SUMMARY:

• 6+ years of experience as Data Analyst with a proficient understanding of Machine Learning technologies, social network analyzing, computer version, nature language processing, data visualization, algorithms, statistical methods, statistical modeling, sampling optimization, data mining and data Warehousing.
• Deep - sighted knowledge in Data Analysis, data Validation, data Cleaning, data mapping, data profiling, Data Verification, and identifying data mismatch.
• Experienced in Python and R to manipulate datafor dataloading and extraction. Worked with Python libraries like Matplotlib, NumPy, SciPy, Pandas, Seaborn, Bokeh, nltk, Scikit - learn, OpenCV, TensorFlow, Keras and on R packages for data analysis.
• Experience in data project life cycle and actively involved in all the phases including data extraction, data cleaning, statistical modeling, data and cloud integration, data profiling, and data visualization using multiple BI and ETL tools like Tableau, Talend, and Power BI
• Experience in data modeling and data warehousing concepts with expertise in creating star & snow-flake schemas, FACT and dimensions tables and physical and logical data modeling
• Expertise in designing and creating various analytical reports and dashboards to help users to identify critical KPIs and facilitate strategic planning in the organization
• Strong experience in manipulating and deriving insights from large sets of structured, semi-structured, and unstructured data.
• High-level knowledge of statistical machine learning algorithms, ability to develop solutions to complex data problems, utilize exploratory analysis to identify abnormalities in data, and implement the appropriate set of algorithms. (regression, decision tree, clustering)
• Designed effective statistical/predictive models for diverse datasets to predict risk factors.
• Proficient in Machine Learning algorithms and Predictive Modeling including Linear Regression, Logistic Regression, Naive Bayes, Decision Tree, Neural Networks, Random Forest, Ensemble Models, SVM, KNN, and K-means clustering.
• Experience in Deep Learning techniques including Feedforward Neural networks, Convolutional Neural networks (CNN), Recursive Neural networks (RNN), pooling, and regularization.
• Excellence in handling Big Data Ecosystems like Hadoop, MapReduce, Spark, HDFS Architecture, HBase, Hive, Pig, MLlib, and ELT.
• Worked in supporting integrated testing of interfaces and validation of data mapping, data migration, as well as data conversion activities conducted during pre-go-live and post-go-live
• Good Data Warehousing concepts including Metadata and Data Marts.
• Experience in conducting Joint Application Development (JAD) sessions with end-users, Subject Matter Experts (SMEs), and Development and QA teams.
• Experience in building the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using NoSQL and SQL from AWS & Big Data
• Experience in understanding Stored Procedures, Functions, Triggers and Packages using SQL
• Strong skills in Statistics methodologies such as hypothesis testing, ANOVA, Monte Carlo simulation, principle component analysis, and correspondence analysis, ARIMA time series analysis, structural equation model.
• Extensive experience in designing, developing, and publishing visually rich and intuitively interactive Tableau workbooks and dashboards for executive decision-making.
• Experience building and optimizing ‘big data’ data pipelines, architectures, and data sets. (HiveMQ, Kafka, Cassandra, S3, Redshift)
• Experience in using R packages such as quantmod, neural net, MASS, rpart, kernlab, ggplot2, fArma, fBasic, and Shiny App to analyze data, construct models, produce data products and evaluate results.
• Good understanding of Agile, Scrum, and Waterfall management processes.
• Self-motivated exuberant learner and adequate with challenging projects and working in ambiguity to solve complex problems independently or in a collaborative team.

TECHNICAL SKILLS:

Languages: Python, R Programming, HTML5, CSS3, JavaScript

Databases: SQL and Bootstrap

Packages/Libraries: Python (NumPy, Pandas, Scikit-learn, SciPy, and Matplotlib), R (Caret, dplyr, ggplot2, Shiny)

ETL Tool: Talend, SSIS

Machine Learning: Regression, Polynomial Regression, LDA, Naive Bayes, Decision Trees, Neural Networks, SVM, Random Forests, K-means

Big Data Technologies: Spark, Redshift, Hadoop (Map Reduce, HDFS), Data Mining, Data Warehousing, Machine Learning, Descriptive, Prescriptive and Predictive Analytics, Tableau, HiveMQ, Kafka, Cassandra, S3, Redshift

Cloud Platforms: AWS (S3, Amazon RDS), Azure Cloud, Google Cloud

Natural Language Processing: Convolutional Neural Network (CNN), RNN, LSTM, GAN

Statistics Methodologies: Hypothesis Testing, ANOVA, Monte Carlo Simulation, PCA and Correspondence Analysis, ARIMA Time Series Analysis, Structural Equation Model

Visualization/Reporting: Tableau, MS Excel, Advanced Excel, MS Access, Power BI

Version Control: Git, GitHub

Operating System: Windows, Linux, Unix

PROFESSIONAL EXPERIENCE:

Confidential, Addison, Texas

Data Analyst/ Machine Learning

Responsibilities:

• Participated in all phases of Data mining, Data-collection,Data-Cleaning, Developing-Models,Validation,Visualization, and Performed Gap Analysis.
• Used Pandas, NumPy, Seaborn, SciPy,Matplotlib,Scikit-learn and NLTK in Python for developing various data analysis techniques and machine learning algorithms.
• Worked on a project to analyze real-time reviews posted on Google PlayStore. My job required me to create visualizations using R studio and Tableau to help application developers get an insight into the category of their application as well as related information to enhance their application and know the competition, market trends, and industry forecast, especially in the ultra-competitive mobile application industry to maintain and grow the business
• Performed application data cleansing, data irregularity resolution and developed cleansing rule sets for ongoing cleansing and data synchronization.
• Highly efficient in data Wrangling techniques such as datacleaning and transforming data in usable format, data quality, data profiling and data organization.
• Extracted Data from multiple sources collected data, manipulated, cleansed, interpretation and then used for reporting
• Implemented Classification using supervised algorithms likeLogistic Regression,Decision trees,KNN,and Naive Bayes.
• Used Pandas as API to put the data as time series and tabular format for manipulation and retrieval of data
• Implemented end-to-end systems for Data Analytics, Data Automation and integrated with custom visualization tools usingR and Hadoop.
• CreatedSQLtables with referential integrity and developed queries usingSQL
• UtilizedSpark,Scala,Hadoop,HBase,MLLib,Python, and a broad variety of machine learning methods including classifications, dimensionality reduction, etc., and utilized the engine to increase user lifetime by 45% and triple user conversations for target campaigns.
• UsedSpark Data frames,Spark-SQL,and Spark MLLibextensively and developed and designed POCs usingScala,Spark SQL, andMLliblibraries.
• Implemented Agile Methodology for building an internal application.
• Updated Python scripts to match data with our database stored inAWSCloud Search, so that we would be able to assign each document a response label for further classification.

Environment: Python, Tableau, SQL, R, OLTP, OLAP, Metadata, MS Excel, Map-Reduce and MongoDB.

Confidential, Irving, Texas

Data Analyst/ Machine Learning

Responsibilities:

• The model gave an efficiency of 79% and a cross-entropy error of 4.8.
• Performed Data Profiling to learn about user behavior and merged data from multiple data sources and participated in all phases of data mining, data collection, data cleaning, developing models, validation, visualization, and performed Gap Analysis.
• Cleaned and processed third-party spending datainto maneuverable deliverables within a specific format with Excel and python libraries such as NumPy and Pandas.
• Utilizing Pandas, NumPy, SciPy, Matplotlib, and Scikit-learn and Python for developing various data analysis techniques and machine-learning algorithms.
• Utilized Python and SQL Scripts to optimize complex SQL queries, to build and test various ETL data pipelines for Feature Implementation.
• Responsible for developing database objects like Stored Procedures, Functions, Packages and Triggers using SQL.
• Involved in relational and dimensional data Modeling for creating Logical and Physical design of the database and ER diagrams using data modeling like Erwin.
• Used statistical for effective analysis by hypothesis testing to validate data and interpretations.
• Perform data Management duties acquire validate, process, store and protect data for reliable accessibility to data users.
• Generating various reports (graphical) using Python packages like NumPy,Pandas, Matplotlib, SciPy, ggplot2.
• Performed K-means clustering, Multivariate analysis and Support Vector Machines in Python/R.
• Developed Clustering algorithms and Support Vector Machines that improved Customer segmentation and Market Expansion.
• Created various B2B Predictive and descriptive analytics using R and Tableau.
• Extensive experience and proficiency in using Tableau to create output files in a variety of formats including HTML and PDF.
• Worked on different data formats such as JSON, and XML and performed machine learning algorithms in R and Python.
• Responsible for planning & scheduling new product releases and promotional offers.
• Parsing data, producing concise conclusions from raw data in a clean, well-structured, and easily maintainable format.
• Extracted data from HDFS and prepared data for exploratory analysis using data munging.
• Worked on Text Analytics, Naive Bayes, Sentiment analysis, creating word clouds, and retrieving data from Twitter and other social networking platforms.

Environment: Python, R, Agile, Data Quality, SQL Server, Tableau, Data Governance, Supervised & Unsupervised Learning, Matplotlib, Scikit-learn, Shiny

Confidential

Data Analyst

Responsibilities:

• Participated in all phases of data mining, data collection, data cleaning, developing models, validation, and visualization to deliver data science solutions.
• Used Pandas, NumPy, Seaborn, Matplotlib, and Scikit-learn in Python for developing various machine learning models and utilized algorithms such as Linear regression, Logistic regression, Gradient Boosting, SVM, and KNN.
• Project was for an Indonesian Pharmaceutical company. My job responsibilities involved designing a model using a Genetic Algorithm and Support Vector Machine to predict heart diseases for an individual. This system gave an accuracy of 92.38%. Also, performed an analysis and comparison of the results using various ML algorithms to conclude with a best determining algorithm
• Implemented algorithms such as Principal Component Analysis (PCA) for dimensionality reduction and normalizing the large datasets. developed various Clustering algorithms for market segmentation to analyze customer behavior patterns.
• Worked with deep neural networks Convolutional Neural Networks (CNNs) and Recurrent Neural networks (RNNs).
• Developed low-latency applications and interpretable models using machine learning algorithms.
• Used the Talend tool to develop and deploy data integration jobs. Also integrated all the data with other data warehouses and synchronize data between systems
• Used cross-validation to test the models with different batches of data to optimize the models and prevent overfitting.
• Performed Ensemble methods to increase the accuracy of the model with different Bagging and Boosting methods.
• Collected data using Hadoop tools to retrieve the data required for building models such as Hive.

Environment: Python (NumPy, Pandas, Scikit-Learn, Matplotlib), Tableau, SQL Server, MS Excel

Confidential

Data Analyst

Responsibilities:

• Involved in all phases ofdataacquisition,datacollection,datacleaning, model development, model validation, and visualization to deliverdatascience solutions.
• Created classification models to recognize web requests with course associations to classify the orders and score the products/courses for analytics which improved the online sales percentage by 13%.
• Used Pandas, NumPy, seaborn, SciPy, Matplotlib, Seaborn, Scikit-learn, and NLTK in Python at various stages for developing data analysis techniques and machine learning model.
• Built and published customized interactive reports and dashboards with customized parameters and user - filters using Tableau improved the user experience by 48%
• Customer segmentation based on their behavior or specific characteristics like age, region, income, and geographical location and applying Clustering algorithms to group the customers based on their similar behavior patterns.
• Created and maintained reports to display the status and performance of deployed model and algorithm with Tableau.
• Worked with numerousdatavisualization tools in python like Matplotlib, seaborn, ggplot, and pygal.
• Worked on NLTK library in python for doing sentiment analysis on customer product reviews and other third-party websites using web scrapping.
• Used Pandas, NumPy, and Scikit-learn in Python for developing various machine learning models such as Decision Tree and Random Forest.
• Used cross-validation to test the models with different batches ofdatato optimize the models and prevent overfitting.
• Worked with ANN (ArtificialNeuralNetworks) and BBN (Bayesian BeliefNetworks).
• Used pruning algorithms to cut away the connections and perceptron to significantly improve the performance of the back-propagation algorithm.
• Implemented a structured learning method that is based on the search and scoring method.

Environment: Python, Tableau, MLlib, Pandas, NumPy, Scikit-learn, Regression, Cluster analysis, SVM, Random Forest

Confidential

Data Analyst

Responsibilities:

• Performed Data Analysis, Data Migration, Data Cleansing, Transformation, Integration, Data Import, and Data Export using Python.
• Generated different types of daily and weekly reports based on requirements using SQL.
• Analyzed student enrolment data and applied machine learning techniques for the prediction of students opting for other courses within a set range of timelines.
• Gained Comprehensive knowledge and experience in process improvement, normalization/de-normalization, data extraction, data cleansing and data manipulation.
• Date time to transform date variables and worked on transformation for age variables.
• Utilized SQL queries and Python for data cleaning and conceptualizing large datasets.
• Responsible for creating analysis reports and documentation and reporting to the team.
• Used cross-validation techniques to avoid overfitting of the model to make sure the predictions are accurate and measured the performance using Confusion matrix and Classification report.
• Organized data sets into pivot tables to showcase important pieces of information.
• Performed quantitative analysis and data visualizationsin Python.

Environment: Python, NumPy, Pandas, SQL