Category Archives: Data Science
Fill the Application for These Top Data Scientist Jobs in MNCs Today – Analytics Insight
Data scientist jobs in MNCs require applicants to be technically wise and use advanced tools.
Data scienceseems to be one of the few career options that survived unscathed during the pandemic. As companies rely more on big data and artificial intelligence, thedemand for data scientistsand otherdata scienceprofessions has drastically surged. Data will be generated every day, and in return, there will always be a need for someone to make sense of it. Although many companies open their door to welcome talenteddata scienceprofessionals, there is hype fordata scientistsworking in multi-national corporations (MNCs).Data scientist jobs in MNCsrequire applicants to be technically wise and use advanced tools to extract knowledge and insights from structured and unstructured data. Not just big tech companies, even MNCs in healthcare, communication, banking, education, etc. are also looking for skilleddata scientists. According to a report,top data scientist jobs are expected to grow by 16% through 2028. Besides, many MNCs are also readily providing work-from-home facilities fordata scienceprofessionals. Analytics Insight has listed topdata scientist jobs in MNCsthat aspirants can apply for today.
Location: Kochi, Kerala
Roles and Responsibilities: As a data scientist at IBM, the candidate will be working with a worldwide team on financial products. He/she will be participating in various solutions and come up with a common approach to be able to solve the issue by using machine learning capabilities. Thye should propose and design the solution and approach to solve a machine learning problem. The candidate needs to develop and code the solution from end to end and be able to integrate with existing offerings. As part of the worldwide team, they should also work on complex problems dealing with financial crimes.
Qualifications:
Applyherefor the job.
Location: Bangalore, Karnataka
Roles and Responsibilities: The data scientist will be part of the Enterprise IT, Information, and Data Science team and drive a strategic and actionable data science architecture to activate the needed business capabilities. He/she should work to deliver business use cases like Care Provider 360 initiative, Product Bundling, Value-Based pricing, Conversational AI, Indirect Trade partner classification, etc. They should ensure the strategic direction for data science capabilities for Philips is created and kept up to date on regular basis. The candidate should continuously evaluate the latest techniques in artificial intelligence, machine learning, robotics, statistical analysis, etc.
Qualifications:
Applyherefor the job.
Location(s): Chennai, Bengaluru
Roles and Responsibilities: As a data scientist at Cognizant, the candidate is expected to discover the information hidden in structured and unstructured data by applying data mining techniques, doing statistical analysis, and building high-quality prediction systems. He/she should evaluate and identify the right machine learning and data science techniques and toolsets to address a variety of predictive analytics problems. The candidate should be an individual contributor as well as a guide to fellow team members in the discovery, design, and development of analytical models.
Qualifications:
Applyherefor the job.
Location: Bengaluru
Roles and Responsibilities: LinkedIn is seeking to recruit a candidate who will work with a team of high-performing analytics, data science professionals, and product managers to identify business opportunities and optimize members experience at the company. He/she should do reporting and monitoring such as designing, creating, and automating reports and dashboards to track key business metrics in security product areas. They should be able to provide end-to-end deep-dive analytics. The candidate should develop and improve predictive models to optimize the user experience and operational efficiency.
Qualifications:
Applyherefor the job.
Location: Bengaluru
Roles and Responsibilities: Oracle is looking for a candidate who has prior knowledge in data science, predictive modeling, and validation. They are also expected to have PL, SQL, and SQL skills.
Qualification:
Applyherefor the job.
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Fill the Application for These Top Data Scientist Jobs in MNCs Today - Analytics Insight
The rise of the autonomous data science teams – ETCIO.com
By Dhrumil Dhakan
First, there was just a single IT team, then came along a data analytics team. But given the pace at which data science is evolving, it is high time that companies start looking at building autonomous data science teams to not just gain insight from data but also be able to take action based on it.
Autonomous data science teams are structured to find insights in a self-managing manner. The notion of team autonomy most common in agile methods of operations is to enable teams to make decisions of their own is central to todays world. Organizations need an Autonomous Data science team today because the need of using data is cross-functional.
But how important is this autonomy when building a data science team?
Srivastava further explains that for an organization, this co-dependence is of immense value as the nature of AI-powered products means that the core has to be customized to each customer. Building AI products requires a heavy data transformation and ML customization phase. This phase requires immense creativity and patience. Data science teams have to submerge themselves into the problem and be able to rapidly experiment, innovate and push the envelope. In such a situation, hierarchy and gates only serve to slow down progress and impede creativity.
While this may seem like the norm, it is quite difficult for data science teams to attain such autonomy as the freedom itself is not the problem but it is the ability to take action on them that becomes the problem. Most enterprises either struggle to trust leaving a business solution to a data science team or are not adequately prepared to transfer business context, constraints and problems to a data science team. This typically leads to a design-by-committee environment which leads to subpar progress and solutions.
This is not a one-way street as it takes an equally qualified team to trust them to take important decisions. While it is more than important to know the technical know-how, what makes for an ideal team member, when it comes to an autonomous data science team.
Khare believes that trust is a major part of what it takes to form a functional autonomous team, as the organization needs to know that, lets say, the insights are not shared before the necessary steps are taken.
Healthy communication and the ability to constantly ask questions and learn about the business problem adds to the skills required. Data scientists should be able to collaborate with other functional teams including product, engineering, marketing, support, customer success, operations, and sales. Data scientists have to work with stakeholders who do not share their technical depth and expertise, so they need to have a high degree of emotional intelligence, patience, and the ability to listen and educate.
An autonomous team does not exist in a vacuum, it operates within the confines of the organization, answering to their Chief Officer, and interacting with the other members of the firm on various topics. The team needs to be codependent with the other branches of the organization so function at its best.
So, how to build a culture that thrives on the autonomy of its data science team?
Ravi Pathak, Co-founder & CEO, Tatvic Analytics said, Having trust in your team is fundamental to building a sense of autonomy, however, leaders can take more proactive steps to help employees feel connected to their teams and other leaders in the organization. As a leader, you will always have to be available for guidance, help employees create strategic goals, give them the right tools to shine, and give increased flexibility where possible,
A robust team must be built that has the right levels of seniority, experience, and exposure. A mix of skill sets, backgrounds, experiences, and diversity in the team ensures that the team has access to a wide variety of ideas and thoughts. Clear objective metrics need to be defined and established and communicated to the team. This ensures that success criteria and governing KPIs are clear and understood enabling the team to self-determine their progress.
Another way leaders can develop a sense of autonomy tactically is by setting employees up with opportunities to grow, develop, and work on special projects. As they create and work on things outside of their immediate job role, their sense of autonomy increases, says Pathak on different ways to build the right culture in your organization for an autonomous team.
The most important benefit of having autonomy in a data science team is to drive a data-centric culture within the organization and establish a Learn-Plan-Test-Measure process in the other functions. It helps in creating more business leaders who are adaptable/flexible and can successfully drive organizational change.
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Learn the wonders of big data with this super-sized learning bundle for under $60 – The Next Web
TLDR: The 2021 Big Data Certification Super Training Bundle includes 15 courses for unlocking everything there is to learn about data science, machine learning, artificial intelligence and more.
We all know what Big Data and the monumental impact of the massive data science explosion on every industry from banking to telecommunications to agriculture. But there are also areas where you might be surprised to learn data analysis is also driving change, including fields as traditionally non data-driven as psychology.
Clinicians are now studying conversations between psychologists and their patients to help detect changes in how those patients form thoughts and present themselves to others to help better treat their issues. What used to seem like the realm of science fiction, data science is now commonplace practice in dozens of industries.
With the training in The 2021 Big Data Certification Super Training Bundle ($59.99, over 90 percent off, from TNW Deals), students get hands-on training with the tools and tactics for understanding and using large data sets to unlock hidden truths found inside all those numbers.
This collection is a massive gathering of training, featuring 15 courses covering more than 113 hours of instruction in everything a budding data scientist needs to know to start using those number-crunching tools to help make informed decisions on almost anything.
Modern data science often begins and ends with the coding at the heart of it all, so learners will find a handful of courses in understanding and programming using the Python coding language. Courses like Deep Dive Into Python for Data Science and Python Data Science break it all down, showing new Python users the fundamentals of coding with the versatile and power language, from tools like NumPy and Pandas to using Python libraries to better organize and process data.
From that foundation, the training expands, introducing new components like using R programming data, mathematics, and other programs like Hadoop, Spark, Storm, and more to use data faster and more effectively.
Meanwhile, further coursework delves even deeper into more specialized uses of big data from its role in tracking stock performance to data visualization to its key role in facilitating both machine learning and artificial intelligence.
Each course in The 2021 Big Data Certification Super Training Bundle is valued at $200, but rather than a $3,000 package of training, this whole collection is available now for just $59.99.
Prices are subject to change
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Learn the wonders of big data with this super-sized learning bundle for under $60 - The Next Web
Wildfire smoke may have contributed to thousands of extra COVID-19 cases and deaths in western US in 2020 – Harvard School of Engineering and Applied…
In 2020, at the same time the nation was contending with the COVID-19 pandemic, huge wildfires swept across the western U.S., including some of the largest ever in California and Washington. Wildfires produce high levels of fine particulate matter (PM2.5), which has been linked with a host of negative health outcomes, including premature death, asthma, chronic obstructive pulmonary diseases (COPD), and other respiratory illnesses. In addition, recent studies have found a link between short- and long-term exposure to PM2.5 and COVID-19 cases and deaths.The researchers from Harvard Chan School, the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), the Department of Earth and Planetary Sciences at Harvard, and the Environmental Systems Research Institute in Redlands, Calif.built and validated a statistical model to quantify the extent to which wildfire smoke may have contributed to excess COVID-19 cases and deaths in California, Oregon, and Washington, three states that bore the brunt of the 2020 wildfires. They looked at the connection between county- and daily-level data on PM2.5 air concentrations from monitoring data, wildfire days from satellite data, and the number of COVID-19 cases and deaths in 92 counties, which represented 95% of the population across the three states. The researchers accounted for factors such as weather, population size, and societal patterns of social distancing and mass gatherings.
The researchers relied on satellite data of smoke plumes to identify the locations and days affected by wildfires.
By combining satellite data with ground measurements of total PM2.5, we could more confidently distinguish smoke from other types of particles, said co-author Tianjia (Tina) Liu, a fifth-year graduate student in the Department of Earth and Planetary Sciences, who led the validation of the satellite data.The study found that from August 15 to October 15, 2020, when fire activity was greatest, daily levels of PM2.5 during wildfire days were significantly higher than on non-wildfire days, with a median of 31.2 micrograms per cubic meter of air (g/m3) versus 6.4 (g/m3). In some counties, the levels of PM2.5 on wildfire days reached extremely high levels. For instance, from September 14 to September 17, 2020, Mono County, Calif., experienced four days in a row with PM2.5 levels higher than 500 g/m3 as a result of the Creek Fire. Such levels are deemed hazardous by the U.S. Environmental Protection Agency.Wildfires amplified the effect of exposure to PM2.5 on COVID-19 cases and deaths, up to four weeks after the exposure, the study found. In some counties, the percentage of the total number of COVID-19 cases and deaths attributable to high PM2.5 levels was substantial.On average across all counties, the study found that a daily increase of 10 g/m3 in PM2.5 each day for 28 subsequent days was associated with an 11.7% increase in COVID-19 cases, and an 8.4% increase in COVID-19 deaths. The biggest effects for the COVID-19 cases were in the counties of Sonoma, Calif., and Whitman, Wash., with a 65.3% and 71.6% increase, respectively. The biggest effects for the COVID-19 deaths were in Calaveras, Calif., and San Bernardino, Calif., with a 52.8% and 65.9% increase, respectively.
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Seed grant to explore using AI to model subsurface rock formations | Penn State University – Penn State News
UNIVERSITY PARK, Pa. It is difficult for geoscientists to map sedimentary rocks' compositional and mechanical properties at high resolution, according to Yashar Mehmani, assistant professor in the John and Willie Leone Family Department of Energy and Mineral Engineering. He recently received a seed grant from the Institute for Computational and Data Sciences (ICDS) to investigate using artificial intelligence (AI) to develop a new method to model the Earths subsurface.
The ICDS seed grant program is designed to help Penn State scientists use the latest computational technology and cutting-edge data science techniques to deepen understanding and develop innovation across fields and disciplines. Mehmani received the grant for his proposal, "Using AI to Map Infrared Spectra to Geomechanical Properties from the Micron to Meter Scale."
"I am super excited," said Mehmani, who also is a co-funded faculty member of the Institutes of Energy and the Environment. "This seed grant is significant because the underlying idea is experimental to the point that there is a finite probability of failure. But if successful, the rewards are really high because they could potentially change how geoscientists model subsurface formations.
"What is also exciting is the promise of machine learning in this specific problem, which I have not so far formally applied in my research. The potential lies in extrapolating data from small to large and translating 'cheap but less useful' information to 'expensive but more useful' information. The speed with which this could be done opens up extraordinary possibilities," said Mehmani.
According to Mehmani, it is difficult to map sedimentary rocks' compositional and mechanical properties at high resolution because the instruments available either lack resolution or are too expensive to use on new, previously unobserved sections of a subsurface formation.
Determining the formation's mechanical properties requires drilling 100-meter-long cores of rock and then extracting smaller sample for testing. While indispensable, the approach is time-consuming, leaves gaps between measurements and must be repeated whenever a new section needs analyzing even from the same formation. Mehmani proposes a new approach that would expose sedimentary rocks to infrared light and record its reflections. His team will then analyze the reflections at multiple wavelengths to understand the compositional makeup of minerals and organics within the rock. The compositional information would then be related to mechanical properties measured on lab samples using AI.
According to Mehmani, the proposed approach only needs to occur once to build the initial database for the formation. The entire process of producing the infrared spectra and mapping them to a high-resolution mechanical property could take only a few hours. This reduction of time and cost could dramatically change how subsurface formations are analyzed.
"When deployed, the AI would instantaneously translate data from a few lab samples into meter-scale information," said Mehmani."AI is that bridge. You train it on a few small samples and when you deploy it, you get something that no instrument can measure on its own."
The use of infrared imaging builds on Mehmani's previous research, which successfully used near-infrared spectra to develop models of organic-rich shales from the Green River Formation.
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Augmented analytics capabilities mark the new era of BI – TechTarget
After the era of self-service analytics, it's now the era of augmented analytics.
The rise of self-service analytics was driven by the idea of giving business users the capabilities to work with data without requiring the skills of a trained data scientist or data analyst.
It was about giving them tools such as dashboards and other data visualizations that enabled them to look at pre-aggregated data and make data-driven decisions on their own, in the moment, without first having to consult with data experts.
Now, however, analytics is moving beyond self-service.
Fueled by augmented intelligence capabilities and machine learning, vendors are developing tools that enable business users to do more than simply look at pre-aggregated data to inform their decisions. They're developing capabilities that enable users without expertise in data science to do some of the tasks that previously required the skills of a data scientist.
Augmented analytics capabilities are now enabling organization to develop data-driven cultures and give business users the tools to prepare their own data, develop their own data models, query their own data, build and run their own reports, and even get automated insights that lead to action.
"We're coming out of the self-service era," said Doug Henschen, principal analyst at Constellation Research, on Aug. 11 during a webinar hosted by analytics vendor Tellius. "Now, the trends are around augmented capabilities, which are bringing the ability of the computer to the fore. This is what's shaping the market today."
We're coming out of the self-service era. Now, the trends are around augmented capabilities, which are bringing the ability of the computer to the fore. This is what's shaping the market today. Doug HenschenPrincipal analyst, Constellation Research
And according to Henschen, four emerging augmented intelligence capabilities in particular -- augmented data preparation, guided analysis, natural language processing and smart predictions -- are moving analytics beyond self-service and into its new era.
"Not all of these are used by everybody," Henschen said. "Some are still aimed at the traditional analysts and power users, while some are aimed at broadening the tent and getting to more business users."
Augmented data preparation tools are capable of automating the tedious, time-consuming process of wrangling the right data for a given project, and then extracting, transforming and loading that data to make it actionable and drive decisions.
Using machine learning algorithms, they're capable of both lightening the workload for data scientists and enabling business analysts to manipulate data on their own.
"Augmented data prep is [mostly] for traditional users -- analysts and power users -- who like to get hands-on and are data-savvy and comfortable," Henschen said. "The idea is improving their productivity, helping them take on more of the data prep and data engineering tasks that would otherwise be done by IT departments."
Key features of augmented data preparation tools include automated data profiling, formatting and cleansing recommendations, data-join recommendations and data governance measures, Henschen added.
Among the analytics vendors offering augmented data preparation tools are Tableau with Tableau Prep Builder and Microsoft with Power BI's Dataflows. Meanwhile, data management vendors including Trifacta and Alteryx are automating the data preparation process.
Guided and intent-driven analysis is augmented by analytics capabilities aimed at providing a data workflow for users who aren't particularly data-savvy.
Guided analysis tools automatically direct users as they navigate the steps of data analysis, providing a roadmap for them to follow as they explore their data with the goal of arriving at a data-driven decision.
"They're very helpful," Henschen said. "They help more ordinary business users, but also improve the productivity of more traditional users to help them do things more quickly."
Intent-driven analysis tools, meanwhile, go a step further and use machine learning to understand the habits of individual users, users within certain departments and even users across entire organizations to make recommendations.
"These are powerful features that help broaden the tent of data and analytics to more users that may not be familiar with all the nuances of exploration," Henschen said.
Tellius, which has a tool called Guided Insights, is one vendor offering guided analysis and ThoughtSpot is among those offering automated recommendations as users work with their data.
Natural language processing (NLP) eliminates the need to know code.
By simply typing words into a search bar or even speaking into a device, users can search and query their data and receive automatically-generated responses from their analytics tools.
The tools are able to automatically translate the natural language -- most often English but also other prominent European and Asian languages, depending on the vendor -- into SQL to run the requested search or query and then translate responses back into natural language.
"It's definitely a tent-broadener, bringing more people into data and analytics," Henschen said. "They're certainly comfortable having a Google-like experience."
NLP also includes natural language generation -- using AI and machine learning to produce narratives about data, whether data stories or short explanations of the data.
"A lot of business users aren't sure what they're looking at when they see a dashboard; they're not sure what to make of a data visualization, so natural language generation develops a paragraph describing what's important in the dashboard or report," Henschen said. "It's drawing on the metadata behind the scenes and giving a textual description."
Most analytics vendors now offer some NLP capabilities. For example, Qlik acquired NLP capabilities with its 2019 acquisition of Crunch Data, while Yellowfin is among the vendors providing NLG capabilities.
Predictive analytics involves using the past to predict the future. Based on historical patterns, what can be expected next?
Predictive analytics, however, is complex, and has historically required data scientists to build and train models.
But now, using augmented analytics capabilities including automated machine learning, business users can use their BI platforms to look forward rather than just back at what's already happened, and do so without having to write code.
More advanced users, meanwhile, can also make use of smart predictive features and enable others within their organizations by embedding those predictions within dashboards so they're consumable by anyone who works with data as part of their workflow.
"It brings a broader base of users to predictive capabilities and predictive insights," Henschen said.
And that, ultimately, is the focus of augmented analytics. Using AI and machine learning, augmented analytics tools are designed to broaden the reach of analytics beyond trained data analysts and data scientists to give business users the power to make data-driven decisions.
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Augmented analytics capabilities mark the new era of BI - TechTarget
AdTheorent, a Leader in Data Science and Machine Learning Optimized Advertising, to List on NASDAQ via Merger with MCAP Acquisition Corporation -…
NEW YORK & CHICAGO--(BUSINESS WIRE)--AdTheorent, Inc., a programmatic digital advertising leader using advanced machine learning technology and solutions to deliver real-world value for advertisers and marketers, and MCAP Acquisition Corporation (NASDAQ: MACQ) (MCAP), a publicly-traded special purpose acquisition company, sponsored by an affiliate of Chicago-based asset manager Monroe Capital LLC, announced today that they have entered into a definitive business combination agreement in which AdTheorent will be merged with MCAP. Upon closing of the transaction, the combined company will be named AdTheorent, Inc. and it is expected to remain listed on the NASDAQ Capital Market. The transaction reflects an implied enterprise value for the company of approximately $775 million. The AdTheorent executive team, led by Chief Executive Officer Jim Lawson, will continue to execute the growth and strategy for the company. Given AdTheorents strong profitability and cash flow characteristics, the net cash provided by the transaction is expected to be used to support an M&A and international expansion strategy, complementing its robust organic growth profile.
Since 2012 we have pioneered a new way to target digital ads programmatically without relying on user-specific personal profiles and individualized data, said Jim Lawson, CEO of AdTheorent. AdTheorent Predictive Advertising delivers a level of superior performance only possible with advanced machine learning and our privacy-forward platform is changing what digital ad targeting can be. We are excited by the opportunities this transaction represents as we work to expand our capabilities for the most sophisticated and data-driven advertisers in the world.
Our world class team is thrilled to have this opportunity to perform on a bigger stage, said Lawson. The public company structure and proceeds provided by the transaction will allow us to enhance our growth plans beyond the already robust organic growth we are delivering in 1H21 34% year-over-year revenue ex-TAC growth in Q1 and over 70% projected in Q2.
Company Overview
AdTheorent's programmatic platform uses award-winning data science and machine learning (ML) capabilities to deliver advertiser-specific business outcomes for top consumer brands. The companys proprietary suite of tools, methodologies and vertical solutions maximizes campaign performance and ROI for advertisers, while operating in a privacy-first manner, which has quickly escalated as an essential element for brand marketers worldwide. AdTheorent's performance focus is centered around ingesting non-personalized data signals and using statistical data for modeling and targeting, representing a growing strategic advantage as regulatory and industry changes reduce marketers access to individual user identifiers such as cookies and device IDs.
Operating at massive scale, AdTheorent is able to optimize ad targeting by evaluating and providing predictive scores for more than 87 billion impressions daily, bidding on less than .01% of impressions scored. The company also leverages advanced machine learning and data science to drive platform efficiencies by optimizing against ad impressions which represent a greater risk of IVT/fraud, poor viewability and brand safety, or impressions that may not be measurable by third party measurement providers.
According to the Winterberry Group, digital media spending will exceed $171 billion in the US in 2021 and is poised for exceptional growth, driven in large part by programmatic advertising. Programmatic digital spending in the US is a $90 billion Total Addressable Market (TAM) in 2021, forecasted to grow at a 17.6% CAGR to $141 billion by 2024 and AdTheorents industry-leading Artificial Intelligence (AI) and ML powered platform and a foundational privacy-forward approach to data and targeting position it to outpace industry growth. AdTheorent serves a diverse roster of the most sophisticated and discerning advertisers in the world across diverse and attractive industry verticals, including: Healthcare & Pharmaceuticals; Banking, Financial Services and Insurance (BFSI); Government, Education & Non-Profit; Retail; Dining & QSR; and Travel & Hospitality.
AdTheorent Investment Highlights
There has never been more demand for AdTheorent capabilities and solutions, said Lawson. Our platform uses machine learning and data science in unprecedented and highly differentiated ways and our opportunities for continued innovation and advancement on this premise are vast. The future is bright for AdTheorent and our team because we created a better way for advertisers to derive provable value from their digital advertising, and we have a lot more to achieve.
Theodore Koenig, Chairman and Chief Executive Officer of MCAP, commented, AdTheorents machine learning advertising technology platform positions the company to continue to take market share in a large and rapidly growing market as consumers, regulators, and corporations alike increasingly demand advertisers shift away from outdated and less effective competitors that rely on harvesting the personal data of consumers.
Zia Uddin, Co-President of MCAP added The ability to deliver a superior ROI to the worlds largest brands with a product focused on privacy provides a clear path to continuing AdTheorents compelling combination of high growth and profitability. We are delighted to announce this business combination, which we expect to accelerate the companys growth and create value for MCAP stockholders.
Transaction Overview
The business combination values AdTheorent at a $775 million enterprise value and at a pro forma market capitalization of approximately $1 billion, assuming a $10.00 per share price and no redemptions by MCAP stockholders. The transaction will provide a minimum of $100 million of net proceeds to the company, including an oversubscribed and upsized $121.5 million fully committed common stock PIPE anchored by top-tier institutional and strategic investors including Hana Financial Group and Monroe Capital and/or one or more of its affiliates, along with Palantir Technologies, a global software company specializing in providing enterprise data platforms for use by organizations with complex and sensitive data environments.
The Boards of Directors of both MCAP and AdTheorent have unanimously approved the transaction. Completion of the proposed transaction is subject to approval of MCAP stockholders and other customary closing conditions, including the receipt of certain regulatory approvals. The transaction is expected to close in Q4 2021.
AdTheorent is currently majority owned by H.I.G. Growth Partners (H.I.G.), an affiliate of H.I.G. Capital, a leading global alternative investment firm with over $44 billion of equity capital under management. H.I.G. will continue to hold a substantial ownership position in AdTheorent.
Additional information about the proposed transaction, including a copy of the business combination agreement and investor presentation, will be provided in a Current Report on Form 8-K to be filed by MCAP with the Securities and Exchange Commission and will be available at http://www.sec.gov.
Advisors
Canaccord Genuity acted as exclusive financial advisor to AdTheorent. Bank of America Securities, Cowen and Canaccord Genuity were engaged as PIPE placement agents. Greenberg Traurig and Nelson Mullins Riley & Scarborough are serving as legal advisors to MCAP while Paul Hastings and Kirkland & Ellis are serving as legal advisors to AdTheorent.
Investor Webcast and Conference Call
MCAP and AdTheorent will host a pre-recorded joint investor conference call to discuss the proposed transaction Tuesday July 27, 2021 at 8:00AM ET. To access the call visit http://public.viavid.com/index.php?id=146011. The recording will also be available as a webcast, which can be accessed at http://www.mcapacquisitioncorp.com.
About AdTheorent
AdTheorent uses advanced machine learning technology and solutions to deliver impactful advertising campaigns for marketers. AdTheorent's industry-leading machine learning platform powers its predictive targeting, geo-intelligence, audience extension solutions and in-house creative capability, Studio AT. Leveraging only non-sensitive data and focused on the predictive value of machine learning models, AdTheorent's product suite and flexible transaction models allow advertisers to identify the most qualified potential consumers coupled with the optimal creative experience to deliver superior results, measured by each advertiser's real-world business goals.
AdTheorent is consistently recognized with numerous technology, product, growth and workplace awards. AdTheorent was awarded "Best AI-Based Advertising Solution" (AI Breakthrough Awards) and "Most Innovative Product" (B.I.G. Innovation Awards) for four consecutive years. Additionally, AdTheorent is the only five-time recipient of Frost & Sullivan's "Digital Advertising Leadership Award." AdTheorent is headquartered in New York, with fourteen offices across the United States and Canada. For more information, visit adtheorent.com.
About MCAP Acquisition Corporation
MCAP Acquisition Corporation raised $316 million in March 2021 and its securities are listed on the NASDAQ Capital Market under the ticker symbols MACQU, MACQ and MACQW. MCAP is a blank check company organized for the purpose of effecting a merger, capital stock exchange, asset acquisition, or other similar business combination with one or more businesses or entities. MCAP is sponsored by an affiliate of Monroe Capital LLC (Monroe Capital), a boutique asset management firm specializing in investing across various strategies, including direct lending, asset-based lending, specialty finance, opportunistic and structured credit, and equity. Monroe Capital is headquartered in Chicago and maintains offices in Atlanta, Boston, Los Angeles, Naples, New York, and San Francisco.
MCAP is the third SPAC in which Monroe has participated as a sponsor. In 2018, Monroe co-sponsored Thunder Bridge Acquisition, Ltd. and supported its successful business combination with Repay Holdings Corporation (NASDAQ: RPAY). In 2019, Monroe co-sponsored Thunder Bridge Acquisition II, Ltd. and supported its successful business combination with indie Semiconductor (NASDAQ: INDI).
MCAP is led by Chairman and Chief Executive Officer Theodore Koenig, who is President, CEO & Founder of Monroe Capital and has been the CEO and Chairman of Monroe Capital Corporation (NASDAQ: MRCC) since 2011. He is joined by Co-President Zia Uddin, who is a Partner at Monroe Capital; Co-President Mark Solovy, who serves as a Managing Director and Co-Head of the Technology Finance Group at Monroe Capital; and CFO Scott Marienau, who is the CFO of Monroe Capitals management company.
As of July 1, 2021, Monroe Capital had approximately $10.3 billion in assets under management. Monroe Capitals assets under management are comprised of a diverse portfolio of over 475 current investments. From Monroe Capitals formation in 2004 through March 31, 2021, Monroe Capitals investment professionals have invested in over 1,450 loans and related investments in an aggregate amount of $21.5 billion, including over $6.1 billion in 330 software, technology-enabled and business services companies.
To learn more please, visit http://www.mcapacquisitioncorp.com. The information that may be contained on or accessed through this website is not incorporated into this release.
Additional Information and Where to Find It
For additional information on the proposed transaction, see MCAPs Current Report on Form 8-K, which will be filed concurrently with this press release. In connection with the proposed transaction, MCAP intends to file relevant materials with the Securities and Exchange Commission (the SEC), including a registration statement on Form S-4 with the SEC, which will include a proxy statement/prospectus of MCAP, and will file other documents regarding the proposed transaction with the SEC. MCAPs stockholders and other interested persons are advised to read, when available, the preliminary proxy statement/prospectus and the amendments thereto and the definitive proxy statement and documents incorporated by reference therein filed in connection with the proposed business combination, as these materials will contain important information about AdTheorent, MCAP and the proposed business combination. Promptly after the Form S-4 is declared effective by the SEC, MCAP will mail the definitive proxy statement/prospectus and a proxy card to each stockholder entitled to vote at the meeting relating to the approval of the business combination and other proposals set forth in the proxy statement/prospectus. Before making any voting or investment decision, investors and stockholders of MCAP are urged to carefully read the entire registration statement and proxy statement/prospectus, when they become available, and any other relevant documents filed with the SEC, as well as any amendments or supplements to these documents, because they will contain important information about the proposed transaction. The documents filed by MCAP with the SEC may be obtained free of charge at the SECs website at http://www.sec.gov, or by directing a request to MCAP Acquisition Corporation, 311 South Wacker Drive, Suite 6400, Chicago, Illinois 60606.
Participants in the Solicitation
MCAP and its directors and executive officers may be deemed participants in the solicitation of proxies from its stockholders with respect to the business combination. A list of the names of those directors and executive officers and a description of their interests in MCAP will be included in the proxy statement/prospectus for the proposed business combination when available at http://www.sec.gov. Information about MCAPs directors and executive officers and their ownership of MCAP common stock is set forth in MCAPs prospectus, dated February 25, 2021, as modified or supplemented by any Form 3 or Form 4 filed with the SEC since the date of such filing. Other information regarding the interests of the participants in the proxy solicitation will be included in the proxy statement/prospectus pertaining to the proposed business combination when it becomes available. These documents can be obtained free of charge from the source indicated above.
AdTheorent and its directors and executive officers may also be deemed to be participants in the solicitation of proxies from the stockholders of MCAP in connection with the proposed business combination. A list of the names of such directors and executive officers and information regarding their interests in the proposed business combination will be included in the proxy statement/prospectus for the proposed business combination.
Forward Looking Statements
This communication contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. Such statements include, but are not limited to, statements about future financial and operating results, our plans, objectives, expectations and intentions with respect to future operations, products and services; and other statements identified by words such as will likely result, are expected to, will continue, is anticipated, estimated, believe, intend, plan, projection, outlook or words of similar meaning. These forward-looking statements include, but are not limited to, statements regarding AdTheorents industry and market sizes, future opportunities for AdTheorent and MCAP, AdTheorents estimated future results and the proposed business combination between MCAP and AdTheorent, including the implied enterprise value, the expected transaction and ownership structure and the likelihood, timing and ability of the parties to successfully consummate the proposed transaction. Such forward-looking statements are based upon the current beliefs and expectations of our management and are inherently subject to significant business, economic and competitive uncertainties and contingencies, many of which are difficult to predict and generally beyond our control. Actual results and the timing of events may differ materially from the results anticipated in these forward-looking statements.
In addition to factors previously disclosed in MCAPs reports filed with the SEC and those identified elsewhere in this communication, the following factors, among others, could cause actual results and the timing of events to differ materially from the anticipated results or other expectations expressed in the forward-looking statements: inability to meet the closing conditions to the business combination, including the occurrence of any event, change or other circumstances that could give rise to the termination of the definitive agreement; the inability to complete the transactions contemplated by the definitive agreement due to the failure to obtain approval of MCAPs stockholders; the failure to achieve the minimum amount of cash available following any redemptions by MCAP stockholders; redemptions exceeding a maximum threshold or the failure to meet The Nasdaq Stock Markets initial listing standards in connection with the consummation of the contemplated transactions; costs related to the transactions contemplated by the definitive agreement; a delay or failure to realize the expected benefits from the proposed transaction; risks related to disruption of managements time from ongoing business operations due to the proposed transaction; changes in the digital advertising markets in which AdTheorent competes, including with respect to its competitive landscape, technology evolution or regulatory changes; changes in domestic and global general economic conditions; risk that AdTheorent may not be able to execute its growth strategies, including identifying and executing acquisitions; risks related to the ongoing COVID-19 pandemic and response; risk that AdTheorent may not be able to develop and maintain effective internal controls; and other risks and uncertainties indicated in MCAPs final prospectus, dated February 25, 2021, for its initial public offering, and the proxy statement/prospectus relating to the proposed business combination, including those under Risk Factors therein, and in MCAPs other filings with the SEC. AdTheorent and MCAP caution that the foregoing list of factors is not exclusive.
Actual results, performance or achievements may differ materially, and potentially adversely, from any projections and forward-looking statements and the assumptions on which those forward-looking statements are based. There can be no assurance that the data contained herein is reflective of future performance to any degree. You are cautioned not to place undue reliance on forward-looking statements as a predictor of future performance as projected financial information and other information are based on estimates and assumptions that are inherently subject to various significant risks, uncertainties and other factors, many of which are beyond our control. All information set forth herein speaks only as of the date hereof in the case of information about MCAP and AdTheorent or the date of such information in the case of information from persons other than MCAP or AdTheorent, and we disclaim any intention or obligation to update any forward-looking statements as a result of developments occurring after the date of this communication. Forecasts and estimates regarding AdTheorents industry and markets are based on sources we believe to be reliable, however there can be no assurance these forecasts and estimates will prove accurate in whole or in part. Annualized, pro forma, projected and estimated numbers are used for illustrative purpose only, are not forecasts and may not reflect actual results.
Non-GAAP Financial Measures
This press release also includes certain non-GAAP financial measures that AdTheorents management uses to evaluate its operations, measure its performance and make strategic decisions, including Revenue ex-TAC and Adjusted EBITDA. We believe that Revenue ex-TAC and Adjusted EBITDA provide useful information to investors and others in understanding and evaluating AdTheorents operating results in the same manner as management. However, Revenue ex-TAC and Adjusted EBITDA are not financial measures calculated in accordance with GAAP and should not be considered as substitutes for revenue, net income, operating profit or any other operating performance measures calculated in accordance with GAAP.
No Offer or Solicitation
This press release shall not constitute a solicitation of a proxy, consent, or authorization with respect to any securities or in respect of the proposed business combination. This press release shall also not constitute an offer to sell or the solicitation of an offer to buy any securities, nor shall there be any sale of securities in any states or jurisdictions in which such offer, solicitation, or sale would be unlawful prior to registration or qualification under the securities laws of any such jurisdiction. No offering of securities shall be made except by means of a prospectus meeting the requirements of Section 10 of the Securities Act of 1933, as amended, or an exemption therefrom.
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Why is it Necessary for Engineers to Learn Data Science in 2021? – Analytics Insight
Whether you agree or not, the hype for engineering is dying out real quick in the third decade of the 21st century. Although the trend forengineerswas at its peak just five to eight years ago, technology is currently popularizingdata scienceprofessionals overengineers. But this is not the end for people who did engineering in the first place. They still have an opportunity to make an amazing comeback with the help ofdata science. Yes, it is necessary forengineers to learn data sciencein 2021, in order to keep their place in the job market.
Data science is a blend of mathematics, machine learning, business decision tools, and algorithms. It helps businesses bring out knowledge and insight from structured and unstructured data. With data becoming the center of decision-making in almost every industry, the demand fordata scienceprofessionals has also surged in the recent past. On the other hand,engineersare highly skilled professionals who need a switch. Most engineers are looking for ways to shift from their engineering jobs todata scienceor the big data industry to stay ahead in the job market. But adopting such a massive change involves challenges. As it is mandatory forengineers to learn data scienceto survive, they are willing to take the risk. Besides, the collaboration betweenengineering and data scienceis also bringing hope among many sectors including healthcare and pharmaceuticals, telecommunication, energy, automobile, banking, etc. They know how to enhance productivity and algorithm code quality by writing simple, performant, readable, and maintainable code. Engineers get to use engineering tactics along with business tools like Tableau, R, Apache Spark, SAS, Python, and many others.
As mentioned earlier,data scienceis a blend of many engineering necessities. Therefore, switching from engineering todata scienceinvolves expanding your skills in more data science-related tools. For example, if you are from Mechanical Engineering, then you must have a strong background in mathematics and physics, which can help you learn data analytics, machine learning tools, and other technological aspects easily. If you are a Computer, IT, or Software Engineer, then your existing software, hardware, networking tools, and knowledge in big data will help you embracedata sciencequickly.
Engineers who have worked for a long time in the industry might feel at ease while they are trying their hand at data science in the 21st century. But it is totally different for beginners. Engineers who started working just a couple of years might find it extremely daunting. The extreme void is because of their different inexperience in the market. Experienced engineers have a statistical mindset and reasoning, which is important in data science. On the other hand, freshers are not much into statistical point of view as they have just begun their career. To patch this gap, new engineers should work extra to become well-versed in data science in 2021. They should learn to generate hypotheses, analyze graphs, plots, and reasoning. Engineers should become experts in handling structured and unstructured data.
Besides planning for a shift from engineering to data science, engineers can also embrace the techniques of data science and streamline their current working process. As engineers are exposed to data constantly, their decision-making skills are already highly based on predicted big data outcomes. But dealing with massive data is different. Fortunately, data science can help you handle large data and take effective decisions based on that.
Engineers who have learned data science can easily connect the dots of the data ecosystem within a company or institution. Besides, learning data science comes with a list of advantages as listed below.
Data science is evolving to be the backbone of decision-making. Engineers who have learned data science are responsible for both the works of a data analyst and data scientist.
Engineers can understand coding better when they mend their skills with data science. They find easy and convenient ways to create abstract, broad, efficient, and scalable solutions.
Learning data science comes with great financial rewards. Over a short period of time, engineers gain value and can demand a high salary or switch to a job with a high salary after learning data science.
Even if you dont want to carry on your job as an engineer, but want to work in data science, it can be very useful to have basic knowledge from engineering courses.
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Why is it Necessary for Engineers to Learn Data Science in 2021? - Analytics Insight
Alliance formed to create new professional standards for data science – FE News
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The FE News Channel gives you the latest education news and updates on emerging education strategies and the#FutureofEducation and the #FutureofWork.
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In Jan 2021, FE News had over 173,000 unique visitors according to Google Analytics and over 200 new pieces of news content every week, from thought leadership articles, to the latest education news via written word, podcasts, video to press releases from across the sector, putting us in the top 2,000 websites in the UK.
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Alliance formed to create new professional standards for data science - FE News
Hypertension: Comparing ACE inhibitors and ARBs – Medical News Today
ACE inhibitors and ARBs are equally recommended as first-line medications in the treatment of high blood pressure.
Currently, doctors prescribe ACE inhibitors more often than they do ARBs. However, few studies have compared the two classes of drugs directly.
A recent study published in Hypertension, an American Heart Association journal, set out to do just that. Study authors investigated whether there were any differences between the two sets of medication in terms of effectiveness and side effects.
ACE inhibitors and ARBs act on the renin-angiotensin-aldosterone system, which is a system of hormones that help regulate blood pressure. While both ACE inhibitors and ARBs are effective, the way they reduce hypertension is different.
Angiotensin is a hormone that narrows blood vessels, thereby restricting blood flow and increasing blood pressure. ACE inhibitors block an enzyme that triggers the production of angiotensin, which therefore reduces blood pressure.
ARBs block angiotensin receptors in the blood vessels. This diminishes the blood vessel-constricting effects of the angiotensin.
While people who are beginning treatment for high blood pressure can benefit equally from either of these medications, the recent study reports that ARBs may have fewer medication-related side effects than the ACE inhibitors.
The large-scale study focused on over 3 million participants with no history of heart disease or stroke who began high blood pressure treatment using ACE inhibitors or ARBs.
Eight electronic health record and insurance claim databases in the United States, Germany, and South Korea provided data for the study.
While prior research points to the similar effectiveness of these medications, information was limited or missing with regard to head-to-head comparisons of medication side effects in those who are starting hypertension treatments.
In addition, disagreement exists between studies as to whether ACE inhibitors, due to their longer history of use, should be the preferred form of treatment.
With so many medicines to choose from, we felt we could help provide some clarity and guidance to patients and healthcare professionals, says author RuiJun Chen, assistant professor in translational data science and informatics at Geisinger Medical Center in Danville, PA.
Researchers compared the occurrence of heart-related events and stroke among nearly 2.5 million people treated with ACE inhibitors with almost 700,000 patients treated with ARBs.
They also considered 51 different medication side effects between the two groups.
While finding no significant differences in the occurrence of any cardiac event, the study authors noticed major differences in observed side effects.
Compared with those taking ARBs, people who took ACE inhibitors were around 30% more likely to develop a persistent dry cough.
Dr. Matthew Tomey, a cardiologist and assistant professor of medicine and cardiology at the Icahn School of Medicine at Mount Sinai in New York City, NY, told Medical News Today that the chronic cough associated with ACE inhibitors is often the reason a prescriber will switch a patient from an ACE inhibitor to an ARB.
Results from the study also show that people taking ACE inhibitors were three times more likely to develop fluid accumulation, swelling of the deeper layers of the skin and mucous membranes, and a sudden inflammation of the pancreas.
Finally, those taking ARBs were 18% more likely to develop gastrointestinal bleeding.
In an interview with MNT, Dr. Gosia Wamil, Ph.D., a cardiologist at Mayo Clinic Healthcare in London, United Kingdom, made the following point after reviewing the study,
Given the likely potentially life threatening consequences of these adverse events, these are important warnings, which we will need to watch carefully when prescribing ACE [inhibitors].
However, Dr. Wamil also made it clear that retrospective observational studies such as these are limited by residual confounding and bias. She explained that, when the authors conducted further analyses with corrections, they did not fully reproduce the level of statistical significance.
While this study is notably strong in the number of patients tracked, the authors note several limitations. Among these is the possibility that because all the participants were just beginning treatment for hypertension, the results may not be applicable to people who were being treated and switched medications.
Dr. Wamil commented on the need for more head-to-head analyses between these two drug types. She believes approaching the study from an economic perspective, such as evaluating and comparing generic forms of these medicines, would be especially valuable for the public.
Agreeing with the need for further study, Dr. Tomey said, Observational studies, such as this one, are important tools to generate hypotheses, but they seldom provide final answers. For that, he explained, we need randomized clinical trials.
Dr. Tomey mentioned patients who may have other preexisting medical conditions that need to be treated along with hypertension. He concluded: We need to be sensitive to the fact that certain specific groups of patients may yet get superior benefits from one drug over the other.
Although the authors of this study suggest that their findings support preferential prescribing of ARB over ACE due to their better safety profile, Dr. Wamil concluded, I believe the main message from that study supports the use of these two groups of antihypertensive drugs in the prevention of major cardiovascular events.
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Hypertension: Comparing ACE inhibitors and ARBs - Medical News Today