Welcome to the Data Science Study Path Draft Page📜, in this section(many pages) we will preview the drafts of our upcoming book. Whether you are an eager learner of data science or a well-grounded data science practitioner, you can take advantage of this Data Science Study Path. You can use it to the fullest if you already have at least some previous experience in dealing with data.

We made the idea of this book to get you up to speed with data science studying, data scientist has been called “the sexiest job of the 21st century,🔥🔥🔥” presumably by someone who has never visited a fire station. Nonetheless, data science is a hot and growing field, and it doesn’t take a great deal of sleuthing to find analysts breathlessly prognosticating that over the next 10 years, we’ll need billions and billions more data scientists than we currently have.

Data science Venn Diagram

But what is data science? After all, we can’t produce data scientists if we don’t know what data science is. According to a Venn diagram that is somewhat famous in the industry, data science lies at the intersection of:

Hacking skills.

Math and statistics knowledge.

Substantive expertise.

At this book📗, I decided to focus on the first two. My goal is to help you develop the hacking skills that you’ll need to get started doing data science. And my goal is to help you get comfortable with the mathematics and statistics that are at the core of data science.

By the end of the book, you will be well-versed with the theory of that power the data science path with its real-world applications. I also hope to give you a sense that playing with data is fun, because, well, playing with data is fun!

This book departs from traditional data science texts and reference/supplement books and study guides in these ways:

Clear and concise step-by-step procedures that intuitively explain how to work through data problems and remember the process.

Focused, intuitive explanations empower you to know you’re doing things right and whether others do it wrong.

Nonlinear approach so you can quickly zoom in on that concept or technique you need, without having to read other material first.

Easy-to-follow examples reinforce your understanding and help you immediately see how to apply the concepts in practical settings.

Understandable language helps you remember and put into practice essential statistical concepts and techniques.

About this Study Path 📜📜📜

There are lots and lots of data science libraries, frameworks, modules, and toolkits that efficiently implement the most common (as well as the least common) data science algorithms and techniques. If you become a data scientist, you will become intimately familiar with NumPy, with scikit-learn, with Pandas, and with a panoply of other libraries. They are great for doing data science. But they are also a good way to start doing data science without actually understanding data science.

In this book, we will be approaching data science from scratch. That means we’ll be building tools and implementing algorithms by hand in order to better understand them. I put a lot of thought into creating implementations and examples that are clear, well-commented, and readable. In most cases, the tools we build will be illuminating but impractical. They will work well on small toy data sets but fall over on “web scale” ones.

Throughout the book, I will point you to libraries you might use to apply these techniques to larger data sets. But we won’t be using them here.

There is a healthy debate raging over the best language for learning data science.

Many people believe it’s the statistical programming language R. (We call those people wrong.) A few people suggest Java or Scala. However, in my opinion, Python is the obvious choice.

Python has several features that make it well suited for learning (and doing) data science:

It’s free.

It’s relatively simple to code in (and, in particular, to understand).

It has lots of useful data science–related libraries.

Who this #StudyPath is for? 🤔🤔🤔

This path is for all who want to learn data science either you are new and want to learn Python coding from scratch or a practitioner who know Python and Data Science and want to learn more or even a machine learning engineer who want to get new things from the path also those interested in deep learning who have a basic foundation in machine learning and some Python programming experience.

If you don’t have background in either Python or mathematics and conceptual understanding of calculus and statistics, we will help you to gain maximum benefit from this path, by teaching you all from ground up.

Is It Free Study Path and Materials?

As you heard this path is free, and will always be free, we did used our books in purpose to provide free contents.

You might wonder, why the books are paid?

First, you don’t need the whole books, as I will provide you the pieces of each book that you need to learn. So, you don’t have to buy any books.

Second, each month I will make a small quiz and the top 3 will get the books for free to learn more and more. Also top 3 students help others will get the free books too.

So, as I said, you don’t need to buy any of our books. You will get all what you need for free.

How this #StudyPath Works?

This page is like the table of content, here you will get all the updates and what you should learn now, and what to learn next.

You will see below that the path is divided into fragments, before getting on a new fragment, you need to finish all before it. I know that you might hate the waterfall style, but believe me that this is better, and you will get it that the basics is important later.

So before getting to the studying and the content of the book, make sure to subscribe to our newsletter to get any updates in this books and any new and good contents too.

Or even better, you can donate with a small donation to keep this #studypath is running, and to help us to create a great rich content.

Start Study Now.

Let’s not waste more time and get to the chapter of the book, make sure to get you coffee mug, set in comfortable place and let’s get started.

Fragment One: Introduction to Data Science.

this one of the books, we did use in this fragment.

In this fragment, we introduce Data Science Field to you, and how this field is important in our life, we also intended to give you a small motivation to keep going the study path to it’s end, and finally we will introduce a real-life data set and how it looks to deal with data came from real-life observations.

You only have to read the followings, and then you are done:

And this is the introduction fragment, easy isn’t it? We tried to make it easier to read, motivative and also kept it informative.

So, what you need to do before going to the next fragment? There are no readings or quizzes for this fragment, just share with us what you have learning in each post of the above list, we are waiting you in the comments.

Fragment Two: Learning Python.

You can choose any programming language to use as a data scientist. But in this chapter, we will choose our favorite programming language, which is Python.

If you are not new to python or expert and comfortable using it, you can skip this chapter, but it is preferred to check those two post as a refresher to it and to make sure that you have everything for the book, consider it as warming up.

On the other hand, if you are new to python, and do not know anything about it, check this series out:

Fragment Three: Linear Algebra

What we will learn in this chapter is essential for you in any data fields such as data science, machine/deep learning, or even statistician. Linear Algebra is one of these hot topics that considered to be the base of dealing with data.

So, if you need a refresher, you can take these posts to refresh you knowledge:

Fragment Four: Probability

It is hard to do data science and be a data scientist without some sort of understanding of probability and its mathematics. As with our treatment of statistics will be in the next Chapter, we’ll wave our hands a lot and elide many of the technicalities.

For our purposes you should think of probability as a way of quantifying the uncertainty associated with events chosen from a some universe of events. Rather than getting technical about what these terms mean, think of rolling a die. The universe consists of all possible outcomes. And any subset of these outcomes is an event; for example, “the die rolls a one” or “the die rolls an even number”.

Notation-ally, we write P(X) to mean “the probability of the event X”.

We’ll use probability theory to build models. We’ll use probability theory to evaluate models. We’ll use probability theory all over the place.

Fragment Five: Statistics

Statistics is the discipline that concerns the collection, organization, displaying, analysis, interpretation and presentation of data. So as a data scientist we need to learn all the mathematics and techniques with which we understand data. It is a rich, enormous field, more suited in a library rather than a chapter in a book or even few posts, and so our discussion will necessarily not be a deep one. Instead, I’ll try to teach you just enough to be dangerous, and pique your interest just enough that you’ll go off and learn more.

Fragment Six: Hypothesis and Inference

// on going

Fragment Seven: Math and Calculus

// on going

Fragment Eight: Defining Data

// on going

Fragment Nine: Getting and Collecting Data

// on going

Fragment Ten: Data Understanding

// on going

Fragment Eleven: Data Visualization

// on going

Get in touch 🤝🤝🤝

Feedback and sharing from our readers is always welcome.

General feedback: If you have questions about any aspect of this book, mention the book title in the subject of your message and email me at hishamelamir001@gmail.com and I will respond as soon as possible.

If you have questions about any aspect of this book, mention the book title in the subject of your message and email me at hishamelamir001@gmail.com and I will respond as soon as possible. Social media: You always can find me on twitter @hisham_elamir where you can DM me or tweet with things you liked with us.

You always can find me on twitter @hisham_elamir where you can DM me or tweet with things you liked with us. Errata: Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you have found a mistake in this book, we would be grateful if you would report this to us. Please visit https://dataisutopia.com/contact-us/, and entering the details.

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