Cloud Hosting

Luke Roquet recently spoke to a customer who recounted the shock of getting a $700,000 bill for a single data science workload running in the cloud. When Roquet, who is senior vice president of product marketing at Cloudera, related the story to another customer, he learned that that company had received a $400,000 tab for a similar job just the week before.

Such stories should belie the common myth that cloud computing is always about saving money. In fact, most executives Ive talked to say that moving an equivalent workload from on-premises to the cloud results in about a 30% cost increase, said Roquet.

This doesnt mean the cloud is a poor option for data analytics projects. In many scenarios, the scalability and variety of tooling options make the cloud an ideal target environment. But the choice of where to locate data-related workloads should take multiple factors into account, of which only one is cost.

Data analytics workloads can be especially unpredictable because of the large data volumes involved and the extensive time required to train machine learning (ML) models. These models often have unique characteristics that can cause their costs to explode, Roquet said.

Whats more, local applications often need to be refactored or rebuilt for a specific cloud platform, said David Dichmann, senior director of product management at Cloudera. Theres no guarantee that the workload is going to be improved and you can end up being locked into one cloud or another, he said.

Cloud march is on

That doesnt seem to be slowing the ongoing cloudward migration of workloads. Foundrys 2022 Data & Analytics study found that 62% of IT leaders expect the share of analytics workloads they run in the cloud to increase.

Although cloud platforms offer many advantages, cost- and performance-sensitive workloads are often better run on-prem, Roquet said.

Choosing the right environment is about achieving balance. The cloud excels for applications that are ephemeral, need to be shared with others, or use cloud-native constructs like software containers and infrastructure-as-code, he said. Conversely, applications that are performance- or latency-sensitive are more appropriate for local infrastructure where data can be co-located, and long processing times dont incur additional costs.

The goal should be to optimize workloads to interact with each other regardless of location and to move as needed between local and cloud environments.

The case for portability

Dichmann said three core components are needed to achieve this interoperability and portability:

Once you have one view of all your data and one way to govern and secure it then you can move workloads around without worrying about breaking any governance and security requirements, he said. People know where the data is, how to find it, and were all assured it will be used correctly per business policy or regulation.

Portability may be at odds with customers desire to deploy best-of-breed cloud services, but Dichmann said fit-for-purpose is a better goal than best-of-breed. That means its more important to put flexibility ahead of bells and whistles. This gives the organization maximum flexibility for deciding where to deploy workloads.

A healthy ecosystem is also just as important as robust points solutions because a common platform enables customers to take advantage of other services without extensive integration work.

The best option for achieving workload portability is to use an abstraction layer that runs across all major cloud and on-premises platforms. The Cloudera Data Platform, for example, is a true hybrid solution that provides the same services both in the cloud and on-prem, Dichmann said. It uses open standards that give you the ability to have data share a common format everywhere it needs to be, and accessed by a broader ecosystem of data services that makes things even more flexible, more accessible and more portable.

Visit Cloudera to learn more.

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Data analytics in the cloud: understand the hidden costs - CIO

Is AI hype here to stay? What problems and risks come with it? Get answers to these questions and more from this survey.

According to Domino Data Labs survey from the REV 4 conference, 90% of data scientists think generative AI hype is justified. Respondents are professionals who are leading, developing and operating generative AI initiatives across Fortune 500 companies.

The findings validate the incredible business potential of Generative AI and its expected near-term impact, wrote Kjell Carlsson, Domino Data Labs head of data science strategy & evangelism, in a blog post. However, it also confirms key challenges governance, control, privacy and fairness as well as the severe limitations of the current, commercially available Generative AI offerings.

The San Francisco-based Domino Data Lab collected responses from 162 data science executives, data science team leaders, data science practitioners and IT platform owners. Some additional opinions in the report were sourced from Domino Data Lab customers.

Jump to:

More than half (55%) of the data science professionals and IT platform owners surveyed said generative AI will have a significant impact on their business within the next one to two years. Additionally, almost half of the respondents (45%) believe the hype is only rising, expecting generative AI to have an even greater impact than todays expectations suggest.

Other data from G2, EY and others show the same large impact of AI. In a recent survey of tech executives, CNBC found that AI is their top priority for tech spending over the next year, starting in June 2023; the second priority is cloud computing.

According to Statista, artificial intelligence startups (a category in which Statista includes machine learning, robotics, neural networks and language processing) received a total yearly investment of $5 billion from 2020 to 2022.

Most (55%) of the data science professionals and IT platform owners Domino Data Lab surveyed prefer to use foundation models from large third parties like OpenAI, Microsoft or Google but create different experiences for their customers on top of the base model. Another 39% want to build their own proprietary generative AI from scratch. Just 6% want to use AI features solely planned and provided by independent software vendors and other third parties.

The respondents believe the biggest problems with commercially available generative AI, such as ChatGPT, are security (54%), reliability (44%) and IP protection (42%).

SEE: Learn what AI technologies Amazon just poured $100 million into. (TechRepublic)

These concerns mean that organizations need to invest in tools to make it easier to fine-tune generative AI models, as 41% of those surveyed plan to do. Some (35%) also plan to implement governance capabilities for tracking and managing the development of those AI models.

There are still challenges facing generative AI adoption today. The data science professionals and IT platform owners surveyed said they foresee challenges around governance (57%), mitigating bias and ensuring fairness (51%) and control (49%), as well as finding employees with the skills for developing generative AI solutions (49%).

Data leakage is another problem cited by survey participants. Some are concerned about generative AI having low accuracy or leading to bad business decisions (35%) and budget overreach (33%).

Senior leadership in particular cited concerns about governing generative AI solutions generally (76%), as well as the reliability (76%) and security (71%) of solutions on the market today.

Other industry experts are warning the tech world to temper the hype.

AI has great potential, but it is a huge high-risk bet, and a large percentage of your investment will likely go nowhere, said Saurajit Kanungo, president of the consulting firm CG Infinity, in an email. Only invest if you can measure the ROI in business terms is it going to decrease costs or increase revenue?

He points toward Gartners 2022 AI Hype Cycle graph, in which generative AI approaches the point labeled Peak of Inflated Expectations.

I absolutely believe that AI (including generative AI) has the potential to drive value for every organization, big or small. However I would advise executives to adopt AI as an evolution, not a revolution, Kanungo said.

He finds the case for generative AI to be stronger than the case for the last hot technology investment trend: cryptocurrencies. Cryptocurrencies require a whole new ecosystem or market to be made. Business cases to justify investing in generative AI in an organization are an easier challenge compared to making a whole market with cryptocurrencies, Kanungo said.

See the original post here:

Data Scientist Survey: Do Tech Leaders Believe the AI Hype? - TechRepublic

Risk managementis critical for financial organizations in today's fast-paced andinterconnected world of finance. Identifying and reducing risks is critical forasset protection, regulatory compliance, and long-term stability.

Big dataanalytics has evolved as a significant risk management tool in recent years,allowing financial organizations to examine huge volumes of data, identifyhidden patterns, and make informed judgments. In this article, we will look atthe role of big data analytics in risk management for financial institutions,as well as how it is changing the way risks are found, assessed, and mitigated.

The process ofanalyzing massive and complicated datasets to extract important insights andcreate data-driven decisions is referred to as big data analytics. Big dataanalytics in risk management provides new possibilities for collecting,processing, and analyzing different data sources including as transactionaldata, customer data, market data, social media data, and more. Financial organizationscan acquire a full and holistic perspective of risks and make more accuratepredictions and assessments by leveraging the power of big data analytics.

The ability toidentify and detect threats in real-time or near real-time is one of theprimary benefits of big data analytics in risk management. Traditional riskmanagement systems frequently rely on historical data and periodic reporting,which may miss new threats or abrupt changes in market conditions. Financialinstitutions can use big data analytics to monitor and analyze data in realtime, allowing for proactive risk identification and early response.

Keep Reading

Big dataanalytics, for example, can detect probable anomalies or fraudulent behaviorsas they occur by examining transactional data. This enables financialorganizations to react promptly and reduce potential losses. Real-time marketdata and news sentiment monitoring can also assist in identifying marketconcerns, allowing institutions to adapt their investment strategies andportfolios accordingly.

Furthermore,big data analytics improves risk assessment by offering a more detailed andprecise understanding of risks. Risk assessments have traditionally been reliedon aggregated and generalized data, which may not represent the nuances andcomplexities of individual situations. Big data analytics allows financialorganizations to look deeper into data, identify hidden patterns, and assessrisks in greater depth.

Financialcompanies can acquire a comprehensive perspective of risk indicators by mergingstructured and unstructured data sources, such as text data from news storiesor social media. Sentiment analysis of social media data, for example, canprovide insights into public perception and sentiment toward certainorganizations or industries, which can be useful in analyzing reputationalconcerns.

Furthermore,big data analytics makes predictive modeling and scenario analysis for riskmanagement easier. Financial organizations can construct predictive models thatestimate future risks and their possible impact by examining historical dataand employing modern statistical and machine learning techniques. These modelsallow institutions to assess the chance of specific hazards occurring andestimate the financial implications.

Another usefulapplication of big data analytics is scenario analysis, which allows financialinstitutions to model and evaluate the impact of various risk scenarios ontheir portfolios and business operations. Institutions can better recognizepotential vulnerabilities and implement risk mitigation strategies byevaluating multiple scenarios. This proactive risk management technique assistsinstitutions in staying ahead of prospective dangers and minimizing potentiallosses.

Big dataanalytics also improves the effectiveness of regulatory compliance in riskmanagement. Financial institutions operate in a highly regulated environment,and regulatory compliance is critical. Big data analytics can assistorganizations in analyzing massive amounts of data in order to uncover anynon-compliance issues. Institutions can ensure that they meet regulatorystandards and avoid penalties by automating compliance monitoring activities.

Furthermore,big data analytics makes it easier to deploy Know Your Customer (KYC) andanti-money laundering (AML) safeguards. Institutions can discover suspiciousactivity and potential hazards by evaluating client data, transaction patterns,and other relevant data sources. This enables institutions to meet regulatoryobligations and effectively combat financial crime.

There are,however, several considerations to make when applying big data analytics inrisk management. When dealing with huge amounts of sensitive financial data,data privacy and security are critical concerns. Financial firms must developstrong data governance procedures, follow data privacy legislation, andguarantee that adequate data security measures are in place.

One of theprimary challenges in leveraging big data analytics for risk management lies inthe quality and integration of data. Organizations accumulate vast amounts ofdata from disparate sources, including structured and unstructured data.Ensuring data accuracy, completeness, and consistency is crucial to producereliable risk assessments and actionable insights.

To overcomethis challenge, organizations need robust data governance frameworks thatestablish data quality standards, data integration protocols, and datacleansing processes. Data integration technologies, such as data lakes and datawarehouses, can help centralize and harmonize diverse data sources.Implementing data validation procedures, data lineage tracking, and dataquality checks can enhance the accuracy and reliability of risk analyses.

As big data analytics involves handlingsensitive and confidential information, privacy and data security posesignificant challenges in risk management. Data breaches, unauthorized access,and misuse of data can lead to severe legal, reputational, and financialconsequences. Additionally, regulatory frameworks, such as the General DataProtection Regulation (GDPR), impose strict guidelines on the collection,storage, and use of personal data.

To addressprivacy and data security concerns, organizations must implement robust dataprotection measures, including encryption, access controls, and secure datastorage. Anonymizing and de-identifying data can help strike a balance betweendata utility and privacy. Compliance with relevant data protection regulationsis crucial, requiring organizations to establish comprehensive data protectionpolicies and conduct regular audits to ensure compliance.

The scarcity of skilled professionals withexpertise in big data analytics and risk management poses a significantchallenge for organizations. Leveraging the full potential of big dataanalytics requires a multidisciplinary approach, combining knowledge in datascience, statistics, risk management, and domain-specific expertise. Findingindividuals who possess these diverse skill sets can be a daunting task.

To bridge thetalent and expertise gap, organizations can invest in training and upskillingtheir existing workforce. Encouraging cross-functional collaboration andknowledge-sharing can help cultivate a data-driven culture within theorganization. Partnering with academic institutions and industry experts canalso provide access to specialized training programs and foster a pipeline ofskilled professionals.

Finally, bigdata analytics is transforming risk management for financial organizations. Byleveraging the power of big data, institutions may discover and detect hazardsin real time, analyze risks at a more granular level, forecast future risks,and more effectively comply with regulatory requirements. As the volume andcomplexity of data increase, big data analytics will become increasinglyimportant in assisting financial institutions in navigating the problems ofrisk management and maintaining stability in an ever-changing financiallandscape.

Risk managementis critical for financial organizations in today's fast-paced andinterconnected world of finance. Identifying and reducing risks is critical forasset protection, regulatory compliance, and long-term stability.

Big dataanalytics has evolved as a significant risk management tool in recent years,allowing financial organizations to examine huge volumes of data, identifyhidden patterns, and make informed judgments. In this article, we will look atthe role of big data analytics in risk management for financial institutions,as well as how it is changing the way risks are found, assessed, and mitigated.

The process ofanalyzing massive and complicated datasets to extract important insights andcreate data-driven decisions is referred to as big data analytics. Big dataanalytics in risk management provides new possibilities for collecting,processing, and analyzing different data sources including as transactionaldata, customer data, market data, social media data, and more. Financial organizationscan acquire a full and holistic perspective of risks and make more accuratepredictions and assessments by leveraging the power of big data analytics.

The ability toidentify and detect threats in real-time or near real-time is one of theprimary benefits of big data analytics in risk management. Traditional riskmanagement systems frequently rely on historical data and periodic reporting,which may miss new threats or abrupt changes in market conditions. Financialinstitutions can use big data analytics to monitor and analyze data in realtime, allowing for proactive risk identification and early response.

Keep Reading

Big dataanalytics, for example, can detect probable anomalies or fraudulent behaviorsas they occur by examining transactional data. This enables financialorganizations to react promptly and reduce potential losses. Real-time marketdata and news sentiment monitoring can also assist in identifying marketconcerns, allowing institutions to adapt their investment strategies andportfolios accordingly.

Furthermore,big data analytics improves risk assessment by offering a more detailed andprecise understanding of risks. Risk assessments have traditionally been reliedon aggregated and generalized data, which may not represent the nuances andcomplexities of individual situations. Big data analytics allows financialorganizations to look deeper into data, identify hidden patterns, and assessrisks in greater depth.

Financialcompanies can acquire a comprehensive perspective of risk indicators by mergingstructured and unstructured data sources, such as text data from news storiesor social media. Sentiment analysis of social media data, for example, canprovide insights into public perception and sentiment toward certainorganizations or industries, which can be useful in analyzing reputationalconcerns.

Furthermore,big data analytics makes predictive modeling and scenario analysis for riskmanagement easier. Financial organizations can construct predictive models thatestimate future risks and their possible impact by examining historical dataand employing modern statistical and machine learning techniques. These modelsallow institutions to assess the chance of specific hazards occurring andestimate the financial implications.

Another usefulapplication of big data analytics is scenario analysis, which allows financialinstitutions to model and evaluate the impact of various risk scenarios ontheir portfolios and business operations. Institutions can better recognizepotential vulnerabilities and implement risk mitigation strategies byevaluating multiple scenarios. This proactive risk management technique assistsinstitutions in staying ahead of prospective dangers and minimizing potentiallosses.

Big dataanalytics also improves the effectiveness of regulatory compliance in riskmanagement. Financial institutions operate in a highly regulated environment,and regulatory compliance is critical. Big data analytics can assistorganizations in analyzing massive amounts of data in order to uncover anynon-compliance issues. Institutions can ensure that they meet regulatorystandards and avoid penalties by automating compliance monitoring activities.

Furthermore,big data analytics makes it easier to deploy Know Your Customer (KYC) andanti-money laundering (AML) safeguards. Institutions can discover suspiciousactivity and potential hazards by evaluating client data, transaction patterns,and other relevant data sources. This enables institutions to meet regulatoryobligations and effectively combat financial crime.

There are,however, several considerations to make when applying big data analytics inrisk management. When dealing with huge amounts of sensitive financial data,data privacy and security are critical concerns. Financial firms must developstrong data governance procedures, follow data privacy legislation, andguarantee that adequate data security measures are in place.

One of theprimary challenges in leveraging big data analytics for risk management lies inthe quality and integration of data. Organizations accumulate vast amounts ofdata from disparate sources, including structured and unstructured data.Ensuring data accuracy, completeness, and consistency is crucial to producereliable risk assessments and actionable insights.

To overcomethis challenge, organizations need robust data governance frameworks thatestablish data quality standards, data integration protocols, and datacleansing processes. Data integration technologies, such as data lakes and datawarehouses, can help centralize and harmonize diverse data sources.Implementing data validation procedures, data lineage tracking, and dataquality checks can enhance the accuracy and reliability of risk analyses.

As big data analytics involves handlingsensitive and confidential information, privacy and data security posesignificant challenges in risk management. Data breaches, unauthorized access,and misuse of data can lead to severe legal, reputational, and financialconsequences. Additionally, regulatory frameworks, such as the General DataProtection Regulation (GDPR), impose strict guidelines on the collection,storage, and use of personal data.

To addressprivacy and data security concerns, organizations must implement robust dataprotection measures, including encryption, access controls, and secure datastorage. Anonymizing and de-identifying data can help strike a balance betweendata utility and privacy. Compliance with relevant data protection regulationsis crucial, requiring organizations to establish comprehensive data protectionpolicies and conduct regular audits to ensure compliance.

The scarcity of skilled professionals withexpertise in big data analytics and risk management poses a significantchallenge for organizations. Leveraging the full potential of big dataanalytics requires a multidisciplinary approach, combining knowledge in datascience, statistics, risk management, and domain-specific expertise. Findingindividuals who possess these diverse skill sets can be a daunting task.

To bridge thetalent and expertise gap, organizations can invest in training and upskillingtheir existing workforce. Encouraging cross-functional collaboration andknowledge-sharing can help cultivate a data-driven culture within theorganization. Partnering with academic institutions and industry experts canalso provide access to specialized training programs and foster a pipeline ofskilled professionals.

Finally, bigdata analytics is transforming risk management for financial organizations. Byleveraging the power of big data, institutions may discover and detect hazardsin real time, analyze risks at a more granular level, forecast future risks,and more effectively comply with regulatory requirements. As the volume andcomplexity of data increase, big data analytics will become increasinglyimportant in assisting financial institutions in navigating the problems ofrisk management and maintaining stability in an ever-changing financiallandscape.

More:

The Role of Big Data Analytics in Risk Management for Financial Institutions - Finance Magnates

The upcoming months of 2023 promise a wealth of learning and networking opportunities for data professionals, with an exciting array of analytics conferences scheduled across the United States. These events will host some of the most brilliant minds in the field of analytics and artificial intelligence (AI), providing a platform to discuss current trends, share valuable insights, and explore what the future holds for the industry.

Here are the top ten analytics conferences to attend from July to December 2023:

MachineCon 2023 (July 21, New York) An exclusive gathering for leaders in the world of analytics and AI, MachineCon explores the transformative potential of advanced AI technologies and innovative analytics solutions that are changing the face of various industries. Organised by AIM Media House, a leading global technology media firm, this conference celebrates those who have mastered the art of turning data into a competitive advantage.

DataConnect Conference (July 20-21, Columbus, OH) As reported by KDnuggets, the DataConnect Conference is a major event in the field of data analytics. It also offers virtual participation, making it accessible to a global audience.

The 2023 International Conference on Data Science (July 24-27, Las Vegas, NV) This conference emphasises the latest developments in data science, serving as a platform for researchers and practitioners to share their discoveries and insights.

Ai4 2023 (August 7-9, Las Vegas, NV) Ai4 2023 is an all-encompassing conference that covers a broad spectrum of topics related to AI and analytics, bringing together business leaders and data practitioners to facilitate the adoption of AI and machine learning technologies.

Chief Data & Analytics Officer (CDAO), Chicago 2023 (August 8-9, Chicago, IL) The Chicago edition of the CDAO conference is a significant event that unites Chief Data Officers and other analytics leaders to deliberate on strategies, trends, and challenges in the data analytics industry.

SAS Explore 2023 (September 11-14, Las Vegas, NV) SAS Explore is a prominent conference that focuses on analytics and data science. The event includes a wide range of sessions and workshops, providing an excellent opportunity for learning and networking.

Chief Data & Analytics Officer (CDAO), Government 2023 (September 19-20, Washington, DC) This iteration of the CDAO conference highlights the use of data analytics in the government sector. It provides a forum for discussion about how data and analytics can be used to enhance government services and operations.

ODSC West 2023 (October 31 November 3, San Francisco, CA) The Open Data Science Conference (ODSC) West is one of the worlds largest applied data science conferences. The event encompasses a wide range of topics, including AI, machine learning, data visualisation, and data engineering. The conference also features a virtual component, making it accessible to attendees worldwide.

Data Science Salon SF: Applying AI & ML in the Enterprise (November 29, San Francisco, CA) This conference focuses on the application of AI and machine learning in enterprise settings. It provides an opportunity for data science professionals to learn about the latest trends, techniques, and best practices in the industry.

Chief Data And Analytics Officers, APEX West (CDAO) (December 5, Arizona City, United States) The CDAO APEX West conference is a significant event for data and analytics officers. It provides an opportunity for these leaders to come together to discuss the latest trends, strategies, and challenges in the field of data analytics.

These ten conferences represent some of the most influential and anticipated events in the data analytics and AI industry for the second half of 2023. Whether youre a data scientist, AI practitioner, or business leader, these events offer a wealth of knowledge, networking opportunities, and a glimpse into the future of data-driven technologies. Be sure to mark your calendars and register in advance to secure your spot.

Read the original:

Top 10 Analytics Conferences in the USA for the Second Half of 2023 - Analytics India Magazine

ATK New Delhi [India], June 28: In the dynamic field of Data Science, where the demand for skilled professionals is constantly growing, having a strong foundation in an industry-relevant curriculum is essential. Boston Institute of Analytics has solidified its reputation as a leading institution in the Data Science domain, with its course curriculum recently being ranked as the most industry-relevant curriculum by the prestigious Indian Analytics Forum (IAF). This recognition speaks volumes about the institute's commitment to providing students with a comprehensive and up-to-date curriculum that aligns with the evolving needs of the industry.

Catering to Industry Demands: The IAF's ranking of the Data Science course curriculum at Boston Institute of Analytics is a testament to the institute's ability to understand and cater to the demands of the industry. The curriculum is designed in collaboration with industry experts, renowned data scientists, and top organizations to ensure that it addresses the latest trends, technologies, and methodologies. By integrating practical skills, theoretical knowledge, and hands-on experiences, the curriculum prepares students to tackle real-world challenges effectively.

Holistic Coverage of Data Science Concepts: The curriculum at Boston Institute of Analytics offers a holistic coverage of Data Science concepts, encompassing both fundamental and advanced topics. It includes comprehensive modules on statistics, programming languages, data visualization, machine learning, natural language processing, and more. Students gain a deep understanding of the underlying principles and techniques that drive the field of Data Science. This broad-based approach equips them with the necessary skills to handle diverse data-related projects across industries.

Integration of Real-World Case Studies: To bridge the gap between theory and practice, the curriculum at Boston Institute of Analytics incorporates real-world case studies. These case studies expose students to actual industry challenges and provide them with hands-on experience in solving complex data problems. By working with authentic datasets and exploring various analytical methodologies, students gain valuable insights into the practical applications of Data Science. This integration of real-world scenarios ensures that graduates are well-prepared to tackle similar challenges in their professional careers.

Cutting-Edge Tools and Technologies: Boston Institute of Analytics understands the importance of equipping students with proficiency in the latest tools and technologies used in the industry. The curriculum includes dedicated modules on popular tools like Python, R, SQL, Tableau, Power BI, and more. Students learn to leverage these tools effectively for data analysis, visualization, and model building. This hands-on experience with cutting-edge technologies prepares students to handle real-world projects and enables them to remain competitive in the rapidly evolving Data Science landscape.

Industry Collaboration and Networking: The curriculum's industry relevance is further enhanced through Boston Institute of Analytics' strong collaboration with industry partners. The institute actively engages with leading organizations to understand their evolving needs and incorporate industry-specific knowledge and best practices into the curriculum. Additionally, students benefit from networking opportunities with industry professionals through guest lectures, workshops, and industry-driven events. This exposure provides students with valuable insights, enhances their industry awareness, and creates potential avenues for internships and job placements.

The recognition of the Data Science course curriculum at Boston Institute of Analytics as the most industry-relevant curriculum by the IAF underscores the institute's commitment to providing students with a top-notch education that aligns with industry demands. As a validation of their commitment to academic excellence, Boston Institute of Analytics (website: http://www.bostoninstituteofanalytics.org) was recently ranked as the best data science training institute in India by IFC. With leading data scientists from the industry as trainers, industry oriented curriculum and collaborations with 350+ hiring partners, Boston Institute of Analytics (BIA) secured the top spot to become the number one ranked data science and analytics training institute in India in the classroom training space. Recognized as industry's best data science and analytics training program by globally accredited organizations and top multi-national corporates, Boston Institute of Analytics classroom training programs have been training students and professionals on industry's most widely sought after skills, and make them job ready in data science, machine learning and artificial intelligence field.

Through a holistic coverage of concepts, integration of real-world case studies, focus on cutting-edge tools and technologies, and strong industry collaboration, the institute ensures that students are well-prepared to excel in their Data Science careers. By choosing to pursue the Data Science course at Boston Institute of Analytics, students can be confident in acquiring the skills and knowledge needed to make a significant impact in the data-driven world. (Disclaimer: The above press release has been provided by ATK. ANI will not be responsible in any way for the content of the same)

(This story has not been edited by Devdiscourse staff and is auto-generated from a syndicated feed.)

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Data Science course curriculum at Boston Institute of Analytics ranked as the most industry-relevant curriculum by IAF - Devdiscourse