All language subtitles for 05_explore-your-data-toolbox.en

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