Category Archives: Data Science
Getting Prices Right in 2021 – Progressive Grocer
People will always want to shop and interact with others, so some elements of 2020 will only last as long as there is a virus risk, but for the most part, the trends that grew last year will stay in place, he notes.
Two examples of those newer trends? Grocery deliveries and curbside pickup. But keeping up with consumer demand for those services in 2021 and beyond, and keeping ahead of competitors, will require more food retailers to invest in software as a service (SaaS) or other forms of optimization technology. Price optimization remains one of the best ways to translate what consumers like into viable actions to stay relevant as trends evolve over time, Pavich says.
Such Saas technology can provide the benefit of a team to retailers that deploy those systems a reflection of a larger trend in the food retail world in 2021, as business becomes ever more digital, and new and real-time data points accumulate more quickly.
Retailers using SaaS pricing platforms also benefited from being part of a community of customers, having strategic partners who were able to monitor broader pricing trends and advise on best practices during very challenging times, Pavich points out. Imagine how Apollo 13 would have turned out if the crew didnt have all of their instruments, gauges, and a room full of experts and scientists in Houston advising them and helping them through their crisis.
Even if a particular food retailer isnt quite ready to deploy new price and promotional optimization strategy or has yet to earmark the money for doing so work toward that goal can be completed now.
The main thing that retailers should be doing is re-evaluating and refreshing their strategies to reflect the new realities of the current market, Pavich advises. This includes taking a new look at pricing zones, competitive indexes, KVIs, category pricing roles and other key aspects of their pricing strategy. High-quality analytics and industry best practices can help retailers build a pricing strategy to meet todays needs while driving sustainable value in 2021 and beyond.
Its hard to go even a day in the world of food retail without hearing about some new deployment of artificial intelligence, or fresh boasting about its near-term promise. That holds true when it comes to pricing and promotional optimization but dont make the mistake that everything is about AI.
Other technologies and processes also matter significantly, according to Maia Brenner, a data scientist for Tryolabs, a San Francisco-based data science consulting firm.
That includes machine learning kind of like the less sophisticated but useful older cousin of AI. Brenner says that mastering price optimization in 2021 requires food retailer tech departments to either master machine-learning techniques or find a partner that can. To do that, retailers need to break past old habits and embrace the future. Many grocery operators proved they could do that in 2020 when they went full force with e-commerce and associated services. Now, that same attitude is required for better price optimization.
Old statistical univariate forecasting tools arent sufficient, since forecasting future sales cant be predicted only by having a look at last years or months sales, Brenner observes. Without doubt, handling new real-time structured or unstructured data sources also helps anticipate the future and make better decisions. For instance, price optimization technologies can be powered up with computer vision and natural language-processing techniques by providing more information to perform better recommendations and personalization.
She also stresses the importance of investing in real-time data sources and crafting a pricing strategy with enough flexibility to be able to analyze all of that information and respond accordingly and before competitors do.
Another anticipated change serves to further underscore why food retailers this year need to take price optimization even more seriously than has been the case.
In 2020, consumers took fewer trips, went to fewer retailers and spent more per trip, notes Edris Bemanian, CEO of Engage3, a price optimization firm with offices in Davis, Calif., and Scottsdale, Ariz. During this period, prices skyrocketed due to lack of supply, and a significant reduction in promotions driven by the lack of supply. At the same time, a greater percentage of sales than ever before shifted to e-commerce, and retailers had to be more sensitive to pricing actions that would be perceived as unethical or unfair.
In 2021, according to Bemanian, ongoing economic constraints promise to place an even higher focus on price. Price optimization will need to shift to be able to deal with smaller trip sizes, households with less money to spend on each trip, shifts to private label, and smaller pack sizes, he predicts. Historical price optimization models of increasing price to drive profit wont work. Retailers will need to focus on the items that drive their price image to keep traffic up.
In other words, the future is pretty much now when it comes to better pricing and promotions. The race toward better optimization could even prove more important in the longer term than the fight to win over online shoppers.
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Why Artificial Intelligence May Not Offer The Business Value You Think – CMSWire
PHOTO:Adobe
Last September, Gartner published its Hype Cycle for AI in which it identified two emerging trends (and five new AI solutions) that would have an impact on the workplace. One of those trends was what Gartner described as the democratization of AI. While there are many ways that this can be interpreted, in simple terms what it meansfor workersis the general distribution and use of AI across the digital workplace to achieve business goals.
In the enterprise, the target deployment of AI is now likely to include customers, business partners, business executives, salespeople, assembly line workers, application developers and IT operations professionals. As AI reaches a larger set of employees and partners, it requires new enterprise roles to deliver it to a wider audience.
While this was an emerging trend last summer, with COVID-19 and the adoption of many new technologies to enable remote working, the widespread use of AI, while still only anecdotal, now appears to be an established fact in the workplace.
Bill Galusha of senior director of marketing at Calsbad, Calif.-based digital intelligence company ABBYY points out, however, that this is not a new phenomena. In the past couple of years, weve seen AI enabling technology like OCR and machine learning become more accessible to non-technical employees and partners through no code/low code platforms, he said.
He points out that thetechnologies designed to help workers understand and extract insights from content have been in high demand as more digital workers increase the number of tasks a knowledge workers have to perform.
In practical terms these new AI platforms enable users to design cognitive skills that are can be easily trained to take unstructured data from type of document like invoices, utility bills, IDs, and contracts, or access trained cognitive skills available through online digital marketplaces. This new approach to making it easy to train machine learning content models and deliver them as skills in a marketplace are certainly going to fuel the online growth and reusability of AI as businesses look to automate all types of content-centric processes across the enterprise, he said.
Related Article:The Risks and Rewards of the Citizen Developer Approach
However, if AI is being used widely across the enterprise, it does not necessarily follow that it is providing business value to every organization, according to Chris Bergh, CEO of Cambridge, Mass.-based DataKitchen, a DataOps consultancy and platform provider.
AI is being deployed everywhere we look, but there is a problem that no one talks about. Machine learning tools are evolving to make it faster and less costly to develop AI systems. But deploying and maintaining these systems over time is getting exponentially more complex and expensive, he told us.
Data science teams are incurring enormous technical debt by deploying systems without the processes and tools to maintain, monitor and update them. Further, poor quality data sources create unplanned work and cause errors that invalidate results.
This is the heart of the problem and one that is likely to impact the bottom line of any business that uses AI. The AI code or model is a small fraction of what it takes to deploy and maintain a model successfully. This means that the delivery of a system that supports an AI model in an application context, is an order of magnitude more complex than the model itself. You can't manage the lifecycle complexity of AI systems with an army of programmers. The world changes too fast. Data constantly flows and models drift into ineffectiveness. The solution requires workflow automation, he said.
There is another problem for businesses too. Given the explosion in the amount of data that is available to them, at first glance you would think that developing AI was getting easier and, consequently, easier to deploy democratized across the enterprise. Not so, according to Chris Nicholson, CEO of San Francisco-based Pathmind, which develops a SaaS platform that enables businesses to apply reinforcement learning to real-world scenarios without data science expertise.
The real problem, he argues is that you cannot decouple algorithms from data, and the data is not being democratized, or made available, across the organization. In many cases, as with GDPR, the data is getting harder to access and because the data is not being democratized, most startups and companies will not be able to train AI models to perform well, because each team is limited to the data it can access.
In a few cases, a general-purpose machine-learning model, can be trained and made available behind an API. In this case, developers can build products on top of it, and that very particular type of AI is slowly percolating into products and impacting customers lives. But, in most cases, businesses have custom needs that can only be met by training on custom data, and custom data is expensive to collect, store, label and stream, he said. At best, AI is a feature. In the best companies, data scientists embed with developers to understand the ecosystem of the data and the code, and then they embed their algorithms in that flow.
Like the discussion around citizen data scientists (and democratizing data science), business leaders need to know what they want this new democratized AI to do. They will not be able to design and build AI models from scratch; that will always require an understanding of what the underlying methods and parameters do, which requires theoretical knowledge.
Given some gray box AI systems, one can envision such systems learning to solve well-defined classes of problems when they are trained or embedded by non-AI experts, Michael Berthold, Switzerland-based KNIME CEO and co-founder, said. Examples he cites are object recognition in images, speech recognition, or probably also quality control via noise and image tracking. Note that already here choosing the right data is critical so the resulting AI is not already biased by data selection.
I think this area will see growth, and if we consider this democratization of AI, then yes, it will grow, he added. But we will also see many instances where the semi-automated system fails to do what it is supposed to do because the task did not quite fit what it was designed to do, or the user fed it misleading information data.
It is possible to envision a shallower training enabling people to use and train such preconfigured AI systems without understanding all the algorithmic details. Kind of like following boarding instructions to fly on a plane vs. learning how to fly the plane itself.
If organizations take this path to develop AI, there are two ways enterprises can push AI to a broader audience. Simplify the tools and make them more intuitive, David Tareen, director of AI and analytics at Raleigh, N.C-based SAS told us.
Simplified Tools - A tool like conversational AI helps because it makes interacting with AI so much simpler. You do not have to build complex models but you can gain insights from your data by talking with your analytics.
Intuitive Tools - These tools should make AI easier to consume by everyone. This means taking your data and algorithms to the cloud to become cloud native. Becoming cloud native improves accessibility and reduces the cost of AI and analytics for all.
In organizations do this, they will see benefits everywhere. He cites the example of an insurance company that uses AI throughout the organization will reduce the cost of servicing claims, reduce the time to service claims, and improve customer satisfaction compared to the rest of the industry. He adds that some enterprise leaders are also surprised to learn that enabling AI across the enterprise itself involves more than the process itself. Often culture tweaks or an entire cultural change must accompany the process.
Leaders can practice transparency and good communication in their AI initiatives to address concerns, adjust the pace of change, and result in a successful completion of embedding AI and analytics for everyone, everywhere.
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Why Artificial Intelligence May Not Offer The Business Value You Think - CMSWire
Awful Earnings Aside, the Dip in Alteryx Stock Is Worth Buying – InvestorPlace
Shares of Alteryx (NASDAQ:AYX) dropped big in February after the data analytics company reported fourth-quarter numbers that while beating estimates revealed structurally weakening growth trends. The guide also called for these weaknesses to persist for the foreseeable future. Naturally, AYX stock plunged after the print.
Source: rafapress / Shutterstock.com
Bad news aside, this dip is an opportunity for AYX bulls to buy in at a discount.
Theres no sugarcoating it, though Alteryxs growth trends look awful. Customer growth is slowing. Revenue growth is slowing. Margins are compressing. Profits are turning into losses. Pretty much nothing looks good right now.
But we dont value businesses based on what they are today. We value them based on what will be tomorrow. And tomorrow, Alteryx will once again be a hypergrowth, hyper-profitable company with great prospects.
So, buy the dip in AYX stock!
Heres a deeper look:
Alteryx had a bad fourth quarter like a really, really bad fourth quarter.
The company added just 128 customers in the quarter. In the year ago quarter, Alteryx added 474 customers. In each of the past 18 quarters, Alteryx has added more than 200 customers. So, adding just 128 customers in the quarter is unusually bad for Alteryx. Indeed, it rounds out to 16% customer growth, its slowest growth rate ever.
Meanwhile, average revenue per customer dropped 15%, leading to meager revenue growth of just 3%. Revenues are expected to drop next quarter. Last year, this was a 50%-plus revenue growth company. Clearly, things are decelerating and fast.
Worse yet, this slowdown in growth is killing margins, because the company isnt able to cut back on expense growth as fast revenue growth is falling. Operating margins two points year-over-year in Q4, and are expected to drop nearly 20 points next quarter.
Things are not going well for Alteryx right now. Theres no other way to put it.
Its no wonder that AYX stock fell off a cliff.
When it comes to Alteryx, you have a classic case of near-term pain, long-term gain.
Data-driven decision making is the future of the business world. Alteryx provides an end-to-end platform, which enables this data-driven decision making, by giving enterprises the analytics and tools necessary to turn raw mountains of messy data into clean, actionable insights.
Importantly, Alteryx does this in a friendly, low-code, easy-to-learn and easy-to-use software environment. That is, you dont need to be a data scientist or have a computer science degree in order to make use of the Alteryx platform. Alteryx enables regular Joes to make advanced data-driven decisions.
Thats big, because most companiesdont have big data skills. All the data scientists in the world are going to work forFacebook(NASDAQ:FB) andMicrosoft(NASDAQ:MSFT), while only 6% of large companies and very few small businesses employ even a single data scientist.
So, as we pivot into a data-driven future, most companies are going to lean into low-code, easy-to-use data science platforms to help them make data-driven decisions. Alteryx is the best-in-breed provider of these solutions and, to that end, the company is going to sell a lot of enterprise seats to its data science platform over the next several years.
The company just hit a rough spot amid the pandemic because businesses leaned up their budgets. But we have multiple highly-effective Covid-19 vaccines that are being distributed rapidly, and it increasingly appears that normal is coming back at some point in 2021/22. As normal returns, businesses will re-up their budgets, and the Alteryx growth narrative will once again fire on all cylinders.
So, amid this ephemeral choppiness, its best to buy AYX stock and ignore the noise.
Ive revised my long-term model to account for Alteryxs slowdown in growth in 2020. Still, I believe AYX stock is worth much than its current price tag.
I see Alteryx as a company that will, post-2020, maintain 10%-plus revenue growth into 2030. Gross margins are up at 90%, so double-digit revenue growth should drive positive operating leverage and allow for sustainable and steady operating margin expansion.
Plugging those assumptions into my model, I see Alteryx growing earnings to $8 per share by 2030. Based on a 35X forward earnings multiple and an 8% annual discount rate, that implies a 2021 price target for AYX stock of $280.
Alteryx had an awful fourth quarter. Oh well. It happens.
This is still a great company, with great growth prospects, and a highly scalable business model. It will sprint back into hypergrowth mode throughout 2021/22 as Covid-19 headwinds on enterprise spending fade.
As the company does that, AYX stock will rebound.
Buy the dip before that rebound.
On the date of publication, Luke Lango did not have (either directly or indirectly) any positions in the securities mentioned in this article.
By uncovering early investments in hypergrowth industries, Luke Lango puts you on the ground-floor of world-changing megatrends. Its how his Daily 10X Report has averaged up to a ridiculous 100% return across all recommendations since launching last May.Click hereto see how he does it.
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Awful Earnings Aside, the Dip in Alteryx Stock Is Worth Buying - InvestorPlace
The chief data scientist: Who they are and what they do – Siliconrepublic.com
Anodots Ira Cohen shares what it means to be a chief data scientist, particularly in the era of Covid-19.
Weve been exploring the topic of working in data for some time on Siliconrepublic.com. Weve looked at the difference between a data scientist and a data analyst, the types of skills data professionals need and how much you could expect to earn in the industry. But something we havent discussed in depth is the role of a chief data scientist.
Ira Cohen is co-founder and chief data scientist at Anodot, a US analytics company that helps businesses detect anomalies in revenue, customer interactions and more. He also previously worked as chief data scientist at Hewlett-Packard Enterprise.
In his current position, Cohen draws on AI and machine learning to develop real-time algorithms to carry out this detection. Here, he explains the differences between his role and that of a CTO, and why chief data scientists will be critical after Covid-19.
Truly great chief data scientists know how to walk a fine line between driving creative innovation and pragmatic solutions IRA COHEN
The chief data scientist manages a range of data-driven functions including overseeing data management, creating data strategy and improving data quality. They also help their organisations extract the most valuable and relevant insights from their data, leveraging data analytics and business intelligence.
Perhaps most importantly, organisations rely on the chief data scientist to bridge the gap between management and the data science teams, helping them understand what machine learning can achieve and, conversely, not accomplish. The chief data scientist has a much deeper understanding of these technologies than the CTO, who likely has a broader knowledge base but not necessarily the deep expertise.
Machine learning is a remarkable innovation when supported by large amounts of data but the journey from big data ideas to successful machine learning implementations is often a complex and arduous one. This path requires a trusted navigator who can help the data science team overcome potential challenges that is where the chief data scientist comes in.
Experienced chief data scientists understand that data is the fuel behind key initiatives and know the non-deterministic risk of developing these capabilities. They bridge the gap between organisational expectations and the reality of what machine learning can accomplish, while understanding how to mitigate the risks associated with complex data-driven endeavours.
Many organisations are discovering that they really need a chief data scientist. For data-driven organisations, this role has become a must-have position rather than a luxury.
Since Covid-19 we have seen the rise of the chief data scientist, especially as organisations accelerate their digital transformations. Right now, everyone is engaging customers and partners in different ways in the digital world, launching new business models and finding better ways to bring products and services to market. This has led organisations to embrace more ambitious data strategies that require more experienced data science leadership.
We are seeing chief data scientists become heavily involved with board-level and C-suite-driven corporate strategies as data becomes even more central to critical company decisions. IDC recently completed a survey that revealed that 59pc of chief data scientists now report to their CEO or another C-suite executive, which illustrates just how far this role has come in a short period of time.
One of the most important things a chief data scientist can do over the next few months is to use machine learning to solve the most pressing business problems created by Covid-19 and the global recession.
For example, churn prediction is a key dilemma for organisations right now specifically, they must forecast which customers are most likely to stop being customers. This important task requires superior data analysis know-how. Moreover, assessing churn predictions requires different levels of technical and data science expertise qualities that the chief data scientist already has.
For example, some organisations need to predict churn in real time, while others must assess churn for each customer once a month. This requires different expertise and product sets with unique machine learning requirements.
Having a chief data scientist navigate these varied scenarios would likely deliver positive outcomes as that individual could understand the scope of the data science work required and complete a thorough analysis of the approaches that will or wont work all while balancing the business and technical trade-offs of taking one approach over the other.
This level of understanding will make all the difference when it comes to finding the most effective solutions that yield the desired results under the right cost and time parameters.
Truly great chief data scientists know how to walk a fine line between driving creative innovation and pragmatic solutions. As data scientists are researchers at heart, they need the time and space to explore different problem sets and possible data-driven solutions. At the same time, they must also deliver real-world data management solutions that solve their organisations pressing business problems. The ideal chief data scientist knows how to rally teams to deliver both.
For researchers, it is all too easy to go down rabbit holes searching for the best solutions. Sometimes you find the gold, but many times you do not uncover it. Talented and resourceful chief data scientists know when to pull their teams out of the rabbit holes; when they have asked all the right questions and done the hard work but still cannot find the treasure. That is when they must be pulled out to avoid wasting too much time, and then you can move them on to the next hole.
Many organisations let their data science teams spend too much time with their heads buried in rabbit holes that end up not bearing any fruit. Finding the right balance between exploration and pragmatic solutions is a key role for the chief data scientist.
Machine learning is the most important technology for chief data science officers in the year ahead.
Machine learning is particularly critical for data science professionals right now. Many of them are engaged in a build-versus-buy debate regarding products or services that offer machine learning as a core feature.
Organisations that opt to build their own platforms with machine learning capabilities should only do so if they are creating mission-critical applications. Otherwise, they will likely spend a great deal of time and expense building an internal technology that will not deliver as much value as the time and effort they put into it.
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The chief data scientist: Who they are and what they do - Siliconrepublic.com
Berkeley’s data science leader dedicated to advancing diversity in computing – UC Berkeley
Jennifer Chayes, associate provost of the Division of Computing, Data Science, and Society, and dean of the School of Information, discussed her vision for the future of data science at UC Berkeley. (UC Berkeley video)
From dictating which posts appear in our social media feeds to deciding whether or not a suspect might be guilty of a crime, data and computing have come to permeate nearly all aspects of our lives. But while these systems can offer many benefits, their faults whether data breaches, unintentional biases in algorithms or the proliferation of misinformation can have disastrous effects, especially on already marginalized individuals and communities.
Thats why Jennifer Chayes, UC Berkeleys new data science leader, is dedicated to creating an environment where data and computing are informed by leaders from all disciplines, including ethics and the humanities, and where people of all races, genders and socioeconomic backgrounds are welcomed at the table.
Chayes, associate provost of the Division of Computing, Data Science, and Society (CDSS) and dean of the School of Information at Berkeley, discussed her vision for the future of CDSS at a virtual Campus Conversations event on Wednesday.
More and more of our public systems (our) criminal justice system, our health system, our education system, our social welfare system [are] being mediated by computing. As [data science] becomes the fabric of our society, [we need to ensure) that it is a fabric that will serve its purpose properly, Chayes said. We need women, we need Black people, we need Latinx and Indigenous people building this fabric, because they will understand in ways different from the majority how [data] may be used.
Chayes left her position as a technical fellow at Microsoft Research to lead CDSS in January 2020. Part of what drew her to Berkeley was the sheer scale of the data science research happening on campus, coupled with the wide variety of fields data scientists were working in from climate change and sustainability to biomedicine and public health to human rights.
I think, at Berkeley, we are going to have just many, many more disciplines interacting with each other, Chayes said, when asked about her hopes for the future of the division. I will feel like a failure if we dont have joint faculty with every division and school and college on campus because I think that all voices have to be here, everyone has to be at the table for this to be a success.
To help increase racial diversity in data science fields, Chayes said that the division has approached historically Black colleges and universities about creating joint masters programs. The data science major also tends to attract a diverse array of students, many of whom didnt necessarily intend to go into data and computing when they entered Berkeley.
The CDSS is also planning the construction of a new data science building that will include extensive convening space for students, staff and faculty to collaborate.
People really need to mix with each other, Chayes said. Its something that I learned at Microsoft. I tried to have as flat of organizations as possible with philosophers, anthropologists and biologists and physicists and mathematicians and computer scientists and lawyers coming together and talking with each other. Its not just learning the language of another discipline, it is really understanding what are the important problems of other disciplines and why.
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Berkeley's data science leader dedicated to advancing diversity in computing - UC Berkeley
Software AG’s TrendMiner 2021.R1 Release Puts Data Science in the Hands of Operational Experts – Yahoo Finance
HOUSTON, TX, HASSELT, BELGIUM and DARMSTADT, GERMANY / ACCESSWIRE / January 28, 2021 / Software AG's TrendMiner has announced the release of TrendMiner 2021.R1. This latest release brings a completely new functionality of notebook integration, which helps users access both data dashboards and code-based data analysis. Also in 2021.R1 are extended capabilities to support multiple asset frameworks and many new user-driven features to help end users improve operational performance and overall profitability.
Bridge the knowledge-gap between engineers and data scientists with embedded notebooksTrendMiner enables operational experts in process industries to analyze, monitor, and predict operational performance using sensor-generated time-series data. The goal of TrendMiner has always been to empower engineers with analytics for improving operational excellence, without the need to rely on data scientists. In doing so, TrendMiner brought data science to the engineer. In the 2021.R1 release, TrendMiner makes the next step of this journey by integrating notebook functionality into the software so that users can easily jump from looking at data in a TrendMiner view to working with it in a code-based data science environment.
With their data science libraries of choice (e.g. Pandas, NumPy, SciPy, SciKit-Learn), engineers can create and run custom scripts themselves for advanced statistical analyses and use AutoML capabilities to build machine learning models for anomaly detection. On top of that, they can operationalize the resulting notebook visualizations (also created with libraries of their choice such as Matplotlib, Plotly, Seaborn) as dashboard tiles in TrendMiner DashHub.
Thomas Dhollander, CTO at TrendMiner commented, "Classical data science depends on bringing process / asset know-how to the data scientist, while self-service analytics aims at packaging a subset of data science modeling capabilities and bringing these to the subject matter expert as a robust set of features (no technical tuning parameters, no data science training needed). Companies that recognize the potential in interweaving these complementary approaches will be the ones that can accelerate their operational efficiency and competitive advantage."
Story continues
Support for multiple asset frameworks for globally operating usersTo support enterprise rollouts and the increased complexity of existing IT-landscapes, TrendMiner has extended its capabilities for handling multiple plant breakdown structures also known as asset frameworks. OSIsoft PI users can easily connect multiple OSIsoft PI Asset Framework servers and set access permissions. Besides support for multiple PI AF structures, multiple CSV asset trees can be imported for use as a data source within TrendMiner. As a result, System Administrators can better control accessibility with the ability to publish and unpublish structures, while the users have more flexibility to analyze the operational performance of multiple plants and production lines, each with their separate plant breakdown structures.
Further informationIn each release, TrendMiner adds a new range of features and enhancements that are requested by its users. There are many more improvements in the TrendMiner 2021.R1 release, which users can find in the TrendMiner release notes on the website: http://www.trendminer.com. To see TrendMiner's functionality in-action and learn how analytics-empowered process and asset experts can help accelerate operational performance and increase profitability, click here to request a demo.
About TrendMinerTrendMiner, a Software AG company and part of the IoT & Analytics division, delivers self-service data analytics to optimize process performance in industries such as chemical, petrochemical, oil & gas, water & wastewater, pharmaceutical, metals & mining, and other process industries. TrendMiner software is based on a high-performance analytics engine for time-series data that allows users to question data directly, without the support of data scientists. The plug-and-play software adds immediate value upon deployment, eliminating the need for infrastructure investment and long implementation projects. Search, diagnostic, and predictive capabilities enable users to speed up root cause analysis, define optimal processes, and configure early warnings to monitor production. TrendMiner software also helps team members to capture feedback and leverage knowledge across teams and sites. In addition, TrendMiner offers standard integrations with a wide range of historians such as OSIsoft PI, Yokogawa Exaquantum, AspenTech IP.21, Honeywell PHD, GE Proficy Historian, and Wonderware InSQL.
Founded in 2008 and now part of Software AG, TrendMiner's global headquarters is located in Belgium and has offices in the U.S., Germany, Spain, and the Netherlands.
Media Contact:
Dawn FontaineRipple Effect Communicationsdawn@rippleeffectpr.com+1-617-536-8887
SOURCE: TrendMiner
View source version on accesswire.com: https://www.accesswire.com/626468/Software-AGs-TrendMiner-2021R1-Release-Puts-Data-Science-in-the-Hands-of-Operational-Experts
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The 12 Best R Courses and Online Training to Consider for 2021 – Solutions Review
The editors at Solutions Review have compiled this list of the best R courses and online training to consider for 2021.
R is a language and environment for statistical computing and graphics. R can be considered as a different implementation of S, and while there are some important differences, much of the code written for S runs unaltered on R. The language provides a variety of statistical and graphical techniques including linear and nonlinear modeling, classical statistical tests, time-series analysis, and classification and clustering. R capabilities are enhanced via user-created packages that allow for special statistical techniques, graphical devices and reporting.
With this in mind, weve compiled this list of the best R courses and online training to consider if youre looking to grow your data science programming skills for work or career advancement. This is not an exhaustive list, but one that features the best R courses from trusted online platforms. We made sure to mention and link to related courses on each platform that may be worth exploring as well. Click GO TO TRAINING to learn more and register.
Note: The best R courses and training modules are listed alphabetically by online learning platform name.
Platform: Codecademy
Description: Part of the Analyze Data with R skill path, this course will expose you to fundamental programming concepts in R. After the basics, youll learn how to organize, modify and clean data frames, a useful data structure in R. Then youll learn how to create data visualizations to showcase insights in data! Finish up with statistics and hypothesis testing to become a data analysis expert. You do not need to know how to code to enroll in this course.
Related path/track: Learn Statistics with R
Platform: Coursera
Description: In this course, you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which include programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting on R code. Topics in statistical data analysis will provide working examples.
Related path/track: Introduction to Probability and Data with R (Duke University)
Platform: DataCamp
Description: Part of DataCamps robust R course directory, this module will enable you to master the basics of this widely used open-source language, including factors, lists, and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis. Oracle estimated over 2 million R users worldwide in 2012, cementing R as a leading programming language in statistics and data science.
Related path/track: Intermediate R, Exploratory Data Analysis in R
Platform: Edureka
Description: Edurekas Data Science Training lets you gain expertise in machine learning algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R. Data science training encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction to Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation, and HR.
Related path/track: Data Analytics with R Certification Training, Advanced Predictive Modelling in R Certification Training
Platform: edX
Description: The first in edXs Professional Certificate Program in Data Science, this course will introduce you to the basics of R programming. You can better retain R when you learn it to solve a specific problem, so youll use a real-world dataset about crime in the United States. You will learn the R skills needed to answer essential questions about differences in crime across the different states. The demand for skilled data science practitioners is rapidly growing, and this series prepares you to tackle real-world data analysis challenges.
Related path/track: Statistics and R
Platform: Experfy
Description: This R course is step-by-step. In every new tutorial, students build on what they had already learned and move one extra step forward. After every video, you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples. This training is packed with real-life analytical challenges which you will learn to solve. In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course.
Related path/track: Data Wrangling in R, Data Science Masterclass with R, Probability and Statistics for Data Science with R
Platform: Intellipaat
Description: This data scientist course lets you master skills, such as data analytics, R programming, statistical computing, machine learning algorithms, k-means clustering, and more. It includes multiple hands-on exercises and project work in the domains of banking, finance, entertainment, etc. Intellipaats online data science courses are well recognized across 500+ employers helping you to land your dream job.
Platform: LinkedIn Learning
Description: Learn the basics of R and get started finding insights from your own data, in this course with professor and data scientist Barton Poulson. The lessons explain how to get started with R, including installing R, RStudio, and code packages that extend Rs power. You also see first-hand how to use R and RStudio for beginner-level data modeling, visualization, and statistical analysis. By the end of the course, youll have a thorough introduction to the power and flexibility of R, and understand how to leverage this tool to explore and analyze a wide variety of data.
Related path/track: R for Data Science: Lunchbreak Lessons, R Programming in Data Science: Setup and Start, R for Excel Users, Data Wrangling in R
Platform: Pluralsight
Description: In this course, Programming with R, you will learn how to manipulate different objects. First, you will learn the basic syntax. Next, you will explore data types and data structures available in R. Finally, you will discover how to write your own functions by implementing control flow statements. When you are finished with this course, you will have a foundational knowledge of R programming that will help you as you move forward to data science.
Related path/track: Data Science with R, Understanding Machine Learning with R
Platform: Simplilearn
Description: The Data Science with R programming certification training covers data exploration, data visualization, predictive analytics, and descriptive analytics techniques with the R language. You will learn about R packages, how to import and export data in R, data structures in R, various statistical concepts, cluster analysis, and forecasting. This module enables several different training options.
Platform: Udacity
Description: Learn the programming fundamentals required for a career in data science. By the end of the program, you will be able to use R, SQL, Command Line, and Git. There are no prerequisites for this program, aside from basic computer skills. With real world projects and immersive content built in partnership with top tier companies, youll master the tech skills companies want.
Platform: Udemy
Description: This course is truly step-by-step. In every new tutorial, instructors build on what students had already learned and move one extra step forward. After every video, you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples. This training is packed with real-life analytical challenges which you will learn to solve. In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course.
Related path/track: Data Science and Machine Learning Bootcamp with R, R Programming: Advanced Analytics In R For Data Science
Tim is Solutions Review's Editorial Director and leads coverage on big data, business intelligence, and data analytics. A 2017 and 2018 Most Influential Business Journalist and 2021 "Who's Who" in data management and data integration, Tim is a recognized influencer and thought leader in enterprise business software. Reach him via tking at solutionsreview dot com.
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The 12 Best R Courses and Online Training to Consider for 2021 - Solutions Review
Book Review: Hands-On Exploratory Data Analysis with Python – insideBIGDATA
The new data science title Hands-On Exploratory Data Analysis with Python, by Suresh Kumar Mukhiya and Usman Ahmed from Packt Publshing is a welcome addition to the growing list of books directed to help newbie data scientists improve their skills. Im always on the lookout for texts that can help my students find their way along the challenging path toward becoming a data scientist. I think this book fills a void for Exploratory Data Analysis (EDA) learning resources. But as Ill discuss, the book goes beyond just EDA, and is maybe mistitled its really an introduction to data science and machine learning using the Python language.
The book includes important EDA topics like Descriptive Statistics (Chapter 5), Grouping Datasets (Chapter 6), Correlation (Chapter 7), Time Series Analysis (Chapter 8), and Hypothesis Testing (first part of Chapter 9). These are all critical pieces of the data science process, and lucid discussions along with clear and simple code examples help the reader get moving. The publisher provides all the Python code from the book so the reader can hit the ground running.
My favorite part of the book is Chapter 4 on Data Transformation (aka data munging, or data wrangling). This is a very important area that often accounts for a majority of a projects time and cost budget, and the examples provided in this chapter cover the most commonly needed tasks for a typical data science project (e.g. missing data handling, discretization, random sampling, etc.). Interestingly, data transformation isnt really part of EDA, but I welcome the discussion as it broadens the scope of the book.
Chapter 2 on data visualization is a nice adjunct to the EDA discussions, because these two areas typically go hand-in-hand. Chapter 3 offers up an interesting use-case for demonstrating data access, data transformation, EDA, and data viz. The example centers around reading in all the emails from your Google account and performing a useful data analysis on the data. Nice touch!
Finally, the book also enters the realm of supervised machine learning, starting with the last part of Chapter 9 on regression models. Then Chapter 10 is a short introduction to various machine learning techniques. This chapter, however, is too brief to be a standalone learning resource, but it does kick-start the reader into thinking about this important topic.
The presumed goal of the last chapter, Chapter 11, is to offer a comprehensive data science example using the well-known Wine Quality data set from the UCI Machine Learning Repository. Ive used this data set in my own class materials many times, and its well-suite for this purpose. My only caveat about this chapter is that its too simplistic and too short. But it does give a correct feel for the steps in the data science process, culminating in the use of a number of common ML algorithms and their interpretation.
I would say Hands-On Exploratory Data Analysis with Python is a good addition to the library of a newbie data scientist as it contains many of the most common techniques for putting together a solid machine learning solution. I will be adding this title to my data science bibliography given out to my Introduction to Data Science students.
Contributed by Daniel D. Gutierrez, Editor-in-Chief and Resident Data Scientist for insideBIGDATA. In addition to being a tech journalist, Daniel also is a consultant in data scientist, author, educator and sits on a number of advisory boards for various start-up companies.
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Book Review: Hands-On Exploratory Data Analysis with Python - insideBIGDATA
O’Reilly Analysis Unveils Python’s Growing Demand as Searches for Data Science, Cloud, and ITOps Topics Accelerate – Business Wire
BOSTON--(BUSINESS WIRE)--OReilly, the premier source for insight-driven learning on technology and business, today announced the findings of its annual platform analysis, identifying the most-searched technology content from OReilly online learning. Each year, OReilly gathers usage data across OReilly online learning, publishing partners and learning modes, live online training courses, and virtual events to provide technology leaders with an overview of key trends, tools, and topics to watch and adopt within their own businesses.
Of note, the research found Python continues to be the most popular programming language to learn, building on the growing demand from the previous year by 27%. This is a significant increase for a language that was already topping the list. While scikit-learn, Pythons machine learning (ML) library, remains a front-runner in usage with 11% year-over-year growth, the popularity of PyTorch, an ML framework used for deep learning, increased by a staggering 159%.
Additional findings from the analysis include:
At the rate new technology emerges, its important to identify the trends and tools that really impact learning among the tech practitioners themselves, said Mike Loukides, vice president of emerging technology content at OReilly. Analyzing yearly trends in technology usage while keeping tabs on whats gaining traction helps our community stay on track to remain current and competitive in the real world. What were seeing within programming languages, AI/ML, data science, IT operations, and security provides a forecast on the systems and tools that will fuel innovation in 2021 and beyond.
For the full platform analysis and data, please visit: https://www.oreilly.com/radar/where-programming-ops-ai-and-the-cloud-are-headed-in-2021/.
About OReillyFor 40 years, OReilly has provided technology and business training, knowledge, and insight to help companies succeed. Our unique network of experts and innovators share their knowledge and expertise through the companys SaaS-based training and learning solution, OReilly online learning. OReilly delivers highly topical and comprehensive technology and business learning solutions to millions of users across enterprise, consumer, and university channels. For more information, visit http://www.oreilly.com.
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Data, AI and babies – BusinessLine
Researchers at IIT Madrass Initiative for Biological Systems Engineering (IBSE) are poring over millions of data points to see why babies are delivered prematurely in India. They want to develop models that will predict the possibility of preterm births and help pregnant women guard against such deliveries. The IBSE is an interdisciplinary group using data science to solve biological problems with machine learning.
Professors Himanshu Sinha, quantitative geneticist, and Karthik Raman and Raghunathan Rengaswamy, chemical engineers, have the basic raw material for their research oodles of data. The Translational Health Science and Technology Institute (THSTI), a government clinical research institute of the Department of Biotechnology, under a programme called Garbh-Ini a pregnancy cohort to study preterm birth in India led by Dr Shinjini Bhatnagar has gathered from the Gurugram Civil Hospital since 2015 a mind-boggling 1,300 parameters for each of the 8,000 pregnant women surveyed.
Some of these are microbiome data collected from saliva, feces, vagina; some, information ultrasound scans, and some more on clinical parameters such as blood samples, temperature and blood pressure. Other pieces of data relate to socio-economic factors income levels, number of rooms in the house, the type of cooking stoves used (for possible smoke effects) and so on.
Now, using machine learning, the researchers will develop a model that will show, early during a pregnancy, if a woman runs the risk of a preterm delivery.
India is the preterm delivery capital of the world. Thirteen per cent of the deliveries in India are preterm, which works out to a quarter of all preterm deliveries in the world. Half of the babies delivered early in India dont survive beyond five months. (Preterm is before 37 weeks, while normal term is 40 weeks.) Obviously, this situation needs correction.
Sinha and Raman told Quantum that they got really clean data all numbers checked, outliers verified and properly formatted for machine learning. But some challenges popped up.
One was class imbalance, a common problem in machine learning. The algorithm will learn more from the majority class in the sample and less from the minority, explains Sinha. In this case, the majority of the pregnant women are normal term; only about 13 per cent are preterm. If this is not factored in there are many techniques to do that the predictions will be less accurate. The factors that cause preterm would be not learnt, Sinha says.
Non-linear effects posed another challenge. Simply put, the effect of something the pregnant woman does in the first three months may pop up, not in the second three months, but much later in the pregnancy. It is easier to predict linear effects than non-linear ones.
The researchers have analysed the data of the first three months and developed the first India-specific model to date the pregnancy in the first trimester. Data pertaining to the next two trimesters is being processed right now.
In the end, the doctors will have tools to know if there is a high likelihood of a preterm birth which will point to the need for corrective measures. The outcome will be healthy babies and happy mothers.
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