I am looking for opportunities in Data and Business Analytics and have strong Quantitive and Analytical skills through my degree program of Business Analytics along with prior 2 years work experience in Project management.
I am passionate about learning and growing with the organization. I am driven by dedication and hard work and I believe an opportunity would bring me on the fast track of my career growth.I look forward to a new and challenging career with development in skills and deep knowledge towards particular technology I would be working with.
The Hadoop market is currently growing at a faster pace due to the rising demand for quicker access to relevant data for everyday functioning across industry sectors.
I am looking for opportunities in Data and Business Analytics and have strong Quantitive and Analytical skills through my degree program of Business Analytics along with prior 2 years work experience in Project management.
I am passionate about learning and growing with the organization. I am driven by dedication and hard work and I believe an opportunity would bring me on the fast track of my career growth.I look forward to a new and challenging career with development in skills and deep knowledge towards particular technology I would be working with.
The Hadoop market is currently growing at a faster pace due to the rising demand for quicker access to relevant data for everyday functioning across industry sectors.
Data Analyst with over 4 years of experience in business intelligence, data management and analytics. Strong understanding and practical knowledge of technologies such as Java, SQL, Python, Hadoop, Tableau, HTML. Background in leading significant projects and managing technical teams in a fast paced environment.
Solutions-oriented Analysis possessing a unique combination of Analytical skills includes data-science, machine learning, business analysis, big-data technology like Hadoop and application development experience in top-tier Retail organizations.
Solutions-oriented Analysis possessing a unique combination of Analytical skills includes data-science, machine learning, business analysis, big-data technology like Hadoop and application development experience in top-tier Retail organizations.
Data Science enthusiast, performing data analysis and predictive modelling using various machine learning algorithms. Sound knowledge of statistics used to gather insights from data and making data driven decisions.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
1. KOMPELLA KAUSHIK
901, Apt #1002, S. Ashland Ave, Chicago, IL – 60607 (312)-871-8673, kkompe2@uic.edu
SUMMARY
A data science enthusiast who wants to explore uncharted territories of big data
Experience leadinganalyticsprojectsusingandcreating executive level reportsbasedonfindings
SKILL SET
Statistical Software:RapidMiner,Python,R,Hadoop,MapReduce,SASEnterprise Miner
Basic Programming: C, Java, html,SQLServer,MySQL
Data Visualization:Tableau
EDUCATION
Masterof Science inBusinessAnalytics Expected12/2017
Universityof Illinois atChicago–LiautaudGraduate School of Business
Bachelorof TechnologyinComputerScience Engineering,GPA-3.72/4.00 06/2014
Jawaharlal NehruTechnological University–Hyderabad,India
EXPERIENCE
Business& MarketingAnalyst,Co-Founder–Valmore.in –Hyderabad,India 06/2014-12/2014
Createda platformforstudentcommunity togetupdatedaboutvariousoffersand events
Analyzedbusiness requirementsfordevelopingandmarketingthe product
Managed A-Zinthe start-upwithmore stresson customerandmarketinganalytics
AssistantSystemsEngineer–TATA ConsultancyServicesLtd – Hyderabad,India 12/2014-06/2016
Led the automation team of 15 and developed automation scripts for a healthcare project
Prepared Return of Investment which saved 85% efforts for automation execution over manual
Exposed to Software Testing process with various Life cycle models like Agile
ACADEMICPROJECTS
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Movie RecommenderSystem:Ledateamof 3 graduate students todevelopandanalyze a movie
recommender system based on a data set consisting of over 100,000 ratings from around 1000
users and 1700 movies.Evaluated the performance using collaborative filtering algorithms like
Matrix Factorization for predicting ratings. Movies with high ratings were recommended to a
user. Reduced RMSE by 22% compared to the state of the art. Tool used: RapidMiner
Analyses of Airline’s On-Time Performance: Led a team of 8 to effectively perform exploratory
analysis and present a visualization of American Airlines on-time performance metrics &
performance withrespect toothercompetitorsusing Bureauof TransportationStatistics dataset
containing more than 1 million rows & 147 variables for the year 2015-2016. Tool used: Tableau
Target Fund Raising: Developed a classification model for a national veteran’s organization to
improve the cost effectivenessof theirdirectmarketingcampaignand capture potential donors.
Technique - Cleaned the data set containing around 500 attributes and 10,000 data points.
Reduced the dimensions in the data using PCA. Selected final set of variables using Random
Forestsvariable importance techniques.Appliedmachine learningalgorithms likeDecisionTrees,
Logistic Regression, Naïve Bayes, Gradient Boosting, and SVM’s to build the model
LEADERSHIP
ProjectLead – InkindConsulting- RyanBanksAcademy (Non-Profit) - Chicago 09/2016-Current
Vice-President–TCS WaverockToastmastersClub, HyderabadIndia 07/2015-07/2016
HONORS and CERTIFICATIONS
Got selectedandwonthe best“ProductDesignandBusinessModel”presentationatthe
prestigious IndianInstitute ofTechnology,Madras inthe year2013
WorldRank 1 inHackerankPythonchallenge
Certifiedin DataDriven DecisionMaking fromPwC and Coursera
Won the Humorous Speechcontestconductedby Toastmasters Public SpeakingGroupin 2015
National Record Holderfor doingthe largestcube art with8000 Rubik’s Cubes
