Data Science Tutorial for Beginners

Data Science Tutorial Overview

This is the age of data! As soon as you open your Facebook account, you are inundated with a huge amount of data. You get to see posts from your friends, which could be in the format of text, pictures, and videos. Now, just imagine if you could tap into this data and use it to gain insights. That would be just wonderful, wouldn’t it? And, this is exactly where Data Science comes in. In this Data Scientist tutorial for beginners, we are going to dive into this magical field.

Why Data Science?

In this Data Science tutorial for beginners, we will start by understanding what exactly data is! This entity called data is present all around us; it’s omnipresent like God! Simply put, data is just a collection of facts.

A bunch of numbers like -0.879 and 348 is data. When we say statements like ‘My name is Sam’ or ‘I love Pizza’, this again is data. A mathematical formula such as ‘A = …’ is nothing but data, and well, when it comes to computers, data is nothing but the binary code, i.e., 0s and 1s.

Now, why is this necessary?

Because data has gone from scarce to super-abundant in the past 2 decades and will keep on increasing exponentially for the next 2 decades. Around 2 or 3 decades back, the data we had with us was small, structured, and most of a single format and the analytics performed on it was quite simple.

But with the advent of technology, this data started to explode; multiple sources started to generate huge amounts of unstructured data of different formats. The data, which was just a few kilobytes or megabytes earlier, started blowing up exponentially, and today, we generate around 2,500 zettabytes of data every single day!

Now, a huge amount of data is being generated every second from every corner of the world, but we do not know what to do with it. In other words, we have a lot of data with us, but we are not trying to find out any insights from it. And this need to understand and analyze data to make better decisions is what gave birth to Data Science.

What is Data Science?

Data Science is nothing short of magic, and a Data Scientist is a magician who performs tricks with the data in his hat. Now, as magic is composed of different elements, similarly, Data Science is an interdisciplinary field. You can consider it to be an amalgamation of different fields such as data manipulation, data visualization, statistical analysis, and Machine Learning. Each of these sub-domains has equal importance in this Data Science tutorial.

Now, let’s go ahead and understand each of these in detail.

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Data Manipulation

Let’s say, you are working with an employee dataset that comprises 1000 columns and 1 million rows. Now, by just looking at the dataset, you would be overwhelmed. To make matters worse, your boss asks you to find out all the male employees whose salary is exactly US$100,000. This is a daunting task, isn’t it? So, how would you go about finding the solution? Would you manually go through each of these 1 million records and check the gender and salary of each employee? Well, that would be a time-consuming and unwise idea.

So, what is the solution to this? Well, this is where data manipulation comes in. With the help of data manipulation techniques, you can find interesting insights from the raw data with minimal effort. Let’s take the below example to understand this better.

So, we have this census dataset that comprises 15 columns and 32,561 rows.

Now, from this dataset, we want to extract only those records where the age of the person is 50. So, let’s see how can we do this with the R language:

census %>% filter(age==50)

So, all it takes is one line of code, and we are able to extract all those records where the age of the person is exactly 50. Now, just imagine, if you had to manually go through each of the 32,561 records to check the age of the person!! Thank God that we can manipulate data with just a single line of code.

Now, if we want to extract all those records where the education of the person is ‘Bachelors’ and the marital status is ‘Divorced.’ Here, we can use the below line of code:

census %>% filter(education==" Bachelors" && marital.status==" Divorced")

Again, just a single line of code, and we are able to get our desired result. So, with these examples, we can understand that data manipulation helps us find insights from the data with the smallest amount of effort.

Now, let’s head onto the next sub-field in the Data Science tutorial, which is data visualization.

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Data Visualization

Data Scientists are sometimes called artists, not because of their skills with the paintbrush, but because they can represent the data in the form of aesthetic graphs. As they say, pictures speak louder than words, and obviously, you wouldn’t want to deal with numerous Excel data when you can visualize it with beautiful graphs.

Let’s take this iris dataset to understand data visualization:

This dataset comprises different species of the iris flower: ‘Setosa’, ‘Versicolor’ and ‘Virginia, along with their ‘sepal length’, ‘sepal width’, ‘petal length’, and ‘petal width.’ Now, we want to understand what is the relationship between the ‘sepal length’ and ‘petal length’ of different species. By just looking at the dataset, we would not get to know any patterns. This is where we can visualize the data.

Now, let’s go ahead and build a scatter plot between ‘Sepal.Length’ and ‘Petal.Length’:

ggplot(data = iris,aes(x=Sepal.Length,y=Petal.Length,col=Species)) + geom_point()

Now isn’t this just a beautiful depiction of the underlying data? This scatter-plot tells us that as the sepal length of the flower increases, petal length would also increase. Not just this, we can also see that ‘Setosa’ has the lowest values of petal length and septal length, and ‘Virginica’ has the highest values.

Now, let’s head onto the most important part of a Data Scientist role: Machine Learning.

Machine Learning

Machine Learning is where the real magic happens. This is the field of Data Science where machines are fed with data so that they can make insightful decisions. Let’s understand the concept of Machine Learning with an example.

How do you know that all of these are cars?

As a kid, you might have come across a picture of a car, and you would have been told by your kindergarten teachers or parents that this is a car and it has some specific features associated with it, such as it has 4 wheels, a steering wheel, windows, and so on. Now, whenever your brain comes across an image with this set of features, it automatically registers it as a car because your brain has learned that it is a car.

That’s how our brain functions, but what about a machine?

If the same image is fed to a machine, how will the machine identify it to be a car?

This is where Machine Learning comes in. We will keep on feeding images of cars to a computer with the tag ‘car’ until the machine learns all the features associated with a car.

Once the machine learns all the features associated with a car, we will feed it with new data to determine how much it has learned.

In other words, raw data/training data is given to a machine so that it learns all the features associated with the training data. Once, the learning is done, it is given new data/test data to determine how well it has learned, and this is the underlying concept of Machine Learning.

You may have stuck on captchas on the internet where out of 10 images, consisting of different items and cars, the system asks you to select the one that has cars.
In short, you are building a trained data set for the system to identify cars from a different set of pictures.

Data Science Life Cycle

It is an iterative process that aims to produce insights and make predictions to achieve business goals. Various steps are involved in the Data Science life cycle such as business understanding, data preparation, data cleaning, visualization, modeling, and deployment. Let’s go through these steps in detail:

• Business understanding: Before processing data, it is important to understand what the problem is or the objectives the business wants to achieve. For example, if a business wants to reduce credit loss, then it needs to find out the factors that affect it. For this, we need to understand our data by its structure, sources, relevance, and its type.
• Data preparation: It is the most important step in the Data Science life cycle that involves data extraction, merging different data sources, cleaning, and dealing with missing values. Although it takes a lot of time to clean and transform the data, it is a crucial step to create a good model.
• Exploratory data analysis: Before building the actual model, we have to gather information about the possible solutions and the affecting factors. We have to find the best possible solution that provides suitable results after processing the data. That is what the underlying objective of the step exploratory data analysis.
• Data modeling: The prepared data is fed to the data model, which provides the desired output. After selecting the model, we need to select the algorithm that provides the perfect results. To achieve the desired results, we can also use hyperparameters while maintaining a balance between generalization and performance.
• Model evaluation: After the model is trained and modified based on the requirements, it is tested by unused datasets and evaluation metrics. If the desired results are not achieved, we must re-iterate the model until it gets it right.
• Model deployment: Model deployment is the final step in the Data Science life cycle, where the model is deployed in the desired channel and format. After rigorous evaluation and modifications, the data model will become ready to provide the results in real time. MLOps is the practice critically utilized across the industry to manage the complete data science process. 

Now that we have understood what exactly is Data Science and looked at its sub-domains, let’s go through some of its applications of Data Science in the real world.

Applications of Data Science

Data Science has a lot of real-world applications. Let’s have a look at some of those in this section.

Chatbots:

Chatbots are basically automated bots, which respond to all our queries. All of us have heard of Siri and Alexa! They are examples of chatbots. These chatbots are perfect applications and are used across different sectors, including hospitality, banking, retail, and publishing.

Self-driving Cars

Another very interesting application is self-driving cars. These self-driving cars are the future of the automotive industry.

A car that drives by itself, without any human intervention, is just mind-boggling, isn’t it?

Image Tagging
All of us have Facebook accounts! Whenever you hover over a person’s picture, Facebook automatically tags a name to that person, and this again is possible with the help of Data Science.

Data Science vs Machine Learning

Below are the key points that show the difference between Data Science and Machine Learning.

In this data science tutorial, let’s dive into the different types of data science jobs.

Types of Data Science Jobs

From this best Data Science tutorial, you will not only learn the basics of Data Science but will also find out various job roles in the domain of Data Science for beginners and experts, which are listed as below:

Data Analyst

A Data Analyst is entrusted with the responsibility of mining huge amounts of data, looking for patterns, relationships, and trends, and coming up with compelling visualizations and reports for analyzing the data to make business decisions.

Data Engineer

A Data Engineer is entrusted with the responsibility of working with large amounts of data. He/she should be available to perform data cleansing, data extraction, and data preparation for businesses working with large amounts of data.

Machine Learning Expert

A Machine Learning expert works with various Machine Learning algorithms such as regression, clustering, classification, decision tree, random forest, and so on.

Data Scientist

A Data Scientist works with huge amounts of data to come up with compelling business insights through the deployment of various techniques, methodologies, algorithms, Data Science tools, etc.

Qualities of a Data Scientist

If you want to learn more about data science, you should be aware of its potential. In this tutorial, you will also see that there are a lot of skills that you need to master to become a successful data scientist.

Some of the skills that an accomplished data scientist must possess include technical acumen, statistical thinking, an analytical bent of mind, curiosity, a problem-solving approach, big data Analytical skills, and so on.

How to become a Data Scientist?

If you want to be an expert data scientist, then you need to practice the following:

• Familiarize yourself with the real-world Data Science problems from this data science beginner tutorial: The whole world is one big data problem, and as a Data Scientist, it is your job to learn more and more about various problems in the real world. This way, you will have a deep understanding of this domain.
• Participate in forums and competitions: There are a lot of forums that are regularly hosting data science contests and competitions. By participating in these highly exciting contests, you would learn more. That way, the knowledge that you get from this data science tutorial can be built up and put into practical use.
• Regularly work on huge datasets: There is a huge amount of data that is available on the Internet. It could be real data or just a practice dataset. But, whatever be the nature of the data, it will be beneficial to work on it to implement your knowledge and get hands-on practice in the domain of data science.
• Have a collaborative and interactive approach: Since the data scientist job role is very vast, in the initial days, it would be good to have a collaborative approach for learning data science. That way, you will learn it in an interactive way and will be on your way to becoming an accomplished data scientist.
• Practice every day and gain a definitive edge: In this data science for Beginners tutorial, you learned about data science, but that would not be enough. If you want to build your skills and hone them to perfection, then you need to practice every day. To be a data scientist, you need to practice a lot to achieve perfection. Whether be coding, algorithms, or analysis- data science requires it all.

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Comparison of Data Science with Data Analytics

A lot of people confuses the role of a Data Scientist with the role of a Data Analyst. So, we will go ahead and understand the similarities and differences between Data Science and Data Analytics in this Data Science tutorial.

The above table gives you a high-level understanding of what the major difference is between a Data Scientist and a Data Analyst. One more key difference between the two domains is that data analysis is a necessary skill for a Data Scientist. Thus, Data Science can be thought of as a big set, where data analysis is a subset of it.

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