Data scientists and IT push the limits of what's possible -- whether that's operating more efficiently, taking advantage of new opportunities, or innovating. Here are 5 ways businesses can boost their effectiveness.
For more: http://blog.tyronesystems.com/
Big data. Small data. All data. You have access to an ever-expanding volume of data inside the walls of your business and out across the web. The potential in data is endless – from predicting election results to preventing the spread of epidemics. But how can you use it to your advantage to help move your business forward?
Data is growing exponentially and it’s now possible to mine and unlock insights from data in new and unexpected ways. Empower your business to take advantage of this data by harnessing the rich capabilities of Microsoft SQL Server and the familiarity of Microsoft Office to help organize, analyze, and make sense of your data—no matter the size.
What do you need to succeed in working with Big Data? RedMonk analyst Donnie Berkholz will present quantitative research on the state of the field, covering the breadth of languages, tools, and infrastructure, to show you which choices to make today and which ones you'll need to get ready for, soon.
Idiots guide to setting up a data science teamAshish Bansal
Some nuggets of how I started the data science practice at Gale Partners on a budget. Presented at the Toronto Hadoop Users Group (THUG) in April, 2015.
How can we nurture these uniquely human traits in workplace culture to create a high-performing organization?
In this webinar, you’ll walk away with:
- Why our ability to collaborate, envision different futures, and tell stories allow us to excel
- The four key human traits that make us poised to embrace technology and not fear it
- Real-world case examples of high-performing organizations that are embracing human characteristics to achieve better business results
DISUMMIT - Rishi Nalin Kumar from DatakindDigitYser
Rishi Nalin Kumar
Chief Scientist at eBench
Half professional, half collaborator, one quarter mathematician. Currently at eBench helping brands understand their consumers and win with their content. Previously leading data science & analytics in large-corporate consumer goods with a light touch of news & media. Proud volunteer at DataKind and a regular on the data & analytics speaker circuit.
Join our #DataTalk on Thursdays at 5 p.m. ET. This week, we tweeted with Dr. Michael Wu, the Chief Scientist at Lithium, where he applies data-driven methodologies to investigate the complex dynamics of the social web.
Michael works with big data and has developed many predictive and prescriptive social analytics with actionable insights. His R&D won him the recognition as a 2010 Influential Leader by CRM Magazine.
You can see all tweets and resources here:
http://www.experian.com/blogs/news/about/data-scientists/
The big data landscape for most enterprises is a vast wilderness. It is a growing and complex ecosystem of different data types from multiple sources, including new data from social media and raw data collected from sources like sensors. Only after effectively exploring and navigating this terrain can businesses begin to mine and refine their data resources to extract value—using trusted information to pave the roads to new insights and smarter decision making.
For more information: www.ibm.com/bigdata
Art by Angela Tuminello
The big data landscape for most enterprises is a vast wilderness. It is a growing and complex ecosystem of different data types from multiple sources, including new data from social media and raw data collected from sources like sensors. Only after effectively exploring and navigating this terrain can businesses begin to mine and refine their data resources to extract value—using trusted information to pave the roads to new insights and smarter decision making.
For more information: www.ibm.com/bigdata
Art by Angela Tuminello
The presentation talks about "Data Science being the sexiest job of the 21st century". What are the challenges faced by the industry and how to Overcome them, is the main theme of the presentation
Data scientists and IT push the limits of what's possible -- whether that's operating more efficiently, taking advantage of new opportunities, or innovating. Here are 5 ways businesses can boost their effectiveness.
For more: http://blog.tyronesystems.com/
Big data. Small data. All data. You have access to an ever-expanding volume of data inside the walls of your business and out across the web. The potential in data is endless – from predicting election results to preventing the spread of epidemics. But how can you use it to your advantage to help move your business forward?
Data is growing exponentially and it’s now possible to mine and unlock insights from data in new and unexpected ways. Empower your business to take advantage of this data by harnessing the rich capabilities of Microsoft SQL Server and the familiarity of Microsoft Office to help organize, analyze, and make sense of your data—no matter the size.
What do you need to succeed in working with Big Data? RedMonk analyst Donnie Berkholz will present quantitative research on the state of the field, covering the breadth of languages, tools, and infrastructure, to show you which choices to make today and which ones you'll need to get ready for, soon.
Idiots guide to setting up a data science teamAshish Bansal
Some nuggets of how I started the data science practice at Gale Partners on a budget. Presented at the Toronto Hadoop Users Group (THUG) in April, 2015.
How can we nurture these uniquely human traits in workplace culture to create a high-performing organization?
In this webinar, you’ll walk away with:
- Why our ability to collaborate, envision different futures, and tell stories allow us to excel
- The four key human traits that make us poised to embrace technology and not fear it
- Real-world case examples of high-performing organizations that are embracing human characteristics to achieve better business results
DISUMMIT - Rishi Nalin Kumar from DatakindDigitYser
Rishi Nalin Kumar
Chief Scientist at eBench
Half professional, half collaborator, one quarter mathematician. Currently at eBench helping brands understand their consumers and win with their content. Previously leading data science & analytics in large-corporate consumer goods with a light touch of news & media. Proud volunteer at DataKind and a regular on the data & analytics speaker circuit.
Join our #DataTalk on Thursdays at 5 p.m. ET. This week, we tweeted with Dr. Michael Wu, the Chief Scientist at Lithium, where he applies data-driven methodologies to investigate the complex dynamics of the social web.
Michael works with big data and has developed many predictive and prescriptive social analytics with actionable insights. His R&D won him the recognition as a 2010 Influential Leader by CRM Magazine.
You can see all tweets and resources here:
http://www.experian.com/blogs/news/about/data-scientists/
The big data landscape for most enterprises is a vast wilderness. It is a growing and complex ecosystem of different data types from multiple sources, including new data from social media and raw data collected from sources like sensors. Only after effectively exploring and navigating this terrain can businesses begin to mine and refine their data resources to extract value—using trusted information to pave the roads to new insights and smarter decision making.
For more information: www.ibm.com/bigdata
Art by Angela Tuminello
The big data landscape for most enterprises is a vast wilderness. It is a growing and complex ecosystem of different data types from multiple sources, including new data from social media and raw data collected from sources like sensors. Only after effectively exploring and navigating this terrain can businesses begin to mine and refine their data resources to extract value—using trusted information to pave the roads to new insights and smarter decision making.
For more information: www.ibm.com/bigdata
Art by Angela Tuminello
The presentation talks about "Data Science being the sexiest job of the 21st century". What are the challenges faced by the industry and how to Overcome them, is the main theme of the presentation
Data analytics with managerial application ass 2Nishant Kumar
This presentation depicts insights of the article "Data Scientist: The Sexiest Job of the 21st Century", and also how these insight are relevant to a manager in india.
"Unlock your data science potential with Digicrome's comprehensive student Welcome to the Digicrome Student Handbook! This comprehensive guide is designed to provide you with all the information you need to excel in your journey to becoming a data scientist. Let's dive in!
At Digicrome, we offer a top-notch online Data Science course that covers a wide range of essential concepts and tools. From Python programming to advanced Python concepts, from data visualization to statistics, from machine learning to SQL, our course encompasses all the key areas you need to master. Our logical and structured approach makes it easy to comprehend complex topics, ensuring a solid foundation in data science.
Our course features 250 hours of intensive live training, where you'll receive hands-on learning experiences. We believe in practical training, even for students with little or no technical background. You'll work on 25+ projects and 4+ capstone projects, allowing you to apply your knowledge to real-world scenarios. This practical exposure will make you job-ready and equip you with the necessary skills to tackle the challenges of the industry.
We understand the importance of placements, which is why we provide 100% job guarantee to our students. Upon completing our course, you'll have the opportunity to be placed in renowned multinational companies such as Nykaa, Myntra, Cred, Meesho, Razorpay, Wipro, Infosys, TCS, Microsoft, Hungama, PharmEasy, and many more. Our industry connections and partnerships ensure that you have ample opportunities for career growth and development.
To further enhance your learning experience, we offer a 3-month paid internship, allowing you to gain practical experience in a professional setting. You'll also have access to mock interviews conducted by hiring managers, helping you refine your interview skills and boost your confidence.
Our course is backed by seven types of certifications, validating your expertise in various data science domains. These certifications will add significant value to your resume and demonstrate your commitment to professional growth.
To ensure individual attention and guidance, we provide 1:1 sessions with industry mentors. These experienced professionals will guide you through your learning journey, offering personalized support and insights. You'll have the opportunity to learn from their experiences and gain valuable industry insights.
We understand that financial constraints can sometimes hinder educational pursuits. That's why we offer a no-cost EMI option, allowing you to manage your payments conveniently. We also provide discounts of up to 60% off academic fees, making our course more accessible and affordable.
Don't miss the chance to embark on an exciting career as a data scientist. Enroll in the Digicrome Python Data Science course, prepare yourself for the future, and become a professional data scientist. Unlock your potential with Digicrome today!
What does a data scientist actually do? Here at Good Rebels we wanted to outline a profile of this new profession, with the help of various industry leaders from academia, business and institutions. In short, we concluded that the main tasks of a data scientist are to identify data, transform it when incomplete, categorize it, prepare it for analysis, perform the analysis, visualize the results and communicate them.
Data science and the art of persuasionAlex Clapson
The presentation of data science to lay audiences—the last mile—hasn’t evolved as rapidly or as fully as the science’s technical part. It must catch up, and that means rethinking how data science teams are put together, how they’re managed, and who’s involved at every point in the process, from the first data stream to the final chart shown to the board. Until companies can successfully traverse that last mile, data science teams will under deliver. They will provide, in Willard Brinton’s words, foundations without cathedrals.
"Learn the concept of Data Science by uniting Statistics, mathematics, computer science and domain knowledge and information science to extract structured and unstructured data’s.
12 Months Duration | Capstone Projects | 07+ Certifications"
What does it_takes_to_be_a_good_data_scientist_2019_aim_simplilearnPraj H
Over the years, the term ‘data scientist’ has evolved greatly. From describing a person who handles data, to a professional who leverages machine learning — this definition has seen a great deal of change. Now, circa 2019, there are numerous blogs, Reddit pages and Quora threads dedicated to the discussion about “how to become a good data scientist”.
The job of a citizen data scientist is relevant and important; however, a lot of what goes into a successful citizen data scientist project is still unprecedented in the data science community.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
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.
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.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
3. Data scientists are big data wranglers.
They take an enormous mass of messy data
points (unstructured and structured) and use
their formidable skills in math, statistics and
programming to clean, massage and organize
them.
6. If “sexy” means having rare
qualities that are much in
demand, data scientists are
already there.
7. There simply aren’t a lot of people with
their combination of scientific
background and computational and
analytical skills.
They are difficult and expensive to hire.
10. Companies are now wrestling with
information that comes in varieties and
volumes never encountered before.
11. Data Scientists help decision makers shift
from ad hoc analysis to an ongoing
conversation with data.
12. The shortage of Data Scientists is
becoming a serious constraint in some
sectors.
13. Why and How are these insights
relevant to a manager in India?
14. Managers should have faith in the power
of Analytics.
Managers shouldn’t take long to
recognize a good idea and utilise it to its
fullest.
15. The challenge for Managers is to learn
how to identify the talent of Data
Scientists, attract it to an enterprise, and
make it productive.
16. As the story of Jonathan Goldman illustrates,
Data Scientists’ greatest opportunity to add value
is in innovating with customer-facing products
and processes.
Data Scientist should have the freedom to
experiment and explore possibilities.
17. Data scientists want to build things, not
just give advice. One describes being a
consultant as “the dead zone.”