HOOD CODING - Python Data Science: After work guide to start learning Data Science on your own. Avoid common beginners mistakes of coding. Approach Panda and NumPy to become a brilliant computer programmer.

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PYTHON FOR DATA SCIENCE

After work guide to start learning Data Science on your own. Avoid common beginners mistakes of coding. Approach Panda and NumPy to become a brilliant computer programmer.

Michail Klling

Contents

Introduction

The purpose of it is to teach you the process of data science, while also providing you with all the fundamental skills and tools to support your learning process. This book is intended for complete beginners looking for a way to understand the basics of data science easily.

Learning data science with Python can be intimidating at times due to the requirements of programming and mathematical knowledge.

Results can often be challenging to communicate because they can be along string of numbers that arent easily understood by beginners. You will learn some basic tools that can help you create interactive charts and make it possible for you to share your results with others.

Keep in mind that to benefit the most from this book, you should work through the examples presented in each section. If you have a difficult time understanding some of them, break them apart line by line, and slowly push through them until you understand the concepts. Each chapter will teach you about the tools you need, show you how to install them, and give you enough information so that you can work on your own as well. Make sure to practice everything you read because, without practical work, you wont succeed at fully understanding the theory.

There are a lot of options that we can work with when it comes to data science, and almost all businesses are going to be able to handle this kind of process and see some of the benefits. If you have been wanting to know more about your customers and what they are looking for from you and your business, then the data science process is going to be the best option to help you out.

To start this guidebook, we are going to take a look at what data science is all about, why it is important, and why we would want to work with this process in the first place as well. We will then take some time to learn the lifecycle of data science, and how we need to go through a series of steps like the finding the right data, preparing the data, coming up with the right model, and more.

Then we are going to spend some time taking a look at the basics that come with the Python language, and how we can use this to help with our data science project. There are a lot of great coding languages out there that we can work with when it is time to handle data science, but many agree that the power, the libraries, and the ease to use and learn of Python make it one of the best choices to handle with this idea. We will also spend some time looking at a few of the libraries that are going to work with when it comes to Python, including NumPy and its arrays, Seaborn, and Matplotlib to get all of the work done in no time.

This is just the start of some of the fantastic things that we can do when it is time to start on data science. We can spend our time looking at what machine learning is all about, the different types of machine learning, and how we can put it all together when it is time to sort through our data and find the right patterns and insights in the process.

There are a lot of benefits that we can see when it comes to working on data science. Many companies in a lot of different industries are going to work with this to ensure that we will be able to handle how to work with their customers, how to beat out the competition, and so much more. When you are ready to work with the idea of data science, and you want to work with all of the different parts that are found with it, then make sure to check out this guidebook to help you get started.

Chapter 1: What is the difference between data science and analysis

What is Data Science?

Data science is likely a term that you have heard about at least a few times. If you are running a company, and you have ever taken a look at some of the steps that you can take to make yourself more efficient and to reduce the risks. When it is time to make some big and important decisions, then you have most likely heard about data science from others who are in the same boat as you. But this doesnt explain that much about data science and what we can do with it. And that is where this guidebook is going to come into play.

As the world started to enter the era of big data, the need for the right storage to use with it was something else that seemed to grow like crazy as well too. This was one of the main challenges and concerns that happen with these industries, at least until a few years ago. The main focus because of this issue was that companies might be able to collect and use a lot of data for their needs, they didnt have an excellent place to store all of that data to work with later.

Now, thanks to some of the changes that are there in the technology that we have, this is no longer the big issue that it was in the past. And this means that we can make a few adjustments and work with more and more data in the process. And you will find that the secret sauce that is going to bring it all together and helps us not only to gather up the data that we need but will ensure that we can learn what is found in all of that data is going to be data science.

There are a lot of companies that are looking to get into the world of data science because they know all of the benefits that come with this as well. When we are ready to handle some of the data that is going on with all of this, we need to make sure that we are getting the right data, and that we understand all of the information that we are dealing with at the same time. But that is all going to be covered in the data analysis that we are doing along the way.

Why is Data Science So Important?

The first thing that we need to take a look at when we are doing some of our work here is why data science is so important, and why a company may need to work with the process of data science along the way. The first issue is the way that this data has been structured in our modern world, especially when it is compared to how it was done in the past. Traditionally, the data that companies were able to get their hands on was going to be smaller in size, and it was pretty much structured the whole time. This allowed us to work with simple business intelligence tools to analyze what is going on.

However, this is not the case any longer. Some benefits and negatives come with this, of course. It allows us the option to know more about a situation because we can gather up the data and understand more with more data. But often, this data is going to be unstructured, and that makes it harder to sort through and understand as well.

Unlike some of the data that was found in those traditional systems, the ones that were structured and easier to work with, today, we will find that most of our data is unstructured or at least semi-structured. This is going to make it harder to work with and can take longer. But because we can find more information to help shape the decisions that we are making, this is not necessarily a bad thing all of the time.

This data is going to be generated from a lot of different sources, including things like instruments, sensors, multimedia forms, text files, and even some financial logs along the way. Because of all these sources that we are getting the data from, it is essential to see that we are not able to work with some of the simple business intelligence tools because they are not capable of handling all the data. This is why it is important to work with data science to work with algorithms and analytical tools that are more advanced and complex as well. This ensures that we can really analyze and processes meaningful insights out of the data as well.

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