Data scientists, data engineers, and data businesspeople are critical to leveraging data in any organization. A common complaint from data science managers is that data scientists invest time prototyping algorithms, and throw them over a proverbial fence to engineers to implement, only to find the algorithms must be rebuilt from scratch to scale. This is a symptom of a broader ailment -- that data teams are often designed as functional silos without proper communication and planning.
This talk outlines a framework to build and organize a data team that produces better results, minimizes wasted effort among team members, and ships great data products.
Here's the deck we used for our Series-B round. We raised $150M 6 months after our Series-A and 8 months prior our Seed. It was led by Altimeter and Coatue.
Even though we didn't necessarily show the appendix slides, we sent them along with the rest of the deck.
See https://airbyte.com
This document summarizes Mint, a personal finance management tool. It allows users to track spending, savings goals, and net worth. Mint makes money through referral fees when users switch financial products based on Mint's recommendations. The document outlines Mint's market size and opportunity, competitors, value proposition, user and partner acquisition strategies, business model, and projected financials. It expects rapid user and revenue growth as it acquires users through viral and partnership channels and converts them through intelligent suggestions.
The document outlines Square's business proposition of providing a simple and low-cost way for merchants to accept credit card payments using a mobile device. Key points include:
- Square charges merchants a flat 2.75% fee per transaction with no setup costs or monthly fees.
- The company has experienced rapid growth, processing over $1 million in payments per day.
- Square targets small businesses and individuals by offering a simple interface and device that plugs into smartphones.
- Backed by experienced founders and investors, Square aims to become the dominant platform in mobile payments by acquiring new merchants through wide exposure and competitive pricing.
ChatGPT and not only: how can you use the power of Generative AI at scaleMaxim Salnikov
This document discusses Microsoft's Azure OpenAI Service and how it can be used to build applications using large language models. Some key points:
- Azure OpenAI Service provides access to models from OpenAI like GPT-3 through Microsoft's Azure cloud platform while ensuring security, privacy and responsible AI.
- It allows generating complex documents, steering models with nuanced instructions, and customizing models for any language or dialect.
- Example capabilities include content generation, summarization, code generation, and semantic search. These can be applied to use cases like call center analytics, software documentation, and marketing content creation.
- Tools are discussed for developing applications using prompt engineering, grounding models with domain-specific
Solve for X with AI: a VC view of the Machine Learning & AI landscapeEd Fernandez
What you'll get from this deck
1. The M&A race for AI: by the numbers
2. Watch out! hype ahead: definitions & disclaimers
3. Machine Learning drivers: why is Machine Learning a ‘thing’ now (vs before)
4. Venture Capital: forming an industry, the AI/ML landscape
5. The One Hundred (+13) AI startups to watch in the Enterprise
6. The great Enterprise pivot: applying Machine Learning at scale
7. - where to go next -
Here's the deck we used for our Seed round. We raised $5M led by Accel.
Even though we didn't necessarily show the appendix slides, we sent them along with the rest of the deck.
See https://airbyte.io
The document discusses how generative AI can be used to scale content operations by reducing the time it takes to generate content. It explains that generative AI learns from natural language models and can generate new text or ideas based on prompts provided by users. While generative AI has benefits like speeding up content creation and ideation, it also has limitations such as not being able to conduct original research or ensure quality. The document provides examples of how generative AI can be used for tasks like generating ideas, simplifying complex text, creating visuals, and more. It also discusses challenges like bias in AI models and the low risk of plagiarism.
Unlocking the Power of ChatGPT and AI in Testing - NextSteps, presented by Ap...Applitools
The document discusses AI tools for software testing such as ChatGPT, Github Copilot, and Applitools Visual AI. It provides an overview of each tool and how they can help with testing tasks like test automation, debugging, and handling dynamic content. The document also covers potential challenges with AI like data privacy issues and tools having superficial knowledge. It emphasizes that AI should be used as an assistance to humans rather than replacing them and that finding the right balance and application of tools is important.
An expert in prompt engineering provides guidelines on designing effective prompts for natural language models. The document discusses prompt engineering principles, what makes a good prompt, and various prompt frameworks including priming, focused prompts, and practical everyday prompts. Effective prompts are clear, concise, unambiguous, and provide the necessary context and task to generate a desired response from a model. Iteration and adapting the prompt based on the response is important.
Our AI connects enterprises and their customers over messaging apps to reduce costs by 70% per interaction. It has reached $2 million in annual recurring revenue after 6 months and expects $36 million in pipeline deals. The AI was created by a team with experience at companies like PayPal, Slack, and others to provide customizable chatbot solutions for enterprises.
The Future Of Work & The Work Of The FutureArturo Pelayo
What Happens When Robots And Machines Learn On Their Own?
This slide deck is an introduction to exponential technologies for an audience of designers and developers of workforce training materials.
The Blended Learning And Technologies Forum (BLAT Forum) is a quarterly event in Auckland, New Zealand that welcomes practitioners, designers and developers of blended learning instructional deliverables across different industries of the New Zealand economy.
Pendo is a Raleigh, NC-based company founded in 2013 that provides an integrated platform for capturing user behavior data, providing product analytics, and delivering personalized in-app guidance. The platform helps various teams across organizations like customer success, marketing, engineering, and product management. Some key customers highlighted in the presentation include Infor, Sprinklr, and Henry Schein. Pendo is targeting continued growth in annual recurring revenue and moving further upmarket towards larger enterprise customers. The company is seeking a $15 million Series B funding round in Q1 of fiscal year 2018.
Unlocking the Power of Generative AI An Executive's Guide.pdfPremNaraindas1
Generative AI is here, and it can revolutionize your business. With its powerful capabilities, this technology can help companies create more efficient processes, unlock new insights from data, and drive innovation. But how do you make the most of these opportunities?
This guide will provide you with the information and resources needed to understand the ins and outs of Generative AI, so you can make informed decisions and capitalize on the potential. It covers important topics such as strategies for leveraging large language models, optimizing MLOps processes, and best practices for building with Generative AI.
This document discusses AI and ChatGPT. It begins with an introduction to David Cieslak and his company RKL eSolutions, which provides ERP sales and consulting. It then provides definitions for key AI concepts like artificial intelligence, generative AI, large language models, and ChatGPT. The document discusses OpenAI's ChatGPT tool and how it works. It covers prompts, commands, and potential uses and impacts of generative AI technologies. Finally, it discusses concerns regarding generative AI and the future of life institute's call for more oversight of advanced AI.
What is ChatGPT and how can we use it? This is a talk given at Affiliate Summit West -- January 2023 to explain what ChatGPT is and isn't and how we can use it in Search.
All images were created using Dall-e.
This document is a presentation about generative AI and Microsoft's ChatGPT, Copilot, and other AI tools. It discusses real-life scenarios where generative AI can be applied, such as communications, note-taking, coding, and more. It also covers Microsoft's Copilot tools for various applications like Dynamics 365, Power Platform, GitHub, and Microsoft 365. The presentation provides examples and screenshots of these tools and discusses next steps for getting started with generative AI.
Highlights and summary of long-running programmatic research on data science; practices, roles, tools, skills, organization models, workflow, outlook, etc. Profiles and persona definition for data scientist model. Landscape of org models for data science and drivers for capability planning. Secondary research materials.
Which institute is best for data science?DIGITALSAI1
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A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
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A comprehensive up-to-date Data Science course that includes all the essential topics of the Data Science domain, presented in a well-thought-out structure.
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Grasp the key fundamentals of data science, coding, and machine learning. Develop mastery over essential analytic tools like R, Python, SQL, and more.
Comprehend the crucial steps required to solve real-world data problems and get familiar with the methodology to think and work like a Data Scientist.
Learn to collect, clean, and analyze big data with R. Understand how to employ appropriate modeling and methods of analytics to extract meaningful data for decision making.
Implement clustering methodology, an unsupervised learning method, and a deep neural network (a supervised learning method).
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The world has witnessed explosive digital growth in the last two decades, which has led to a data deluge. This data may be
holding some key business insights or solutions to crucial problems. Data Science is the key that unlocks this possibility
to extract vital insights from the raw digital data. These findings can then be visualized, and communicated to the
decision-makers to be acted upon.Online Data Science Training is the best choice for the students to begin a new life. We
provide Data Science Training and Placement for the students .
Similar to Bridging the Gap Between Data Science & Engineer: Building High-Performance Teams (20)
DESIGN AND DEVELOPMENT OF AUTO OXYGEN CONCENTRATOR WITH SOS ALERT FOR HIKING ...JeevanKp7
Long-term oxygen therapy (LTOT) and novel techniques of evaluating treatment efficacy have enhanced the quality of life and decreased healthcare expenses for COPD patients.
The cost of a pulmonary blood gas test is comparable to the cost of two days of oxygen therapy and the cost of a hospital stay is equivalent to the cost of one month of oxygen therapy, long-term oxygen therapy (LTOT) is a cost-effective technique of treating this disease.
A small number of clinical investigations on LTOT have shown that it improves the quality of life of COPD patients by reducing the loss of their respiratory capacity. A study of 8487 Danish patients found that LTOT for 1524 hours per day extended life expectancy from 1.07 to 1.40 years.
Combined supervised and unsupervised neural networks for pulse shape discrimi...Samuel Jackson
Our methodology for pulse shape discrimination is split into two steps. Firstly, we learn a model to discriminate between pulses using "clean" low-rate examples by removing pile-up & saturated events. In addition to traditional tail sum discrimination, we investigate three different choices for discrimination between γ-pulses, fast, thermal neutrons. We consider clustering the pulses directly using Gaussian Mixture Modelling (GMM), using variational autoencoders to learn a representation of the pulses and then clustering the learned representation (VAE+GMM) and using density ratio estimation to discriminate between a mixed (γ + neutron) and pure (γ only) sources using a multi-layer perceptron (MLP) as a supervised learning problem.
Secondly, we aim to classify and recover pile-up events in the < 150 ns regime by training a single unified multi-label MLP. To frame the problem as a multi-label supervised learning method, we first simulate pile-up events with known components. Then, using the simulated data and combining it with single event data, we train a final multi-label MLP to output a binary code indicating both how many and which type of events are present within an event window.
Towards an Analysis-Ready, Cloud-Optimised service for FAIR fusion dataSamuel Jackson
We present our work to improve data accessibility and performance for data-intensive tasks within the fusion research community. Our primary goal is to develop services that facilitate efficient access for data-intensive applications while ensuring compliance with FAIR principles [1], as well as adoption of interoperable tools, methods and standards.
The major outcome of our work is the successful creation and deployment of a data service for the MAST (Mega Ampere Spherical Tokamak) experiment [2], leading to substantial enhancements in data discoverability, accessibility, and overall data retrieval performance, particularly in scenarios involving large-scale data access. Our work follows the principles of Analysis-Ready, Cloud Optimised (ARCO) data [3] by using cloud optimised data formats for fusion data.
Our system consists of a query-able metadata catalogue, complemented with an object storage system for publicly serving data from the MAST experiment. We will show how our solution integrates with the Pandata stack [4] to enable data analysis and processing at scales that would have previously been intractable, paving the way for data-intensive workflows running routinely with minimal pre-processing on the part of the researcher. By using a cloud-optimised file format such as zarr [5] we can enable interactive data analysis and visualisation while avoiding large data transfers. Our solution integrates with common python data analysis libraries for large, complex scientific data such as xarray [6] for complex data structures and dask [7] for parallel computation and lazily working with larger that memory datasets.
The incorporation of these technologies is vital for advancing simulation, design, and enabling emerging technologies like machine learning and foundation models, all of which rely on efficient access to extensive repositories of high-quality data. Relying on the FAIR guiding principles for data stewardship not only enhances data findability, accessibility, and reusability, but also fosters international cooperation on the interoperability of data and tools, driving fusion research into new realms and ensuring its relevance in an era characterised by advanced technologies in data science.
[1] Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016) https://doi.org/10.1038/sdata.2016.18
[2] M Cox, The Mega Amp Spherical Tokamak, Fusion Engineering and Design, Volume 46, Issues 2–4, 1999, Pages 397-404, ISSN 0920-3796, https://doi.org/10.1016/S0920-3796(99)00031-9
[3] Stern, Charles, et al. "Pangeo forge: crowdsourcing analysis-ready, cloud optimized data production." Frontiers in Climate 3 (2022): 782909.
[4] Bednar, James A., and Martin Durant. "The Pandata Scalable Open-Source Analysis Stack." (2023).
[5] Alistair Miles (2024) ‘zarr-developers/zarr-python: v2.17.1’. Zenodo. doi: 10.5281/zenodo.10790679
[6] Hoyer, S. & Hamman, J., (20
The Rise of Python in Finance,Automating Trading Strategies: _.pdfRiya Sen
In the dynamic realm of finance, where every second counts, the integration of technology has become indispensable. Aspiring traders and seasoned investors alike are turning to coding as a powerful tool to unlock new avenues of financial success. In this blog, we delve into the world of Python live trading strategies, exploring how coding can be the key to navigating the complexities of the market and securing your path to prosperity.
Introduction to Data Science
1.1 What is Data Science, importance of data science,
1.2 Big data and data Science, the current Scenario,
1.3 Industry Perspective Types of Data: Structured vs. Unstructured Data,
1.4 Quantitative vs. Categorical Data,
1.5 Big Data vs. Little Data, Data science process
1.6 Role of Data Scientist
How AI is Revolutionizing Data Collection.pdfPromptCloud
Artificial Intelligence (AI) is transforming the landscape of data collection, making it more efficient, accurate, and insightful than ever before. With AI, businesses can automate the extraction of vast amounts of data from diverse sources, analyze patterns in real-time, and gain deeper insights with minimal human intervention. This revolution in data collection enables companies to make faster, data-driven decisions, enhance their competitive edge, and unlock new opportunities for growth.
AI-powered tools can handle complex and dynamic web content, adapt to changes in website structures, and even understand the context of data through natural language processing. This means that data collection is not only faster but also more precise, reducing the time and effort required for manual data extraction. Furthermore, AI can process unstructured data, such as social media posts and customer reviews, providing valuable insights into customer sentiment and market trends.
Embrace the future of data collection with AI and stay ahead of the curve. Learn more about how PromptCloud’s AI-driven web scraping solutions can transform your data strategy. https://www.promptcloud.com/contact/
Overview of Statistical software such as ODK, surveyCTO,and CSPro
2. Software installation(for computer, and tablet or mobile devices)
3. Create a data entry application
4. Create the data dictionary
5. Create the data entry forms
6. Enter data
7. Add Edits to the Data Entry Application
8. CAPI questions and texts
7. Which leads to job requirements like this…
• MSc/PhD in Computer Science, Electrical Engineering, Math or Statistics
• At least 5 years of experience in solving real-world practical problems using Machine Learning
• At least 5 years of experience on mining and modeling large-scale data (hundreds of terabytes)
• Extensive in-depth knowledge of Data Mining, Machine Learning, Algorithms
• Knowledge of at least one high-level programming language (C++, Java)
• Knowledge of at least one scripting language (Perl, Python, Ruby)
• Knowledge of SQL and experience with large relational databases
• Knowledge of at least one ML toolset (R, Weka, KNIME, Octave, Mahout, scikit-learn)
• Strong ability to formalize and provide practical solutions to research problems
• Strong communication skills and ability to work independently to get an idea from inception to
implementation.
• Knowledge of the state of the art in at least one of Bayesian Optimization, Recommendation
Systems, Social Network Analysis, Information Retrieval
• At least 5 years of experience with storing, sampling, querying large-scale data (hundreds of
terabytes) and experimentation frameworks
• At least 5 years of experience with Hadoop, Spark, Mahout or Giraph
13. Machine Learning,
Statistics, Domain Knowledge
Softw
are
Engineering
Business
Acum
en
Distributed
Com
puting
Com
m
unication
Look for T-shaped people
14. • Compose teams of individuals who
have overlapping skill-sets and
deep expertise in one area
(machine learning, statistics,
engineering, business, etc.)
• The overlap allows them to speak
the same language and work
collaboratively on solving problems
15. How do I structure my data science team within
my organization?
17. Centralized
Data Scientists sit on a team that
acts as internal consultants, fielding
and answering questions from
multiple teams within the
organization, defining tools for the
organization, and acting as highly
powered consultants.
18. Embedded
• Data Scientists are almost wholly
embedded within one particular team
and focus on solving problems for that
team.
• Teams are assigned to one particular
product or function within the company
and define and answer questions for
that product or function.
19. Hub & Spoke
• The data science team sits
together physically and works
collaboratively to solve problems.
• However, each data scientist (or
a combination of them) gets
deployed to work on problems
within the organization.
• Tends to apply to companies
who have a lot of users.
23. Data Science Software Engineering
Python R Java/C++ RoR/Javascript
modeling & prototyping production
24. Data scientists learn
to write prototypes
in production
languages
Engineers learn the
basics of data
science so they can
understand how
the models work
Goal is to have both teams speak
the same language and engender
trust through communication
25. Data Science Data Engineering
Common Core
Data Science
Curriculum
Data Engineering
Curriculum
Data Science Data Engineering
Projects
27. • Don’t look for unicorns, build collaborative
teams of T-shaped people
• Pay attention to how your data science team is
structured within your organization
• Get your data science and engineering teams to
speak the same language, allowing them to build
trust and work collaboratively
Summary
28. We believe an opportunity belongs
to anyone with aptitude and ambition.
29. 29Galvanize 2015
NODES ON THE NETWORK
COLORADO (BOULDER, DENVER, FORT COLLINS)
SEATTLE, WA
SAN FRANCISCO, CA
AUSTIN, TX (OPENING Q1 2016)
Programs: Full Stack Immersive, Data Science Immersive,
Entrepreneurship
Programs: Full Stack Immersive, Data Science Immersive,
Entrepreneurship
Programs: Full Stack Immersive, Data Science Immersive, Data
Engineering Immersive, Masters of Science in Data Science,
Entrepreneurship
Programs: Full Stack Immersive, Data Science Immersive,
Entrepreneurship
[Explanation Text]
30. 30Galvanize 2015
PLACEMENT STATS
FULL STACK IMMERSIVE DATA SCIENCE IMMERSIVE
$43K $77KPre-program Salary
Average Starting Salary
97% Placement
Rate*
*Galvanize is a founder member of NESTA (New Economy Skills Training Association), a trade organization founded to regulate the new “bootcamp” market.
This place rate is more rigorous than that requested by state licensure agencies. The placement rate is calculated 6 months after graduation.
$72K $114KPre-program Salary
94%Placement
Rate*
Average Starting Salary
31. 31Galvanize 2015
5 PROGRAMS
• Full Stack Immersive
• Data Science Immersive
• Data Engineering Immersive
Project over 500 Student Member Graduates in 2015
Currently over 1500 Members
• Master of Science in Data Science
(University of New Haven)
• Startup Membership
32. 32Galvanize 2015
FULL STACK IMMERSIVE
• 97% Placement Rate
within 6 months
• $77K Average Starting Salary
• 6 Month Program