Hello.
Welcome to Statistics for Data Science-1.
I am Usha Mohan, Professor at the Department
of Management Studies, IIT Madras.
Over the next 12 weeks, we would travel together
to understand the basics of statistics that
is needed for your online degree programme.
So, we first begin with understanding what
is statistics.
So, first we look at what is the definition
of statistics.
So, one of the most popular definitions of
statistics tells us statistics is the art
of learning from data which includes collection
of data, analysis of data, presentation of
data, and then, drawing conclusions from data.
So, one of the main things you are going to
learn in this course is to first, we are going
to look at how you collect data So, that is
one of the main things which we will do.
So, we start with data collection.
So, we look at how you collect data and then,
we go on to see how you actually present this
data.
During this course, we learn about how you
do present data in a tabular form; we introduce
a concepts of frequency tables; we introduce
a concept of relative frequency and we look
at how you tabulate data.
We also learn about the different types of
data that you encounter.
Then, we look at how you further look at describing
data using graphical techniques.
Some of the graphical techniques that you
would learn are bar charts, pie charts, histograms,
stem leaf plots etcetera.
We then, also go about learning how you actually
come up with numerical measures of data.
What we look here mainly is at the measures
of central tendency; namely, mean, median
and mode and the measures of variation; namely,
range, variance, standard deviation.
So, far we have just talked about describing
a single variable, we also look at answering
questions about association between variables.
Towards this, we also introduce the concept
of contingency tables, scatter plots and we
also introduce what is a correlation matrix.
So, predominantly in the descriptive statistics
module, you will be learning about collecting
data, organising data and describing data.
Describing data both using graphical techniques
and numerical techniques.
Now, once you are comfortable with just describing
data, where the question is you just want
to describe whatever data you have.
The next step is to try and see I said that
statistics is the art of using data to draw
conclusions.
So, you want to infer something from data.
So, this lays the playground for you to go
next level which is inferential statistics.
Inferential statistics will be taught in the
next course on statistics.
But, however, in this course, we will help
you by laying the foundation or the building
block to understand inferential statistics.
What do I mean by building block to understand
inferential statistics?
We motivate this through a small introduction
to the probability theory.
When we go to probability theory, we start
by what we mean by permutation and combinations.
This is something which you are going to revisit.
Most of you would have learned this in high
school, but you are going to revisit the concept
of permutation and combinations.
Then, we introduce the notion of probability
through random experiments like rolling a
die, tossing a coin and then, afterwards we
talk about probability of events, the combination
of events and why they are necessary.
Finally, we end this course by introducing
to two main distributions; namely, the binomial
distribution and the normal distribution.
Throughout the course, the focus is going
to be on teaching at a conceptual level and
applying these concepts to real world problems.
So, you will have a lot of assignments which
will test your knowledge, both at a conceptual
level and an application level.
There would be at every stage, we will try
and motivate the learner to look at understanding
the concepts and applying their concepts through
a set of case studies specifically designed
for this course.
Welcome again to the world of statistics and
this course which is Statistics for Data Science-1.
