Data Python

RESUME

G. PRUDHVI KUMAR MOBILE: 799-***-****

DATA SCIENTIST EMAIL ID: *****************@*****.***

PROFESSIONAL SUMMARY:

Having 4 years of experience in the field of Core Analytics and advanced business analytics (Data Science) to build predictive framework/ML to draw insights at scale for different aspects of business.

CORE COMPETENCIES:

To develop and implement advanced analytics approaches including statistical modelling, machine learning principles etc. to answer business questions, drive actionable insights using R & Python Programming Languages.

Hands on experience in Analytics - deriving key business impact from data and in creating algorithms, implementing an analytical solution based on analysis with large, complex, structured data sets to provide better insights.

Analyzed business problems using data from different sources to provide strategic and actionable business insights.

Very good knowledge at various Machine Learning techniques, such as, Linear & Logistic Regression, Model Selection; Cross Validation, Decisions trees, SVM (Support Vector Machines), Random Forests Algorithm, Cluster analysis.

Deep understanding of statistical modeling /machine learning / data mining concepts.

Ability to work well under pressure and on multiple and conflicting priorities.

EDUCATION:

MBA (finance) from Osmania University.

WORK EXPERIENCE:

Working as an Associate Data Scientist in Tech Mahindra Organization, September 2016.

KEY SKILLS AND TOOLS:

Data Science Tools : R Studio & Python

Data visualization Tools : R Studio

Programming Languages : R, Python

Environments : Windows

PROFESSIONAL EXPERIENCE:

Project # 1

Project

Customer default Analysis

Role

Data Scientist

Technologies

R Studio, Python, Excel

Domain

Telecommunications

Description:

AT&T is the world's largest telecommunications company. AT&T is the second largest provider of mobile telephone services and the largest provider of fixed telephone services in the United States, and also provides broadband subscription television services through DirecTV.

Objective: To predict, who all are going to become as a delinquent customer.

Contribution:

Gathered the large volume of data

Created data patterns by using the key metrics are like Billing information and payment information

Did driver analysis to understand most important variables

Built the logistic regression analysis to find who are likely to become as a delinquent

Created metrics are like Sensitivity, Specificity, Roc curve, AUC, and precision

Applied ridge regression and lasso regression to regularize the coefficients.

Project # 2

Project

Network Intrusion

Role

Associate Data Scientist

Technologies

R Studio, Excel

Domain

Network

Description:

Nowadays, most of the industries facing unauthorized accessing of networks and it is very painful for the organizations to overcome this sort of activities.

Objective: To detect authorized and unauthorized access to networks.

Contribution:

Identifying the correct data required for solving a business problem.

Cleaning and pre-processing the dataset to appropriate format for predictive models.

Exploratory data analysis, to figure out important features for machine learning.

Creating appropriate training, test, and validation set for training the model.

Understanding the issues of underfitting, overfitting, out of sample errors.

Project # 3

Project

Exploratory Data analysis for Billing and Complaints Data

Role

Associate Data Scientist

Technologies

R Studio, Excel, Python

Domain

Telecom

Description:

Ciena Corporation is a United States-based global supplier of telecommunications networking equipment, software, and services. The company was founded in 1992 and is headquartered in Hanover, Maryland.

Objective: Identify the drivers for Billing and complaints data.

Contribution:

Identify the key metrics for Billing and complaints data (Usage information, Roaming information, network information)

To perform data sanity check (Missing values and Outliers)

Created univariate analysis for all the key metrics

Created various data patterns for Billing and complaints data

Identified relation between numeric variables by using the correlation techniques

Applied various Anova methods to finding relation between the variables

Created multi variate analysis for few variables

G. PRUDHVI KUMAR

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