what is a data scientist

#DataTalk What is a Data Scientist?

LIVE TWEETCHAT

FEATURING:Dr. Michael Wu

Chief Scientist, Lithium@mich8elwu

Join our #DataTalk on Thursdays at 5 p.m. ETThis week, we tweeted with Dr. Michael Wu, the Chief Scientist at Lithium, where he applies data-driven methodologies to investigate the complex dynamics of the social web.

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What type of work doesa data scientist do?

Dr. Michael WuChief Scientist, Lithium@mich8elwu

“A data scientist’s work includes everything from data infrastructure (capture, store,

process) to data service (retrieval).

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Shanji XiongGlobal Chief Scientist, Experian@ShanjiXiong

“A data scientist converts data into business intelligence.



“ A data scientist’s work includes: decision science, business intelligence, customer

analytics, marketing analytics, fraud, security, etc.


What are attributes of agood data scientist?


To be a data scientist, you need the technical expertise in computer science,

statistics, and knowledge/experience with large data sets.


“

Data scientists should have good intuition, strong coding capability, solid training in statistics & machine learning.


“Shanji XiongGlobal Chief Scientist, Experian@ShanjiXiong


Educational background for data scientists can be computational genomics, astrophysicists, fluid dynamics, chemistry,

biophysics (like me) ...


“


To be a good data scientist, you need more than tech expertise. You must be

a good communicator to explain complex data/analysis.


“


Good data scientists also need to be passionate about data. I’d highly value curiosity, creativity and perseverance

when hiring one.


“

What kinds of companies have (or need) data scientists?


Any company that already is investedin modern big data infrastructure

will need data scientists to crunch the data.“

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All companies need to havedata scientists to stay competitive.“




All businesses use data, and data willgrow so big that our brain and databaseseventually can’t handle … they all need

data scientists eventually.

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What types of teams do data scientists work with?


It all depends how the data organizationis structured within the enterprise:independent team, hub & spoke,

or silo in dept.

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Data scientists can work in R&D, product development and support

business operations.

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SMBs use hub and spoke data teamswhere they report to different departments,

but collaborate and work together,so data expertise is shared.

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Large companies typically have entire teams of data scientists

within each department and they usually don’t collaborate.

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Personally, I work internally withengineering, product, marketing,best practice, service, consulting,

strategy, even sales and human resources.

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I process data, build models, engagewith clients, and facilitate collaboration

among Experian Data Labs.

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What are some big challenges that data scientists face?


One of the biggest challenges fordata scientists is communication.

Many data scientists speak tech & stats,but they don’t speak business.

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Challenges for data scientists: data governance and what

data can be used for“




Other challenges for data scientists:Data access, data integration, and motivation.“



If a company is starting a data scienceinitiative, their data scientist may not

have access to all data due to securityand compliance.

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Is there an art and science toworking with big data?

Absolutely! Good intuition and domain knowledge are the keys for

successful big data projects.“




There’s definitely science to workingwith big data… there are rigorous

stats and implementation details youlearn from statistics and computer science.

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There’s also art in working with big data,and this only comes with years

of experience on working with big data.“



Picking a good problem is sort of an art,choosing right features from an infinite

number of features, too (feature engineering).“



Exploratory data analysis (EDA): getting a feel or a hunch for how the

data behaves is definitely an art.“


How can data scientists make a big impact in their business?


First, data scientists need to learnabout the business, so they have the

context to interpret the data andresult of models/analyses.

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Second, they need to pick a goodproblem: the most impactful

problem that can be addressed withdata they have access to.

“



Third, they must communicateeffectively and make businesses

understand the analysis and businessimplication of the insight they found.

“


To be impactful, data scientists needto keep an open mind and concentrate

efforts on most impactful problems.“




A good data scientist must bea good communicator, storyteller,

teacher, etc. who can simplifycomplex data science for business.

“


What are some big data trends?

Big data will be embraced by moreand more businesses. More decisionswill be driven by data and analytics.




In past 5 years, most of the big datatech operate at the infrastructure layer.Now more people are focused on the

algorithm layer.


“


If there’s a big data trend, it’s theshift from infrastructure to analytics/

algorithms on people’s big data asset.


“


It used to be that data scientists can doeverything about any data, now there is data

engineering, algorithm, decision science.


“


Now there’s experts in natural languageprocessing, image analysis, video/audio

processing, streaming data, etc.


“

Why should companies invest more in data science?

Businesses invested in big data wiselywill have a huge competitive advantage

over their peers.“




Companies should invest more in datascience. They will need to eventually anyway.“


Any final tips for those whowant to work in data science?

Tips for new data scientists:Keep an open mind, think outside the

box, and work hard. The future is bright.




Final Tips: Learn the tech and stats,learn the business context, learn to

communicate the tech/stats to businessto bridge the gap


“


Be patient, follow your passion (which should be data), and pick a good

problem to solve.


“

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