## Data Science Tutorial for Beginners

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 the 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. So, in this Data Science tutorial, we are going to dive into this magical field. So. Let’s look at the agenda for this data science tutorial:

- Need of Data Science
- What is Data Science
- Applications of Data Science
- Types of Data Science Jobs
- Qualities of a Data Scientist
- How to learn Data Science from scratch
- Comparison of Data Science with Data Analytics

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**Need of Data Science**

In this Data Science tutorial for beginners, we will start off 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.

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Now, why is this necessary?

Because this data has gone from scarce to super-abundant in the past two decades and will keep on increasing exponentially for the next two decades. Around two or three decades back, the data which we had with us was small, structured, and mostly of a single format and then the analytics performed 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 of 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, huge amount of data was being generated every second from every corner of the world, but we did not know what to do with it. In other words, we had a lot of data with us, but we were 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.

Now that we know what is the need of data science, we will move ahead in this data science tutorial and understand the concept of 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 data science to be an amalgamation of different fields such as Data Manipulation, Data Visualization, Statistical Analysis, and Machine Learning. Each of these sub-domains is equally important when it comes to data science.

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

**Data Manipulation**

Let’s say, you are working with an employee dataset which comprises of 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 $100,000. This definitely 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 the employee? Well, that would be a time-consuming and stupid 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 this example to understand this better.

So, we have this census data-set which comprises 15 columns and 32,561 rows.

Now, from this dataset, I 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 took was one line of code and we were 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.

Similarly, let’s say if I want to extract all those records where the education of the person is “Bachelors” and Marital Status is “Divorced”:

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

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

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

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**Data Visualization**

Data Scientists are sometimes called as artists, not because of their skills with the paint-brush but because they can actually 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 excel sheets after excel sheets of data, when you can visualize it with beautiful graphs.

Let’s take this iris data-set to understand data visualization:

This dataset comprises of different species of the iris flower: ‘setosa’, ‘versicolor’ & ‘virginica’, along with their ‘Sepal length’, ‘sepal width’, ‘petal length’ & ‘petal width’. Now, I want to understand what is the relationship between the ‘Sepal length’ & ‘Petal length’ of different species. So, by just looking at the data-set, we don’t really get to know about any patterns. So, this is where we can visualize the data.

Now, let’s go ahead and build a scatter-plot between ‘Sepal.Length’ & ‘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? So, this scatter-plot tells us that as the Sepal Length of the flower increases, it’s petal length would also increase. Not just this, we 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 data science: machine learning.

**Machine Learning**

Machine learning is where the real magic happens. This is the field of data science where machines are fed data so that they can make insightful decisions.

So, let’s understand the concept of machine learning with this example:

**How do you know 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 like it has 4 tyres, a steering wheel, windows and so on. Now, whenever your brain comes across an image with those 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 M*achine Learning* comes in. We’ll keep on feeding images of a car 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 new data to determine how much has it learned. Study the Machine Learning Course for more details.

In other words,* Raw Data/Training Data *is given to the 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 the machine has learned, and this is the underlying concept of machine learning.

Now that we have understood what exactly is data science and looked at its sub-domains, let’s go through some applications of data science.

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**Applications of Data Science**

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

**Chatbots**

Chatbots are basically automated bots, which respond to all our queries. I believe all of you must have heard of Siri and Cortana! They are examples of chatbots. These chatbots are perfect applications of Data Science and are used across different sectors like hospitality, banking, retail, and publishing.

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**Self-driving Car**

Another very interesting application of Data Science is the self-driving car. This self-driving car is 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**

I believe all of you 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.

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**Types of Data Science Jobs**

In this Data Science tutorial, you will not only learn Data Science but will also find out various job roles in the domain of Data Science 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, trends, and so on, and coming up with compelling visualization and reporting for analyzing the data to take 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 clear data cleansing, data extraction, and data preparation for businesses for working with large amounts of data.

**Machine Learning Expert**

A Machine Learning expert is the one who is working with various Machine Learning algorithms like regression, clustering, classification, decision tree, random forest, and so on.

**Data Scientist**

A Data Scientist is the one who works with huge amounts of data to come up with compelling business insights through the deployment of various tools, techniques, methodologies, algorithms, and so on.

**Qualities of a Data Scientist**

If you want to learn Data Science, you should be aware of the various strengths of a Data Scientist. In this Data Science tutorial, you will also see that there are a lot of skills that you need to master in order to become a successful Data Scientist.

Some of the skills that an accomplished Data Scientist possesses include technical acumen, statistical thinking, analytical bent of mind, curiosity, problem-solving approach, big data analytical skills, and so on.

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**How to Learn Data Science to Be an Expert?**

If you want to be an expert Data Scientist, then you need to practice the following things:

**Familiarize yourself about the real-world Data Science problems**

Like one famous person once said that the whole world is one big data problem. So, as a Data Scientist, it is your job to learn more and more about various Data Science problems in the real world. This way, you will have an inside understanding of this domain.

**Participate in Data Science forums and competitions**

There are a lot of forums that are regularly hosting Data Science contests and competitions for Data Scientists. You would do well not only learn Data Science but also participate in these highly exciting contests. 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 this 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 Data Science is a very vast field, in the initial days, it would be good to have a collaborative approach to learn 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**

So far in this Data Science tutorial, you have learned Data Science, but that would not be enough. If you want to build your skills and hone it to perfection, then you need to practice every day since, as we all know, practice makes a man perfect. To learn Data Science, the rule is not much different; you need to practice a lot to achieve perfection.

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**Comparison of Data Science with Data Analytics**

A lot of people confuse 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.

Criteria | Data Science | Data Analytics |

Skills Needed | Data capturing, statistics, and problem-solving | Analytical, mathematical, and statistical skills |

Type of Data Used | All types of data | Mostly structured and numeric data |

Standard Life Cycle | Explore, discover, investigate, and visualize | The report, predict, prescribe, and optimize |

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 Data Science. Thus, Data Science can be thought of a big set, where data analysis can be a subset of it.

In this Data Science tutorial, you have learned top tools, technologies, and skills of Data Science from scratch. This is your preliminary step to learn Data Science and become an accomplished Data Scientist.

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I heard that in data science there is no codeing, is it true ? Because I am a non programming background.

Data Scientists do deal with coding and statistical skills, they work on making data useful in various ways But non programming background people can learn data science as well.

I want to learn data science ..Can you please tell me what is the prerequisites for learning Data Science?

There are no particular prerequisites for this Training Course. If your intersted in mathematics, it is helpful.

According to me Data Science is the practice of formulating hypothesis, Defining the data & Identifying the type of analysis, Am I right?

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