Data science tutorial

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Table Of Content :
What is Data Science? 4
Why Data Science? 5
Role of a Data Scientist 6
Solving Problems with Data Science 7
Tools for Data Science 9
i. R 9
ii. Python 9
iii. SQL 10
iv. Hadoop 10
v. Tableau 11
vi. Weka 11
Applications of Data Science 11
i. Data Science in Healthcare 11
ii. Data Science in E-commerce 12
iii. Data Science in Manufacturing 12
iv. Data Science as Conversational Agents 12
v. Data Science in Transport 12
Summary 12

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Data Science has become one of the most demanded jobs of the 21st century.
It has become a buzzword that almost everyone talks about these days. But
what is Data Science? In this article, we will demystify Data Science, the role
of a Data Scientist and have a look at the tools required to master Data
Science.
So, let’s start Data Science Tutorial.
What is Data Science?
“Data Science is about extraction, preparation, analysis, visualization, and
maintenance of information. It is a cross-disciplinary field which uses
scientific methods and processes to draw insights from data. ”

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With the emergence of new technologies, there has been an exponential
increase in data. This has created an opportunity to analyze and derive
meaningful insights from data. It requires special expertise of a ‘Data Scientist’
who can use various statistical & machine learning tools to understand and
analyze data. A Data Scientist, specializing in Data Science, not only analyzes
the data but also uses machine learning algorithms to predict future
occurrences of an event. Therefore, we can understand Data Science as a
field that deals with data processing, analysis, and extraction of insights from
the data using various statistical methods and computer algorithms. It is a
multidisciplinary field that combines mathematics, statistics, and computer
science.
Why Data Science?
So, after knowing what exactly Data Science is, you must explore why Data
Science is important. So, data has become the fuel of industries. It is the new

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electricity. Companies require data to function, grow and improve their
businesses. Data Scientists deal with the data in order to assist companies in
making proper decisions. The data-driven approach undertaken by the
companies with the help of Data Scientists who analyze a large amount of data
to derive meaningful insights. These insights will be helpful for the companies
who wish to analyze themselves and their performance in the market. Other
than commercial industries, healthcare industries also use Data Science.
where the technology is in huge demand to recognize microscopic tumors and
deformities at an early stage of diagnosis.
The number of roles for Data Scientists has grown by 650% since 2012. About
11.5 Million jobs will be created by 2026 according to the U.S. Bureau of
Labor Statistics. Also, the job of Data Scientist ranks among top emerging jobs
on Linkedin. All the statistics point towards the growing demand for Data
Scientists.
Role of a Data Scientist
You might want to know who is a Data Scientist and what are his/her roles in
different fields. A Data Scientist deals with both unstructured and structured
data. The unstructured data is present in a raw format that requires extensive
data pre-processing, cleaning and organization in order to impart a
meaningful structure to a dataset. The Data Scientist then investigates this
organized data and analyzes it thoroughly to derive information from it using
various statistical methodologies. We use these statistical methods to describe,
visualize and hypothesize information from the data. Then with the usage of
advanced machine learning algorithms, the data scientist predicts the
occurrence of events and takes data-driven decisions.
A Data Scientist deploys vast arrays of tools and practices to recognize
redundant patterns within the data. These tools range from SQL, Hadoop to

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Weka, R, and Python. Data Scientists usually act as consultants employed by
companies where they participate in various decision-making processes and
creation of strategies. In other words, Data Scientists use meaningful insights
from data to assist companies in taking smarter business decisions. For
example – Companies like Netflix, Google and Amazon are using Data Science
to develop powerful recommendation systems for their users. Similarly,
various financial companies are using predictive analytics and forecasting
methods to predict stock prices. Data Science has helped to create smarter
systems that can take autonomous decisions based on historical datasets.
Through its assimilation with emerging technologies like Computer Vision,
Natural Language Processing and Reinforcement Learning, it has manifested
itself to form a greater picture ofArtificial Intelligence.
Solving Problems with Data Science
When solving a real-world problem with Data Science, the first step towards
solving it starts with Data Cleaning and Preprocessing. When a Data Scientist
is provided with a dataset, it may be in an unstructured format with various
inconsistencies. Organizing the data and removing erroneous information
makes it easier to analyze and draw insights. This process involves the
removal of redundant data, the transformation of data in a prescribed format,
handling missing values etc.
A Data Scientist analyzes the data through various statistical procedures. In
particular, two types of procedures used are:
● Descriptive Statistics
● Inferential Statistics
Assume that you are a Data Scientist working for a company that
manufactures cell phones. You have to analyze customers using the mobile

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phones of your company. In order to do so, you will first take a thorough look
at the data and understand various trends and patterns involved. In the end,
you will summarize the data and present it in the form of a graph or a chart.
You therefore, apply Descriptive Statistics to solve the problem.
You will then draw ‘inferences’ or conclusions from the data. We will
understand inferential statistics through the following example – Assume that
you wish to find out a number of defects that occurred during manufacturing.
However, individual testing of mobile phones can take time. Therefore, you
will consider a sample of the given phones and make a generalization about
the number of defective phones in the total sample.
Now, you have to predict the sales of mobile phones over a period of two years.
As a result, you will use Regression Algorithms. Based on the given historical
sales, you will use regression algorithms to predict the sales over time.
Furthermore, you wish to analyze if customers will purchase the product
based on their annual salary, age, gender, and credit score. You will use
historical data to find out whether customers will buy (1) or not (0). Since
there are two outputs or ‘classes’, you will use a Binary Classification
Algorithm. Also, if there are more than two output classes we use Multivariate
Classification Algorithm to solve the problem. Both of the above-stated
problems are part of ‘Supervised Learning’.
There are also instances of ‘unlabeled’ data. In this, there is no segregation of
output in fixed classes as mentioned above. Suppose that you have to find
clusters of potential customers and leads based on their socio-economic
background. Since you do not have a fixed set of classes in your historical data,
you will use the Clustering Algorithm to identify clusters or sets of potential
clients. Clustering is an ‘Unsupervised Learning’ algorithm.
Self Driving cars have become a trending technology. The principle behind the
self-driving car is autonomy, that is, being able to take decisions without

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human interference. The traditional computers required human input to yield
output. Reinforcement Learning has solved the problem of
human-dependence. Reinforcement Learning is about taking specific actions
to accumulate maximum reward. You can understand this with the following
instance: Assume that you are training a dog to fetch ball. Then you reward
the dog with a treat or reward each time it fetches the ball. You do not give it a
treat if it does not fetch the ball. The dog will realize the reward of treats if it
fetches the ball back. Reinforcement Learning uses the same principle. We
give a reward to the agent based on its action and it will try to maximize the
reward.
A Data Scientist will require tools and software to tackle the above-mentioned
problems. We will now take a look at some of the tools that a Data Scientist
uses to those problems.
Tools for Data Science
Data Scientists use traditional statistical methodologies that form the core
backbone of Machine Learning algorithms. They also use Deep Learning
algorithms to generate robust predictions. Data Scientists use the following
tools and programming languages:
i. R
R is a scripting language that is specifically tailored for statistical
computing. It is widely used for data analysis, statistical modeling, time-series
forecasting, clustering etc. R is mostly used for statistical operations. It also
possesses the features of an object-oriented programming language. R is an
interpreter based language and is widely popular across multiple industries

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ii. Python
Like R, Python is an interpreter based high-level programming language.
Python is a versatile language. It is mostly used for Data Science and Software
Development. Python has gained popularity due to its ease of use and code
readability. As a result, Python is widely used for Data Analysis, Natural
Language Processing, and Computer Vision. Python comes with various
graphical and statistical packages like Matplotlib, Numpy, SciPy and more
advanced packages for Deep Learning such as TensorFlow, PyTorch, Keras
etc. For the purpose of data mining, wrangling, visualizations and developing
predictive models, we utilize Python. This makes Python a very flexible
programming language.
iii. SQL
SQL stands for Structured Query Language. Data Scientists use SQL for
managing and querying data stored in databases. Being able to extract
information from databases is the first step towards analyzing the data.
Relational Databases are a collection of data organized in tables. We use SQL
for extracting, managing and manipulating the data. For example A Data
Scientist working in the banking industry uses SQL for extracting information
of customers. While Relational Databases use SQL, ‘NoSQL’ is a popular
choice for non-relational or distributed databases. Recently NoSQL has been
gaining popularity due to its flexible scalability, dynamic design, and open
source nature. MongoDB, Redis, and Cassandra are some of the popular
NoSQL languages.
iv. Hadoop

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Big data is another trending term that deals with management and storage of
huge amount of data. Data is either structured or unstructured. A Data
Scientist must have a familiarity with complex data and must know tools that
regulate the storage of massive datasets. One such tool is Hadoop. While being
open-source software, Hadoop utilizes a distributed storage system using a
model called ‘MapReduce’. There are several packages in Hadoop such as
Apache Pig, Hive, HBase etc. Due to its ability to process colossal data quickly,
its scalable architecture and low-cost deployment, Hadoop has grown to
become the most popular software for Big Data.
v. Tableau
Tableau is a Data Visualization software specializing in graphical analysis of
data. It allows its users to create interactive visualizations and dashboards.
This makes Tableau an ideal choice for showing various trends and insights of
the data in the form of interactable charts such as Treemaps, Histograms, Box
plots etc. An important feature of Tableau is its ability to connect with
spreadsheets, relational databases, and cloud platforms. This allows Tableau
to process data directly, making it easier for the users.
vi. Weka
For Data Scientists looking forward to getting familiar with Machine Learning
in action, Weka is can be an ideal option. Weka is generally used for Data
Mining but also consists of various tools required for Machine
Learning operations. It is completely open-source software that uses GUI
Interface making it easier for users to interact with, without requiring any line
of code.

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Applications of Data Science
Data Science has created a strong foothold in several industries such as
medicine, banking, manufacturing, transportation etc. It has immense
applications and has variety of uses. Some of the following applications of
Data Science are:
i. Data Science in Healthcare
Data Science has been playing a pivotal role in the Healthcare Industry. With
the help of classification algorithms, doctors are able to detect cancer and
tumors at an early stage using Image Recognition software. Genetic Industries
use Data Science for analyzing and classifying patterns of genomic sequences.
Various virtual assistants are also helping patients to resolve their physical
and mental ailments.
ii. Data Science in E-commerce
Amazon uses a recommendation system that recommends users various
products based on their historical purchase. Data Scientists have developed
recommendation systems predict user preferences using Machine Learning.
iii. Data Science in Manufacturing
Industrial robots have made taken over mundane and repetitive roles required
in the manufacturing unit. These industrial robots are autonomous in nature
and use Data Science technologies such as Reinforcement Learning and Image
Recognition.

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iv. Data Science as Conversational Agents
Amazon’s Alexa and Siri by Apple use Speech Recognition to understand
users. Data Scientists develop this speech recognition system, that converts
human speech into textual data. Also, it uses various Machine Learning
algorithms to classify user queries and provide an appropriate response.
v. Data Science in Transport
Self Driving Cars use autonomous agents that utilize Reinforcement Learning
and Detection algorithms. Self-Driving Cars are no longer fiction due to
advancements in Data Science.
Summary
While Data Science is a vast subject, being an aggregate of several technologies
and disciplines, it is possible to acquire these skills with the right approach. In
the end, Data Science is a very robust field that best fits people who have a
knack for experimentation and problem-solving. With a large number of
applications, Data Science has become the most versatile career.

Data science tutorial

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