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:

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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.

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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.

census-Data Science Tutorial-Intellipaat

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)

census50-Data Science Tutorial-Intellipaat

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")

census_edu_marital-Data Science Tutorial

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:

iris-Data Science Tutorial-Intellipaat

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()

Rplot-Data Science Tutorial-Intellipaat

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:

ml1-Data Science Tutorial-Intellipaat

ml2-Data Science Tutorial-Intellipaat

ml3-Data Science Tutorial - Intellipaat

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 Machine 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.

ml4-Data Science Tutorial-Intellipaat

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.

ml5-Data Science Tutorial-Intellipaat

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 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.

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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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Frequently Asked Questions

Why learn Data Science?

According to the Harvard Business Review, Data scientists are the best jobs of the 21st century. Today, most organizations are willing to pay high salaries for professionals with the right skills. Thus, you can accelerate your career, get promising jobs, and take your career to the next level by learning Data Science.

What does a Data Scientist do?

Data Scientist’s typical job is to identify data analytics problems, collect structured and unstructured data from multiple sources, clean/verify data, apply models/algorithms to mine Big Data, analyze and interpret data, and communicate the findings.

How do I become a Data Scientist?

Data scientists need knowledge of statistics and programming. You will be happy to know that Intellipaat offers one of the best Data science courses in the country to help you learn about Data Science, its tools and methods. You will also participate in many hands-on projects to learn how to deal with industry-specific solutions.

Who should learn Data Science?

Everyone can learn about data science. In general, learners who want to work as data scientists or professionals belonging to Big Data, business intelligence, information architecture, and machine learning, opt for learning Data Science.

Is learning Data Science hard?

Many people want to learn Data Science, but only a few become Data Scientist because learning Data Science is not easy. It requires a combination of skills/knowledge, such as Algorithms, Python, SQL. However, learning Data science can be easy if you have access to the right Data Science tutorial.

Can I learn Data Science on my own?

Yes, you can become a self-learning data scientist. However, it requires commitment and planning. This data science tutorial will provide you with what you need to learn (Basic Data Science Course). In addition, this field is interdisciplinary, so you need to focus on each topic. If you are unable to self-learn, you can turn to Intellipaat for guidance.

What is the average salary of Data Scientist in the United States and India?

The average salary of Data Scientists in the US is around $120,000 and the average salary in India is close to INR 10,00,000.

Which are the top companies hiring Data Science professional?

Today every company hires data scientists. Some of the top companies hiring data scientists include IBM, Google, Amazon, Oracle, Microsoft, Apple, Facebook, Walmart, Visa, Bank of America and others.

Table of Contents

Introduction of Data Science

What is Data Science?: The simplest Data Science meaning would be, applying some scientific skills on top of data so that we can make this data talk to us. Now, what we exactly mean by ‘applying scientific skills on top of data’? Well, to put it precisely, Data Science is an umbrella term which encompasses multiple skills and scientific techniques. Techniques Read More

Command line Tools

Data Science Command Line Tools: Here, we are going to look at the most convenient and common Data Science Command tools for quick analysis of data. Watch this Data Science Tutorial video [videothumb class="col-md-12" id="pcGePSWo2ew" alt="Data Science Tutorial" title="Data Science Tutorial"] alias It defines or display aliases. It is a Bash built in. $ help alias $ alias ll='ls -alF' bash Read More

Machine Learning Algorithms for Data Science

Machine Learning in Data Science: It is a process or collection of rules or set to complete a task. It is one of the primary concepts in, or building blocks of, computer science: the basis of the design of elegant and efficient code, data processing and preparation, and software engineering. We have the perfect professional Data Science Training Course for Read More

R and RStudio - Installation

Steps to Install R and RStudio: Follow these procedures to  install R and RStudio in your system. Step 1 – Install R Download the R installer from Run the installer. Default settings are fine. If you do not have admin rights on your laptop, then ask you local IT support. In that case, it is important that you also ask Read More

Data Acquisition

What is Data Acquisition?: There are many ways to get a dataset like configuring an API, internet, database, etc. To convert binary data into useful data, we need to perform certain tasks which includes-Decompress files, Querying relational database, etc. It is very much important to track the origin of the database and check whether that data is up to date Read More

Scrubbing Data

Techniques for Scrubbing or Cleaning Data in Data Science: As we know the obtained data has inconsistencies, errors, weird characters, missing values or different problems. In this situation, you have to scrub or clean the data before to use this data. We have the perfect professional Data Science Training Course for you! So for scrubbing the data in Data Science, some Read More

Data Visualization

Data Visualization in R programming: Here we will be using the R programming language to visualize data. It is very important to visualize the result in a graphical format, to analyze the obtained output. Apart from that, we will be deriving statistics to get all the unique values, identifiers, factors, and continuous variables. We can check the overall result through Read More

Modeling the data

Data Modelling Concepts in Data Science: To predict something useful from the datasets, we need to implement machine learning algorithms. Since, there are many types of algorithm like SVM Algorithm in Python, Bayes, Regression, etc. We will be using four algorithms- Dimensionality Reduction It is a very important algorithm as it is unsupervised i.e. it can implement raw data to Read More

Data Extraction

Data Extraction in R: In data extraction, the initial step is data pre-processing or data cleaning. In data cleaning, the task is to transform the dataset into a basic form that makes it easy to work with. One characteristic of a clean/tidy dataset is that it has one observation per row and one variable per column. The next step in Read More

Course Schedule

Name Date
Data Science Architect 2020-12-05 2020-12-06
(Sat-Sun) Weekend batch
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Data Science Architect 2020-12-12 2020-12-13
(Sat-Sun) Weekend batch
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Data Science Architect 2020-12-19 2020-12-20
(Sat-Sun) Weekend batch
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23 thoughts on “Data Science Tutorial - Learn Data Science from Experts”

  1. In-depth, comprehensible and coherent! Each topic is explained in a straightforward manner through appropriate examples. Great Job!!!

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

    1. 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.

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

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

  4. 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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