BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Asia/Kolkata BEGIN:STANDARD DTSTART:19451014T230000 TZOFFSETFROM:+0630 TZOFFSETTO:+0530 TZNAME:IST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20240514T132610Z UID:86D24635-52DD-4068-9ABE-BB2749755EA5 DTSTART;TZID=Asia/Kolkata:20240505T100000 DTEND;TZID=Asia/Kolkata:20240505T110000 DESCRIPTION:The event featured Mr. Sitaram Tadepalli\, Vice Chair of Machin e Learning Systems at\nDBS Tech India\, as the speaker. It was hosted by t he IEEE Hyderabad Section\nStudents Activities Committee in collaboration with the TEMS Chapter of IEEE\nHyderabad Section.\nThe objective of the we binar "Applied Data Science Methodologies in Enterprise\nContext" was to d iscuss how data science is used in banking. It aimed to show how\nartifici al intelligence and data management in banking have changed \, address the \nchallenges banks face today due to internet and geographical growth\, an d explore\nnew data techniques like data warehouses and data lakes. The we binar also aimed\nto emphasize the importance of ethical and fair practice s in creating and using data\nmodels. Lastly\, it provided a platform for attendees to learn from an expert\, ask\nquestions\, and connect with othe rs interested in this field.\nSudeep Banerjee\, a member of IEEE SN from t he Hyderabad section\, commenced\nthe session by addressing the attendees. \nP. Karthik Rao\, also a member of IEEE SN from the Hyderabad section\, d elivered the\nopening remarks and introduced the speaker\, highlighting th e significance of the\ntopic and the speaker's expertise.\nMr. Sitaram Tad epalli started the discussion on the topic\, diving into key aspects and\n providing insights.\nThe following are the takeaways from the webinar:\n • Focus on the Evolution of AI:\nThe session commenced with a discussion on the evolution of AI through\nvarious stages. Mr. Tadepalli presented a graphical representation depicting the\nhighs and lows AI has encountered over time.\n• Mr. Tadepalli's Discussion on Bank Operations from Approx imately 20\nYears Ago:\nMr. Tadepalli dived into the operation of banks ar ound two decades ago\,\nhighlighting the historical context for understand ing data science in banking.\n• Different types of data collected by ban ks were discussed\, focusing on their\nsignificance in predicting future t ransactions and shaping banking operations.\n• Operational Data Stores ( ODS) and SQL Queries:\nMr. Tadepalli shed light on Operational Data Stores (ODS) and the use of SQL\nqueries and nightly jobs during that period\, e xplaining their role in data management\nand analysis.\n• Challenges Ari sing from Internet Expansion and Geographical expansion:\nIssues with SQL memory and nightly jobs were discussed\, due to the\nexpansion of the inte rnet and geographical factors impacting banking operations. It\nbecame evi dent that banks were operating around the clock to accommodate\ntransactio ns occurring in different time zones.\n• To address challenges\, solutio ns such as the implementation of Data\nWarehouses (DWH) and the creation o f data lakes capable of storing both\nstructured and unstructured data wer e proposed.\n• The METHODOLOGY included generating exploratory data anal ysis (EDA)\nreports and charts to summarize the situation. Data scientists used various\ntools to understand data distribution for analysis.\n• Fe ature Mart and modeling were discussed as methods to adapt to changing\nda ta needs and optimize data analysis processes within the banking sector.\n • Methodology Involving Exploratory Data Analysis (EDA) and Feature Mart \nThe methodology encompassed generating exploratory data analysis (EDA)\n reports and utilizing Feature Mart for data analysis and future use.\n• Different models\, categorized into classification and regression modes\, were\ndeployed\, with considerations for batch and online modes.\n• Moni toring models involved evaluating of explainability and fairness for\neffe ctiveness.\nTo sum up\, Mr. Tadepalli's talk gave us valuable information on how data science\nworks in banking. He made sure to answer everyone's q uestions and stressed how\nimportant it is for models to be fair and ethic al. He also mentioned that there's a lot\nof research going on about makin g loan approvals fairer. Mr. Tadepalli has offered his\nemail address\, in viting further discussions for those who are interested in the\nrespective field.\nAttendees posed questions regarding the discussion\, and Mr. Tade palli addressed\neach query\, leading to an engaging and informative excha nge.\n\nSpeaker(s): \, Sitaram\n\nAgenda: \nThe main outcome of the event was to give an overview about how data science is\nused in the banking sec tor. 50+ attendees have attended the webinar and gained\nknowledge on the topic.\n\nVirtual: https://events.vtools.ieee.org/m/420422 LOCATION:Virtual: https://events.vtools.ieee.org/m/420422 ORGANIZER:sac@ieeehyd.org SEQUENCE:37 SUMMARY:Webinar on Applied data science methodologies in enterprise context URL;VALUE=URI:https://events.vtools.ieee.org/m/420422 X-ALT-DESC:Description: <br /><p style="text-align: justify\;">The event fe atured Mr. Sitaram Tadepalli\, Vice Chair of Machine Learning Systems at&n bsp\;<br>DBS Tech India\, as the speaker. It was hosted by the IEEE Hydera bad Section&nbsp\;<br>Students Activities Committee in collaboration with the TEMS Chapter of IEEE&nbsp\;<br>Hyderabad Section.&nbsp\;<br>The object ive of the webinar "Applied Data Science Methodologies in Enterprise&nbsp\ ;<br>Context" was to discuss how data science is used in banking. It aimed to show how&nbsp\;<br>artificial intelligence and data management in bank ing have changed \, address the&nbsp\;<br>challenges banks face today due to internet and geographical growth\, and explore&nbsp\;<br>new data techn iques like data warehouses and data lakes. The webinar also aimed&nbsp\;<b r>to emphasize the importance of ethical and fair practices in creating an d using data&nbsp\;<br>models. Lastly\, it provided a platform for attende es to learn from an expert\, ask&nbsp\;<br>questions\, and connect with ot hers interested in this field.<br>Sudeep Banerjee\, a member of IEEE SN fr om the Hyderabad section\, commenced&nbsp\;<br>the session by addressing t he attendees.<br>P. Karthik Rao\, also a member of IEEE SN from the Hydera bad section\, delivered the&nbsp\;<br>opening remarks and introduced the s peaker\, highlighting the significance of the&nbsp\;<br>topic and the spea ker's expertise.<br>Mr. Sitaram Tadepalli started the discussion on the to pic\, diving into key aspects and&nbsp\;<br>providing insights.<br>The fol lowing are the takeaways from the webinar:<br>&bull\; Focus on the Evoluti on of AI:<br>&nbsp\;The session commenced with a discussion on the evoluti on of AI through&nbsp\;<br>various stages. Mr. Tadepalli presented a graph ical representation depicting the&nbsp\;<br>highs and lows AI has encounte red over time.<br>&bull\; Mr. Tadepalli's Discussion on Bank Operations fr om Approximately 20&nbsp\;<br>Years Ago:&nbsp\;<br>&nbsp\;Mr. Tadepalli di ved into the operation of banks around two decades ago\,&nbsp\;<br>highlig hting the historical context for understanding data science in banking.<br >&bull\; Different types of data collected by banks were discussed\, focus ing on their&nbsp\;<br>significance in predicting future transactions and shaping banking operations.<br>&bull\; Operational Data Stores (ODS) and S QL Queries:<br>&nbsp\;Mr. Tadepalli shed light on Operational Data Stores (ODS) and the use of SQL&nbsp\;<br>queries and nightly jobs during that pe riod\, explaining their role in data management&nbsp\;<br>and analysis.<br >&bull\; Challenges Arising from Internet Expansion and Geographical expan sion:<br>&nbsp\;Issues with SQL memory and nightly jobs were discussed\, d ue to the&nbsp\;<br>expansion of the internet and geographical factors imp acting banking operations. It&nbsp\;<br>became evident that banks were ope rating around the clock to accommodate&nbsp\;<br>transactions occurring in different time zones.<br>&bull\; To address challenges\, solutions such a s the implementation of Data&nbsp\;<br>Warehouses (DWH) and the creation o f data lakes capable of storing both&nbsp\;<br>structured and unstructured data were proposed.<br>&bull\; The METHODOLOGY included generating explor atory data analysis (EDA)&nbsp\;<br>reports and charts to summarize the si tuation. Data scientists used various&nbsp\;<br>tools to understand data d istribution for analysis.<br>&bull\; Feature Mart and modeling were discus sed as methods to adapt to changing&nbsp\;<br>data needs and optimize data analysis processes within the banking sector.<br>&bull\; Methodology Invo lving Exploratory Data Analysis (EDA) and Feature Mart<br>&nbsp\;The metho dology encompassed generating exploratory data analysis (EDA)&nbsp\;<br>re ports and utilizing Feature Mart for data analysis and future use.<br>&bul l\; Different models\, categorized into classification and regression mode s\, were&nbsp\;<br>deployed\, with considerations for batch and online mod es.&nbsp\;<br>&bull\; Monitoring models involved evaluating of explainabil ity and fairness for&nbsp\;<br>effectiveness.<br>To sum up\, Mr. Tadepalli 's talk gave us valuable information on how data science&nbsp\;<br>works i n banking. He made sure to answer everyone's questions and stressed how&nb sp\;<br>important it is for models to be fair and ethical. He also mention ed that there's a lot&nbsp\;<br>of research going on about making loan app rovals fairer. Mr. Tadepalli has offered his&nbsp\;<br>email address\, inv iting further discussions for those who are interested in the&nbsp\;<br>re spective field.<br>Attendees posed questions regarding the discussion\, an d Mr. Tadepalli addressed&nbsp\;<br>each query\, leading to an engaging an d informative exchange.<br><br></p><br /><br />Agenda: <br /><p>The main o utcome of the event was to give an overview about how data science is&nbsp \;<br>used in the banking sector. 50+ attendees have attended the webinar and gained&nbsp\;<br>knowledge on the topic.</p> END:VEVENT END:VCALENDAR