Welcome, everyone, to the course.
In this very first lecture, we're going to have a brief overview of the topics we cover in this course.
First off, we want to thank you so much for enrolling.
And before you begin with this lecture, you should remember to check out the very first lecture in
the course, which is right before this one.
It's an article lecture, so it basically just has a bunch of links with important information like
how to ask a question in the course as well as the link for things like the course slides.
So definitely make sure to check out the first lecture before continuing on with the course.
Okay, so what is the course curriculum and what do we cover in this course?
This course focuses on the mathematics behind the key concepts for data science and analysis, allowing
you to understand the intuition behind many methods and procedures that you'll see being conducted in
the field of data science.
Now, to do this, we start off with this curriculum, core data concepts, visualizing data, combinatorics,
probability, joint distributions and data distributions.
Then we move on to discussing the normal distribution sampling hypothesis, testing and regression.
So we cover all of that in the curriculum and it's a lot of material.
Let me give you a very brief overview of each section.
So we start off the course with core data concepts here.
We really just talk about very basic ideas about data, and we also make sure we start off with a level
playing field.
We're going to talk about measurements of data such as mean median mode and then measurements of dispersion
like variance in standard deviation.
Then we'll move on to visualizing data.
The reason we also cover this earlier in the course is because we use a lot of visualizations when describing
things like data distribution.
So when learning about data distributions like the normal distribution or binomial distribution, it's
important to understand what you're visually looking at.
So we give you a tour of a bunch of different ways to visualize data, because communicating your results
is a really important part of data science.
Then we start moving on to the more math heavy parts of the course.
So we start off with combinatorics.
This is the study of counting, and this section helps you understand concepts like counting combinations
and permutations of objects like the different number of ways to sort a deck of cards.
Then we move on to probability and we talk a lot about the basics of probability in the section, but
we also include things like conditional probability, such as Bayes Theorem.
It's a pretty big section of the course.
Then we move on to talking about things like distributions.
Our very first discussion is about joint distributions, talking about the relationships between data
sets.
So we talk about here about concepts like covariance and the correlation coefficient.
Then we move on to data distributions.
We're going to start off with just an understanding of what is a data distribution like, What's the
difference between a probability mass function versus a probability density function and how those relate
to the idea of random variables.
After that, we take you through a tour of the most popular and common data distributions such as Bernoulli
distribution, Poisson uniform and a bunch of distributions.
I should point out that we actually have a special section for the normal distribution, so we have
an entirely separate section on the normal distribution due to its unique properties.
And also it's one of the most common distributions that occur in real world data sets.
So it's really important to understand this.
And keep in mind this section in particular really focuses heavily on applying the statistics to real
world applications.
But I should mention pretty much all the sections, especially the questions at the end, focus on applying
what you've learned to something in the real world that you would encounter as a practicing data scientist.
Then we'll talk about sampling, where we focus on ideas like the Central Limit theorem, which are
really critical for understanding how to apply statistical concepts to real world data sets.
After this, we discuss hypothesis testing, which is super important for concepts like understanding
terms like P values or significant levels or ideas like AB testing on a website.
This all has to do with hypothesis testing and then we conclude the course with regression.
So we touch on one of the most common statistical modeling techniques at the very end of the course,
allowing you to use your skills to actually perform something like possible forecasting or modeling
of outcomes based on inputs.
All right.
Let's get started.
We'll see you at the next lecture.