Week 1: Introduction to data science concepts

If you’ve ever used an app to find the best
way through traffic or received product recommendations while shopping online, you’re
already familiar with data analytics from the consumer end. According to some estimates, each
person creates at least 1.7 megabytes of data per second, on average. That roughly translates
into over 2.5 quintillion megabytes of data being produced every single day worldwide. As such, there’s a huge demand, now and for the foreseeable future, for people who can organize
data and interpret the stories locked within. As you approach this career pathway, you’re likely
to bring practical experience and problem-solving decision-making, allocating resources,
time management, and many other skills that are particularly suited for the job of a data
professional. Many companies are searching for candidates to fill positions in this fast-growing,
high-paying field. My name is Cassie, and I’ve been a data scientist since before we called it
data science. I lead decision intelligence here at Google and I’ll be your instructor for the
first course of this certificate program. Before I became Google Cloud’s chief decision
scientist, I worked as a data scientist in Google Research, where I was involved
with over 400 projects all across Google. One of my favorite things about the data science
career is the tremendous variety, especially if, like me, you’re a naturally curious person. There are so many different flavors of project and challenge. Some of us choose to work on one
project for years. Others get involved with several new projects every week. The possibilities are endless. Data professional is a term used to describe any individual who works with data and/or
has data skills. At a minimum, a data professional is capable of exploring, cleaning, selecting,
analyzing, and visualizing data. They may also be comfortable with writing
code and have some familiarity with the techniques used by statisticians and
machine learning engineers, including building models, developing algorithmic
thinking, and/or building machine learning models. Machine learning is an alternative
approach to automation, expressing the way you want a task done by using data
instead of explicit instructions. Machine learning is an important component of the
modern data professional’s toolkit. To train a machine learning model, specialists put a bunch
of potential data inputs through algorithms, tweak the settings, and keep iterating until promising
outputs are produced. But training a model  is only one small step in the professional machine learning journey. Machine learning techniques can also be used for data analytics and exploration with far fewer steps. That’s what you’ll learn in this program. You’ll discover additional
opportunities to explore machine learning through the course resources, so do check those out. Data professional work spans a wide range of industries and impact affects a multitude of products
and services. As we’ll discuss later, there are also lots of different roles and titles that
focus on data professional work. Think of them  as data detectives, analyzing and interpreting
their findings to reveal the stories within. I’m  excited for you to get to meet some of them
in this program. Google Career Certificates are designed by industry professionals with
decades of experience here at Google. You’ll  have a different expert from Google to guide
you through each course in this program. We’ll share our knowledge and videos, help you practice
with hands-on activities, and guide you through scenarios that you might encounter on the
job. This certificate is designed to prepare you for a job in 3 to 6 months, if you work
on the certificate part-time. So this program is really flexible. You can complete all of
the courses on your own terms and at your own pace. Throughout the program, we’ll give you
resources that will prepare you to advance in your career as a data professional. As you progress,
you will also build a repository of portfolio projects and a comprehensive capstone project
that will showcase your abilities beyond your resume. You’ll also have a supportive network
of peer learners taking the certificate with you. You can connect with them in the discussion
forums. This program is designed to give you experience, by building upon the knowledge
and skills that you’ve developed to this point. Regardless of your experience with data and
analytics, as you begin the program, you’ll learn about different experiences that are relevant and
helpful for starting or advancing your career. In addition to building your skill set, we’ll examine
how teams of data professionals collaborate and contribute in the workplace. By the end of this program, you’ll be ready to pursue a position in the data career space. By completing a Google
Career Certificate like this one, you will develop the skills and knowledge necessary for a job in
this expanding career field. Once you graduate, you can connect with hundreds of employers
who are interested in hiring Google Career Certificate graduates. Whether you’re seeking
to switch careers, level up your skills, or start your own business, the Google Career
Certificates can help you take that next  step. Throughout this first course, I’ll be
here to help you gain foundational knowledge needed to succeed in the field. Again, I’m
so glad you’re here. I’m excited for you  to take these next steps forward in your
career. I’ll see you in the next video.

Now that you have a
general understanding about this program overall, let’s talk a bit
more specifically about what you can
expect in this course. We’ll start with the basics and a little background on what
the data field offers. While you may already be familiar with work
within the data field, I’m excited to dig deep into some history that shows
us where we’ve been, where we are, and where
we’re going in this field. These key developments and
applications will really showcase all the opportunities this program will
help prepare you for. In this first course, we will discuss the
specific skills and characteristics that
organizations look for in future employees. You’ll develop the
core skills necessary to advance on your
journey as a data professional and
integrate these skills with your own
pre-existing abilities. We’ll focus specifically on technical and workplace
expectations. There will also be plenty of chances to practice
along the way. Then we’ll explore your
job market opportunities. You’ll learn about the
variety of roles and positions that match
your skill set. You’ll also investigate the
responsibilities and ethics that underpin all roles
within the data career space. With the increasing number of industries that are turning
to the data professions, you’re bound to find a role
that fits your interests. We’ll also explore some of these industries so you can see where data professionals are employed to make more
informed decisions. Then we’ll examine how
larger organizations create teams of data professionals to approach
larger-scale projects. We’ll also peek
into the future of data careers and the trajectory
of the field in general, so that you have a good
sense of what the future holds for you after
completing this program. We’ll also investigate elements of effective communication and discover how it can empower
you as a data professional. Throughout this course
and the whole program, you will see how effective
communication can elevate productivity and promote
general understanding during the data
analytics process. As you progress, you will gain hands-on data
analysis experience through your portfolio projects. Beginning with the first
one in this course. We’ve designed a few different
options for you to apply your data skills to actual scenarios with data shared by our industry partners. You can use these projects to showcase your skills
to future employers. Lastly, our instructors
will provide a few career tips and tactics to guide you
on your journey. That’s a short
preview of what to expect later in this course. Next, you’ll have the
opportunity to go over some resources
that will help you get the most out
of this program. I’ll see you in the next video.

Hello again! Let’s discuss some of the course items you’ll encounter in your learning journey. In this program, you’ll code in Python, discover the stories that data holds, develop data visuals, use statistical tools, build models, and even dabble with
some machine learning! Along the way you’ll build a portfolio full of data projects, in addition to this program’s capstone. Whether you’re looking to switch careers, start a new career, improve your skills, or advance beyond your
current role in a company, the Google Career Certificates can help guide you as you take steps towards new opportunities. We’ve gathered some amazing instructors to support you on your journey and they’d like to
introduce themselves now: Hello! I’m Adrian and I am a
Customer Engineer at Google. Together we will explore one of the fastest growing
programming languages, Python. You’ll learn the basics, which will help you write scripts that perform a number of key mathematical
operations on datasets, all designed to help you
unlock the stories within data. Hi there! I’m Robb. I am a Consumer Product Leader. I work on marketing
projects here at Google. I’m excited to talk to you about how to tell stories using data. We’ll discuss the six practices of exploratory data analysis and how to identify the trends and patterns in it. We will also learn about the importance of designing and presenting
data visualizations using Python and Tableau, which can help you understand your data and convey it to others. Hello! My name is Evan. I’m an Economist and I consult with various
teams across at Google. Statistics helps you
generate more complex ideas from the data itself. In our time together, you’ll discover how you
can generate insights, draw conclusions, make inferences, create estimates, and make predictions. Hello! I’m Tiffany, and I’m a
Marketing Science Lead, and I work with marketing
data here at Google. I will guide you through the process of modeling relationships between variables. Together, we’ll explore different regression models and hypothesis tests. We’ll also talk about model assumptions, construction, evaluation, and interpretation as the means for answering data-driven questions. Hello! I’m Susheela. I’m a Data Scientist and I work on projects for
YouTube here at Google. I will guide you through building systems that can learn and adapt without a specific set of instructions. We’ll discuss how machine learning is transforming the
process of data analysis – as you construct your own models. Hello! I’m Tiffany, and I lead teams focused on building AI responsibly here at Google. I’ll introduce you to career resources and portfolio projects, and guide you through the capstone course at the end of the program. I’ll assist you with
different opportunities and tools that will set you up for success on the job market. And of course, you already know I’ll be guiding you through course one. This is such a great time to grow and advance your career
as a data professional. Your path to a career full of new opportunities awaits!

Tutorial on “Explore your data toolbox” in Data Science

Data science is a field that combines computer science, statistics, and mathematics to extract knowledge and insights from data. Data scientists use a variety of tools and techniques to collect, clean, analyze, and visualize data.

One of the most important tools for data scientists is their data toolbox. This toolbox contains a variety of tools that can be used to perform different data science tasks.

Here is a brief overview of some of the most common tools in a data scientist’s toolbox:

• Programming languages: Programming languages are used to write code that can be used to manipulate and analyze data. Some popular programming languages for data science include Python, R, and Julia.
• Data visualization tools: Data visualization tools are used to create charts and graphs that can be used to communicate the insights extracted from data. Some popular data visualization tools include Tableau, Matplotlib, and Seaborn.
• Machine learning libraries: Machine learning libraries are used to build and train machine learning models that can be used to make predictions and classifications. Some popular machine learning libraries include TensorFlow, PyTorch, and scikit-learn.
• Cloud computing platforms: Cloud computing platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) provide a variety of services that can be used for data science tasks. These services can be used to store and manage data, run code, and train machine learning models.

To explore your data toolbox, you can start by learning about the different tools that are available. You can do this by reading articles, watching tutorials, and taking online courses. Once you have a basic understanding of the different tools, you can start to experiment with them and see how they can be used to solve data science problems.

Here are a few ideas for how to explore your data toolbox:

• Build a simple data pipeline: A data pipeline is a series of steps that are used to transform data from one format to another. You can use a data pipeline to clean and prepare data for analysis or to train a machine learning model.
• Create a data visualization: A data visualization is a graphical representation of data. You can use data visualizations to communicate the insights extracted from data to others.
• Build a machine learning model: A machine learning model is a statistical model that can be used to make predictions or classifications. You can use a machine learning model to solve a variety of data science problems, such as predicting customer churn or classifying spam emails.

By exploring your data toolbox, you can learn how to use the different tools to solve data science problems. This will help you to become a more effective data scientist.

Here are some additional tips for exploring your data toolbox:

• Find a mentor: If you know a data scientist, ask them to be your mentor. A mentor can provide you with guidance and support as you learn about data science tools and techniques.
• Join a data science community: There are many online and offline data science communities. Joining a community is a great way to learn from other data scientists and stay up-to-date on the latest trends and technologies.
• Contribute to open source projects: There are many open source data science projects. Contributing to these projects is a great way to gain experience with data science tools and techniques.

By following these tips, you can explore your data toolbox and become a more effective data scientist.

All professions require a certain
set of tools for success, and data driven work is no different. In this video,
we’ll open our analytics tool box and look at some of the most common items. Before we begin, I want to emphasize that each of the items serves
their own individual purpose. However, when used together they help
build and tell stories with data which can then inform, influence,
and impact business decisions. Programming languages are the first
tools we’ll investigate. They allow data professionals to work efficiently
within and dissect large data sets. Most languages have been developed
over time and each data professional, has their own preferences. We’ll mention two in this video that have
become very popular for data analysis. The R programming language and Python.
R is a programming language that’s used extensively by researchers and academics. It was my primary language during
graduate studies in statistics and some people say that R captures
the statisticians mindset. I’d say there’s something
to that sentiment. If you’re after implementations of
the latest statistical breakthroughs, R is a great place to look. But it’s used for more than statistics,
you’ll find many new technologies and ideas programmed with it. One of the best features of R is that you
can create complex statistical models from just a few lines of code. If you’re curious about R, or need a
refresher, be sure to check out our Google Data Analytics certificate
also offered here on this platform. This program teaches the Python programming language. It’s a great choice for a few reasons. First of all, it emphasizes readability, making it one of the easiest programming
languages to learn and write. Second, unlike R,
Python wasn’t born in the data community. While this might sound like a minus,
it can also be a huge plus. In the modern world data is used
in increasingly creative ways. There’s a massive advantage, to learning
a programming language that’s capable not only of handling
the data side of things, but can also be used to build and deploy the applications that
data will be fueling. Although R, was my first love, these days,
I find that I lean more heavily on Python because of its flexibility. Python can perform a wide variety
of data related tasks, which makes it very popular
among data professionals. If you’re a novice or
new to coding completely Python is a very approachable language. Its formatting is visually uncluttered. It’s one of the most beginner
friendly languages and it has enormous online communities and plenty of
resources to help you if you get stuck. We will interact with Python
within a web based computing platform also called Jupyter notebooks, which allows you to run code in real time,
and helps identify errors easily. To visualize
the stories in the data, we’re going to teach you how to share complex
data through a graphical interface. Those who experienced our
data analytics program will be familiar with
a platform called Tableau. In this program, we’ll take a more
detailed look at how this powerful tool can help others understand
the results of your analysis. Additionally, we’ll look at effective
communication in data driven careers. At first glance, it might seem like less
of a concern, but describing the sometimes complex processes of data analytics
to nontechnical stakeholders may be one of the most important skills
a data professional can have. Since communication is
something we all do regularly. It’s easy to forget about the importance
of how data professionals share and process data stories. Our goal here
is to strengthen the communicative skills that you already possess, so that you can leave this program
equipped to excel. In this course specifically, and across other segments of this program,
communication will be a key component that is directly tied to the work
you’ll do as a data professional. Programming languages allow data
professionals to interact with and interpret data. Visual data tools, like Tableau, enrich the
stories within data with visual elements that bring attention to specific details. But the most important element of any
story is the storyteller. That’s you. Your prior experiences and knowledge
inform your storytelling abilities, and your distinct background is what will
set you apart from others in these roles. Regardless of your eventual career path,
remaining determined and developing the proper skills is essential to
personal and professional transformation, and the tools we’re offering you in this
program will also help you along the way. I’m thrilled to continue alongside you in
your journey. The best is yet to come. I’ll see you soon

Fill in the blank: Data professionals use _____ to work efficiently with large datasets.

programming languages

Data analytics professionals use programming languages to work efficiently within large datasets.

Congratulations on completing the first
part of this program. You’ve officially
begun your journey to new opportunities
in the data field. Let’s revisit what
we’ve covered so far. First, we covered the basic logistics of what’s included in
the certificate. You met each of your
instructors and we previewed some of the different course topics you
will encounter. Next, we looked at the
basics of data-focused work and some of the industries that are incorporating data insights. We also discussed the future
of data-driven careers. In addition to
exploring what it’s like to work in the data field, we also discussed the data
professionals toolbox and the skills this program
will help you develop. Congratulations on
your progress so far. I can’t wait to see
you in the next video.

[MUSIC] Hi, my name is Lois An and I’m
an industry intelligence lead at google. What that means is that I helped to
make both our internal teams and our partners and advertisers a lot smarter
about the industry that they work in. Early in my analytics career it
was actually pretty lonely and so I had a really big need to connect. I could not find a job immediately out of
college and I had done lots of things, work different places, didn’t really
build deep enough connections to have a community of people in this area. Early on, I started joining some networks. Networking is critical
when entering a new field. For me, what was most jarring about this new thing
was just the newness of being new, right? So you feel like a fish out of water,
you are also subject to that very pesky imposter syndrome, I’m not good enough to
be here, they’re going to find me out. And so having a network who was able
to bring me back in and say no, you’re qualified and hear all the reasons
why you’re qualified, and actually here’s how these other skill sets you have make
you an asset here was immensely valuable. In my role currently I collaborate
with tons of different people in data analytics. Collaboration is important because there
are so many diverse parts of the story. So you need people who are willing to,
enable to, and good at asking the questions which are,
what exactly are we looking for? Are we looking in the right place? You need people who
are then able to query and find a set of data,
find the needle in the haystack. You need other people to find the pattern,
and to find what the insight is from the data, and
then you need people to tell the story. Practicing curiosity is something that
will always help you in any role that you’re in,
especially when you’re dealing with data. I highly recommend participating in
the forums and this certificate program, because it’s a great way to practice
curiosity, asking questions, getting comfortable, asking questions,
getting comfortable working on a team and asking questions of your
team is super important. And beyond that you have this
opportunity to connect with your cohort, you are the next generation of
advanced data analytics practitioners. And so getting to know one another, getting to understand what you’re all
thinking, and even opening up the door to innovate together is a super
important and exciting opportunity.

As you know, this program asks you to
complete a graded assessment at the end of each section and course, and now is
the time to prepare for your first one. This assessment will effectively verify
your understanding of key data analytics concepts. It will also help build confidence in
your understanding of data analysis while identifying any areas where
you can continue to improve. Assessments can sometimes
feel overwhelming but approaching them with the strategy
makes them more manageable. Here’s a list of tips to set
yourself up for success. Before taking an assessment,
review your notes and the videos, readings and the most recent glossary
to refresh yourself on the content. During the assessment, take your time, review the whole test before
filling in any answers. Then answer the easy questions, skip the ones you don’t know
the answer to right away. For multiple choice questions, focus
on eliminating the wrong answers first. Also, it’s a good idea to
read each question twice, there are often clues that you
might miss the first time. If you start to feel anxious,
calm yourself with some mental exercises. One way to do that is by completing
a simple math problem in your head or spelling your name backwards, this also
helps you recall information more easily. Before you submit the assessment,
check your work but be confident. Sometimes people change an answer
because it feels wrong but it’s actually correct,
your first instinct is often the best one. Finally, remember to trust yourself, often people know a lot more than
they give themselves credit for. Everyone learns at different speeds and
in different ways, but it’s important to maintain your momentum. So, take the time you need and
when you feel ready, keep moving ahead. You’ve got this.