Data Project

SRILEKHA PANDA

Address:Balajinagar,Gosaninuagaon,Ganjam,Odisha,pincode:760003

Phone no:809-***-****

Email id:********.*******@*****.***

OBJECTIVE Looking for a challenging career in the area of IT sector by utilizing the skills and knowledge e ciently and e ectively.

TECHNICAL

SKILLS

Programming Languages: C,C++,Python

Software Tools: MS O ce,LaTeX

Operating Systems: Windows(7,8),UBUNTU(16.04,17.10) EDUCATIONAL

QUALIFICA-

TION

M.Tech July 2016 - Present

University:University Of Hyderabad,Gachibowli,Hyderabad CGPA:7.8

B.Tech June 2011 - July 2015

Board:BPUT,ODISHA

College:PMEC,Berhampur

CGPA:8.2

Intermediate June 2008-May 2010

Board:CHSE,ODISHA

College:S.B.Rath women’s college,Odisha

Percentage:67.3

Matriculation March 2008

Board:Board Of Secondary Education,Odisha

School:G.S.T.S For Womens’,Berhampur,Odisha

Percentage:83.3

ACADEMIC

ACHIVEMENTS

Quali ed Gate-2016 with 95 percentile.

Got Scholarships during 3rd and 5th standard.

B.TECH

PROJECT

Online Auction System:

The purpose of this project is to build an on-line auction management system, place for buyers and sellers to come together and trade almost anything. In fact, the system consists in a web-portal where registered users can propose new auctions, place bids in order to buy the items on auction,send messages to other users and receive automatically news via e-mail (when they receive new o ers for the proposed auctions, when an auction is over etc.)

M.TECH

PROJECT

Topic:Gibb’s Sampling in Topic Modeling

Description:Gibbs sampling is a Markov Chain Monte Carlo algorithm for high- dimensional data. It is called Monte Carlo because it draws samples from speci ed probability distributions and the Markov chain comes from the fact that each sample is dependent on the previous sample. I have taken Topic Modeling as my problem which is a probabilistic model for discovering the abstract "topics" that occur in a collection of documents where the document texts don’t have any labels. Here i am using Latent Dirichlet Allocation (LDA) as my Topic Model which can be learned using Gibbs Sampling. Generally Gibbs Sampling is inherently sequential, but here i am going to show how that parallel approximation of the original algorithm is able to learn as good models as the exact algorithm. I am using GPUs for parallel mechanism to generate samples.

Supervisor:Prof. C.R.Rao (email id:*****.*****@*****.***) and Prof.RajeevWankar(email id:********@*****.*****.**)

COURSE

PROJECTS

Neural Network (M.Tech Semester -II(2016)):

Topic: Building Your Own Neural Network using Python Dataset: MNIST dataset for handwritten number

Description: This mini-project is about implementing our own neural network from scratch in Python.

Time Series Analysis in Python:

Dataset: Temperature data from New York City

Description: Ideas of correlation and autocorrelation for time series, Time Series Models(Autoregressive (AR) Models, Moving Average (MA) and ARMA Models), How to model two series jointly using cointegration models

Pandas basics and manipulations of DataFrames with Pandas: Description:

As Real-world data sets contain strings, integers, time-stamps and un- structured data. How do you store data and manipulate it and easily retrieve important information. That can be easily done through pandas DataFrame.How to import, build, manipulate real world-data. Cleaning Data in Python:

Dataset: Gapminder data

Description:

How to explore the data and nding outliers, missing values, and dupli- cate rows.Tidying data using pivoting and melting.String manipulation and pattern matching to deal with unstructured data, and then explore techniques to deal with missing or duplicate data. Natural Language Processing Fundamentals in Python: Description:

how to identify and separate words, how to extract topics in a text, and how to build your own fake news classi er.

Databases in Python:

Description:

-Basics of Relational Databases

-Applying Filtering, Ordering and Grouping to Queries

-Advanced SQLAlchemy Queries

-Creating and Manipulating our own Databases

Importing in Python:

Description:

Importing data from

at les, Excel spreadsheets, Stata, SAS and MAT- LAB les, from relational databases such as SQLite and PostgreSQL and from the web, Interacting with APIs to import data from the web EXTRA

STUDIES

MACHINE LEARNING :studying and doing practice in machine learning PERSONAL

DETAILS

Date of Birth: 15th April 1993

Gender: Female

Languages Known: English,odia,Hindi

Hobbies: Cooking,Listening music

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