9 answers
afraz
Student
Kalyan, Maharashtra, India
1
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what is the work of data scientist
#groups #scientist #scientists #forensic-scientists #data-science #actuary
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9 answers
Madison Hansen
Data Scientist
7
Answers
New York, New York
Updated
Madison’s Answer
I am a data scientist, I can tell you what kind of work I do every day. Note that the title "Data Scientist" covers a very wide range of skills and responsibilities, so a data scientist's work can vary a lot between different companies.
Similar to other scientists (like biologists, chemists, or physicists) data scientists use the scientific method: Observation, Hypothesis, Experiment, Analysis. However instead of using biological/chemical/physical data, they use data from a business or organization. And instead of trying to answer questions about how life/chemicals/the physical world works, they are trying to answer questions about how the business or the organization works.
In my role, Observation takes the form of finding and understanding data sets. Finding data sets requires talking to people (clients/customers, people in other departments in my organization) and asking them about data they could share with me. Once we have found the data, we need to understand it, which usually requires talking to the people who helped generate or collect it. A data scientist has to be willing to investigate where the data comes from!
For Hypothesis, now we think about what improvements we might try to make to the business/organization based on the data we found. This is a lot of brainstorming. It also requires knowledge of the business, knowing what are the highest priorities. You might have a lot of improvements, but if they only improve the business by 0.000001% then they might be a waste of time. This part of a data scientist's work requires business experience and wisdom.
For Experiment and Analysis, now we apply different mathematical and computational techniques to the data. The goal is to go from raw data to business result. This part of the job requires technical skills such as programming, statistics, mathematical modeling, interpretation, etc. It's also important to be able to explain and communicate the results, so good presentation skills are useful.
So as you can see, being a data scientist includes a lot of different types of work. Talking to people, research, computer skills, mathematical understanding, presentation skills. The projects I work on are very short, sometimes only a few months, so I cycle through these activities quickly, like every week my focus is different. Other data scientists work on longer projects and they may spend months just Observing for example. And the type of data they work on varies a lot, too. So while data science work is always exciting, not every data science job will be the same, you might have to change jobs a few times before you find the flavor of data science that you like the best.
Similar to other scientists (like biologists, chemists, or physicists) data scientists use the scientific method: Observation, Hypothesis, Experiment, Analysis. However instead of using biological/chemical/physical data, they use data from a business or organization. And instead of trying to answer questions about how life/chemicals/the physical world works, they are trying to answer questions about how the business or the organization works.
In my role, Observation takes the form of finding and understanding data sets. Finding data sets requires talking to people (clients/customers, people in other departments in my organization) and asking them about data they could share with me. Once we have found the data, we need to understand it, which usually requires talking to the people who helped generate or collect it. A data scientist has to be willing to investigate where the data comes from!
For Hypothesis, now we think about what improvements we might try to make to the business/organization based on the data we found. This is a lot of brainstorming. It also requires knowledge of the business, knowing what are the highest priorities. You might have a lot of improvements, but if they only improve the business by 0.000001% then they might be a waste of time. This part of a data scientist's work requires business experience and wisdom.
For Experiment and Analysis, now we apply different mathematical and computational techniques to the data. The goal is to go from raw data to business result. This part of the job requires technical skills such as programming, statistics, mathematical modeling, interpretation, etc. It's also important to be able to explain and communicate the results, so good presentation skills are useful.
So as you can see, being a data scientist includes a lot of different types of work. Talking to people, research, computer skills, mathematical understanding, presentation skills. The projects I work on are very short, sometimes only a few months, so I cycle through these activities quickly, like every week my focus is different. Other data scientists work on longer projects and they may spend months just Observing for example. And the type of data they work on varies a lot, too. So while data science work is always exciting, not every data science job will be the same, you might have to change jobs a few times before you find the flavor of data science that you like the best.
Dan Wastchak
Business Operations
6
Answers
Cary, North Carolina
Updated
Dan’s Answer
Hi Afraz,
Great question! The work of a data scientist can vary greatly but at the core a data scientist uses statistical and mathematical methods to analyze data to in order to understand "the story" the data is "telling" or indicating. Often they work to prove or validate a hypothesis.
They often are dealing with large amounts of data collected over month or years. This data may be combined with other data sets (i.e. car performance data combined with car purchasing data to determine if car performance influences customers to buy more of the high-performing cars). So a data scientist must be versed at 1) processing or "cleaning/preparing" the data, 2) analyzing the data using statistical methodologies, 3) interpreting the outcomes of the analysis, and 4) creating a summary of the findings to be shared with other members of their team. Some data scientists perform all of these tasks, while others may specialize in only one of these areas and then work with others to complete the work.
Data Scientists can also take their statistical methods and incorporate them into Machine Learning (ML) where computer systems are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data. This allows the computer to do what it does best and that's to processes a large amount of data all while learning from and adjusting to the changing data.
This is a fascinating field with a lot of demand as the world is generating terabytes of data every second. This data can be analyzed to improve processes, drive efficiencies, understand patterns and in the end make people's lives better.
Skills needed are: mathematics, science, computer programming
Great question! The work of a data scientist can vary greatly but at the core a data scientist uses statistical and mathematical methods to analyze data to in order to understand "the story" the data is "telling" or indicating. Often they work to prove or validate a hypothesis.
They often are dealing with large amounts of data collected over month or years. This data may be combined with other data sets (i.e. car performance data combined with car purchasing data to determine if car performance influences customers to buy more of the high-performing cars). So a data scientist must be versed at 1) processing or "cleaning/preparing" the data, 2) analyzing the data using statistical methodologies, 3) interpreting the outcomes of the analysis, and 4) creating a summary of the findings to be shared with other members of their team. Some data scientists perform all of these tasks, while others may specialize in only one of these areas and then work with others to complete the work.
Data Scientists can also take their statistical methods and incorporate them into Machine Learning (ML) where computer systems are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data. This allows the computer to do what it does best and that's to processes a large amount of data all while learning from and adjusting to the changing data.
This is a fascinating field with a lot of demand as the world is generating terabytes of data every second. This data can be analyzed to improve processes, drive efficiencies, understand patterns and in the end make people's lives better.
Skills needed are: mathematics, science, computer programming
Karthikeyan Natarajan
3
Answers
Bengaluru, Karnataka, India
Updated
Karthikeyan’s Answer
Interesting Question!
At a High level below are the work of Data scientist
+ Identifying data sources and pulling the data (e.g sales data from SAP or SQL server )
+ Understand the data (e.g sales, demand of product etc)
+ Interpretation of the data (sales per region, per product etc)
+ Provide a data insights to the Organization ( which region that has more sales or products with more profits)
+ Use advance algorithms to predict the future state ( product sales for a particular region)
Note : Lot of other areas data science can be applied like optimal resource required for a company, Risk modelling for new programs launched by finance companies etc . Finally, Identifying the data and understanding the data is the more crucial & time taking task.
At a High level below are the work of Data scientist
+ Identifying data sources and pulling the data (e.g sales data from SAP or SQL server )
+ Understand the data (e.g sales, demand of product etc)
+ Interpretation of the data (sales per region, per product etc)
+ Provide a data insights to the Organization ( which region that has more sales or products with more profits)
+ Use advance algorithms to predict the future state ( product sales for a particular region)
Note : Lot of other areas data science can be applied like optimal resource required for a company, Risk modelling for new programs launched by finance companies etc . Finally, Identifying the data and understanding the data is the more crucial & time taking task.
Kartikaya Pershad
Data Scientist
4
Answers
Austin, Texas
Updated
Kartikaya’s Answer
Excellent Question! I am glad that you are interested in Data Science.
I feel that DS is gonna grow even more in the coming years and we would like to have a new Data Scientists and increase our community.
In my work experience, I have learnt that as a Data Scientist, my job is to make sense of the data by transforming it into usable form and presenting that to stakeholders to help them make their decisions, Making sense of data is required because data is mostly stored in its raw form..it could be image, video or machine logs which then needs to be transformed into something that could fit any visualization tool like charts or Machine Learning models and algorithms.
In my opinion its much more fun and interesting than traditional software engineering and you get to work with many different experts like biologist, physicist, banks, artist and many more as its scope is limitless.
Overall, without going into too much details...i think you will love working as a Data Scientist and in case you want to get into details like where to start or what are some of the resources or courses that you can take..please feel free to reach out to me.
I feel that DS is gonna grow even more in the coming years and we would like to have a new Data Scientists and increase our community.
In my work experience, I have learnt that as a Data Scientist, my job is to make sense of the data by transforming it into usable form and presenting that to stakeholders to help them make their decisions, Making sense of data is required because data is mostly stored in its raw form..it could be image, video or machine logs which then needs to be transformed into something that could fit any visualization tool like charts or Machine Learning models and algorithms.
In my opinion its much more fun and interesting than traditional software engineering and you get to work with many different experts like biologist, physicist, banks, artist and many more as its scope is limitless.
Overall, without going into too much details...i think you will love working as a Data Scientist and in case you want to get into details like where to start or what are some of the resources or courses that you can take..please feel free to reach out to me.
Mark Stevens
Strategy
16
Answers
Atlanta, Georgia
Updated
Mark’s Answer
I am no expert in data science. But I do know this. The volume of data is exploding. We are entering a world where every physical movement or human experience can be recorded and analyzed for future decision-making. Data science has already become an incredibly important field and its importance from a career perspective will only increase. Data scientists have the opportunity to understand every movement in our world - people, animals, plants, things - identify patterns of behavior, and prescribe new ideas to make those movements or journeys better. The world needs the best minds to become data scientists!!
Gurjeet Ahluwalia
Engineer
2
Answers
Cary, North Carolina
Updated
Gurjeet’s Answer
Data scientists wear a lot of hats. They’re analyst, computer scientist, and storyteller (just to name a few). And they can also be a real game changer for organizations.
Data scientists are mainly involved in work on huge amount of structured, unstructured and real-time data sets from a variety of sources, and then cleaning and preparing data , in order to filter the most useful& relevant info in the data as per the use cases and requirements, to analyze user's usage patterns and provide impactful insights using metrics, algorithms, and statistics.
Besides Data scientists are also involved in building models for predictive analysis, to predict future events, on the basis on user's past and present behaviour(behaviour analysis).
May also involve in preparing reports for all types of audiences, using some reporting tools, as a result of their analysis. Presenting those reports with the most influencing insights, which can useful for the organiation.
Data scientists are mainly involved in work on huge amount of structured, unstructured and real-time data sets from a variety of sources, and then cleaning and preparing data , in order to filter the most useful& relevant info in the data as per the use cases and requirements, to analyze user's usage patterns and provide impactful insights using metrics, algorithms, and statistics.
Besides Data scientists are also involved in building models for predictive analysis, to predict future events, on the basis on user's past and present behaviour(behaviour analysis).
May also involve in preparing reports for all types of audiences, using some reporting tools, as a result of their analysis. Presenting those reports with the most influencing insights, which can useful for the organiation.
Thammanna Gowda
Data Engineer
2
Answers
San Jose, California
Updated
Thammanna’s Answer
Data Science is every vast subject area. First critical step is your understanding of data and subject area to find the answers to any business questions. Sometimes you try to find why it happened(Sales slowed in last Qtr since we started charging shipping fees) and sometimes you predict what may happen (future based on existing historical data )
aditi tiwari
Presales
20
Answers
Bengaluru, Karnataka, India
Updated
aditi’s Answer
Identifying the data-analytics problems that offer the greatest opportunities to the organization
Determining the correct data sets and variables
Collecting large sets of structured and unstructured data from disparate sources
Cleaning and validating the data to ensure accuracy, completeness, and uniformity
Devising and applying models and algorithms to mine the stores of big data
Analyzing the data to identify patterns and trends
Interpreting the data to discover solutions and opportunities
Communicating findings to stakeholders using visualization and other means
Determining the correct data sets and variables
Collecting large sets of structured and unstructured data from disparate sources
Cleaning and validating the data to ensure accuracy, completeness, and uniformity
Devising and applying models and algorithms to mine the stores of big data
Analyzing the data to identify patterns and trends
Interpreting the data to discover solutions and opportunities
Communicating findings to stakeholders using visualization and other means
Arjun Mukherjee
Data Science
2
Answers
Seattle, Washington
Updated
Arjun’s Answer
Great question. Essentials are:
1. Understanding the problem first and clarity on what we are trying to solve
2. Do we have clean past data around this problem to analyze first
3. Can you get initial insights or relationships from the data about the problem you are trying to solve
4. Can you predict or classify with simple or complex algorithms using past data what will happen in the future or solve the problem
1. Understanding the problem first and clarity on what we are trying to solve
2. Do we have clean past data around this problem to analyze first
3. Can you get initial insights or relationships from the data about the problem you are trying to solve
4. Can you predict or classify with simple or complex algorithms using past data what will happen in the future or solve the problem