Actively looking for paid possibly remote Data Science, Data Analytics, Machine Learning internships, Startups.
Junior year undergraduate at IIIT, BBSR, Head at Programming Society at IIIT-Bh.
● 4 years of experience in software development with Java, React, GraphQL, Python, and Javascript.
● Experienced in developing scalable microservices using spring boot(RESTful, SOAP).
● Experienced in developing custom front ends and SPA's using ReactJS, Redux, and NodeJS.
● Experience in Unix/Linux platform and relational database (SQL, MySQL).
● 1 year of experience in contributing to architecture, design, and scaling of distributed systems.
● Skilled in OOPs, Data Structures, and Algorithms.
● 2 Years of Full Stack Development experience.
● Experienced in creating Technical Documentation, and test automation (Cypress, python).
● Good understanding of Software Development life cycle.
● Quick learner and excited about Machine Learning and Natural language processing.
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.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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.
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.”
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
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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.
1. Nabanita Dash
GitHub:Naba7 LinkedIn:nabanita07 Kaggle:chad7
C/169, Sector-1
Rourkela, India-769008
(+91) 8249665459
dashnabanita@gmail.com
EXPERIENCE
Rourkela Steel Plant (Steel Authority of India Limited),
Rourkela, India — Stocks control intern
MAY 2019 - JUNE 2019
● Developeda model using single-directional LSTMs to predict the
stock-price in 1 month and achieved72.6% accuracy.
● Collaboratively worked in a team to train, evaluate model.
NALCO Damanjodi Private Limited, Damanjodi, India —
Web-developer intern
JUNE 2018 - JULY 2018
● Learnt VB,.NETand improvedNALCO's website by adding more
linked pages and Updates section in 2 weeks.
● Collaboratively worked in a team on developingwebsite for easy
and accessible handling of information for all.
PROJECTS
Sketching images using RNNs
● DevelopedEncoder-Decoder model to learn from sketch strokes,
redraw images using Python, Tensorflow with accuracy 82.6%.
● Low quality sketch-strokes, blunt-edges were major challenges.
Style transfer
● Developeda model that can create new images by mixing
abstractions and outlines of one image and colour and styling of
another image with better optimization techniques using
Python,Pytorch framework, which achievedan accuracy of 85%.
MNIST Autoencoder
● DevelopedSimple Autoencoder, Sparsity Autoencoder model that
learns from modified MNIST and reproduces digit images using
Python, Keras framework, achievedan accuracy of 97%.
Twitter Sentiment analysis using RNNs
● Developeda model that scrapes data from Twitter and predicts
the emotional belief of users using bag-of-words, LSTM
techniques in Python which achievedan accuracy of 96.2%.
OPEN SOURCE CONTRIBUTIONS
Bash Autocomplete script for iterative/ DVC
● Createda bash autocomplete script for open-source DVC.
● Developing Markdown documentation usingdillinger.io for
DVC.org which explains data versioning like Git version control.
Code-base blog for yellowbrick, NumFocus
● Implementedregression model usingyellowbrick with accuracy
of 89.5% which got accolades from NumFocus and yellowbrick.
SKILLS
C++/C, Python,
Tensorflow, Pytorch, Keras,
Linux, Git, Shell Scripting
(Advanced)
Java, Ruby, Julia,
Bash, Statistics, Linear
Algebra, SQL, Tableau,
ARIMA,
BigQuery,(Intermediate)
EDUCATION
- International Institute
of Information
Technology,
Bhubaneswar, India —
Bachelor of Technology in
Computer Science and
Engineering
AUGUST 2017 - PRESENT
CGPA : 7.6
- Ispat English Medium
School, Rourkela, India
— Senior Secondary, ISC
JULY 2015 - JUNE 2017
PERCENTAGE : 89.6%
- Ispat English Medium
School, Rourkela, India
— Secondary, ICSE
JULY 2005 - JUNE 2015
PERCENTAGE : 97.6%
AWARDS
- Head of Programming
Society, IIIT, BBS, India
- Placed in Top 12 in DVC and
Top 15in DVC.org.
- Received scholarship from
Facebook-Udacity on Secure
and Private AI.
2. ● Createda code-base blog describing and implementing
yellowbrick in the regression model which detects crime in U.S.
- Member of ACM India and
ACM India Bhubaneswar.
- Secured Rank 12 Regional
Mathematics Olympiad.