Data Science Tutorial – Learn Data Science from Experts

Data Science Tutorial – Learn Data Science from Experts

Want to start your career as a Data Scientist, but don’t know where to start? You are at the right place! Hey Guys, welcome to this awesome Data Science Tutorial blog, it will give you a kick start into the data science world. To get in-depth knowledge on Data Science, you can enroll for live Data Science Certification Training by ITCourCes with 24/7 support and lifetime access. Let’s look at what we will be learning today.

  • What is Data Science?
  • Why Data Science?
  • Data Science Components
  • Data Science Process
  • Data Science Jobs Roles
  • Tools for Data  Science
  • Difference between Data Science with BI (Business Intelligence)
  • Applications of Data science
  • Challenges of Data Science Technology

Data Science is one of the hottest jobs of the 21st century with an average salary of $123,000 per year. According to LinkedIn, the Data Scientist job profile is among the top 10 jobs in the United States. As per McKinsey’s reports, the United States alone faces a job shortage of 1.5 million Data Scientists. So, Data Science is a hot cake now and every single soul on the planet wants to get a piece of it. Become a Master of Data Science by going through this Data Science Course. So, let’s get started with a Data Science Tutorial!

Why Data Science 

Here, are significant advantages of using Data Analytics Technology:

  • Data is the oil for today’s world. With the right tools, technologies, algorithms, we can use data and convert it into a distinct business advantage
  • Data Science can help you to detect fraud using advanced machine learning algorithms
  • It helps you to prevent any significant monetary losses
  • Allows to build intelligence ability in machines
  • You can perform sentiment analysis to gauge customer brand loyalty
  • It enables you to make better and faster decisions
  • Helps you to recommend the right product to the right customer to enhance your business

What is Data Science?

DATA SCIENCE is the area of study which involves extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes. It helps you to discover hidden patterns from the raw data. The term Data Science has emerged because of the evolution of mathematical statistics, data analysis, and big data.

Data Science is an interdisciplinary field that allows you to extract knowledge from structured or unstructured data. Data science enables you to translate a business problem into a research project and then translate it back into a practical solution.

Who is a Data Scientist?

Data Scientist - Data Science Tutorial - Edureka

Scientist - Data Science Tutorial - Edureka

As you can see in the image, a Data Scientist is the master of all trades! He should be proficient in maths, he should be acing the Business field and should have great Computer Science skills as well. Scared? Don’t be. Though you need to be good in all these fields, even if you aren’t, you’re not alone! There is no such thing as “a complete data scientist”. If we talk about working in a corporate environment, the work is distributed among teams, wherein each team has their own expertise. But the thing is, you should be proficient in at least one of these fields. Also, even if these skills are new to you, chill! It may take time, but these skills can be developed, and believe me it would be worth the time you will be investing. Why? Well, let’s look at the job trends.

Data Scientist Job Trends

Data Science Job Trends - Data Science Tutorial - Edureka

Well, the graph says it all, not only is there a lot of job openings for a data scientist, but the jobs are well-paid too! And no, our blog will not cover the salary figures, go google! 

Well, we now know, learning data science actually makes sense, not only because it is very useful, but also you have a great career in it in the near future.

Let’s start our journey in learning data science now and begin with,

Data Science Jobs Roles

Most prominent Data Scientist job titles are:

  • Data Scientist
  • Data Engineer
  • Data Analyst
  • Statistician
  • Data Architect
  • Data Admin
  • Business Analyst
  • Data/Analytics Manager

Let’s learn what each role entails in detail:

Data Scientist:


A Data Scientist is a professional who manages enormous amounts of data to come up with compelling business visions by using various tools, techniques, methodologies, algorithms, etc.


R, SAS, Python, SQL, Hive, Matlab, Pig, Spark

Data Engineer:


The role of a data engineer is working with large amounts of data. He develops, constructs, tests, and maintains architectures like large scale processing systems and databases.


SQL, Hive, R, SAS, Matlab, Python, Java, Ruby, C + +, and Perl

Data Analyst:


A data analyst is responsible for mining vast amounts of data. He or she will look for relationships, patterns, trends in data. Later he or she will deliver compelling reporting and visualization for analyzing the data to make the most viable business decisions.


R, Python, HTML, JS, C, C+ + , SQL



The statistician collects, analyses, and understands qualitative and quantitative data by using statistical theories and methods.


SQL, R, Matlab, Tableau, Python, Perl, Spark, and Hive

Data Administrator:


Data admin should ensure that the database is accessible to all relevant users. He also makes sure that it is performing correctly and is being kept safe from hacking.


Ruby on Rails, SQL, Java, C#, and Python

Business Analyst:


This professional needs to improve business processes. He/she acts as an intermediary between the business executive team and the IT department.


SQL, Tableau, Power BI and, Python

Machine learning in Data Science

To become a data scientist, one should also be aware of machine learning and its algorithms, as, in data science, there are various machine learning algorithms that are broadly being used. Following are the name of some machine learning algorithms used in data science:

  • Regression
  • Decision tree
  • Clustering
  • Principal component analysis
  • Support vector machines
  • Naive Bayes
  • Artificial neural network
  • Apriori

We will provide you some brief introduction for a few of the important algorithms here,

1.Linear Regression Algorithm: Linear regression is the most popular machine learning algorithm based on supervised learning. This algorithm works on regression, which is a method of modeling target values based on independent variables. It represents the form of the linear equation, which has a relationship between the set of inputs and predictive output. This algorithm is mostly used in forecasting and predictions. Since it shows the linear relationship between input and output variables, hence it is called linear regression.Data Science tutorial

The below equation can describe the relationship between x and y variables: Y=MX+C

Where, y= Dependent variable

X= independent variable

M= slope

C= intercept.

2.Decision Tree: Decision Tree algorithm is another machine learning algorithm, which belongs to the supervised learning algorithm. This is one of the most popular machine learning algorithms. It can be used for both classification and regression problems.

In the decision tree algorithm, we can solve the problem, by using tree representation in which, each node represents a feature, each branch represents a decision, and each leaf represents the outcome.

Following is the example for a Job offer problem:

Data Science tutorial

In the decision tree, we start from the root of the tree and compare the values of the root attribute with the record attribute. On the basis of this comparison, we follow the branch as per the value and then move to the next node. We continue comparing these values until we reach the leaf node with predicated class value.

  1. K-Means Clustering: K-means clustering is one of the most popular algorithms of machine learning, which belongs to the unsupervised learning algorithm. It solves the clustering problem.

If we are given a data set of items, with certain features and values, and we need to categorize those sets of items into groups, such types of problems can be solved using the k-means clustering algorithm.

K-means clustering algorithm aims at minimizing an objective function, which known as squared error function, and it is given as:

Data Science tutorial

Where, J(V) => Objective function

‘||xi – vj||’ => Euclidean distance between xi and vj.

ci’ => Number of data points in the ith cluster.

C => Number of clusters.

How to solve a problem in Data Science?

So now, let’s discuss how one should approach a problem and solve it with data science. Problems in Data Science are solved using Algorithms. But, the biggest thing to judge is which algorithm to use and when to use it? 

Basically there are 5 kinds of problems which you can face in data science.

Questions - Data Science Tutorial - Edureka

Let’s address each of these questions and the associated algorithms one by one:

Is this A or B?

With this question, we are referring to problems that have a categorical answer, as in problems that have a fixed solution, the answer could either be a yes or a no, 1 or 0, interested, maybe or not interested. 

For Example: 

  1. What will you have, Tea or Coffee?

Here, you cannot say you would want a coke! Since the question only offers tea or coffee, and hence you may answer one of these only.

When we have only two types of answers i.e yes or no, 1 or 0, it is called 2 – Class Classification. With more than two options, it is called Multi-Class Classification.

Concluding, whenever you come across questions, the answer to which is categorical, in Data Science you will be solving these problems using Classification Algorithms.

The next problem in this Data Science Tutorial, that you may come across, maybe something like this,

Is this weird?

Questions like these deal with patterns and can be solved using Anomaly Detection algorithms.

For Example:

Try associating the problem “is this weird?” to this diagram,

Anomaly Detection - Data Science Tutorial - Edureka

What is weird in the above pattern? The red guy, isn’t it?

Whenever there is a break in the pattern, the algorithm flags that particular event for us to review. A real-world application of this algorithm has been implemented by Credit Card companies where any unusual transaction by a user is flagged for review. Hence implementing security and reducing human effort on surveillance.

Let’s look at the next problem in this Data Science Tutorial, don’t be scared, deal with maths!

How much or How many?

Those of you, who don’t like maths, be relieved! Regression algorithms are here!

So, whenever there is a problem that may ask for figures or numerical values, we solve it using Regression Algorithms. 

For Example:

Temperature - Data Science Tutorial - Edureka

What will be the temperature for tomorrow?        

Since we expect a numeric value in the response to this problem, we will solve it using Regression Algorithms.

Moving along in this Data Science Tutorial, let’s discuss the next algorithm,

How is this organized?

Say you have some data, now you don’t have any idea how to make sense out of this data. Hence the question, how is this organized?

Well, you can solve it using clustering algorithms. How do they solve these problems? Let’s see:

Clustering Algorithms - Data Science Tutorial - Edureka

Clustering algorithms group the data in terms of characteristics that are common. For example in the above diagram, the dots are organized based on colors. Similarly, be it any data, clustering algorithms try to apprehend what is common between them and hence “clusters” them together.

The next and final kind of problem in this Data Science Tutorial, that you may encounter is,

What should I do next?

Whenever you encounter a problem, wherein your computer has to make a decision based on the training that you have given it, it involves Reinforcement Algorithms.

For Example:

Temperature - Data Science Tutorial - Edureka

Your temperature control system, when it has to decide whether it should lower the temperature of the room, or increase it.

Data Science Components

  1. Datasets

What will you analyze on? Data, right? You need a lot of data which can be analyzed, this data is fed to your algorithms or analytical tools. You get this data from various researches conducted in the past.

R Studio Logo - Data Science Tutorial - Edureka

  1. R Studio

R is an open-source programming language and software environment for statistical computing and graphics that is supported by the R foundation. The R language is used in an IDE called R Studio. 

Why is it used?

  • Programming and Statistical Language  Programming Statistics - Data Science Tutorial - Edureka
    • Apart from being used as a statistical language, it can also be used as a programming language for analytical purposes.
  • Data Analysis and Visualization
    Analysis Visualization - Data Science Tutorial - Edureka
    • Apart from being one of the most dominant analytics tools, R also is one of the most popular tools used for data visualization.
  • Simple and Easy to Learn  Easy - Data Science Tutorial - Edureka
    • R is simple and easy to learn, read & write
  • Free and Open Source  Open - Data Science Tutorial - Edureka
    • R is an example of a FLOSS (Free/Libre and Open Source Software) which means one can freely distribute copies of this software, read it’s source code, modify it, etc.

R Studio was sufficient for analysis, until our datasets became huge, also unstructured at the same time. This type of data was called Big Data.

  1. Big Data
Big Data - Data Science Tutorial - Edureka

Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.

Now to tame this data, we had to come up with a tool, because no traditional software could handle this kind of data, and hence we came up with Hadoop.

  1. Hadoop
Hadoop - Data Science Tutorial - Edureka

Hadoop is a framework that helps us to store and process large datasets in parallel and in a distribution fashion.

Let’s focus on the store and process part of Hadoop.


The storage part in Hadoop is handled by HDFS i.e Hadoop Distributed File System. It provides high availability across a distributed ecosystem. The way it functions is like this, it breaks the incoming information into chunks, and distributes them to different nodes in a cluster, allowing distributed storage.


MapReduce is the heart of Hadoop processing. The algorithms do two important tasks, map and reduce. The mappers break the task into smaller tasks that are processed parallel. Once, all the mappers do their share of work, they aggregate their results, and then these results are reduced to a simpler value by the Reduce process.

Data Science Lifecycle

The life-cycle of data science is explained as below diagram.

Data Science tutorial

The main phases of data science life cycle are given below:

  1. Discovery: The first phase is the discovery, which involves asking the right questions. When you start any data science project, you need to determine what are the basic requirements, priorities, and project budget. In this phase, we need to determine all the requirements of the project such as the number of people, technology, time, data, an end goal, and then we can frame the business problem on the first hypothesis level.
  2. Data preparation: Data preparation is also known as Data Munging. In this phase, we need to perform the following tasks:
  • Data cleaning
  • Data Reduction
  • Data integration
  • Data transformation,

After performing all the above tasks, we can easily use this data for our further processes.

  1. Model Planning: In this phase, we need to determine the various methods and techniques to establish the relation between input variables. We will apply Exploratory data analytics(EDA) by using various statistical formulas and visualization tools to understand the relations between variables and to see what data can inform us. Common tools used for model planning are:
  • SQL Analysis Services
  • R
  • SAS
  • Python
  1. Model-building: In this phase, the process of model building starts. We will create datasets for training and testing purposes. We will apply different techniques such as association, classification, and clustering, to build the model.

Following are some common Model building tools:

  • SAS Enterprise Miner
  • WEKA
  • SPCS Modeler
  1. Operationalize: In this phase, we will deliver the final reports of the project, along with briefings, code, and technical documents. This phase provides you a clear overview of complete project performance and other components on a small scale before the full deployment.
  2. Communicate results: In this phase, we will check if we reach the goal, which we have set on the initial phase. We will communicate the findings and final results with the business team.

Difference between Data Science with BI (Business Intelligence)


Business Intelligence

Data Science


Looking Backward

Looking Forward

Data Sources

Structured Data. Mostly SQL, but some time Data Warehouse)

Structured and Unstructured data. Like logs, SQL, NoSQL, or text


Statistics & Visualization

Statistics, Machine Learning, and Graph


Past & Present

Analysis & Neuro-linguistic Programming


Pentaho. Microsoft Bl, QlikView,

R, TensorFlow

Challenges of Data Science Technology

  • High variety of information & data is required for accurate analysis
  • Not adequate data science talent pool available
  • Management does not provide financial support for a data science team
  • Unavailability of/difficult access to data
  • Data Science results not effectively used by business decision-makers
  • Explaining data science to others is difficult
  • Privacy issues
  • Lack of significant domain expert
  • If an organization is very small, they can’t have a Data Science team


  • Data Science is the area of study which involves extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes.
  • Statistics, Visualization, Deep Learning, Machine Learning, are important Data Science concepts.
  • The Data Science Process goes through Discovery, Data Preparation, Model Planning, Model Building, Operationalize, Communicate Results.
  • Important Data Scientist job roles are: 1) Data Scientist 2) Data Engineer 3) Data Analyst 4) Statistician 5) Data Architect 6) Data Admin 7) Business Analyst 8) Data/Analytics Manager
  • R, SQL, Python, SAS, are essential Data science tools
  • The predictions of Business Intelligence are looking backward while for Data Science it is looking forward.
  • Important applications of Data science are 1) Internet Search 2) Recommendation Systems 3) Image & Speech Recognition 4) Gaming world 5) Online Price Comparison.
  • A high variety of information & data is the biggest challenge of Data Science technology.

Applications of Data Science:

  • Image recognition and speech recognition:
    Data science is currently used for Image and speech recognition. When you upload an image on Facebook and start getting the suggestion to tag to your friends. This automatic tagging suggestion uses an image recognition algorithm, which is part of data science.
    When you say something using, “Ok Google, Siri, Cortana”, etc., these devices respond as per voice control, so this is possible with speech recognition algorithms.
  • Gaming world:
    In the gaming world, the use of Machine learning algorithms is increasing day by day. EA Sports, Sony, Nintendo, are widely using data science for enhancing user experience.
  • Internet search:
    When we want to search for something on the internet, then we use different types of search engines such as Google, Yahoo, Bing, Ask, etc. All these search engines use data science technology to make the search experience better, and you can get a search result with a fraction of seconds.
  • Transport:
    Transport industries also use data science technology to create self-driving cars. With self-driving cars, it will be easy to reduce the number of road accidents.
  • Healthcare:
    In the healthcare sector, data science is providing lots of benefits. Data science is being used for tumor detection, drug discovery, medical image analysis, virtual medical bots, etc.
  • Recommendation systems:
    Most of the companies, such as Amazon, Netflix, Google Play, etc., are using data science technology for making a better user experience with personalized recommendations. Such as, when you search for something on Amazon, and you start getting suggestions for similar products, so this is because of data science technology.
  • Risk detection:
    Finance industries always had an issue of fraud and risk of losses, but with the help of data science, this can be rescued.
    Most of the finance companies are looking for data scientists to avoid risk and any type of losses with an increase in customer satisfaction.

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 ITCourCes 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 Scientists 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 IT CourCes for guidance.

What is the average salary of a 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 professionals?

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 that encompasses multiple skills and scientific techniques. Techniques

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

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

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

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 using this data. We have the perfect professional Data Science Training Course for you! So for scrubbing the data in Data Science.

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

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 algorithms 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

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 time is ripe to up-skill in Data Science and Big Data Analytics to take advantage of the Data Science career opportunities that come your way. This brings us to the end of the Data Science tutorial blog. I hope this blog was informative and added value to you. Now is the time to enter the Data Science world and become a successful Data Scientist.

Got a question for us in the Data Science Tutorial? Please mention it in the comments section and we will get back to you.

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