Welcome to the course!
You've begun one of the most complete overviews on data science tooling that you’ll currently
find on the internet.
This doesn’t mean that we cover each and every tool, but later in the course we’ll
introduce a comprehensive list of tasks a data scientist needs to perform and give you
the top two or three open source and commercial tools available to complete them.
We also explain how the tools overlap in functionality, what their pros and cons are, and how these
tools can address the whole data science pipeline.
Let’s start with data.
Data is obviously central to data scientists.
In this course, we’ll show you how to manage, extract, transform, analyze, and visualize
data.
Now, you might be able to survive data science without programming skills if you use the
right set of tools.
However, we highly recommend getting familiar with programming and the related programming
frameworks for data science.
To help you along, we’ll introduce you to the most commonly used programming languages
and frameworks available for data science.
That said, there is a lot of automation available in the latest tooling that a data scientist
can use.
In this course, we’ll explain how to make use of those tools to save time and uncover
inspiration.
Visual programming is available in many tools.
In this course, you’ll learn how visual programming can be used to speed up development
time and to help non-programmers enter the field of data science.
Open source software is leading the field of data science, but its total costs of ownership,
or "TCO," can be higher at times due to configuration, customization and maintenance costs.
As a result, commercial software also has its place, especially since the new generation
of commercial data science software leverages open source software and open standards.
This makes it easy to migrate between tools and can reduce overall TCO.
In this course, we’ll introduce you to both open source and commercial software and point
out their strengths and weaknesses for data science.
We'll also show you ways that you can take advantage of their respective strengths.
Finally, we'll show you how cloud computing can be used to further speed up and facilitate
data scientists' work.
We'll introduce you to the most commonly used and newly emerging cloud tools for data science.
In addition to lectures, this course, has numerous labs to make you more familiar with
the material and get hands-on experience.
There are also multiple quizzes to test your learning.
Nothing more to say than we’re glad to have you in the course and happy learning.
In case you have trouble in any way, please don’t hesitate to contact us in the discussion
forum.
There's nothing left but to begin!
We're very happy to have you with us as you start your data science journey.
If you have any trouble with any of the course material, please don’t hesitate to contact
us in the discussion forum.
Let's get started!