Data Analyst Sales

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Shashikanth Devulapally *************@*****.*** +916*********

D.O.B: 12-November-1996

CAREER OBJECTIVE:

A proactive and fast learning individual seeking an opportunity to work as a dynamic data analyst utilizing analytical and methodical skills to help the company achieve business goals sticking to vision and values.

ACADEMIC QUALIFICATION:

Qualification Year University/School Percentage

Bachelor of Engineering (E.C.E) 2014-18 Matrusri Engineering College, Hyderabad

70.42%

Intermediate 2012-14 Narayana Junior College,

Hyderabad

96%

S.S.C 2012 Oxford School, Suryapet 9.3 (G.P.A)

CERTIFICATIONS:

Institute Year

Imarticus Learning Institute,

Hyderabad

2019 Data Science Pro Degree in Statistics and Machine Learning

DATA SCIENCE SKILLS:

Statistics Types of Data and Levels of Measurement Statistical Analysis Descriptive Statistics (Measures of Central Tendency and Measures of Variability), Inferential Statistics (Normal distribution, Standard Normal distribution, Student's T distribution, Confidence Interval, Hypothesis Testing, Type1 and Type2 Errors, Z statistic, T statistic, P value, ANOVA, F- Ratio).

Data Analysis Exploratory Data Analysis: Univariate Analysis, Bi-Variate Analysis. Data Visualization (Bar plot, Histogram, Box plot, Scatter plot, Pie chart). Data Preprocessing Treating Missing values, One hot encoding, Feature Scaling, Dimensionality Reduction, Data Bifurication, Feature creation.

Machine Learning Supervised Learning: Regression Analysis (Linear Regression, Logistic Regression, Regularized Regression models), K Nearest Neighbors, SVM, Decision Tree, Ensemble techniques (Bagging and Boosting), Random Forests, Naive Bayes, Gradient Descent Algorithm.

Unsupervised Learning: K- Means Clustering.

Deep Learning Perceptron/Neural Networks, Activation Functions (Sigmoid Function, Hyperbolic Tangent Function, RELU, Softmax), Back Propagation using Gradient Descent, Feed Forward Neural Networks.

Text Analytics NLP, Text Preprocessing, Removing StopWords, Tokenization, Stemming, Lemmatization, Parts of Speech Tag, NER, CountVectorizer, TFIDFVectorizer.

2

IT SKILLS:

Programming Languages Python (Numpy, Pandas, Matplotlib, Seaborn, SciKit-Learn, TensorFlow, Keras)

R programming Language

Database SQL

Operating System Windows

ACADEMIC PROJECTS :

Project Title Description

1 XYZ Corp_Lending Dataset

(Classification Problem)

Based on the data that is available during loan

application, we have to build a model to predict the loan defaulter in the future. This will help the

company in deciding whether or not to pass the

loan.

2 MNIST dataset

(Image Prediction using

neural networks)

It is a large database of handwritten digits that is commonly used for training various image processing systems. It consists of 70,000 images of handwritten digits. Here we have 10 digits (0 to 9) then we have 10 classes. Our objective is to build an algorithm that takes image as input and correctly determines which number is shown in that image.

3 Real or Not? NLP with Disaster

Tweets

(Disaster Prediction using

NLP)

Based on the data obtained from different locations, we have to predict a particular disaster tweet is Real or Not. If disaster is Real then the output is 1 else the output is 0.

4 Loss Given Default

(Regreesion Problem)

The bank wants to automate the loss estimation

caused by the loan defaulter while applying for

new loan, so that they get to know what features

are leading to defaults up to which amount.

5 BigMart Sales from Analytics

Vidhya

(Regression Problem)

This is the BigMart 2013 sales for 1559 products

across 10 stores in different cities. The aim is to build a machine learning model and find out sales of each product at a particular store.

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