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CALSCALE:GREGORIAN
BEGIN:VEVENT
UID:b007485ac42fd9be3e9f15c12691a0c9a8321d19@swoogo.com
DTSTAMP:20240328T215115Z
DESCRIPTION:Join the Analytics Architecture & Technology (AAT) Community f
or an exciting panel discussion on 'Enabling the Citizen Data Scientist wi
th Next-Generation Data Science Technologies'. This session on May 3\, 202
1 is part two from a panel discussion that took place back in September 20
20 on “The Next Generation of Analytics Tools”. During September 2020\, we
covered many topics at a high-level and the members of the community requ
ested that we take a deeper dive on some of the topics discussed in the fu
ture. First up on that deeper dive\, our utility analytics experts will sh
are their perspectives and insight on 'Enabling the Citizen Data Scientist
with Next-Generation Data Science Technologies'.\n\nTo level set\, what d
oes ‘citizen data scientist’ mean? We have learned that “citizen data scie
ntist” is a term initiated by Gartner. Gartner defines it as “a person who
creates or generates models that use advanced diagnostic analytics or pre
dictive and prescriptive capabilities\, but whose primary job function is
outside the field of statistics and analytics.” In a nutshell\, they are n
on-technical employees who can use data science tools and technologies to
solve business problems. Citizen data scientists can provide business and
industry area of expertise that many data science experts lack. Their busi
ness experience and awareness of business priorities enable them to effect
ively integrate data science and machine learning output into business pro
cesses.\n\nWith that said\, our panel of experts will answer questions lik
e:\n\n * Can we successfully convert non-technical employees into data scie
ntists by leveraging and combining their area of expertise with data scien
ce technology to solve business problems? How?\n * How do you ensure Artif
icial Intelligence and Machine Learning becomes more accessible to the ave
rage data analyst/citizen data scientist?\n * In your opinion\, what are t
he factors that are beginning to democratize data science through data sci
ence technology?\n * How do I get started with democratizing data science
through data science technology\, especially using what I’ve learned in th
e past combined with what we are doing today to properly unleash our futur
e vision of enabling the citizen data scientist?\n * When acquiring and im
plementing your analytics and data science technology stack\, what are you
r considerations to ensure success for your average data analysts/citizen
data scientists?\n * There are many tools’ promises to empower citizen dat
a scientists. What are THE must have innovative tools for the next generat
ion of citizen data scientists?\n * How do I balance the different technol
ogies used by Citizen Data Scientists?\n * I’ve got the capability\, i.e.
the data science technology\, the right staff\, trained at the right level
\, so how do I unleash it and use it at scale to add value to the business
?\n * How do I market the concept of democratization of data science throu
gh data science technology to my organization? What are my change manageme
nt considerations? And how best do I communicate it to the business once I
get buy-in?\n * Most of us know that data wrangling is a big part of any
analyst’s job. How do we best educate our “citizen data scientists” in dat
a wrangling (data management\, data tools\, simple queries\, data quality\
, etc.)?\n * How do we best connect our “citizen data scientists” with the
right tools for their level of interest\, ability\, expertise\, and busin
ess need? For instance\, do they just need to learn how to use a pre-train
ed image recognition model\, or do they need to learn how to build their o
wn Convolutional Neural Network in TensorFlow? How do we best match tool t
o talent? \n * I was wildly successful at selling the promise of Data Sci
ence inside my company and the demand quickly outstripped our ability to s
upply. How do I best manage the influx of demand?\n * The data scientist s
killset includes the understanding of databases and data warehouses. Data
scientists are responsible for setting up appropriate data marts that prov
ide users with less complex queries that do not bring down the system\, as
well as maintaining administration over permissions and security is a bui
lding block to establishing self-service analytics. With that said\, do we
allow 'citizen data scientists' to set-up models that pull data from sour
ce systems on-demand\, which could bring a halt to systems\, especially fo
r smaller utilities with more limitations?\n\n
DTSTART:20210503T150000Z
DTEND:20210503T160000Z
LAST-MODIFIED:20240328T215115Z
LOCATION:
SEQUENCE:0
STATUS:CONFIRMED
SUMMARY:Analytics Architecture & Technology Community Conversation: Enablin
g the Citizen Data Scientist with Next-Generation Data Science Technologie
s
TRANSP:OPAQUE
X-ALT-DESC;FMTTYPE=text/html:
Join the Analytics Architecture &\; Tech
nology (AAT) Community for an exciting panel discussion on 'Enabling the
Citizen Data Scientist with Next-Generation Data Science Technologies'. Th
is session on May 3\, 2021 is part two from a panel discussion that took p
lace back in September 2020 on “The Next Generation of Analytics Tools”. D
uring September 2020\, we covered many topics at a high-level and the memb
ers of the community requested that we take a deeper dive on some of the t
opics discussed in the future. First up on that deeper dive\, our utility
analytics experts will share their perspectives and insight on 'Enabling t
he Citizen Data Scientist with Next-Generation Data Science Technologies'.
\n\nTo level set\, what does ‘citizen data scientist’ mean? We have
learned that “citizen data scientist” is a term initiated by Gartner. Gar
tner defines it as “a person who creates or generates models that use adva
nced diagnostic analytics or predictive and prescriptive capabilities\, bu
t whose primary job function is outside the field of statistics and analyt
ics.” In a nutshell\, they are non-technical employees who can use data sc
ience tools and technologies to solve business problems. Citizen data scie
ntists can provide business and industry area of expertise that many data
science experts lack. Their business experience and awareness of business
priorities enable them to effectively integrate data science and machine l
earning output into business processes.
\n\nWith that said\, our pan
el of experts will answer questions like:
\n\n- Can we successful
ly convert non-technical employees into data scientists by leveraging and
combining their area of expertise with data science technology to solve bu
siness problems? How?
\n - How do you ensure Artificial Intelligence
and Machine Learning becomes more accessible to the average data analyst/c
itizen data scientist?
\n - In your opinion\, what are the factors th
at are beginning to democratize data science through data science technolo
gy?
\n - How do I get started with democratizing data science through
data science technology\, especially using what I’ve learned in the past
combined with what we are doing today to properly unleash our future visio
n of enabling the citizen data scientist?
\n - When acquiring and imp
lementing your analytics and data science technology stack\, what are your
considerations to ensure success for your average data analysts/citizen d
ata scientists?
\n - There are many tools’ promises to empower citize
n data scientists. What are THE must have innovative tools for the next ge
neration of citizen data scientists?
\n - How do I balance the differ
ent technologies used by Citizen Data Scientists?
\n - I’ve got the c
apability\, i.e. the data science technology\, the right staff\, trained a
t the right level\, so how do I unleash it and use it at scale to add valu
e to the business?
\n - How do I market the concept of democratizatio
n of data science through data science technology to my organization? What
are my change management considerations? And how best do I communicate it
to the business once I get buy-in?
\n - Most of us know that data wr
angling is a big part of any analyst’s job. How do we best educate our “ci
tizen data scientists” in data wrangling (data management\, data tools\, s
imple queries\, data quality\, etc.)?
\n - How do we best connect our
“citizen data scientists” with the right tools for their level of interes
t\, ability\, expertise\, and business need? For instance\, do they just n
eed to learn how to use a pre-trained image recognition model\, or do they
need to learn how to build their own Convolutional Neural Network in Tens
orFlow? How do we best match tool to talent?
\n - I was wildly succ
essful at selling the promise of Data Science inside my company and the de
mand quickly outstripped our ability to supply. How do I best manage the i
nflux of demand?
\n - The data scientist skillset includes the unders
tanding of databases and data warehouses. Data scientists are responsible
for setting up appropriate data marts that provide users with less complex
queries that do not bring down the system\, as well as maintaining admini
stration over permissions and security is a building block to establishing
self-service analytics. With that said\, do we allow 'citizen data scient
ists' to set-up models that pull data from source systems on-demand\, whic
h could bring a halt to systems\, especially for smaller utilities with mo
re limitations?
\n
BEGIN:VALARM
ACTION:DISPLAY
DESCRIPTION:Join the Analytics Architecture & Technology (AAT) Community f
or an exciting panel discussion on 'Enabling the Citizen Data Scientist wi
th Next-Generation Data Science Technologies'. This session on May 3\, 202
1 is part two from a panel discussion that took place back in September 20
20 on “The Next Generation of Analytics Tools”. During September 2020\, we
covered many topics at a high-level and the members of the community requ
ested that we take a deeper dive on some of the topics discussed in the fu
ture. First up on that deeper dive\, our utility analytics experts will sh
are their perspectives and insight on 'Enabling the Citizen Data Scientist
with Next-Generation Data Science Technologies'.\n\nTo level set\, what d
oes ‘citizen data scientist’ mean? We have learned that “citizen data scie
ntist” is a term initiated by Gartner. Gartner defines it as “a person who
creates or generates models that use advanced diagnostic analytics or pre
dictive and prescriptive capabilities\, but whose primary job function is
outside the field of statistics and analytics.” In a nutshell\, they are n
on-technical employees who can use data science tools and technologies to
solve business problems. Citizen data scientists can provide business and
industry area of expertise that many data science experts lack. Their busi
ness experience and awareness of business priorities enable them to effect
ively integrate data science and machine learning output into business pro
cesses.\n\nWith that said\, our panel of experts will answer questions lik
e:\n\n * Can we successfully convert non-technical employees into data scie
ntists by leveraging and combining their area of expertise with data scien
ce technology to solve business problems? How?\n * How do you ensure Artif
icial Intelligence and Machine Learning becomes more accessible to the ave
rage data analyst/citizen data scientist?\n * In your opinion\, what are t
he factors that are beginning to democratize data science through data sci
ence technology?\n * How do I get started with democratizing data science
through data science technology\, especially using what I’ve learned in th
e past combined with what we are doing today to properly unleash our futur
e vision of enabling the citizen data scientist?\n * When acquiring and im
plementing your analytics and data science technology stack\, what are you
r considerations to ensure success for your average data analysts/citizen
data scientists?\n * There are many tools’ promises to empower citizen dat
a scientists. What are THE must have innovative tools for the next generat
ion of citizen data scientists?\n * How do I balance the different technol
ogies used by Citizen Data Scientists?\n * I’ve got the capability\, i.e.
the data science technology\, the right staff\, trained at the right level
\, so how do I unleash it and use it at scale to add value to the business
?\n * How do I market the concept of democratization of data science throu
gh data science technology to my organization? What are my change manageme
nt considerations? And how best do I communicate it to the business once I
get buy-in?\n * Most of us know that data wrangling is a big part of any
analyst’s job. How do we best educate our “citizen data scientists” in dat
a wrangling (data management\, data tools\, simple queries\, data quality\
, etc.)?\n * How do we best connect our “citizen data scientists” with the
right tools for their level of interest\, ability\, expertise\, and busin
ess need? For instance\, do they just need to learn how to use a pre-train
ed image recognition model\, or do they need to learn how to build their o
wn Convolutional Neural Network in TensorFlow? How do we best match tool t
o talent? \n * I was wildly successful at selling the promise of Data Sci
ence inside my company and the demand quickly outstripped our ability to s
upply. How do I best manage the influx of demand?\n * The data scientist s
killset includes the understanding of databases and data warehouses. Data
scientists are responsible for setting up appropriate data marts that prov
ide users with less complex queries that do not bring down the system\, as
well as maintaining administration over permissions and security is a bui
lding block to establishing self-service analytics. With that said\, do we
allow 'citizen data scientists' to set-up models that pull data from sour
ce systems on-demand\, which could bring a halt to systems\, especially fo
r smaller utilities with more limitations?\n\n
TRIGGER:-PT15M
END:VALARM
END:VEVENT
END:VCALENDAR