In-Plant Training Report

IN-PLANT TRAINING
An Industrial Internship Report
submitted by
RYAN SERRAO
(13BCE0230)
in partial fulfillment for the award of the degree of
B.TECH
in
COMPUTER SCIENCE
SCHOOL OF COMPUTING SCIENCE AND
ENGINEERING
MARCH 2016

DECLARATION BY THE CANDIDATE
I hereby declare that the Industrial Internship report entitled
“IN-PLANT TRAINING” submitted by me to Vellore Institute of
Technology, Vellore in partial fulfillment of the requirement for
the award of the degree of B.TECH in COMPUTER SCIENCE is
a record of bonafide industrial training undertaken by me under
the supervision of Monish Salot and Balakrushna Tripathy. I
further declare that the work reported in this report has not been
submitted and will not be submitted, either in part or in full, for
the award of any other degree or diploma in this institute or any
other institute or university.

TABLE OF CONTENTS
Chapter no. Title Page no.
List of Figures
1 Synopsis of the project 1
2 Organizational
Description
2
2.1 Introduction 2
2.2 Organization focus 4
2.3 Organization products 5
2.4 Technology of the
Organization
6
3 Skill set of the student
before training
7
4 Knowledge aquired from
training
8
4.1 Management Skills 8
4.2 Technical Skills 9
5 Application of the gained
knowledge
12
5.1 Geocode Module 12
5.2 XML Data Scrape 17
6 Comparision of
competency levels before
and after training
19

LIST OF FIGURES
Fig 1: Tecnical Expertise
Fig 2: Management Skills

1. SYNOPSIS OF THE PROJECT
This report begins with the description of the organization. It describes the main
focusses of the organization, followed by its objectives and products.
It then goes on to describe the technology used by the organization and the skills
present in the student before and after the training. It also describes the benefits of
the training does a comparative analysis on the technical skills and management
skills before and after the training.
The nature of the training was demonstration based followed by its practical
implementation. New softwares and technical methodologies was learnt in this
process which has helped me realize the importance of our traditional curriculum
and how it is being applied in the practical world.
The training has provided me an insight into the industry and how things work in
the real world. It has helped me broaden by perspective and has definitely proved
to be beneficial.
1

2. ORGANIZATION DESCRIPTION
2.1 Introduction
The name of the organization is Think Analytics. It is an analytics based company
which provides services of various form to various different comapnies.
It has tie-ups with many different companies which provide it with various kinds
of projects. It deals with a variety of clients from different industries such as:
1. Pharmaceutical industry
It provides data analysis for various pharmaceutical companies from different
parts of the world. It tells them as to which medicine is in demand, which
medicine would be in demand in the future based on the present health scenario,
the various kinds of sickness prevalent in today's society, the profit margin, the
loss, region wise profit and loss, region wise demand and many more.
2. Stock Markets
It also helps customers invest in stocks and provides a recommender service based
on which it suggests it's customers to invest in the right stock options.
3. Sales industry
It has partnership's with many different sales companies and it provides business
stategies on a to how to proceed with offers, or which offer would be the most
beneficial to the company at that point, how much discount is to be given on a
particular product, which product is giving the the profit, which product is having
losses, how to place a product in the store, what type of customer prefers to buy
which product and many more.
2

4. Drone data analysis
It recently has also opened up an analytics service of processing data captured via
drones. It provides analysis on weather information, drone image processing,
DroneData GPU equipped Render Engines deliver the performance to minimize
render times over any other solution, analytics for pixel based data , provides the
environment to develop, distribute and execute applications as sensors advance.
5. Financial Analysis
It has many clients all over the world for which it provides financial data
analysis. This is a private service and is done for big clients to give them a
feedback on their expenditures and finance manangement. It also tells them about
their losses incurred in various business investments and also the upcoming ofers
and profits.
6. Doctors
It provides an insight to doctors from various hospitals about their patients. It
keeps records on how many patients have been diagnosed by a doctor and that
which treatment was given by the doctor. This helps the doctor in treating the
patient better next time. It also keeps track of how effective was a medicine on a
particular illness. It keeps track of the doctors operations and also based on its
prior work recommends the doctor as to which method would be preferred.
3

2.2 ORGANIZATION'S FOCUSSES:
1. Working with clients across the globe to set up their in-house or externalized 2
to 200 member analtyics competency centers. From advisory to execution.
2.Solving complex analytical problems to extract the value of data. From
customer segmentation to LTV models to Risk scorecards to optimization.
3.Build, manage and scale outsourced analytics teams for providing continuous
business support and organizing analytic competencies at optimized investments.
The Organization is an open culture young startup working on building solutions
where high value data sits at the core.
Apps: Technology products and apps that address specific consumer pain points.
Platforms: Building platform solutions that help organizations unlock the value
of their own data using smarter algorithms, robust technical architecture and
standardized data models. Without worrying a bit.
Data | Analytics | Advisory: Solving business problems where intelligent and
creative use of data can unlock value.
4

2.3 ORGANIZATION'S PRODUCTS:
1.VITO
It is an alternative of CouponDunia. It provides a list of offers present accross a
wide variety of stores ranging from cafes such as the cafe coffee daya, baristas to
the various dining places to shops in the malls to various products sold on
amazon, flipkart, ebay, mantra to various coupons and discounts on food meals at
your local stores. It is spread accross channels, is contextual, hyperlocal and
personalized. Its main motto is to provide the customers an offer that you annot
refuse.
2.Credit Potato
Expanding Depth & Breadth of Credit in E-Commerce with Proprietary Alternate
Credit Score .
3.MUSED
µSeD – Micro-Segmented. Helping organizations have one to one conversations
with their customers is the goal of the product.
5

2.4 TECHNOLOGY OF ORGANIZATION
Analytics based
1.R/ SAS, and equivalent tools
2.Map Reduce/Hadoop
3.Python
4.Excel
5.Apache
6.Spark
Development based
1.HTML5
2.CSS3
3.AJAX
4.PHP
5.Python
6.SQL
7.third-party libraries and APIs
8.Hybrid Mobile App Development
9.Javascript
10.Jquery
6

3. SKILL SET BEFORE TRAINING
Before joining the training I was well equipped with the technical skills learnt
from the curriculum provided at the University. This included of:
1. C programming
2.C++ programming
3.Data Structures
4.Microprocessors
5.Good Mathematical skills
6.Operating Systems
7.Digital Logics
8.Networking
9.Algorithm analysis
10.Basic web development
11.OOP's concepts
12.Data mining techniques
On the management side I too had certain skill set:
1.Leadership
2.Team work
7

4. KNOWLEDGE AQUIRED FROM IN_PLANT
TRAINING
4.1 Management Skills
Well the knowledge aquired from the in-plant training has proved to be very
beneficial in my day to day life. It trained me on a overall basis and helped shape
my overall personality.
I learnt a lot specially on the management side such as:
1.How to talk to people in an office environment
2.How to deal with clients
3.How to handle pressure
4.How to balance work with fun
5.How to move on with life
6.There is nothing more important in an organization than having respect for your
fellow employees and being truthful
7.Customers are the king
8.Help every one and try to be polite and humble
For a certain period as a part of training, I was given a task of handling the foreign
customers. I had to deal with their problems, note their observations, take their
orders, provide services, provide analytical solutions on their company stats.
All such tasks taugh me a lot with how to deal with clients.
Another thing that I learnt was how to dress up for an event and to dress up for an
office day.
8

4.2 Technical Skills
Apart from that, I also got to learn a lot of new technical skills:
1.Analytics
I was introduced to the analytics team and got to observe on as to how they used
to work on a daily basis.
I was introducd to R programming and was given small tasks on daily basis.
Hence I learnt on how to visualize data and create plots and graphs using R tool.
I was also introduced to the Apache environment for data analysis.
I played with various machine learning algorithms amd applied them to the data
and was told to report the unusual trends and patterns in the data.
Later at the end I was also asked to perform data analysis with a software named
Enthough Canopy. I had to use python programming language. I was also trained
on how to use python as an alternative for matlab by using the inbuilt libraries in
python named numpy,scipy and matplotlib.
2.Web scraping
I was told to scrape data from various websites for their new product VITO.
For that I was trained to use various softwares and python packages:
Beautiful Soup
Beautiful Soup is a Python package for parsing HTML documents (including
having malformed markup, i.e. non-closed tags, so named after Tag soup). It
creates a parse tree for parsed pages that can be used to extract data from HTML,
which is useful for web scraping.
9

Selenium
Selenium is a portable software testing framework for web applications.
Selenium provides a record/playback tool for authoring tests without learning a
test scripting language (Selenium IDE).
Mozilla web driver
It is an inbuilt mozilla tool used to scrape dynamic data from the internet.
XML/JSON parsing
The DOM or SAX parser parses input XML documents . If you implement a
validating parser, then the processor attempts to validate the XML data document
against any supplied DTDs or XML schemas.
3.API Integration
Stores need to be located on a Google Map and hence inorder to locate the store
we needed to geocode its address.
Hence all scraped address in raw form was geocoded using the geolocation api
provided my google.
Geocoding typically refers to the transformation process of addresses and places
to coordinates, and is sometimes called forward geocoding whereas Reverse
geocoding uses geographic coordinates to find a description of the location, most
typically a postal address or place name
10

4. XML file difference
For purpose of simplicity the data obtained from the scraper had to compared
against the existing data scraped the previous and based on the comparision only
the difference file had to be obtained to prevent redundancy.
5. Handling dynamic data
Various tools were taught to me on how to handle dynamic data from a website.
11

5. APPLICATION OF THE GAINED KNOWLEDGE IN
TRAINING
5.1 GEOCODING MODULE
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import urllib
import time
from bs4 import BeautifulSoup
from xml.dom import minidom
import re
class Geo:
def __init__(self):
self.subloc_1=" "
self.subloc_2=" "
self.subloc_3=" "
self.admin_1=" "
self.admin_2=" "
self.locality=" "
self.country=" "
self.pin=" "
self.premise=" "
self.route=" "
self.street_number=" "
self.lat=" "
self.lng=" "
12

def split_address(self,addr_loc):
url="https://maps.googleapis.com/maps/api/geocode/xml?address=
%s&key=AIzaSyCNUZZcsmT3cDI3DVPNt0FVJ9WGWjKsHcQ"%addr_loc
xml_distant = urllib.urlopen(url)
time.sleep(5)
xmldoc = minidom.parse(xml_distant)
xml_distant.close()
data= xmldoc.toxml()
bs_ex=BeautifulSoup(data)
try:
self.lat=bs_ex.find('lat').text
except Exception as e:
self.lat=" "
pass
try:
self.lng=bs_ex.find('lng').text
self.lat=" "
pass
for tag_ex in bs_ex.find_all('address_component'):
try:
type_loc=tag_ex.find('type').text
type_loc=" "
pass
try:
if type_loc=='sublocality_level_1':
self.subloc_1=tag_ex.find('long_name').text
self.subloc_1=" "
pass
13

try:
self.subloc_2=" "
pass
try:
self.subloc_3=" "
pass
try:
if type_loc=='administrative_area_level_1':
self.admin_1=tag_ex.find('long_name').text
self.admin_1=" "
pass
try:
if type_loc=='administrative_area_level_2':
self.admin_2=tag_ex.find('long_name').text
self.admin_2=" "
pass
try:
if type_loc=='locality':
self.locality=tag_ex.find('long_name').text
self.locality=" "
pass
14

try:
if type_loc=='country':
self.country=tag_ex.find('long_name').text
self.country=" "
pass
try:
if type_loc=='postal_code':
self.pin=tag_ex.find('long_name').text
self.pin=" "
pass
try:
if type_loc=='premise':
self.premise=tag_ex.find('long_name').text
self.premise=" "
pass
try:
if type_loc=='street_number':
self.street_number=tag_ex.find('long_name').text
self.street_number=" "
pass
try:
if type_loc=='route':
self.route=tag_ex.find('long_name').text
self.route=" "
pass
15

def print_addr(self):
print "subloc1 : "+self.subloc_1
print "premise : "+self.premise
print "street_number : "+self.street_number
print "route : "+self.route
print "administrative_area_level_1 : "+self.admin_1
print "administrative_area_level_2 : "+self.admin_2
print "locality : "+self.locality
print "country : "+self.country
print "postal_code : "+self.pin
print "Lat : "+self.lat
print "Lng : "+self.lng
#geo1=Geo()
#geo1.split_address('Famous Studio Lane, Off Dr. E Moses Road, Mahalakshmi,
Mumbai')
#geo1.print_addr()
16

5.2 XML DATA SCRAPED
<Bootlegger Offer="Happyhours">
<Details Category="offers">
<ShopTitle>The Tao Terraces</ShopTitle>
<OfferTitle>Happy Hours: 03:00 PM to 08:00 PM,</OfferTitle>
<Offers_Description> Happy Hours: 03:00 PM to 08:00 PM, Sundays
Happy Hour Deal: Buy 1 Get 1 on IMFL, Beer & Cocktails.
A pint of beer costs: Rs. 225 (approx)
It is designed and decorated with nature, Open air a bar and lounge that breathes
life into the nightlife of the city. On stepping in, youll know why they call it Tao,
which, literally when translated, means the energy behind all the processes of the
world. This sophisticated venue features two floors of restaurant space, a lavish
lounge, and exclusive cocktail bars all under one roof. It is designed with the sole
motive of providing sheer luxury and comfort to its guests which I experienced
first-hand. The menu exhibits an extensive range of Asian international cuisines.
And though, they have branded the Oriental cuisine as their specialty, youll be
hard pressed to pinpoint a dish that didnt make your mouth water.
</Offers_Description>
<Address>1 MG Mall, Opposite Vivanta by Taj, MG Road, Bangalore</Address>
<Telephone>9886640234</Telephone>
<Time>12:00 Noon to 11:00 PM (Sunday to Thursday), 12:00 Noon to 01:00 AM
(Friday to Saturday)</Time>
<Terms_and_Conditions>Bootlegger Insider Offers:
1. Buy 1 Get 1 Offer on Domestic Liquor on Sundays during Happy Hours.
2. Unlimited Drinking Packages available for groups of 15 and
above.</Terms_and_Conditions>
<Category>Bangalore</Category>
<imagelogo>http://www.bootlegger.in/wp-content/uploads/2014/09/the-tao-
terraces-300x296.jpg</imagelogo>
<website>http://www.onemgroad.com/tao.php</website>
17

<subloc_1> </subloc_1>
<admin_1>Karnataka</admin_1>
<admin_2>Bangalore Urban</admin_2>
<locality>Bengaluru</locality>
<country>India</country>
<pin> </pin>
<lat>12.9715987</lat>
<lng>77.5945627</lng>
<premise> </premise>
<route> </route>
<street_number> </street_number>
</Details>
18

6. COMPARISION OF COMPETENCY LEVELS BEFORE AND
AFTER TRAINING
Training has definitely help me improve my competency. It has hepled me
improve on the technical as well as the management side.
I have learnt a lot on how to behave in an organization,how to deal with clients,
behave with employees and many more.
It also gave me insight into the various technicalities needed in an industry and
working and observing from industry experts has definitely proven to be
beneficial.
The following graph shows the competency level comparision:
Fig 1: Technical Expertise
19

In-Plant Training Report

Recommended

Recommended

More Related Content

What's hot

What's hot (19)

Similar to In-Plant Training Report

Similar to In-Plant Training Report (20)

In-Plant Training Report