Alternative Data News. 09, December 2020
Finding sources and uses for alternative data can be difficult. At CloudQuant we regularly read and search the internet for new sources of data that can be used in our mission to find alpha signals and build quantitative trading strategies. We recognize that we are technology and data junkies so we wrote our own crawler that specifically seeks out web pages, posts, and news articles that give us a snapshot of what is going on in the world of Alt Data. The following is a collection of articles that we think you will find interesting from the past week.
When is it acceptable to start playing Christmas music?
By reddit user u/GradientMetrics
Data collected with Dynata, using a representative panel in addition to weighting the data to census levels.
Visualization created in R with ggplot2.
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2020-01-02 Read the Full Story…
CloudQuant Thoughts : Sometimes you just have to go with the most important data at the moment!
20 Core Data Science Concepts for Beginners
With so much to learn and so many advancements to follow in the field of data science, there are a core set of foundational concepts that remain essential. Twenty of these ideas are highlighted here that are key to review when preparing for a job interview or just to refresh your appreciation of the basics.
- Data Wrangling
- Data Visualization
- Data Imputation
- Data Scaling
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- Data Partitioning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Model Parameters and Hyperparameters
- Exciting AI Project Ideas for Beginners
- Bias-variance Tradeoff
- Evaluation Metrics
- Uncertainty Quantification
- Math Concepts
- Statistics and Probability Concepts
- Productivity Tools
2020-12-20 00:00:00 Read the full story…
Weighted Interest Score: 5.5455, Raw Interest Score: 2.3596,
Positive Sentiment: 0.1265, Negative Sentiment 0.1925
CloudQuant Thoughts : kdnuggets regularly pump out these interesting lists!
14 Data Science projects to improve your skills
There’s a lot of data out there and so many data science techniques to master or review. Check out these great project ideas from easy to advanced difficulty levels to develop new skills and strengthen your portfolio.
Let’s not take this for granted. Take this time in isolation to learn new skills, read books, and improve yourself. For those interested in data, data analytics, or data science, I’m providing a list of fourteen data science projects that you can do during your spare time!
There are three types of projects:
- Visualization projects
- Exploratory data analysis (EDA) projects
- Prediction modeling
- Coronavirus visualizations
- Australian Wildfire Visualizations
- Earth Surface Temperature Visualization
Exploratory Data Analysis Projects
- New York Airbnb Data Exploration
- Most Important Factors related to Employee Attrition and Performance
- World University Rankings
- Alcohol and school success
- Pokemon Data Exploration
- Exploring Factors of Life Expectancy
- Time Series Forecast on Energy Consumption
- Loan Prediction Forecast
- Used Car Price Estimator
- Detecting Credit Card Fraud
- Skin Cancer Image Detection
2020-12-14 00:00:00 Read the full story…
Weighted Interest Score: 4.6355, Raw Interest Score: 1.4865,
Positive Sentiment: 0.2973, Negative Sentiment 0.2973
CloudQuant Thoughts : Another great summary article from kdnuggets
5 Free Books to Learn Statistics for Data Science
Learn all the statistics you need for data science for free.
Statistics is a fundamental skill that data scientists use every day. It is the branch of mathematics that allows us to collect, describe, interpret, visualise, and make inferences about data. Data scientists will use it for data analysis, experiment design, and statistical modelling. Statistics is also essential for machine learning. We will use statistics to understand the data prior to training a model. When we take samples of data for training and testing our models we need to employ statistical techniques to ensure fairness. When evaluating the performance of a model we need statistics to assess the variability of the predictions and assess accuracy.
“If statistics are boring, you’ve got the wrong numbers.”, Edward Tufte
These are just some of the ways in which statistics are employed by data scientists. If you are studying data science it is therefore essential to develop a good understanding of these statistical techniques. This is one area where books can be a particularly useful study tool as detailed explanations of statistical concepts is essential to your understanding. Here are my top 5 free books for learning statistics for data science.
2020-12-05 00:00:00 Read the full story…
Weighted Interest Score: 4.5155, Raw Interest Score: 1.9823,
Positive Sentiment: 0.1546, Negative Sentiment 0.0422
CloudQuant Thoughts : I have picked out 3 kdnuggets blog posts this week! I obviously like lists!
Next gen of alt data : Five years of historic Level 3 order book data
While the relationship between systematic hedge funds and alternative data sources is not new, quants have recently raised concerns that alt data is not giving the same alpha as it used to. This is because people are increasingly accessing the same data sets (web scraping, search analytics, satellite feeds etc.), making it difficult to find investing signals and high-frequency insight amongst the noise.
New data sets are needed to satisfy the demand for data-driven insights to spot long-term trends, improve trading decisions and ultimately drive performance. And while the industry understands the benefits of real-time (or near real-time) data, market participants are waking up to the predictive power of pricing data that comes from having access to vast amounts of historic data. Having a minimum of 5 years’ worth of historic Level 3 order book data is what is needed to have a meaningfully predictive data set. Or in other words…the next generation of alternative data.
2020-12-01 Read the Full Story…
CloudQuant Thoughts : Is Level 3 order book data the next gen of alt data? What do you think?
Lenovo Looks to Unify Data Management
A set of data management building blocks unveiled this week by Lenovo’s Data Center Group seeks to unify data from edge to cloud via new data management software, an all-flash storage array that supports object storage as well as AI-driven storage management and a data fabric geared toward data analytics.
The data management suite is among the latest offered by infrastructure vendors who are targeting the growing volumes of data at the network edge with the promise of applying data analytics to achieve business goals.
“The biggest thing customers are trying to do is a new way of…accelerating [and] modernizing their IT,” said Kamran Amini, general manager of Lenovo’s server, storage and software-defined infrastructure.
“What we’re seeing happening is customers are either moving data to the edge or going ahead and modernizing their basic, core data centers” to support private and hybrid clouds, Amini added. The next step is utilizing the data generated by this infrastructure to “get some kind of business outcome.”
2020-12-03 Read the Full Story…
Banking Providers Still Aren’t Ready for Big Data
Big data is an asset and not a technology. Data maturity is about using data and advanced analytics to answer business questions and deliver value to the consumer. Without deployment of insights outside the organization, most of big data’s value never materializes. The question: How do banks and credit unions improve their data maturity?
By Jim Marous, Co-Publisher of The Financial Brand, Owner/CEO of the Digital Banking Report and host of the Banking Transformed podcast.
The penalty for poor business decisions and deficient customer experiences is enormous, so most financial institutions are combining new external data streams to existing internal data sets, applying advanced analytics to find the foundation for faster decisions and better consumer insights. This has never been more important than in a pandemic-impacted marketplace, where change is happening faster than ever and customer expectations are rising exponentially.
2020-12-03 Read the Full Story…
Find the questions to solve with alt data
Buzzwords, buzzwords, buzzwords. Every age has different buzzwords. Today, probably some of the most popular are machine data and alternative data, whether you work in finance or any other industry. Want to make more profits? Well, use machine learning, use alternative data etc. (ok, it isn’t that easy, but I’ll explain more later).
Rather than just hand waving, and chanting, we need to understand how these techniques can be helpful for us. In particular, how can machine learning be useful if we have alternative datasets? Machine learning can provide us with many useful techniques for making sense of alternative data. After all, in order to structure alt data we often use machine learning. Making sense of text, requires NLP, and many NLP models use machine learning these days. The same is true of computer vision. Whereas in the past it was dominated by rules.
Given that banding around buzzwords isn’t really sufficient, rather than thinking about the techniques or resources we’ll use (machine learning and alternative data), we need to think about the types of questions we want to solve, where potentially they might be useful. It’s just taking a plane People travel from A to B and a plane facilitates that. They don’t go from A to B, in order to spend hours queuing at an airport, and to spend time in a plane. Another important point with alternative data, is that it might help you solve questions, which you were never able to ask with “traditional data”.
2020-12-05 Read the Full Story…
Contextualising alternative data is key to garnering true insights
The world has witnessed an unprecedented explosion of data over the last few years. Most of us will be familiar with the term Terabyte, which represents 1012 or 1 trillion bytes of data. But such is the data-drenched world in which we live today that Caltech estimates 463 Exabytes of data will be created, every day, by 2025. One Exabyte is 1018, equivalent to one quintillion bytes!
The numbers are mind-boggling and too much for our human brain to comprehend. For the asset management industry, finding ways to harness technology in a way that can bring a kernel of insight to investment portfolios, is likely to be the next significant phase of evolution, where data management will define the winners from the losers.
In its latest white paper entitled “The exponential pull of innovation”, SEI refers to it as the “Googlisation” of financial services. More than just a placeholder for the idea of big data, “Google plays the role of a reliable means of deriving utilitarian knowledge from data. It is emblematic of data abundance and our strides in using that data effectively,” the white paper suggests.
2020-12-07 00:00:00 Read the full story…
Weighted Interest Score: 4.5314, Raw Interest Score: 1.8634,
Positive Sentiment: 0.2713, Negative Sentiment 0.2241
The Evolution of Data as an Asset
From an Afterthought to a Core Business Asset: The Journey of Data
In recent years, the phenomenal growth of connected devices and advanced data technologies, along with the availability of affordable data storage, transfer, and processing capabilities, has enabled businesses to gain competitive intelligence on demand.Despite recognizing the strategic potential of data, many businesses face challenges due to data silos and lack of understanding among stakeholders. This KPMG Advisory shows what is hindering data from becoming a business asset.
In What is Data Value and Should it be Viewed as a Corporate Asset?, author Asha Saxena shares an interesting anecdote. Steve Todd of Dell EMC conducted a survey to gauge the value of data, where highly profitable companies were found to be:
“Focused more on the challenges of storing, protecting, accessing, and analyzing massive amounts of data, and not as much on transforming or quantifying its business value.”
Asha further states that data is still not featuring on company balance sheets as an asset.
2020-12-09 08:35:31+00:00 Read the full story…
Weighted Interest Score: 4.2559, Raw Interest Score: 2.2607,
Positive Sentiment: 0.2642, Negative Sentiment 0.1321
New hybrid architectures are unlocking the power of data and AI (VB Live)
To stay on top of AI innovation, it’s time to upgrade to next-gen architecture. Join this VB Live event to learn how cutting-edge computer architecture can unlock new AI capabilities, from common use cases to real-world case studies and more.
“Everybody’s heard a lot about big data over the last decade,” says Alan Lee, corporate vice president and head of research and advanced development at AMD. “Put differently, what are we going to do with all this data? How can we use it for the betterment of mankind, business, individuals, and so on?”
All that data needs to be handled or manipulated in some way. AI is enabling new modelling and simulation methods, and dramatically improving visualization. It’s helping unlock new ways to meet the critical needs of enterprise, to connect people and businesses through data sharing and video collaboration when working (or teaching) in-person is not possible.
2020-12-08 00:00:00 Read the full story…
Weighted Interest Score: 3.3887, Raw Interest Score: 1.5707,
Positive Sentiment: 0.3187, Negative Sentiment 0.1138
How to Influence Data Quality Through Data Stewardship
To get value from data, data stewards must understand business requirements and apply them. When business ambiguity arises about best serving data stakeholders, data stewards need to know how to find out this information and with whom to speak. Then these data trustees influence Data Quality for the better by aligning fit for purpose with business needs.
Data stewards understand business standards’ frameworks when taking good care of data assets.
Data Governance, either formal or a non-invasive, reflects these structures and provides context and direction to these frameworks. When a data steward misunderstands the business framework and misapplies Data Governance, Data Quality suffers. Just as a martial arts practitioner in either Kung Fu, Karate, Capoeira, or Neo-Bartitsu needs to understand its concepts and context to best an opponent, data stewards should follow the rules and concepts making data fit for purpose.
2020-12-08 08:35:53+00:00 Read the full story…
Weighted Interest Score: 3.3510, Raw Interest Score: 1.7331,
Positive Sentiment: 0.3944, Negative Sentiment 0.1142
An end-to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku
Predicting sport scores, from data wrangling to model deployment
At the time of the Rugby World Cup in 2019 I did a small data science project to try and predict rugby match results, which I wrote about here. I’ve expanded this into an example end-to-end machine learning project to demonstrate how to deploy a machine learning model as an interactive web app.
Goal : To provide a high-level overview of the key steps needed in going from raw data to a live deployed machine learning app.
Once you’ve gone through this — pick a topic that you’re interested in, find some data, get your hands dirty and aim to build your own machine learning app, from data preparation to deployment.
2020-12-09 13:01:07.764000+00:00 Read the full story…
Weighted Interest Score: 3.3411, Raw Interest Score: 2.5098,
Positive Sentiment: 0.0000, Negative Sentiment 0.0000
Praxis Launches Its Full-Time Data Engineering Program
While COVID-19 pandemic has had a huge impact on people, function and process in innumerable ways, it has brought about an acceleration in the adoption of digital transformation across business and social sectors. The industry needs to rapidly ramp up on skills required to manage this rapid digitalisation. One of these critical skills is Data Engineering – in fact the DICE report of 2020 has labelled Data Engineering (DE) as the fastest-growing tech job with a 45% year-on-year growth.
The pioneers of formal Business Analytics/ Data Science education in India, Praxis Business School, are launching a 9-month full-time post-graduate program in Data Engineering to address the business need for people with these skills. This course by Praxis is supported by industry giants Genpact and LatentView, who are providing industry inputs and know-how to strengthen the program.
2020-12-03 11:30:46+00:00 Read the full story…
Weighted Interest Score: 3.2884, Raw Interest Score: 1.9612,
Positive Sentiment: 0.2992, Negative Sentiment 0.1130
GAM appoints Global Head of Sustainable and Impact Investment
AGM Investments has appointed Stephanie Maier as Global Head of Sustainable and Impact Investment. Maier will report directly to Group Chief Executive Officer Peter Sanderson and will be a member of the Senior Leadership Team. She will join the firm on 4 January.
In this newly created global role, Maier will be responsible for leading GAM’s sustainable investment and ESG (environmental, social and governance) strategy and strengthening the firm’s ESG proposition for clients.
Maier brings 18 years’ experience in responsible investment and ESG strategy. She joins GAM from HSBC Global Asset Management, where she was Director for Responsible Investment. Prior to that, she spent seven years at Aviva Investors, latterly as Head of Responsible Investment Strategy and Research, and was formerly Head of Research for EIRIS, an ESG research and consultancy firm. Maier holds a BA in Biological Sciences from Oxford University and an MSc in Environmental Technology from Imperial College Londo
2020-12-09 00:00:00 Read the full story…
Weighted Interest Score: 3.2485, Raw Interest Score: 1.8950,
Positive Sentiment: 0.3790, Negative Sentiment 0.0000
Sustainable Finance Live: Using real-time measurement of climate change to address risk
Finextra Research and Responsible Risk today hosted Sustainable Finance Live, the second virtual workshop in a series of events designed to create actionable ESGtech strategies and build an ecosystem of partnerships that will turn strategy into reality.
This workshop details how alternative data from sources such as satellites and sensors can augment traditional risk systems and real-time, forward-looking, data can provide insights for the future of sustainable financing.
What are the issues and opportunities for risk management working with alternative data to inform credit decisions? How can these decisions be quantified against physical and transition risk? Richard Peers, founder of Responsible Risk and contributing editor for Finextra Research, provides the answers.
2020-12-08 13:15:00 Read the full story…
Weighted Interest Score: 3.0818, Raw Interest Score: 1.6702,
Positive Sentiment: 0.2563, Negative Sentiment 0.2298
Top 10 Quotes On AI & Data Science In 2020
The year 2020 saw a lot of new developments in the AI and data science field — from Neuralink to GPT-3, along with some significant announcements from events such as Nvidia GTC 2020 and RAISE 2020. These exciting developments were accompanied by quotes and remarks by tech leaders. As the year 2020 comes to an end, we round up a few of these quotes that defined the year 2020.
Narendra Modi, PM, India, while speaking at RAISE 2020 said:
“AI is a tribute to human intelligence power. At every step of history, India has led the world in knowledge and learning.”
2020-12-09 07:30:00+00:00 Read the full story…
Weighted Interest Score: 2.8968, Raw Interest Score: 1.2482,
Positive Sentiment: 0.2591, Negative Sentiment 0.0707
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