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One of the points Andrew Ng repeatedly makes, especially recently, is that we do not focus a lot on the data. ML is a field of models, not data. That's fine, it

by zwaps 5y ago

One of the points Andrew Ng repeatedly makes, especially recently, is that we do not focus a lot on the data. ML is a field of models, not data. That's fine, it's an academic discipline. However, a data scientist should probably focus on something else. Nevertheless, we currently want to be ML people, more or less. It's sexy to download __some__ dataset and develop a new model architecture. It's not really sexy to collect good data - not just any data, but good data. Now, coming from academia, I think this is just a normal thing. It happens in every discipline in some way. In academic fields that are driven by observational data, data collection is usually a difficult and tedious task. Developing theories, methods and, of course, new results is much more interesting. And rewarding! Look at economics and Piketty's work: His main contribution was to collect data on wealth. Nobody really cares about his models much. However, for some reason, that data simply did not exist before. And it's now valuable for hundreds of different projects. As a data scientist, one needs to think about data, first and foremost. One needs to spend most time on collection, on measurement errors, on selection effects, on identification, censoring and all that stuff that nobody likes to think about. Fitting the next best function approximator to the data is the most fun, but probably not the most valuable thing a data scientist can do. From that standpoint, I think data scientist is a proper job that is probably needed in every company in the future. However, no coursera course on the latest ML package will make you a valuable data scientists. I think data scientists who are expert statisticians, survey and experiment designers, causal inference gurus and combine that with proper CS techniques will be valuable.

mateo411 5y ago
I agree with this. I think you really need to be a good data engineer to be a good data scientist. Now, these terms are different roles at different organizations. I think you need to have a well developed software engineering skillset. You need to have some experience with database systems and query optimization. Of course you need a strong background in statistics and mathematics. Often these skillsets are split across teams and departments, which tends to slow the process down. It takes a long time to develop these skills, but if you have them, you are very valuable. Many DS and sometimes companies make a mistake of undervaluing the software engineering skillset. However, if your data comes from the real world, it will be messy, it will need to be organized. If you don't know how to do that, then you aren't going to build your models. If you are bad at querying your data, then you are going to build your models much more slowly.
- edmundsauto 5y ago
  Do you think it makes sense for someone to combine these skills? My (unexamined) belief is that its better to have them split out. My reasoning: 1. Data science is often low latency work, running investigations to provide insight for strategic decisions. This is more of a political/comms role - and the cadence/requirements of the role tend to be counter to the long periods of uninterrupted focus that an engineer needs. 2. DE work is foundational, but once it's built, you probably want someone who is fast at adhoc analysis. (Unless you want to push into production - but that's a 3rd skillset, IMO) 3. It's hard to be both user and designer; having two brains has saved me from many mistakes related to bias and assumptions that I make. The DS can function almost as a product manager, synthesizing information and focusing on deliverables (the "what"). The DE can focus on the how. Would love to hear your thoughts - this is an area that I'm very interested in.
  - nerdponx 5y ago
    I agree with the fact that they need to be treated as separate roles, although a lot of organizations just don't understand this well enough and you end up having data engineers doing data science, or data scientists doing data engineering, or both, maybe without the teams ever really talking to each other. However I think that's a somewhat different issue from the one under discussion here. The problem here is that data scientists can end up wasting time trying to apply fancy techniques, when the real impact would come from basic data analysis using old-school statistics. Interestingly, I have never encountered this in my personal experience. I don't personally know any data scientist who would rather use in neural network win something simpler would suffice. Also, I disagree with the idea that data scientists don't need long periods of concentration. If anything, data science is more of a "creative" enterprise, whereas coding tends to be somewhat methodical and mechanical. For me at least, that means long periods of time spent trying different things and contemplating, which requires just as much deep focus as writing a complicated piece of software.
  - mateo411 5y ago
    I think if you have the desire and the time to combine these skills, then it's a good idea to do this. If you enjoy building and optimizing pipelines and if you enjoy doing creating analytics, stats and building models, you will be very much in demand and it will be pretty lucrative. Most people don't do this, because it takes a long time, and they enjoy one over the other. 1. This really depends on the DS role at a company. You are describing a company where DS is highly valued and has a lot of power. They probably have representation with a VP or in the C suite. Some companies are like this. 2. DE should be foundational, and they should also be able to push to production. You might have another team which works on setting up new platform environments, but DE better be comfortable pushing and debugging issues in production. It's helpful for DE to be able to perform exploratory analysis on a dataset. It's easy to introduce bugs, if DE can catch this, then it really improves the velocity. If they can't do this, then you need to wait for DS or somebody to act like QA for a dataset. You can find cheaper DEs who are good at shoveling data around, but they don't know what a probability distribution is. 3. It's helpful for DS to have some domain knowledge. If they have that, and DS is highly valued at the company, then it makes sense for the DS role to contain a Product Management component. I'm glad you are interested in my thoughts on this. If you have any more follow up questions, feel free to ask.

by zwaps 5y ago

mateo411 5y ago

I agree with this. I think you really need to be a good data engineer to be a good data scientist. Now, these terms are different roles at different organizations. I think you need to have a well developed software engineering skillset. You need to have some experience with database systems and query optimization. Of course you need a strong background in statistics and mathematics. Often these skillsets are split across teams and departments, which tends to slow the process down. It takes a long time to develop these skills, but if you have them, you are very valuable. Many DS and sometimes companies make a mistake of undervaluing the software engineering skillset. However, if your data comes from the real world, it will be messy, it will need to be organized. If you don't know how to do that, then you aren't going to build your models. If you are bad at querying your data, then you are going to build your models much more slowly.

edmundsauto 5y ago
Do you think it makes sense for someone to combine these skills? My (unexamined) belief is that its better to have them split out. My reasoning: 1. Data science is often low latency work, running investigations to provide insight for strategic decisions. This is more of a political/comms role - and the cadence/requirements of the role tend to be counter to the long periods of uninterrupted focus that an engineer needs. 2. DE work is foundational, but once it's built, you probably want someone who is fast at adhoc analysis. (Unless you want to push into production - but that's a 3rd skillset, IMO) 3. It's hard to be both user and designer; having two brains has saved me from many mistakes related to bias and assumptions that I make. The DS can function almost as a product manager, synthesizing information and focusing on deliverables (the "what"). The DE can focus on the how. Would love to hear your thoughts - this is an area that I'm very interested in.
- nerdponx 5y ago
  I agree with the fact that they need to be treated as separate roles, although a lot of organizations just don't understand this well enough and you end up having data engineers doing data science, or data scientists doing data engineering, or both, maybe without the teams ever really talking to each other. However I think that's a somewhat different issue from the one under discussion here. The problem here is that data scientists can end up wasting time trying to apply fancy techniques, when the real impact would come from basic data analysis using old-school statistics. Interestingly, I have never encountered this in my personal experience. I don't personally know any data scientist who would rather use in neural network win something simpler would suffice. Also, I disagree with the idea that data scientists don't need long periods of concentration. If anything, data science is more of a "creative" enterprise, whereas coding tends to be somewhat methodical and mechanical. For me at least, that means long periods of time spent trying different things and contemplating, which requires just as much deep focus as writing a complicated piece of software.
- mateo411 5y ago
  I think if you have the desire and the time to combine these skills, then it's a good idea to do this. If you enjoy building and optimizing pipelines and if you enjoy doing creating analytics, stats and building models, you will be very much in demand and it will be pretty lucrative. Most people don't do this, because it takes a long time, and they enjoy one over the other. 1. This really depends on the DS role at a company. You are describing a company where DS is highly valued and has a lot of power. They probably have representation with a VP or in the C suite. Some companies are like this. 2. DE should be foundational, and they should also be able to push to production. You might have another team which works on setting up new platform environments, but DE better be comfortable pushing and debugging issues in production. It's helpful for DE to be able to perform exploratory analysis on a dataset. It's easy to introduce bugs, if DE can catch this, then it really improves the velocity. If they can't do this, then you need to wait for DS or somebody to act like QA for a dataset. You can find cheaper DEs who are good at shoveling data around, but they don't know what a probability distribution is. 3. It's helpful for DS to have some domain knowledge. If they have that, and DS is highly valued at the company, then it makes sense for the DS role to contain a Product Management component. I'm glad you are interested in my thoughts on this. If you have any more follow up questions, feel free to ask.