Data Analytics Analysis

Name: Siva Jetti

Email id:**********@*****.***

Mobile Number: +1-469-***-****

PROFESSIONAL SKILLS:

● Having 2 years of hands-on experience in SQL.

● Having good knowledge ofData warehousing -OLTP, OLAP.

● Hands-on experience inbulk loading and unloadingdata

● Hands-onexperienceinwritingcomplex,highlyoptimizedSQLQueriesacrosslargedatasets,differentareasof RDBMS, and data loading through SQL loader.

● Extensive knowledge in system analysis,development,implementation,production support,and maintenance of data warehouse business applications.

● Abilitytoworkindependentlyandasateamwithasenseofresponsibility,dedication,andcommitmentwithan urge to learn new technologies.

● Good communication skills,problem-solving skills,and ability to analyze quickly and develop an efficient industry standard solution for a given problem.

● Good interpersonal skills, proactive and hardworking professional. SCHOLASTIC RECORDS:

Masters: University of North Texas, Denton,75007.

Major: Advanced Data Analytics

C.G.P.A:3.8/4.0

Master’s Project Summary

● Integration of Snowflake with Tableau for data analysis.Analyzing retail sales performance by using Tableau to createdashboardsandvisualizations.Asanoutcome,IhaveobservedInteractivedashboardsshowingsalestrends. I have performed data integration and cleaning. Optimizing the visualizations for better experience and accuracy.

● Climatedataanalysisusing DeepLearningtechniquesusingbigdatatoolslikeTensorflow/Keras,PythonJupyter Notebook,and Apache Spark for predicting weather patterns.Excepted outcomes provide accurate forecasts and better predictions and responses to weather changes.

● IdevelopedaSecuritymanagementsystemthatenhancesthedetectionandmigrationofcyberthreats.Ihaveused Apache Hadoop andSparkfordatastorageandpreprocessing.IntegratedtheCNNs,andRNNsmodelsforthreat detection, and used Apache Kafka and Spark for streaming.

● Analyzing the Health care objectives with the help of SAS in predicting the enhancementofpatientcare,medical supplies,andstaffandbettermanagementofthepatientflow,reductionofovercrowding,andbetterutilization of beds.For data collection,we have used previous medical history,treatment procedures,and patient data from different hospitals including demographic information.Followed by Data integration,data cleaning,and performing descriptive statistics.Creating Freature engineering for predicting LOS and model building and splitting the data for testing and training finally evaluating the model performance withRMSE, MAE, and R-Squared.

● The capstone project covers multiple topics in school nutrition standards all over theUnitedStates.Iconducteda literature reviewaboutthebackground,currenttrends,currentissues,andopportunities.Forthemodelingand predictivepart,wehavedoneexploratorydataanalysiswhichinvolvesPython'sdatasciencelibrarydatacleaning, datawrangling,datatypeconversion,removingnullvalues,andremovingduplicates.Comingtotheneedofthe project we have found the total distribution across the different regions,we have observed the correlation between the various types of meal served vs free meal distribution,predicting the overall future prediction percentageofthechildren'sparticipationbasedonthecurrentparticipationrates,Classificationofclassbased onthefuturepredictionbysegressionbasedontheneedofthefreemeal.Concluding,wegiveafewsuggestions to USDA based on our output and a few recommendations for/based on future considerations. Major topics covered in Masters.

Data Analytics -1- Data and Data Preparation, Tabular and Graphical methods, Introduction to Probability, Discrete probability Distributions, Hypothesis testing, sample and sample distribution, and regression analysis. Data Analytics -2- Introduction to SAS, Data Miningintroduction, Linear and logistic Regression, cluster analysis and dimension reductions, decision tree, Neural nets. Application and Deployment of Advanced Analytics-Applyingmachine learning methods using r, to build predictive models and discover Patterns in data to develop analytic solutions to practical business problems and enable more informed decision-making.Understanding the strategies in data wrangling, feature engine, missing values, and data pre-processing techniques. Large Data Visualisations- Introduction to datavisualization, introduction to Tableau, Preparing data in Tableau, Exploratory data analysis, Data visualizations- Graphs and color, Tableau- Dashboards, Parameters and Filters, Power BI.

Harvesting, Storing, and Retrieving Data-Introductionto Big Data, Structured and unstructured data, Data life cycle, introduction to Google Cloud Platform, Exploring Hadoop Ecosystem, Introduction to Linux Operating systems, Creating Tables and Querying in Hive and Spark. Discovery and Learning with Big Data-Python Basics,Data analysis life cycle, Data preprocessing, Exploratory data analysis, Big data analytics and machine learning

-overview, supervised, unsupervised, Evaluating Algorithms, NumPy, Pandas, Scikit-Learn in Python. Deep learning with big data-Google cloud platform,set up Deep Learning Virtual Machine in GCP, connect and explore remote VM using SSH, Linear Algebra for deep learning, Tensorflow and Tensorflow coding in Jupiter, Keras, Multilayer Perceptrons(MLP’S), Build, train and test CNN and MNIST dataset, AI deep learning

-Recurrent Neural Networks, Generative Adversarial Networks. Information and cybersecurity-Introduction to cyber-security and Asset Security, security management, Risk Management, Security Architecture, Security Engineering, Identity and Access Management, and Security in software development.

Information Systems for Healthcare Management- governmentpolicy and healthcare reform, HIT governance, HIT architecture, and infrastructure, HIT project portfolio management, and HIT value analysis. Analysis Capstone Experience-Design and sampling methodologies, Linear regression models, data analysis techniques and data collection process, and program applications for performing data analysis, applying concepts learned in advanced data analytics courses for generating proposed solutions to a real case study.

Technical Skills

● Cloud Technologies: snowflake, AWS.

● Programming Language: Python.PL/SQL, SQL.

● Data Warehousing: Snowflake, Teradata.

● Databases: MySQL, SQL Server, Oracle, MS Access, Teradata, and Snowflake.

● No SQL Database: MongoDB, DynamicDB.

● Development Tools: SQL, AWS.

● Operating systems: Unix, Linux, Windows 10, Mac OS.

● Visualization & ETL Tools: Tableau, Power BI, Informatica, Teradata, and Snowflake.

● Big data technologies: Hadoop, spark, yarn, Hive, Apache Flume. Certifications-DATACAMP

Certification in machine learning with Carter in R. Certification in exploratory data analysis.

Certification in introduction to R.

Certification in building web applications with Shiny in R. Certifications -Coursera

Certification on foundations: Data, Data everywhere. The wonderful world of data.

Set up your data analytics toolbox.

Introduction to Microsoft Excel.

Declaration.

I here by declare that all the particulars stated above are true to the best of my knowledge and belief.

Contact this candidate