Sara Jafari

Address Jersey City, NJ, *****

Phone 201-***-****

E-mail adki1t@r.postjobfree.com

Result-Driven Data Analyst, experienced in Finance and Management, eager to contribute to team success through hard work, excellent organizational skillls. Motivated to learn, grow, and excel in Data Sciences.

Skills

Expert of advanced statistical tools: Python, Java, SQL, R, STATA, EViews, Hadoop.

Professional in Excel, Power Point, Word, and SAP

Data Science- data analysis, R statistics,R with Hadoop framework, Machine Learning, time-series analysis, K-Means Clustering, Naïve Bayes, business analytics, etc

AI- convolutional neural networks (CNN), perceptron in CNN, TensorFlow, TensorFlow code, transfer learning, graph visualization, recurrent neural networks (RNN), Deep Learning libraries, GPU in Deep Learning, Keras and TFLearn APIs, backpropagation, forward propagation and hyperparameters

Python, OOP, NumPy, SciPy, MatPlotLib, JSON, Packages, Functions, Web scraping, Python parser

Tableau- Tableau Desktop and public integration with R and Big Data

B Hadoop- MapReduce, HDFS, Advanced Hive and Impala, Pig, Sqoop, Oozie, Flume and HBase, Spark framework and RDD, Scala and Spark SQL, Machine Learning using Spark, Spark Streaming, etc.

AWS skills - S3, EC2, VPC, EBS, ELB, AMI, Elastic Compute, Storage Volumes, Load Balancing, Autoscaling and DNS, Automation and Configuration management, Access Management and Monitoring Services, Amazon FSx and Global Accelerator

Work History

Jan 2020 - Current

Intern

Intellipaat, Jersey City, NJ

Currently attending Data Scientist program:

Project1: Increased cross-selling of All-Mart, by using association mining in R and find the top 5 rules related to the number of their most frequently used items.

Project2: Selected the most appropriate clients for loan approval at ATM bank by running linear regression model in Python.

Sep 2017 - Sep 2019

Financial Analyst

Publicis Group, Paris, France

Retrieved, analyzed performance data, and presented financial reports to leadership team regarding revenue of products such as Nestle, L'Oreal, and Renault.

Generated invoices to agencies using Hyperion Financial Management (HFM).

Draw charts to show financial status of the organization and variance of actual and forecasted results.

Monitored and supervised the accuracy of timesheet using SAP.

Jun 2015 - Dec 2016

Business Analyst

Jafcom Telecommunication, Jersey City, NJ

Gathered and organized crucial material project information in the advertising database.

Analyzed client's data through implementation of tools such as Excel spreadsheet to interpret added value.

Improved social networks such as LinkedIn, Facebook, and Instagram.

Sep 2013 - Sep 2014

Financial Analyst

BNP Paribas Bank, Paris, France

Investigated new models to address performance issues and profit & loss measurements.

Created weekly analyses of investment portfolios and escalated them to Finance leads.

Registered trade requests features in Murex and Power booking system.

Evaluated financial key figures of the trades and presented the results to managers.

Jul 2012 - Sep 2012

Intern

HSBC Bank, Paris, France

Managed user inquiries regarding services assurance of hardware incidents.

Assisted users with acquiring information from manuals.

Education

M.A. Economics

Rutgers University-Newark - Newark, NJ

(May 2012)

M.A. Control Management

Skema Business School - Paris, France

(Jun 2018))

B.A. Economics and Management

Paris West University - Paris, France

(Jun 2010)

Project

Projects on Data Science with R

Project 1: Loan Approval Prediction

Domain: Banking

Problem Statement: Predict approval rate of a loan by using multiple labels

Topics: Data Analysis, Data preprocessing, Cleaning Ops, Data Visualization, R Language

Highlights:

Performing Data Preprocessing

Building a model

Building a Naïve Bayes model on the training dataset

Prediction of values after performing analysis

Projects on Python for Data Science

Project 2: Analyzing the Naming Pattern Using Python

Industry: General

Problem Statement: How to analyze the trends and the most popular names

Topics: In this Python project, I have worked with the United States Administration which has made data on the frequency of names until 2020 available. The project requires analyzing the data considering different methods. I have visualized the most frequent names, determined the naming trends and come up with the most popular names for a certain year.

Highlights:

Analyzing data using Pandas Library

Deploying Data Frame Manipulation

Bar and box plots with Matplotlib

Projects on Artificial Intelligence & Deep Learning with Tensorflow

Project 3: AI-based Chatbot using IBM watson LAB

Industry: Ecommerce

Problem Statement: Create a chatbot using Artificial Intelligence

Description: In this project, by understanding the client’s needs, I have been able to offer the right services through Artificial Intelligence chatbots. I have learned how to create the right artificial neural network with the right number of layers to ensure that the clients queries are relevant to the Artificial Intelligence chatbot. This has helped me to understand Natural Language Processing, going beyond keywords, data parsing, and providing the right solutions.

Highlights:

Breaking user queries into components

Creating neural networks with TensorFlow

Understanding Natural Language Processing

Projects on Tableau Desktop

Project 4: Analyzing Aggregate Data

Domain: Government

Problem Statement: How is unemployment affecting malnutrition?

Description: In this project, I have worked on vast amounts of data and analyzed trends, insights, and correlations. Datasets include the aggregate unemployment figures for multiple years, world population statistics across several years, and the worldwide nutritional data. By analyzing this data, I have linked the malnutrition problem with the unemployment rates using Tableau.

Highlights:

Cleaning up Excel data and connecting with Tableau

Using pivot tables

Comparative analysis with the Tableau dashboard

Projects on Hadoop

Project 1: Working with MapReduce, Hive, Pig, Impala and Sqoop

Industry: General

Problem Statement: How to successfully import data using Sqoop into HDFS for data analysis

Topics: As part of this project, I have worked on the various Hadoop tools like MapReduce,

Apache Hive and Apache Sqoop. I have worked with Sqoop to import data from relational

database management system like MySQL data into HDFS. I needed to deploy Hive for summarizing data, querying and analysis. I have translated SQL queries using HiveQL for deploying MapReduce on the transferred data.

Highlights: Sqoop data transfer from RDBMS to Hadoop Coding in Hive Query Language Data

querying and analysis

Project 2: Hadoop YARN Project

Industry: Banking

Problem Statement: How to bring the daily data (incremental data) into the Hadoop Distributed

File System

Topics: In this project, we had transaction data which is daily recorded/stored in the RDBMS. Now

this data is transferred everyday into HDFS for further Big Data Analytics. I have worked on live

Hadoop YARN cluster. YARN is part of the Hadoop ecosystem that lets Hadoop work from

MapReduce. I have worked on the YARN central resource manager.

Highlights: Using Sqoop commands to bring the data into HDFS End-to-end flow of transaction

dataWorking with the data from HDFS

Projects on Apache Spark

Project 1: Movie Recommendation

Industry: Entertainment

Problem Statement: How to recommend the most appropriate movie to a user based on his taste

Topics: This is a hands-on Apache Spark project deployed for the real-world application of movie

recommendations. This project helped me gain essential knowledge in Spark MLlib which is a

Machine Learning library; I know how to create collaborative filtering, regression, clustering and

dimensionality reduction using Spark MLlib. Upon finishing the project, I have first-hand

experienced in the Apache Spark streaming data analysis, sampling, testing and statistics, among

other vital skills.

Highlights: Apache Spark MLlib componentStatistical analysis Regression and clustering

Project 2: Twitter API Integration for Tweet Analysis

Industry: Social Media

Problem Statement: Analyzing the user sentiment based on the tweet

Topics: This is a hands-on Twitter analysis project using the Twitter API for analyzing of tweets. I have integrated the Twitter API and do programming using Python or PHP for developing the essential

server-side codes. Finally, I have been able to read the results for various operations by filtering,

parsing and aggregating it depending on the tweet analysis requirement.

Highlights: Making requests to Twitter API Building the server-side codes Filtering, parsing and

aggregating data

Project 3: Data Exploration Using Spark SQL – Wikipedia Data Set

Industry: Internet

Problem Statement: Making sense of Wikipedia data using Spark SQL

Topics: In this project I have been using the Spark SQL tool for analyzing the Wikipedia data. I have gained experience in integrating Spark SQL for various analysis, Machine Learning, visualizing and processing of data and ETL processes, along with real-time

analysis of data.

Highlights: Machine Learning using Spark Deploying data visualization Spark SQL integration

Project on Splunk

Project 1: Creating an Employee Database

Industry: General

Problem Statement: How to build a Splunk dashboard where employee details are readily

available

Topics: In this project, I have made a text file of employee data like full name, salary,

designation, ID,. I had to index the data based on various parameters, use various

Splunk tools for evaluating and extracting the information. Finally, I have created a

dashboard and add various reports to it.

Highlights:

Splunk search and index commands

Extracting field in search and saving results

Editing event types and adding tags

Project 2: Building an Organizational Dashboard with Splunk

Industry: E-commerce

Problem Statement: How to analyze website traffic and gather insights

Topics: In this project, I have built an analytics dashboard for a website and create alerts for

various conditions. I have captured access logs of the webserver and the sample logs and then

the sample are uploaded. I have analyzed the top ten users, the average time spent, peak

response time of the website, the top ten errors and error code description. I have also created a

Splunk dashboard for reporting and analyzing.

Highlights:

Creating bar and line charts

Sending alerts for various conditions

Providing admin rights for dashboard

Project 3: Field Extraction in Splunk

Industry: General

Problem Statement: How to extract the fields from event data in Splunk

Topics: In this project, I have learned to extract fields from events using the Splunk field extraction

technique. I have gained knowledge in the basics of field extractions, understand the use of the

field extractor, the field extraction page in Splunk web, and field extract configuration in files. I

have learned the regular expression and delimiters method of field extraction. Upon the

completion of the project, I have gained expertise in building Splunk dashboard and use the

extracted fields data in it to create rich visualizations in an enterprise setup.

Highlight :

Field extraction using delimiter method

Delimit field extracts using FX

Extracting fields with the search command

Projects on Azure 103

Project 1: Deploying a website using Microsoft Azure

Topics: Traffic manager, Application gateway, Virtual Machines. Blob storage, Virtual Network Peering

Highlights:

Deploying the website servers in two different regions

Controlling, managing and monitoring the traffic geographically

Enabling Path based Routing for the website

Enabling communication between the servers

Storing data on the cloud

Projects on AWS

Project 1: Deploy a multi-tier website on AWS

Problem Statement: Deploying a Custom PHP Website to AWS with functionalities for SQL, NoSQL and file storage

Topics: RDS, SNS, DynamoDB, S3, VPC, EC2, NAT Gateways, Load Balancer and Auto Scaling

Highlights:

Configuring AWS to send emails for every operation using the website

Deploying the web application in private subnet with no internet access

Using Load Balancer to expose the application in the private subnet

Using NoSQL database for metadata storage

Using Auto Scaling for varying traffic workloads

Certification

Big Data and Data Science Master's program certificate by Intellipaat

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