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Showcasing new product capabilities during 10-city World Tour, including new object-centric data model and a more intuitive, open and intelligent platform

NEW YORK & MUNICH, May 23, 2023--(BUSINESS WIRE)--Celonis, the global leader in Process Mining, today showcases a series of powerful new product capabilities that enable companies to rapidly find and capture business value within their processes. At the Celonis World Tour 2023 - that kicked off today in Munich, Germany - the company demonstrates how it helps its customers to improve the performance of their core business processes. The new features demonstrate how the company has extended and enhanced its core platform and underscore how Celonis continues to reinvent process mining.

Object-Centric Data Model

Following on from the launch of Object-Centric Process Mining at Celosphere 2022, Celonis now introduces the Object-Centric Data Model, a new and powerful single data representation of an entire business that enables companies to dramatically accelerate the speed with which they can model their process data, gain critical insights, and take action to start realizing value. Celonis Object-Centric Data Model reduces the work needed to transform the data from source systems and operates side by side with Event Log Data Models, making the transition non-disruptive. It also powers Celonis new End-to-End Lead Times App - also launching today - which enables supply chain leaders to fully comprehend end-to-end lead times and understand the impact of each process to accelerate cash conversion and exceed service levels.

Benefits of Celonis Object-Centric Data Model include:

Simplicity. Businesses can work with a data model that uses the same language they do - for invoices, orders, deliveries rather than the language of source systems, to better model the business;

System agnosticity. Companies can take advantage of Celonis apps and process content, no matter which ERP, SCM, CRM or other source system they use, leveraging standardized business definitions and prebuilt transformations for core processes;

Flexibility. With traditional process mining, a business needs a data model for each process it wants to examine. With the new object-centric data model, organizations can dynamically adjust process analyses, switching perspectives from process to process without needing to go back to the source data. This significantly reduces onboarding time, and allows organizations to get to insights and key actions much faster.

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"I look forward to extending our usage of Celonis market-leading process mining solution throughout our organization," said Travis Cain, Senior Director Enterprise Architecture & Data Science Experience for Dell Technologies. "The Celonis Execution Management System has already provided us with tremendous benefits, including the ability to more easily find bottlenecks in our processes and get to deeper business insights more quickly. Using Celonis process mining capabilities gives us insights into the complexity of our operations and helps us to drive rapid business value by improving the performance of our core business processes."

"Object-centric process mining allows users to easily navigate through processes based on real-life objects and events, and fully unleashes the power of process mining," said Alex Rinke, co-founder and co-CEO at Celonis. "Customers can build and interact with their data more naturally, in a way they are already familiar with. With this new expanded data foundation, as well as the additional capabilities we are unveiling during this years World Tour, I am thrilled to show customers how Celonis provides them with unparalleled visibility, helps them disseminate process intelligence, and enables intelligent actions across the enterprise."

An Intuitive Platform: Process Intelligence Accessible to All Users

Celonis Business Miner, the industrys first intelligent process investigation technology and collaboration workspace, arms every business user with a breakthrough new way to easily, and independently investigate, communicate, and team up around process problems and opportunities. With it, Celonis deepens and democratizes the power of process mining so that everyone in an organization, not just analysts, can understand and improve process performance. No technical background is needed to be able to take advantage of process mining.

Celonis Business Miner is now generally available with process-specific explorations in Celonis core processes: Accounts Payable, Accounts Receivable, Procurement, and Order Management, as well as for additional process-agnostic explorations that focus on topics like cycle times, rework, and undesired activities.

With its intuitive question-and-answer-based exploration that provides quick and consumable insights, Celonis makes this powerful communication and collaboration tool accessible to all users with a Celonis Execution Management System (EMS) license, enabling unparalleled scalability and performance.

An Open Platform: Intelligence API

Celonis is also focused on providing its customers with an open platform - a critical strength recently recognized by Gartner.

First announced at Celosphere 2022 and generally available since March 2023, the Celonis Intelligence API is designed to provide governed access to Celonis process intelligence for use within third party applications.

The Intelligence API enables Celonis to bring unique process intelligence to tools as varied as PowerBI for reporting at scale, Slack for instant communication about process insights, and ServiceNow for rapid action triggered by process mining. Celonis itself uses Intelligence API to surface critical process insights within Salesforce to help its teams within software environments where they already work.

This makes the Intelligence API more open and accessible to an entire organization and enables intelligent orchestration and process-intelligent applications. Emporix, who launched their Commerce Execution Platform (CXP) at Celosphere 2022, are pioneering in this space. The Emporix CXP is a workflow engine that helps organizations optimize their business outcomes by leveraging process insights from Celonis.

Celonis provides the process context while Emporix orchestrates the end customers experience and interactions so that Emporix is delivering a better, more tailored customer experience as well as more efficient and cost-effective operations for customers.

Furthermore, the Intelligence API is a key component of Celonis platform strategy, as the company continuously aims to deliver a more intuitive, more open and more intelligent platform to its customers.

"The current macroeconomic climate demands better ways of working, which means organizations urgently need to improve their core business processes," says R "Ray" Wang, CEO and Principal Analyst, Constellation Research, Inc. "Processes are the fabric of every business, but many do not deliver much value - if any. With features such as the Object-Centric Data Model, Business Miner, and Intelligence APIs, customers can rapidly improve processes to achieve cost savings, increase customer satisfaction, meet regulatory requirements and boost overall performance."

The Celonis World Tour kicks off on May 23 and covers 10 stops across the US, Europe and Japan. It is Celonis global process mining roadshow where customers, prospects and partners share how, with Celonis, they are able to find and capture business value within processes, enabling them to perform at levels they never thought possible. The Celonis World Tour kicked off shortly after Celonis was named a leader in Gartners first-ever Magic Quadrant for Process Mining Tools. The report catapulted process mining into C-level consciousness and placed Celonis highest and farthest to the right on both axes, Ability to Execute and Completeness of Vision.

About Celonis

Celonis enables customers to optimize their business processes. Powered by its leading process mining technology, Celonis provides a unique set of capabilities for business executives and users to continuously find improvement opportunities within and across processes, and execute targeted actions to rapidly enhance process performance. This optimization yields immediate cash impact, radically improves customer experience, and reduces carbon emissions. Celonis has thousands of implementations with global customers and is headquartered in Munich, Germany and New York City, USA with more than 20 offices worldwide.

2022 Celonis SE. All rights reserved. Celonis, Execution Management System, EMS and the Celonis "droplet" logo are trademarks or registered trademarks of Celonis SE in Germany and other jurisdictions. All other product and company names are trademarks or registered trademarks of their respective owners.

View source version on businesswire.com: https://www.businesswire.com/news/home/20230523005621/en/

Contacts

Celonis press@celonis.com

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Celonis Launches New Process Mining Innovations to Deliver Fast, Substantial Value to Customers - Yahoo Finance

High-Speed Engine Market to reach USD 29.9 billion by 2029 at a CAGR of 3.9 percent says Maximize Market Research

Pune, May 25, 2023 (GLOBE NEWSWIRE) -- A global Material & Chemical business consulting firm, Maximize Market Research has published a market intelligence and competitive landscape report on the High-Speed Engine Market . The report is a combination of primary data and secondary data and domain experts have analyzed the High-Speed Engine Market from a global point and regional standpoint. Over the forecast period, Maximize Market Research expects the market to grow from USD 23.6 Bn. in 2022 to USD 29.9 Bn. in 2029 at a CAGR of 3.9 percent.

High-Speed Engine Market Report Scope and Research Methodology

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The High-Speed Engine Market report includes a comprehensive analysis of emerging trends, market drivers, growth opportunities, and major restraints in the industry. It also provides a detailed analysis of the major segments of the High-Speed Engine Market with their sub-segments. The report covers historical data for understanding the past and forecasting future trends in the High-Speed Engine industry. To understand the structure of the market and the prevailing competition in the industry, a thorough regional and competitive analysis of the High-Speed Engine Market is covered in the report. The competitive landscape includes key players in the market along with new entrants. Regional analysis of the market is covered at global, regional, and country levels for understanding the market penetration, dominant players, and growth strategies used by them.

The bottom-up approach was used to estimate the global and regional High-Speed Engine Market size. The main research methodology used by the Maximize Market Research team is data triangulation which involves data mining , analysis of the impact of data variables on the High-Speed Engine Market, and primary (industry expert) validation. The report includes extensive use of secondary sources directories and databases such as Bloomberg, Hoovers, Statista and other government associations. The company websites and private websites have also been used to identify and collect information useful for the technical, market-oriented and commercial study of the High-Speed Engine Market. To provide strengths, weaknesses, opportunities, and threats in the High-Speed Engine Market, a SWOT analysis was used. PESTLE was employed to understand the potential impact of the micro-economic and macro-economic factors affecting the High-Speed Engine Market.

High-Speed Engine Market Overview

High-Speed Engine engines are typically used in high-performance vehicles, including sports cars, supercars, and race cars. They are designed to deliver exceptional power, torque, and acceleration, enabling the vehicles to achieve top speeds. Continuous advancements in engine technology contribute to the growth of the high-speed engine market.

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High-Speed Engine Market Dynamics

Advancements in engine technology are crucial for the high-speed engine market, playing a pivotal role in enhancing engine performance, efficiency, and reliability. Manufacturers are continuously innovating to improve these aspects by integrating technologies such as direct injection, turbocharging, variable valve timing, and lightweight materials into high-speed engines. These advancements result in increased power output, responsiveness, and fuel efficiency, fostering competition among manufacturers and driving the development of more powerful and efficient high-speed engines.

The automotive industry is currently undergoing a notable transition towards electric and hybrid powertrains. This shift presents both challenges and opportunities for the high-speed engine market. The increasing demand for electric vehicles (EVs) and hybrid vehicles has significantly influenced the market dynamics. In this context, Ferrari holds a prominent position in the high-speed engine market. The company is renowned for its exceptional V8 and V12 engines, which power its sports cars and supercars, solidifying its presence in the industry.

High-Speed Engine Market Regional Insights

North America is expected to be the leading revenue contributor in the High-Speed Engine market during the forecast period. The presence of a well-established industrial sector, technological advancements, and a strong focus on energy efficiency and emissions regulations drive the demand for high-speed engines. The North American high-speed engine market is highly competitive with the presence of both domestic and international manufacturers. Prominent manufacturers in the region include Cummins Inc., Caterpillar Inc., and MTU America among others. These companies offer a wide range of high-speed engines with varying power outputs and applications catering to the diverse needs of industries in North America.

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High-Speed Engine Market Segmentation

By Speed

By Power Output

High-Speed Engine Market's Key Competitors include:

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Key Offerings:

Key questions answered in the High-Speed Engine Market are:

Maximize Market Research is leading research firm, has also published the following reports:

Automotive Engineering Services Market - The market size is expected to reach USD 303.23 Bn by 2029 at a CAGR of 8.9 percent during the forecast period. The consistently rising consumer trust in engineering services and the significant RD investments made by the automobile industry .

Aircraft Engines Market - Market size was valued at US$ 84.96 Bn and expected to reach US$ 137.47 Bn by 2029, at a CAGR of 6.2% throughout the forecast period.

Heavy Duty Automotive Aftermarket Market - The total Market size is expected to reach USD 105.38 Billion by 2029 at a CAGR of 5.2 percent during the forecast period. The growing popularity of e-commerce channels is driving the growth of the heavy-duty automotive aftermarket market.

Automotive Suspension System Market - The total market size is expected to reach USD 95.94 Bn. by 2029 at a CAGR of 6 percent during the forecast period. The market is expected to be driven by Government and automaker initiatives to introduce commercial electric vehicles.

Automotive Data Cable Market - The market size is expected to reach USD 16.48 Billion by 2029 at a CAGR of 11.5 percent during the forecast period. The rise of electric vehicles (EVs) and autonomous vehicles (AVs) is a major driver impacting the automotive data cable market.

About Maximize Market Research:

Maximize Market Research is a multifaceted market research and consulting company with professionals from several industries. Some of the industries we cover include medical devices, pharmaceutical manufacturers, science and engineering, electronic components, industrial equipment, technology and communication, cars and automobiles, chemical products and substances, general merchandise, beverages, personal care, and automated systems. To mention a few, we provide market-verified industry estimations, technical trend analysis, crucial market research, strategic advice, competition analysis, production and demand analysis, and client impact studies.

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High-Speed Engine Market to reach USD 29.9 billion by 2029 at a ... - InvestorsObserver

Regional context

Indonesia (officially the Republic of Indonesia) is the largest archipelago country (over 17,000 islands) and the 14th largest country by area (about 2 million km2) in the world 19. The archipelago covers tropical rainforest, tropical monsoon, and tropical savanna climates, where there are more than 300 ethnic groups 19. Indonesia consists of 38 provinces, and the governance system is decentralized since the end of the twentieth century 19,20. In Indonesia, regencies (kabupaten) and representative cities (kota) are positioned at the same administrative level (level 2); the average size of regencies/cities is 3622 km2 (ranging from 10 to 44,013 km2). Regencies/cities are administratively separated by geographic conditions and historical (social, cultural, and political) backgrounds, with each regency/city having homogeneous environmental conditions and a socioeconomic status. 16.

The main drivers of the rapid deforestation in Indonesia were population growth and economic expansion, including the illegal clearance of forests 21,22. Indonesias economy has grown by>5% per year on average since 2000 until the Covid-19 pandemic; GDP PPP (international dollars) increased about three times from 1 Trillion USD in 2000 to 3.33 Trillion USD in 2019 23. Additionally, its population has increased by 1.14% annually, leading to an approximately 30% growth during this period (i.e., from 211 Million in 2000 to 273 Million in 2020), making it the most populous and prosperous country in Southeast Asia 19. Indonesia is also one of the largest emitters of greenhouse gases (GHG) 24.

In contrast, the Indonesian government is committed to unconditionally reduce its GHG emission by 29% and up to 41% if international assistance for finance, technology transfer, and capacity building are provided25. The land-use and energy sectors are to minimize the national GHG emission. Therefore, Indonesia first introduced moratorium on new forest clearance in 2011 26 and made it permanent in 2019 3. This moratorium mainly targeted Kalimantan, Sumatra, and Papua (New Guinea), which had been center of deforestation in the beginning of 2000s 3,27.

Meanwhile, agriculture has expanded in this century, with the primary export goods (e.g., oil palm and rubber plantations) coming from Sumatra and Kalimantan 5. Conversely, since the turn of the century, sustainable forestry policies in Java have caused a surge in the forestation of local smallholders 6. There are indications that forests on Java Island are recovering 7,8. Due to improved forestry policies and reforestation activities, the deforestation rate has decreased since 2011 9. Deforestation has been reported from all over Indonesia, but the causes of deforestation were different from one region to another because of geographic conditions and socioeconomic statuses 28.

Many studies have demonstrated that the NDVI is related to the leaf area, green biomass, percent green cover, and a fraction of absorbed photosynthetically active radiation (fAPAR) 18,29,30; moreover, NDVI is a global-based vegetation index. We utilized the product of NASA's MODIS Terra Program version 6.1. This product provides data at 16-day intervals (i.e., composite data for a 16-day period derived from images that are acquired almost every day) at 250m spatial resolution after correction for atmospheric effects (aerosols and gases) and sensor degradation, angular consideration, and minimization of the influence of daily cloud covers for consistent spatial and temporal comparisons of vegetation 17,18. The NDVI runs from1 to+1 (acceptable range for the NDVI of the MODIS: from0.2 to+1) and is determined from the visible and near-infrared light reflected by the quantity (biomass) and/or composition of vegetation.

The information used was from LP DAAC, a part of NASAs Earth Observing System Data and Information System run in cooperation between the US Geological Survey and NASA. We utilized the shapefile of Administrative Level 2 in April 2020 created by the Indonesian Bureau of Statistics (BPS). This was made available through the Humanitarian Data Exchange program (HDX) of the United Nations Office for Coordination of Humanitarian Affairs (OCHA) (https://data.humdata.org/) 31. The Administrative Level 2 consisted of 93 cities (kota), five administrative cities (kota administrasi), 415 regencies (kabupaten), and one administrative regency (kabupaten administrasi) (Supplementary Table 1). Each MOD13Q1 picture was dissected for the Administrative Level 2 and pixel reliability (1: no data; 0: excellent data; 1: poor data; 2: snow/ice; and 3: cloud). The coastal regions, where the pixels had recorded water cover, experienced the pixel reliability of MOD13Q1 with1 (no data). The NDVI was unreliable when the pixels were classified as level 2 (snow/ice). No NDVI was available for the pixels covered in clouds (pixel reliability=3). These cases were disregarded from further analysis. Hence, the average NDVI was determined for regions in each regency/city at each time point with a pixel reliability range of 0 or 1. Seribu Islands was the sole administrative regency, which was omitted from the studies because it was composed of a number of tiny islands, and all pixels covering it had some sea in them. Accordingly, further evaluations were conducted for 513 regencies and cities throughout a 20-year span (i.e., every 16days; 460 time points). We downloaded a total of 5520 pictures since 12 MOD13Q1 images at each time point cover the whole Indonesia.

We split the NDVI changes in the MOD13Q1 data into trends, seasonal changes, and residuals using a stochasticlevel and deterministic seasonal state space model (SSM). We performed time-series studies based on SSM 32 using two steps 16: (1) the NDVI data were averaged for each geographic unit (regency or city); and (2) stochasticlevel and deterministic seasonal models were used. The time-series change in this model was divided into trends, cycles, and residuals while excluding noises. The slopes and the levels were determined by a stochastic process, seasonal changes (annual cycle), and irregular changes with interporation of missing datasmoothed by the Kalman filter. The maximum likelihood estimation was made for the following equations:

$${y}_{t}={mu }_{t}+ {gamma }_{t}+ {varepsilon }_{t}, {varepsilon }_{t}sim mathrm{NID} left(0, {sigma }_{varepsilon }^{2}right)$$

$${mu }_{t+1}={mu }_{t}+{xi }_{t}, { xi }_{t}sim mathrm{NID}(0,{sigma }_{xi }^{2})$$

$${gamma }_{1, t+1}= {-gamma }_{1, t} {-gamma }_{2, t}dots {-gamma }_{22, t}$$

$${gamma }_{2, t+1}= {gamma }_{1, t}$$

$${gamma }_{22, t+1}= {gamma }_{21, t}$$

for t=1, n, where ({y}_{t}) is the observation (NDVI) at time t; ({mu }_{t}) is the unobserved level at time t; ({gamma }_{t}= {gamma }_{1, t}) denotes the seasonal component; ({varepsilon }_{t}) is the observation disturbance term at time t; and ({xi }_{t}) is called the level disturbance term at time t. The level ({mu }_{t}) was allowed to vary over time in the stochasticlevel and deterministic seasonal model. The seasonal changes, trends, and residuals are represented by , , and , respectively. R Software version 4.1.2 with dlm package was used for the analysis 33.

The USGS Earth Resources Observation and Science Center and the Climate Hazard Center of the University of California in Santa Barbara developed climate hazards group infrared precipitation with station (CHIRPS) v2p0, which provide data on rainfall estimates from rain gauge and satellite observations and is available for the entire world, including areas with sparse surface data 34. CHIRPS provide moderate resolution (0.05) gridded precipitation information. We obtained the CHIRPS monthly data for Indonesia from 2001 to 2020 and masked them at the administrative 1 (province) level (data compiled by Indonesian Statistical Office and available at OCHA HDX) because the resolution of CHIRPS is coarser than that of MOD13Q1. Using the same SSM model with the NDVI data, we decomposed the precipitation data into trends and seasonal cycles while excluding noises.Furthermore, the monthly average precipitation for each province over the course of 20 years was calculated from this dataset (rainfall level).

We used the population density data and the GDP at the regency/city level issued by the Ministry of Internal Affairs of the Republic of Indonesia 35. However, we were unable to study the time-series changes in socioeconomic development over a 20-year period because the administrative units increased from 397 in 2001 to 514 in 2020 due to the administrative reforms brought about by population and economic growth. The data used in this study were (1) the population densities in 2020, (2) GDP proportion from agriculture, forestry, and fisheries (as an indicator of the land-use intensities for agricultural and forestry development), and (3) GDP proportion from financial and insurance activities (as urban development). These indicators reflected the socioeconomic conditions of Indonesia, where the inequality in development among regions is very high. The total GDP was not used because it was closely correlated with the three variables used in this work.

After obtaining results of time-series analyses, we also conducted field observations in 2022 in North Sumatra, the Special Capital Region of Jakarta, Central Java, the Special Region of Yogyakarta, and South Sulawesi. We visited regencies and cities showing very consistent increases or decreases in NDVI, rapid loss or growth in NDVI, or dramatically irregular changes (e.g., disasters); furthermore, we observed the reasons behind such changes. In addition, we observed vegetation changes between 2014 and 2017 in parts of East Nusa Tenggara 16.

We defined the Pearsons correlation coefficients of the NDVI trend (after noise and cycle elimination) with time (every 16day) as the NDVI consistent trend. Furthermore, we defined the NDVI variation between 2001 and 2020 (in other words, differences in the average NDVI between 2020 and 2001) as the NDVI value change. Pettitts Test was used to identify the trend change-points 36. For precipitation, the correlation of the trend with time and the difference in the NDVI between 2020 and 2001 were also computed (CHIPRS data).

We used classification and regression trees (CART), a decision tree model data mining method that explains how a target variable is predicted by other variables based on categorizing samples into binary classes 37; exponential was used for NDVI consistent trend. The decision tree regression analysis was conducted to explore factors contributing to the consistent trend of the NDVI and the NDVI value changes. To minimize overfitting, the complexity parameter (cp) value for the biggest cross-validated prediction error of less than the minimal relative error plus the cross-validated prediction standard deviation was used as the cut-off (pruning tree model). To reflect the differences in agricultural intensities and main tree crops, all regencies and cities were classified into Sumatra, Western Kalimantan, Eastern Kalimantan, Western Java, Eastern Java, Nusa Tenggara, Northern Sulawesi, Southern Sulawesi, Maluku, and Papua (Supplementary Fig.1). QGIS 3.22.4 Biaowiea (QGIS Development Team) (https://qgis.org/) was utilized for the map creation.

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Time-series analysis of satellite imagery for detecting vegetation ... - Nature.com

Data Science graduates have endless opportunities as long as data abounds in the digital universe. PHOTOS FROM FACEBOOK/MAPUA MATHEMATICS DEPARTMENT

DATA science has been called the "sexiest job of the 21st century," and experts deem it will continue to be sexy well into the future.

Since data is practically used everywhere, it has become one of the most prized commodities today, and data science, one of the fastest-growing fields. Large and well-known companies use data to develop business strategies, create or improve products, understand customers, finalize transactions, and gain profit.

Dr. Mylen Aala-Capuno, chairman of the Mapua University Department of Mathematics, said with data science, organizations gain relevant insights to formulate accurate decisions and corresponding business actions. This wealth of information allows them to optimize their operations and services.

"Data science is an interdisciplinary field for the study of data to extract meaningful insights that will lead to effective decisions. It combines the principles and techniques of mathematics, statistics, and computer science," she said in a press statement.

Many people may not be aware, but data science is not exclusive to tech and finance giants; it is widely used across various industries. In fact, ordinary Filipinos experience its application and conveniences in everyday life.

Whether browsing for content on video or audio streaming sites, searching for new business connections on professional networks, buying items online or looking up lost long friends on social media, the breadth and potential of data science are limitless.

"With data growing at an astronomical rate, the demand for data scientists is also growing, and forecasts show that this demand will grow to 36 percent in the next 10 to 15 years. Being a data scientist is a very lucrative job and is usually in the top 3 highest-paying jobs today," Aala-Capuno added in the statement.

Data science professor Edgar Adina added that the career options of data science graduates are much like the data they explore and analyze boundless.

"Data Science graduates have endless opportunities as long as data abounds in the digital universe. This includes data manager, data architect, data engineer, business analyst, machine learning scientist and engineer, statistician, data modeler, marketing analyst, fraud investigator, business intelligence developer, including a position in the academe," Adina said.

Although the field seems highly technical, Aala-Capuno said anyone passionate about learning could pursue data science since the analytical skills highly valued in the specialization could be learned and developed. Nonetheless, having basic knowledge of mathematics, statistics, and computer programming is also a good foundation for aspiring data science professionals.

While superior mathematical skills could be an advantage, these are not a requirement since the needed competencies will be taught and honed during the program, the university said.

Mapua University said it has been raising Filipino youth to become proficient in the field as its data science program is known for its highly personalized mentorship that empowers students to learn beyond theories.

The three-year program exposes students to numerous training sessions with real-life data using industry-accepted programming languages like Python, R, and software Power BI and MatLab. All courses are technology-integrated so that students can have multiple learning platforms and modalities.

Adina said students will master essential skills like data management, data visualization, data mining and modeling, machine learning, and deep learning, including artificial intelligence and natural language processing. They will also have apprenticeship programs with Mapua's industry partners and job placement opportunities after graduation.

In today's fast-changing world, a career in data science is not only lucrative; it is future-ready. The field's unlimited opportunities and relevance to everyday life assure graduates of professional growth and stability decades after they receive their degrees.

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Right course for 'sexiest job of the 21st century' - The Manila Times