. We’ve added data. Now let’s label it. This is important
for organization. Adding labels to the top of the columns will
make it easier to reference and find data later on when you’re doing analysis. These column labels are
usually called attributes. An attribute is a
characteristic or quality of data used to label a
column in a table. More commonly, attributes are referred to as column names, column labels, headers, or the header row. Let’s add some
headers to our table. I’ll click in cell A1 and
type the words first name. In cell B1 I’ll type last name. We’ll make these attributes
bold so they stand out more. Spreadsheets can get really big, so you want to make
sure your data is clearly labeled
and easy to find. I can use my cursor to select the cells with the attributes. Then I’ll click the bold
icon to make them bold. Looking good so far. Ready to add some more data? Let’s start with
some new attributes. First, I’ll add a column for
the number of siblings by typing siblings in cell C1. Then I’ll add two
more attributes in the next two columns. Let’s go with favorite
color and favorite dessert. I’ll make them bold too. To fit the labels in the cells, I’ll adjust the size of the
columns just like before. Now, keep in mind, there are more ways to adjust the size of the
columns and rows. If you have questions
about using spreadsheets, a quick search online will usually help you
find what you need. We’ve also included
a reading with more tips and information
about spreadsheets. OK, let’s get back to it. Now, I can add my own
data to the dataset. I’ll type in how many
siblings I have and my favorite color and dessert
in the appropriate cells. Next, I’ll add data
for two more people. We now have three rows of data. In a dataset, a row is also
called an observation. An observation includes
all of the attributes for something contained
in a row of a data table. In this case, row 3
is an observation of Willa Stein because we see all of her
attributes in this row. So now we know spreadsheets let you do lots of things with data. You can store and organize data like we’ve done
in this spreadsheet. But you can go even further and recognize existing data too. Here, I’ll show you how. Let’s say we want to
organize our data by how many siblings
each person has. There’s a simple way to do that. First, we’ll need to select
all of our columns with data so that all of it
is reorganized together. Then we can go to our data menu. Here we have some options. Let’s select sort range. This will let us choose how
to organize the column. Next, we’ll choose A to Z, which will organize
our numbers in order from smallest to largest. Now, we want to watch
out for header row, which is the word siblings, the attribute for this column. We’ll check this box. This makes sure the word
siblings stays in place. Now we’re ready to sort. Voila, we just reorganized our data by sorting it from the smallest number
to the largest. As we go further, you discover lots of other ways to work with
data in a spreadsheet, including functions
and formulas. Let’s finish with a quick
example of a formula. You can think of formulas as one way of manipulating
data in a spreadsheet. Formulas are like a
calculator, but more powerful. A formula is a set of
instructions that performs a specific action using
the data in a spreadsheet. To do this, the formula uses cell references for the
values it’s calculating. Let me show you. Here we go. We’ll click in the next cell
in the siblings column. Then we’ll type an equal sign. All formulas begin
with this symbol. Next, we’ll type in the cells
we want to add together. In this case, we’ll type
in C2 plus C3 plus C4. Now we can press “Enter”. There it is. The
formula has given us the total number of siblings
represented in this dataset. We’ve just analyzed some data. We’ll want to store the
data for later use. In Google sheets,
a spreadsheet is automatically saved
in your Google Drive. For Excel and other
spreadsheets, you’ll save them as a file. Now you know some basics
for using spreadsheets. Once you’re used
to these concepts, you’ll be able to learn even more about spreadsheet tools. Feel free to re-watch this video and
practice on your own. You can even make your own
version of the spreadsheet with your own data. Bye for now.

In a table, an attribute is a characteristic or quality of data used for what purpose?

To label a column

In a table, an attribute is a characteristic or quality of data used to label a column.

What is SQL?

SQL stands for Structured Query Language. It is a language used to manage data in relational databases. Relational databases are databases that store data in tables. Each table has a set of columns that define the data that can be stored in the table.

What can SQL do?

SQL can be used to do many things with data in relational databases, including:

• Create and delete tables
• Insert, update, and delete data from tables
• Select data from tables
• Join tables together
• Sort and filter data
• Aggregate data
• Create views
• Grant and revoke permissions

Why is SQL important for data analytics?

SQL is an essential tool for data analysts. Data analysts use SQL to access and manipulate data in relational databases. This allows them to extract insights from data that would not be possible to obtain otherwise.

How to learn SQL

There are many resources available to help you learn SQL. Here are a few suggestions:

• Online courses: There are many online courses available that teach SQL. Some of these courses are free, while others require a fee.
• Books: There are many books available that teach SQL. Some of these books are aimed at beginners, while others are more advanced.
• Tutorials: There are many tutorials available online that teach SQL. These tutorials can be a great way to learn the basics of SQL.
• Practice: The best way to learn SQL is to practice. Try to find a dataset that you are interested in and use SQL to query the data.

SQL in action

Here is an example of how SQL can be used in data analytics:Let’s say you are a data analyst for a retail company. You want to find out which products are the most popular among customers. You can use SQL to query the company’s sales database to find the products with the highest sales.

The following SQL query would return the products with the highest sales:

SELECT product_name, SUM(quantity_sold) AS total_sales
FROM sales_table
GROUP BY product_name
ORDER BY total_sales DESC

This query first selects the product name and the total sales for each product. It then groups the results by product name and orders the results by total sales in descending order. This will return the products with the highest sales at the top of the results.

This is just one example of how SQL can be used in data analytics. There are many other things that you can do with SQL, such as joining tables together, filtering data, and aggregating data.

Fill in the blank: A data analyst uses a SQL query to retrieve information from a database. They add a WHERE statement to _____ the data based on certain conditions.

filter

They add a WHERE statement to filter the data based on certain conditions.

As you might remember, earlier we touched on the
query language SQL. In this video, you’ll see SQL in action and learn what
you can do with it, with some examples
of specific queries. I guess you can call
this the SQL sequel. We’ll try to make this one
even better than the original. Remember, SQL can do lots of the same things with data
that spreadsheets can do. You can use it to store, organize and analyze your
data, among other things. But like any good sequel, it’s on a larger scale,
bigger, more action-packed. Think of it as
supersize spreadsheets. For example, you might
want to consider a spreadsheet when you
have a smaller dataset, such as one with just 100 rows. But if your data set
seems to go on forever, and your spreadsheet is
struggling to keep up, SQL would be the way to go. When you use SQL, you need a place where the SQL
language is understood. If you’ve ever gone somewhere
and not known the language, it can be challenging
to communicate. You might think
you’re asking for one thing and get something
completely different. Well, SQL knows the feeling. SQL needs a database that
will understand its language. Let’s talk. There are a number of databases out
there that use SQL. You may use several of them during your time
as a data analyst. But here’s the thing, no matter which
database you use, SQL basically works
for the same in each. For example, in SQL,
queries are universal. We’ve talked about
queries before, but it never hurts
to have a refresher. A query is a request for data or information
from a database. Here’s the structure
of a basic query. You can see that
with this query we can select specific data from a table by adding where we can filter the data based
on certain conditions. Let’s get started. We’ll open
our database and see how SQL can communicate with it
to do some simple data task. First, let’s select
our dataset. We’ll use an asterisk to select all of the
data from the table. With that simple query, the database calls up
the table we need. Magic. Let’s add Where to our query to show how that
changes what data we get. You can see the data
now only shows, movies that are in
the action genre. That’s it, a basic query in SQL. Pretty cool, huh? Soon you’ll learn about building
more complex queries. For now, though,
we can celebrate learning about the structure
of a basic SQL query, select, from, and where. As you continue the program, you have the opportunity
to use SQL yourself. I hope this video was a useful sneak peek at
what’s coming later.

I’m Angie, I’m a Program Manager of Engineering at Google. I’m currently working on the
Data Analytics certificate. Previously, I was a researcher
in people analytics. I was also what I call
an analytical mercenary working for a lot of
different companies to help them make
sense of their data. Every time I learn a new skill, I feel like I’m learning how
to speak all over again. I remember the first
time I learned SQL, I was so frustrated because
everyone around me, it felt like they were fluent, they knew exactly
what they were doing. I remember struggling with
the most basic things, just like getting the data out
of the table or I remember somebody asked me just
to find an average of something and I kept
on getting an error. It really does feel like you’re learning a new
language and you’re at toddler level and everyone around you
is like maybe fluent. My parents immigrated to this country when they
are in their 30s. After they had learned another language and
they had to start over and learn English. I remember as a
child watching them struggle every day to
pick up a new language, to do really basic things, like ask for help at
the grocery store. I remember calling the cable
company when I was six, asking them questions about the bill because my
parents couldn’t. I remember how hard
they worked to learn this new
language and to become fluent and every
time I’m learning a new data language like SQL or R, I think about how
hard that must have been. I think if they can do
that, I can learn SQL. If they can ask for help for
the most basic of things, I can ask the Data
Analysts next to me how to write a SQL statement and how to get data out of a table. That really helped me, is just having that mindset and knowing that I
can ask for help.

1. Understand the basics of data visualization

The first step to becoming a data viz whiz is to understand the basics of data visualization. This includes understanding the different types of data visualizations, the principles of good design, and the different tools that can be used to create data visualizations.

2. Practice creating data visualizations

The best way to learn data visualization is by practicing. There are many online resources that offer tutorials and exercises on data visualization. You can also find data visualization competitions and challenges that you can participate in.

3. Get feedback on your work

Once you have created some data visualizations, it is important to get feedback on your work. This feedback can help you identify areas where your visualizations can be improved. You can get feedback from friends, colleagues, or mentors.

4. Stay up-to-date on the latest trends

The field of data visualization is constantly evolving. It is important to stay up-to-date on the latest trends so that you can create the most effective visualizations. You can do this by reading blogs, attending conferences, and taking online courses.

5. Build a portfolio

A portfolio is a great way to showcase your data visualization skills. It can help you get a job in data analytics or freelancing gigs. When creating your portfolio, be sure to include a variety of visualizations that demonstrate your skills.

6. Never stop learning

The field of data visualization is constantly evolving, so it is important to never stop learning. There are always new techniques and tools to learn. By staying up-to-date on the latest trends, you can become a data viz whiz and create the most effective visualizations.

Here are some additional tips for becoming a data viz whiz:

• Choose the right tool for the job. There are many different data visualization tools available, so it is important to choose the right one for the task at hand. Consider the type of data you are working with, the audience you are creating the visualization for, and your own skill level.
• Keep your audience in mind. When creating a data visualization, always keep your audience in mind. What are they trying to learn from the visualization? How can you make the visualization as clear and concise as possible?
• Use color effectively. Color can be a powerful tool in data visualization. However, it is important to use color effectively. Use colors that are easy to distinguish and that make sense for the data you are visualizing.
• Tell a story. A good data visualization should tell a story. The visualization should be more than just a collection of numbers and charts. It should help the viewer understand the data and draw conclusions.

With hard work and dedication, you can become a data viz whiz and create powerful visualizations that communicate data effectively.

What are some reasons why a data analyst might use data visualizations? Select all that apply.

To create interesting graphs

To explain complex data quickly

To reinforce data analysis

Data analysts use data visualizations to explain complex data quickly, reinforce data analysis, and create interesting graphs and charts.

Your data analysis
toolbox is getting full. Learning about both
Spreadsheets and SQL will get you far in the
world of data analysis. There’s more to learn, of course, and lots more tools
you’ll be able to use, but your future is
looking bright. It’s about to look even brighter, because we’re here to talk
more about data visualization. I’ll tell you a little
more about the role of data visualization tools
and data analytics, and give you a chance to see those tools in action
later in this video. You might remember that
data visualization is the graphical
representation of information. For tons of data analysts, it’s the most exciting part of their job because they get to see their hard work pay off
with something interesting. Not to mention that
data visualization is beautiful and useful. I was floored when
I got to Google and started to get a
quarterly data report in my e-mail and had a big slide deck where people contributed
their visualizations. It was definitely a
source of light as I started to build my
own visualizations. If you’re not
impressed by my story, let me tell you about
Florence Nightingale. Does that name ring a bell? She is responsible for much of the philosophy of modern nursing, and believe it or not, she was also a data analyst. During the Crimean
War in the 1850s, thousands of soldiers
were dying every day, Nightingale wanted to find a way to reduce the number of deaths. After examining the data, she found that the majority of soldiers were dying from
preventable conditions. To convince hospital
administrators that they needed to focus
on these conditions, she created a chart showing the number of deaths
over several months. The much larger blue sections in the visualization represent
the preventable deaths. Her work directly led to major
changes in patient care. She did all of this over 150 years ago without a computer. One of the main reasons
Nightingale created this visualization was to make the data easier to
digest for her audience. She felt she’d be more successful convincing
the stakeholders using visuals instead of
just words and numbers. She was right, tables
filled with data, while necessary for analysis, just aren’t able to show
trends and patterns as quickly and clearly as
visualizations can. Imagine, you receive
an assignment that needs to be completed
the same day. You gather the data
you need in a table, could you explain your
findings using the table? Yes, you probably could, but a better idea will be to use a visualization like
this bar graph. Something like this makes it much easier for you to
explain quickly, and you’ve got the benefit of a cool graphic to
backup your analysis. As a data analyst, you’ll want to create
visualizations that make the data easy to understand, and interesting to look
at, so show it off. Stakeholders may not have much time to devote to the data, your job will be to make
their time worthwhile. Let’s go back to
that data table we created earlier in the course. If you created your
own for practice, you can open it up now
or try this out later. Here’s the data we added before. Let’s create a
visualization of the data by inserting a
chart, a bar graph. You can see that the
spreadsheet visualized the data from our table in a way that made the most sense. It created a bar graph or column chart to compare the
ages of each person by name, but you might have
figured that out already. That’s the beauty
of visualization, it shows data analysis
quickly and clearly. We can use chart editor
to adjust the chart. Different spreadsheet
programs might have different ways to do this, but they all have
visualization functions and ways to edit
those visualizations. For now, let’s just look
at the suggested charts. We can make the bars go
horizontally using a bar chart. That looks great, so let’s
close the Chart editor. There are lots of
options to look at, but we’ll keep it basic for now. Feel free to try
other visualizations if you practice later. Now, we can adjust
our chart to make our whole spreadsheet look
clean and professional. Excellent. I hope
you learn to love data visualization
as much as I do. Maybe you’ll become a data
visualization pioneer, just like Florence Nightingale. As a budding data analysts, you started to fill
your utility belt with valuable tools that you’ll use throughout the
rest of the program. Having spreadsheets, SQL
and data visualization know-how will help make
you an ace data detective. You’ll be able to use
these tools throughout the data analytics process
as you move forward. Coming up next, you complete a few activities to wrap up
this part of the program. You’ll also complete
an assessment to check your understanding
of all that you learn. This is a great opportunity to think about some
of the areas that you’ll continue to explore in this course and
in your career. As always, feel free to
review the videos and readings to help remind you
of certain topics and ideas, even if you already
feel prepared. You’re just a few steps
away from the next course, that’s great
progress. Keep it up.

[MUSIC] My name is Lilah Jones, and
I am 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. Data visualizations,
that’s a long word and that can also make your eyes glaze over. But I wonder if, when you were little and
you were with your parents, maybe they had a bedtime routine or
maybe you have children, you’re doing a bedtime routine with them. You very rarely are going to come to
those children with a bunch of facts and figures before they go to bed. But I bet you probably are telling them
a story, you’re showing them pictures, I know I always loved comic books,
pictures tell a story. Data visualizations are pictures,
they are a wonderful way to take very basic ideas around data and
data points and make them come alive. You can do all different types of
combinations of visualizations, but the ones that are interactive,
wow, those are huge. Can you imagine being executive of an
organization and trying to figure out how
should we open up another site in Bangkok? Does that make sense and
us being able to walk in and saying, here’s why it makes sense in having
great data visualizations to support all of our points of view,
makes it a no brainer. Interestingly enough, I do recall
the first time I came across a super amazing visualization,
it was in my personal life. I switched my budgeting software
from one provider to another, and the provider that I switched to was really
focused on every dollar has a job and making sure you’re budgeting,
every single dollar. They gave visualizations that change,
depending on what input you would add to it and it really just changed my
entire perspective, the entire thing. So having the data is like having
the answer sheet for a test, it really just lets you know that you’re
going to make good decisions because it’s backed up by data.