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Hello, everybody. Welcome to introduction to
descriptive statistics with Python.

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My name is Professor David Lubeck.

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Let me take a minute or two to introduce the
content of this module part to you.

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This part of the module at hand stands on two
legs:

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There's descriptive statistics and there's
Python.

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Descriptive statistics is all about communication.

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It is about answering the question:

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"What have we got?" and then being able to
actually get the answer across.

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Be it to collaborators or decision makers.

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Of course, this requires an understanding of
the techniques employed

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as well as of the resulting product.

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Incidentally, you will need that very same
understanding

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as you grow into the role of a decision maker
yourself.

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To this aim, we will handle data or other data
sets.

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Let me throw in some keywords that highlight
topics and statistics that we will deal with:

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Measures of central tendency

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measures of variability,

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data distribution,

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and frequency distributions.

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In order to do all this, we need tools,

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and because sticks and stones are a bit old
fashioned,

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and because we don't want to be the guy who
remains stuck with Excel and its limitations,

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we are going to make use of a proper programming
language.

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Python.

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Python, which is a scripting language, is a
good choice because it has a pleasant syntax.

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It is also widely used in industry for data
analysis of all types.

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Another great choice would have been the R
language,

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which however is not as easily accessible to
a newbie.

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I also have standards and so that settles that.

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Welcome to Python!

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Actually Python comes with a whole universe
of tools and libraries,

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including for data analysis.

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This is to say that beyond Python proper,

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you will get exposed to Pandas, Numpy and also
to Seaborn.

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The latter being a graphing tool.

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In order to write code it is customary to use
a code editor,

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indeed a so-called IDE

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that stands for integrated development environment

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and is a software solution that offers you
supporting tools for your coding tasks.

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The IDE we will use is called Jupyter,

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which is also in wide use, be it in academia
or in the business world.

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With all these tools at our hands, we can now
get started to work with datasets

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in order to approach the statistical analysis
I mentioned earlier.

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The first step typically is the so-called ETL
process.

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And ETL stands for extract, transform, load
and aims at the provision of data.

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A more general term for all this is data wrangling.

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The completion of this hands-on part of the
course will stand you in good stead,

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not only for follow-up modules within your
degree at HTW,

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but it will qualify you for a career in any
field that requires more advanced techniques

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than simply adding up numbers in an Excel sheet.

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While the content taught is not particularly
challenging in terms of academic depth,

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I appreciate that for many of you this will
be the very first foray into coding.

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That is to say, you're faced with a new way
of thinking and working,

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which may be challenging as you take your very
first steps in this field.

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I encourage you to roll up your sleeves, pick
up this challenge,

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and to collaborate intensively with your classmates.

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I am looking forward to supporting you in this
endeavour.