Doing data science with clojure

DataCanvas: Big Data Analytic Flow in Cloud

Lei Fang

Data science team (new version)

Omid Mogharian

Machine Learning with GraphLab Create

The 3 Insights Defining Modern Analytics

Webinar - Fraud Detection - Palombo (20160428)

Beyond Predictive Analytics : Deploying apps to production and keep them improving Some smart companies have been putting predictive application in production for decades. Still, either because of lack of sharing or lack of generality, there is still no single and obvious way to put a predictive application in production today. As a consequence, for most companies, transitioning analytics from development to production is still “the next frontier”. Behind the single word "production” lays a great number of questions like: what exactly do you put in production: data, model, code all three ? Who is responsible for maintenance and quality check over time : business, tech or both ? How can I make my predictive app continuously improve and check that it delivers the promised business value over time ? What are the best practice for maintenance and updates by the way ? Will my data scientists keep working after first development or should I lay half of them off ? etc… Let’s make a small analogy with the development of web sites in the 90’s and early 00’s : Back then, the winners where not necessarily the web sites with an amazing design, but a winner had clearly made the necessary efforts and had a robust way to put their web site reliabily in production Today, every web developper can enjoy the confort of Heroku, Amazon, Github, docker, Angular, bootstrap … and so we forget. How much time before we get the same confort for the predictive world ?

Data-driven design (UX Antwerp 24/09/19)

Peter Vermaercke

Semantic Web in Enterprise: an agile startup perspective

Jeen Broekstra

Dataiku productive application to production - pap is may 2015

Dataiku

Watch the recorded webinar at http://bit.ly/1MeX7QK Everywhere you look, companies are using external-facing analytics to maximize the value derived from their data assets, by moving customers up the value chain, increasing stickiness, and offering a more competitive product on the marketplace. Listen to learn about how to bring an external-facing data product to market by embedding BI software, and what that can add to your offering. Presentation covers: -Top uses cases for embedding business intelligence software Case studies from different companies currently embedding BI -Build vs buy considerations -Evaluating ROI

When and Where to Embed Business Intelligence

Join 2017_Deep Dive_Integrating Looker with R and Python

This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/VAW2eDht7JA Bio: Krish Swamy is an experienced professional with deep skills in applying analytics and BigData capabilities to challenging business problems and driving customer insights. Krish's analytic experience includes marketing and pricing, credit risk, digital analytics and most recently, big data analytics and data transformation. His key experiences lie in banking and financial services, the digital customer experience domain, with a background in management consulting. Other key skills include influencing organizational change towards a data and analytics driven culture, and building teams of analytics, statisticians and data scientists. Bio: Balaji Gopalakrishnan has over 15 years experience in the Machine Learning and Data Science space. Balaji has led cross functional data science and engineering teams for developing cutting-edge machine learning and cognitive computing capabilities for insurance fraud and underwriting, telematics, multi-asset class risk, scheduling under uncertainty, and others. He is passionate about driving AI adoption in organizations and strongly believes in the power of cross functional collaboration for this purpose.

Krish Swamy + Balaji Gopalakrishnan, Wells Fargo - Building a World Class Dat...

Sri Ambati

Helping Business Leaders Get Over Their Learning Curve in Advanced Analytics

IDEAS - Int'l Data Engineering and Science Association

Webinar - Know Your Customer - Arya (20160526)

In this session you will build a sentiment analysis solution step by step, using Azure Platform. We will talk about sentiment analysis and how you can get this introduced in your application. We will run live demo and extract data from live twitter feeds and work together in processing the data and performing sentiment analysis on the data. We encourage audience to come with mobile phones to tweet :P but if not, not to worry as we can use the historical data.

Let's analyze how world reacts to road traffic by sentiment analysis final

Sajeetharan

People have been using Excel for 35 years. There are over 750 million Excel users. People are making magic with Excel every day. With the surging interest in big data, advanced analytics, and the cloud, how does Excel stay relevant and how extreme can Excel get? In this presentation, we will examine: o Traditional limits of Excel performance, scale, dataset sizes o Cloud technologies that make Excel better o Defining the new extremes for Excel power users Speaker Bio: Rachel Beddor is a Solutions Engineer for Kyligence where she creates technical content to enhance the learning experience for new Apache Kylin and Kyligence users. She has dedicated her career to making technology more accessible, fun, and inviting to people of all backgrounds.

Extreme Excel: How a 35-Year-Old Desktop App Smashed Through the Big Data Bar...

SamanthaBerlant

Quick iteration and reusability of metric calculations for powerful data exploration. At Looker, we want to make it easier for data analysts to service the needs of the data-hungry users in their organizations. We believe too much of their time is spent responding to ad hoc data requests and not enough time is spent building, experimenting, and embellishing a robust model of the business. Worse yet, business users are starving for data, but are forced to make important decisions without access to data that could guide them in the right direction. Looker addresses both of these problems with a YAML-based modeling language called LookML. This paper walks through a number of data modeling examples, demonstrating how to use LookML to generate, alter, and update reports—without the need to rewrite any SQL. With LookML, you build your business logic, defining your important metrics once and then reusing them throughout a model—allowing quick, rapid iteration of data exploration, while also ensuring the accuracy of the SQL that’s generated. Small updates are quick and can be made immediately available to business users to manipulate, iterate, and transform in any way they see fit.

Data Modeling in Looker

There is certainly a growing demand for incorporating machine learning solutions into various types of business. Yet, the knowledge base is not always keeping up with hype around this subject. Some reports [source: Global CIO Point of View] tell us that 9 out of 10 CIOs plan to use machine learning solutions to achieve certain goals in their companies. Yet only around 20% actually have something in production and only 5% use machine learning extensivelly. Why? There are quite a few reasons, and that’s what this talk is all about. Arturs is a data scientist in Intrum Global Technologies, has experience in developing machine learning solutions ranging from scoring to automated self learning systems.

Yellowbrick MicroStrategy webcast

Yellowbrick Data

“How to Succeed with Machine Learning” by Arturs Valujevs from Intrum Global ...

DevClub_lv

LogiqApps LowCode Norwegian Hackathon Challenge №3

LogiqAppsAS

Mais procurados (20)

Webinar - Patient Readmission Risk

DataCanvas: Big Data Analytic Flow in Cloud

Data science team (new version)

Machine Learning with GraphLab Create

The 3 Insights Defining Modern Analytics

Webinar - Fraud Detection - Palombo (20160428)

Data-driven design (UX Antwerp 24/09/19)

Semantic Web in Enterprise: an agile startup perspective

Dataiku productive application to production - pap is may 2015

When and Where to Embed Business Intelligence

Join 2017_Deep Dive_Integrating Looker with R and Python

Krish Swamy + Balaji Gopalakrishnan, Wells Fargo - Building a World Class Dat...

Helping Business Leaders Get Over Their Learning Curve in Advanced Analytics

Webinar - Know Your Customer - Arya (20160526)

Let's analyze how world reacts to road traffic by sentiment analysis final

Extreme Excel: How a 35-Year-Old Desktop App Smashed Through the Big Data Bar...

Data Modeling in Looker

Yellowbrick MicroStrategy webcast

“How to Succeed with Machine Learning” by Arturs Valujevs from Intrum Global ...

LogiqApps LowCode Norwegian Hackathon Challenge №3

Semelhante a Doing data science with clojure

No doubt Visualization of Data is a key component of our industry. The path data travels since it is created till it takes shape in a chart is sometimes obscure and overlooked as it tends to live in the engineering side (when volume is relevant), an area where Data Scientist tend to visit but not the usual Web/Marketing Data Analyst. Nowadays the options to tame all that journey and make the best of it are many and they don't require extensive engineering knowledge. Small or Big Data, let's see what "Store, Extract, Transform, Load, Visualize" is all about.

Store, Extract, Transform, Load, Visualize. Untagged Conference

Ani Lopez

Lendon mc kinney resume 2019 updated

LendonMcKinney

Leading brands such as Pepsi and Macy’s use Celtra’s technology platform for brand advertising. To inform better product design and resolve issues faster, Celtra relies on Databricks to gather insights from large-scale, diverse, and complex raw event data. Learn how Celtra uses Databricks to simplify their Spark deployment, achieve faster project turnaround time, and empower people to make data-driven decisions. In this webinar, you will learn how Databricks helps Celtra to: - Utilize Apache Spark to power their production analytics pipeline. - Build a “Just-in-Time” data warehouse to analyze diverse data sources such as Elastic Load Balancer access logs, raw tracking events, operational data, and reportable metrics. - Go beyond simple counting and group events into sequences (i.e., sessionization) and perform more complex analysis such as funnel analytics.

How Celtra Optimizes its Advertising Platformwith Databricks

Grega Kespret

Creating a Culture of Data @ Facebook - TCCEU13

Andy Kriebel

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.

Data Culture Series - Keynote & Panel - 19h May - London

Jonathan Woodward

Webinar with SnagAJob, HP Vertica and Looker - Data at the speed of busines s...

Joe Caserta, President at Caserta Concepts addressed the challenges of Business Intelligence in the Big Data world at the Third Annual Great Lakes BI Summit in Detroit, MI on Thursday, March 26. His talk "Architecting for Big Data: Trends, Tips and Deployment Options," focused on how to supplement your data warehousing and business intelligence environments with big data technologies. For more information on this presentation or the services offered by Caserta Concepts, visit our website: http://casertaconcepts.com/.

Mongo db ops mug pres

David Erickson

Architecting for Big Data: Trends, Tips, and Deployment Options

Caserta

Continuum Analytics and Python

Travis Oliphant

Data Engineer's Lunch #85: Designing a Modern Data Stack

Anant Corporation

The Briefing Room with Mark Madsen and IBM Live Webcast on Aug. 27, 2013 Visit: www.insideanalysis.com What's old is often new again, especially in the world of information management. The innovation of OLAP cubes years ago transformed business intelligence by empowering analysts with significantly faster number-crunching capabilities. Today, with data volumes exploding, a new kind of cube is offering similar value, thanks in large part to in-memory analytics. of The Briefing Room to learn from veteran Analyst and practitioner Mark Madsen of Third Nature, who will explain how this new wave of in-memory technology can give analysts a needed boost for dealing with the rising tide of data volumes and types. He'll be briefed by Chris McPherson of IBM Business Analytics, who will tout IBM Cognos Dynamic Cubes, which were specifically designed to let business users maintain the speed and agility they need for their analytical solutions.

Thinking Outside the Cube: How In-Memory Bolsters Analytics

Inside Analysis

The Briefing Room with Dr. Robin Bloor and WhereScape Live Webcast on April 1, 2014 Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=7b23b14b532bd7be60a70f6bd5209f03 In the Big Data shuffle, everyone is looking at Hadoop as “the answer” to collect interesting data from a new set of sources. While Hadoop has given organizations the power to gather more information assets than ever before, the question still looms: which data, regardless of source, structure, volume and all the rest, are significant for affecting business value – and how do we harness it? One effective approach is to bolster the data warehouse environment with a solution capable of integrating all the data sources, including Hadoop, and automating delivery of key information into the rights hands. Register for this episode of The Briefing Room to hear veteran Analyst Robin Bloor as he explains how a rapidly changing information landscape impacts data management. He will be briefed by Mark Budzinski of WhereScape, who will tout his company’s data warehouse automation solutions. Budzinski will discuss how automation can be the cornerstone for closing the gap between those responsible for data management and the people driving business decisions. Visit InsideAnlaysis.com for more information.

The Right Data Warehouse: Automation Now, Business Value Thereafter

Inside Analysis

Partner webinar presentation aws pebble_treasure_data

Treasure Data, Inc.

Moving from BI to AI : For decision makers

zekeLabs Technologies

Pairing your real-time operational data stored in a modern database like MongoDB with first-class business intelligence platforms like Tableau enables new insights to be discovered faster than ever before. Many leading organizations already use MongoDB in conjunction with Tableau including a top American investment bank and the world’s largest airline. With the Connector for BI 2.0, it’s never been easier to streamline the connection process between these two systems. In this webinar, we will create a live connection from Tableau Desktop to a MongoDB cluster using the Connector for BI. Once we have Tableau Desktop and MongoDB connected, we will demonstrate the visual power of Tableau to explore the agile data storage of MongoDB. You’ll walk away knowing: - How to configure MongoDB with Tableau using the updated connector - Best practices for working with documents in a BI environment - How leading companies are using big data visualization strategies to transform their businesses

Webinar: Introducing the MongoDB Connector for BI 2.0 with Tableau

MongoDB

From Business Intelligence to Big Data - hack/reduce Dec 2014

Adam Ferrari

Power BI - 2016 - Public

Julian Payne

An overview of modern scalable web development

Tung Nguyen

Infectious Media runs on data. But, as an ad-tech company that records hundreds of thousands of web events per second, they have have to deal with data at a scale not seen by most companies. You can not make decisions with data when people need to write manual SQL only for queries take 10-20 minutes to return. Infectious Media made the switch to Google BigQuery and Looker and now every member of every team can get the data they need in seconds. Infectious Media shares: - Why they chose their current stack - Why faster data means happier customers - Advantages and practical implications of storing and processing that much data Check out the recording at https://info.looker.com/h/i/308848878-power-to-the-people-a-stack-to-empower-every-user-to-make-data-driven-decisions

Power to the People: A Stack to Empower Every User to Make Data-Driven Decisions

A tenet of the Agile Manifesto is ‘Working software over comprehensive documentation’, and many have interpreted that to mean that data models are not necessary in the agile development environment. Others have seen the value of data models for achieving the other core tenets of ‘Customer Collaboration’ and ‘Responding to Change’. This webinar will discuss how data models are being effectively used in today’s Agile development environment and the benefits that are being achieved from this approach.

Agile & Data Modeling – How Can They Work Together?

DATAVERSITY

Semelhante a Doing data science with clojure (20)

Store, Extract, Transform, Load, Visualize. Untagged Conference

Lendon mc kinney resume 2019 updated

How Celtra Optimizes its Advertising Platformwith Databricks

Creating a Culture of Data @ Facebook - TCCEU13

Data Culture Series - Keynote & Panel - 19h May - London

Webinar with SnagAJob, HP Vertica and Looker - Data at the speed of busines s...

Mongo db ops mug pres

Architecting for Big Data: Trends, Tips, and Deployment Options

Continuum Analytics and Python

Data Engineer's Lunch #85: Designing a Modern Data Stack

Thinking Outside the Cube: How In-Memory Bolsters Analytics

The Right Data Warehouse: Automation Now, Business Value Thereafter

Partner webinar presentation aws pebble_treasure_data

Moving from BI to AI : For decision makers

Webinar: Introducing the MongoDB Connector for BI 2.0 with Tableau

From Business Intelligence to Big Data - hack/reduce Dec 2014

Power BI - 2016 - Public

An overview of modern scalable web development

Power to the People: A Stack to Empower Every User to Make Data-Driven Decisions

Agile & Data Modeling – How Can They Work Together?

Mais de Simon Belak

Recommendation algorithms and their variations such as ranking are the most common way for machine learning to find its way into a product where it is not the main focus. In this talk we’ll dig into the subtleties of making recommendation algorithms a seamless and integral part of your UX (goal: it should completely fade into the background. The user should not be aware she’s interacting with any kind of machine learning, it should just feel right, perhaps smart or even a tad like cheating); how to solve the cold start problem (and having little training data in general); and how to effectively collect feedback data. I’ll be drawing from my experiences building Metabase, an open source analytics/BI tool, where we extensively use recommendations and ranking to keep users in a state of flow when exploring data; to help with discoverability; and as a way to gently teach analysis and visualization best practices; all on the way towards building an AI data scientist.

The subtle art of recommendation

Metabase Ljubljana Meetup #2

Metabase lj meetup

In this talk we will look at how to efficiently (in both space and time) summarize large, potentially unbounded, streams of data by approximating the underlying distribution using so-called sketch algorithms. The main approach we are going to be looking at is summarization via histograms. Histograms have a number of desirable properties: they work well in an on-line setting, are embarrassingly parallel, and are space-bound. Not to mention they capture the entire (empirical) distribution which is something that otherwise often gets lost when doing descriptive statistics. Building from that we will delve into related problems of sampling in a stream setting, and updating in a batch setting; and highlight some cool tricks such as capturing time-dynamics via data snapshotting. To finish off we will touch upon algorithms to summarize categorical data, most notably count-min sketch.

Sketch algorithms

Transducers -- composable algorithmic transformation decoupled from input or output sources -- are Clojure’s take on data transformation. In this talk we will look at what makes a transducer; push their composability to the limit chasing the panacea of building complex single-pass transformations out of reusable components (eg. calculating a bunch of descriptive statistics like sum, sum of squares, mean, variance, ... in a single pass without resorting to a spaghetti ball fold); explore how the fact they are decoupled from input and output traversal opens up some interesting possibilities as they can be made to work in both online and batch settings; all drawing from practical examples of using Clojure to analize “awkward-size” data.

Transducing for fun and profit

You have defined your metrics, setup dashboards, and started to incorporate data into your everyday. Great, but I have some bad news for you. Almost certainly some of you metrics are wrong. At best these mistakes mean that you are not getting all the insights you could have, at worse some of the conclusions you have drawn from them are wrong. In this talk we will go through the most common but pernicious mistakes and unravel the mechanisms behind them so by the end of the talk you will be equipped with an analytical toolset to spot them on your own. The main classes of errors we will cover are: viewing data as a static process; not considering error margins and variance; picking the wrong reference point; assuming your population is homogeneous; and improperly accounting for costs.

Your metrics are wrong

Writing correct smart contract is hard (a recent study estimated that 3% of Ethereum contracts in the wild have some sort of security vulnerability; we all know of the DAO and Parity exploits, …). There are two main reasons for this. First and foremost developing for the blockchain is quite different than what most programmers are used to. The level of concurrency is far beyond our (von Neumann) intuition and mental models. And you can’t stop and inspect running code like you can in other systems. Taken together blockchain is closer to a physical/living system than conventional software — a fact not reflected in the tools available. Compared to other domains our tooling and programming languages are somewhere between rudimentary and bad; and a far cry from where they would need to be to augment developers and help make programming for the blockchain less alien and less error prone. In this talk we will first unpack what makes programming for the blockchain hard, and what are the most common types of vulnerabilities and their causes. Then we will look at the state of art programming language research in correctness proving and programming massively concurrent systems; and how these can be applied to programming smart contracts; revisit some technologies from the past that didn’t get traction at the time, but are nevertheless worth studying; and finishing off by trying to imagine how programming for the blockchain should, and perhaps one day will, look like.

Writing smart contracts the sane way

Online statistical analysis using transducers and sketch algorithms

OpenAI recently published a fun paper where they showed using evolution algorithms to train policy networks to perform on par with state of the art reinforcement deep learning. In this talk we’ll try to reimplement the main ideas in that paper using Neanderthal (blazing fast matrix and linear algebra computations) and Cortex (neural networks); make it massively distributed using Onyx; build a simulation environment using re-frame; and of course save our princess from no particular harm in our toy game example

Save the princess

Data driven going to market strategy

You can do cool and unexpected things if your entire type system is a first class citizen and accessible at runtime. With the introduction of spec, Clojure got its own distinct spin on a type system. Just as macros add another -time (runtime and compile time) where the full power of the language can be used, spec does to describing data. The result is an entire additional type system that is a first class citizen and accessible at runtime that facilitates validation, generative testing (a la QuickCheck), destructuring (pattern matching into deeply nested data), data macros (recursive transformations of data) and a pluginable error system. And then you can start building on top of it. The talk will be half introduction to spec and the ideas packed within it, and half experience report instrumenting 15k loc production codebase (primarily ETL and analytics) with spec.

Spec: a lisp-flavoured type system

Clojure has always been good at manipulating data. With the release of spec and Onyx (“a masterless, cloud scale, fault tolerant, high performance distributed computation system”) good became best. In this talk you will learn about a streaming data layer architecture build around Kafka and Onyx that is self-describing, declarative, scalable and convenient to work with for the end user. The focus will be on the power and elegance of describing data and computation with data; the inferences and automations that can be built on top of that; and how and why Clojure is a natural choice for tasks that involve a lot of data manipulation, touching both on functional programming and lisp-specifics such as code-is-data. We will look at how such an approach can be used to manage a data warehouse by automatically inferring materialized views from raw incoming data or other views based on a combination of heuristics, statistical analysis (seasonality, outlier removal, ...) and predefined ontologies. Doing so is a practical way to maintain a large number of views, increasing their availability and abstracting the complexity into declarative rules, rather than having an ETL pipeline with dozens or even hundreds of hand crafted tasks. The system described requires relatively little effort upfront but can easily grow with one's needs both in terms of scale as well as scope. With its good introspection capabilities and strong decoupling it is for instance an excellent substrate for putting machine learning algorithms in production, which is the final use-case we will dive into.

A data layer in clojure

Segmentacija je ključna za učinkovito nagovarjanje in konvertiranje potancialnih strank. Simon Belak, vodja analitike pri GoOptiju in transmedijski urednik pri kritičnem časopisu Tribuna, je razkril, kako odkrivati segmente iz podatkov. Po njegovih besedah je povsem neupravičeno, da je segmentacija povečini statična in narejena na slepo, neupoštevajoč podatke. V predavanju je predstavil aletrnativo: analitično delno avtomatično odkrivanje segmentov iz podatkov. Na konkretnih primerih je pokazal, kako preslikati podatke o interakcijah s strankami (obisk strani kot pokazatelji interesov, odgovori na ankete, vzorci premikanja po straneh, odpiranje emailov…) v model strank in nadaljeval z razdelitvijo v segmente. Simon je za konec izpostavil najpogostejše pasti in drobne trike za primere, ko imamo malo podatkov, ali so le-ti nejasni.

Odkrivanje segmentov iz podatkov

@sbelak Simon Belak Using Onyx in anger Clojure has always been good at manipulating data. With the release of spec and Onyx ("masterless, cloud scale, fault tolerant, high performance distributed computation system") good became best. In this talk I will walk you through a data layer architecture build around Kafka an Onyx that is self-describing, declarative, scalable and convenient to work with for the end user. The focus will be on the power and elegance of describing data and computation with data; and the inferences and automations that can be built on top of that.

Using Onyx in anger

Spec + onyx

Whenever a programming language comes out with a new feature, us smug lisp weenies shrug and point out how lisp had that in the early seventies; and if you look at the list of influences of a given language, there is bound to be a lisp in there. In this talk I will try to unpack what makes lisp special, why it is called programming programming language , how it changes one’s thinking, and how that thinking can be applied elsewhere.

Dao of lisp

Successfully forecasting future demand is key in allowing GoOpti its low prices while isolating transport partners from risk. It this talk Simon Belak, Chief Data scientist at GoOpti, will take you through how he approaches forecasting and the lessons that he learned along the way. The focus is going to be on models that do not require excessive amounts of data, are legible and work well as part of a continuous process (rather than being a one-of problem).

Predicting the future with goopti

Living with-spec

Living with-spec

Having programmers do data science is terrible, if only everyone else were not even worse! The problem is tools – either a bunch of libraries and an agnostic IDE, or some point-and-click wonder which no matter how glossy never quite fits our need. The dual lisp tradition of grow-your-own-language and grow-your-own-editor gives me hope there is a third way. This talk is a meditation on how I do data science with Clojure, what the ideal process would look like, and the tools needed to get there. Some already exists (or can at least be bodged together); others can be made with relative ease (and we are already working on some of these); but a few will take a lot more hammock time. Clojure is fantastic for data manipulation and rapid prototyping, but falls short when it comes to communicating your insights. What is lacking are good visualization libraries and (sharable) notebook-like environments. I'll show my workflow which weaves Clojure with R (for ggplot) and Python (for scikit-learn) and tell you why it's wrong; how IPythons of the world have trapped us in a local maximum and why we need a reconceptualization similar to what a REPL does to programming. All this interposed with my experience doing data science with Clojure (everything from ETL to on-the-spot analysis during brainstormings) and how these are interwoven into the design of Huri my library for the lazy data scientist.

Doing data science with Clojure

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Mais de Simon Belak (20)

The subtle art of recommendation

Metabase Ljubljana Meetup #2

Metabase lj meetup

Sketch algorithms

Transducing for fun and profit

Your metrics are wrong

Writing smart contracts the sane way

Online statistical analysis using transducers and sketch algorithms

Save the princess

Data driven going to market strategy

Spec: a lisp-flavoured type system

A data layer in clojure

Odkrivanje segmentov iz podatkov

Using Onyx in anger

Spec + onyx

Dao of lisp

Predicting the future with goopti

Living with-spec

Doing data science with Clojure

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SAC 25 Final National, Regional & Local Angel Group Investing Insights 2024 0...

Elaine Werffeli

Discover Why Less is More in B2B Research

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