MD ASIF NASKAR

Data Analyst, Python, MIS, SQL, Power BI

PROFILE

I want to pursue a career in

Analytics. I am good enough

In Statistical analysis &

Machine Learning with

Python, R, SQL, Power BI and

MS Excel. I also have good

(Both written and oral)

Communication skill.

CONTACT

PHONE:

+91-700*******/ +91-801*******

EMAIL:

adz8mt@r.postjobfree.com

LANGUAGES

Read Write Speak

English

Bengali

Hindi

EDUCATION

Gargi Memorial Institute of Technology (B.Tech)

2013 - 2016

GPA – 6.7

JLD College Of Engineering & Management (Diploma)

2010-2013

GPA – 7.6

Baruipur High School (12th)

2009

WBCHSE – 50%

Baruipur High School (10th)

2007

WBBSE – 51%

WORK EXPERIENCE

RT NETWORK & SOLUTIONS PVT LTD – DEPLOYMENT ENGINEER & CMP LEAD July 2019 – Aug 2022

CERAGON IP20 C, ERICSSON

6613/63ALA/028/04kL/F78, HF2 UBR, C5X, UBR & troubleshooting, CISCO 920O Router configuration. I was also creating MIS reports using MS Excel. I have used R to automate some of the MIS report of our Company curtailing down the time by more than 90%. I used to analyze the frequencies using Python to find our any possible anomalies which might cause some disruption. I have also helped the company build machine learning algorithms to predict maintenance of the towers. PROJECTS DONE

1. Predictive Analytic

I was manage and maintain a large number of devices that are continuously running all the time. I did predictive analytics on the data collected by client of devices for gaining valuable insights. These insights helps me in making some smarter data- driven decisions for becoming faster and better.

2. Increase Network Security

One of the biggest concerns of the Telecom Industry is to ensure the security of the networks. I helped to identify the problems. I also helped to analyze the previous data and make predictions about any problem or complications that might appear in the near future. This analysis helps me to take suitable actions for any problem before its severe consequences.

3. Network Optimization

Telecoms need to ensure that speed and performance are always in top shape. To do this, I did machine learning algorithms to identify patterns in data that help me to detect and predict irregularities before customers experience any service degradation. TVS Logistics Pvt Ltd - VODAFONE BSS

MAY 2018 – JULY 2019

Ericsson RBS 2964, 2954, Ericsson RBS 6601, 6602, Huawei905,910,950,950A Vivan Infratel Pvt Ltd - MICROWAVE ENGG

Sept 2016 – APRIL 2018

IP 20C Commissioning & Troubleshooting, HF2 UBR, C5X, UBR & troubleshooting, CISCO 920O router configuration

My Skillset in Details:

Used R for building different statistical models using predictive analytics and Machine Learning algorithms. Used Linear Regression techniques to predict price of the house from different independent variables area location etc. Deployed Logistic regression to create propensity model to identify frauds for BFSI clients. Expert in using forecasting techniques like ARIMA to forecast sales for manufacturing clients. Various other Machine Learning techniques were used to execute classification problems by using Clustering, Decision Tree and Random Forest. Extensively used different packages of R, like Reshape2, Dplyr, ggplot etc, to clean and format data to make them ready to be used in the models. I have also used R to automate excel based reporting like a Robotic Automation; thereby curtailing the manual time by more than 90% with 100% accuracy.

Extensive experience in Python, using all types of data manipulation, filtering, sorting, duplicate removals, text & numeric functions. Expert in using visualization of Python like matplotlib. I have used different types of machine learning algorithms like linear regression, Time series forecasting ARIMA for regression problems. I am also proficient in classification techniques like Logistic regression, Decision tree, Random Forest, KNN, Naïve Bayes, ANN, XGB etc. Unsupervised machine learning like clustering is used as the prerequisite of model building using python. I have also worked on deep learning (ANN) and recommendation engine line Market Basket Analysis. Most of the machine learning were done using packages like statsmodel, scikit learn etc. Kaggle data are used to participate in model building competition on a regular basis.

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