7 ms·

How to Become a Data Scientist, Part 2

184 points by iamjeff 10y ago

darkhorn 10y ago
BS in Statistics? If you don't have one you are very likely not a data SCIENTIST. You will be data guy. I know that some meteorologist have used normality in forcasting. This is an example of why you cannot become a data SCIENTIST. Another example; applying regression to your data. If you think that regression is as simple as its formula then you need at least 4 years to understand what I mean.
- ibab 10y ago
  Why should not having a BS in statistics prevent anyone from properly learning and applying statistics in a rigorous scientific fashion? There are a lot of people with a rigorous mathematical background (mathematicians, physicists, biologists, computer scientists, ...) who are perfectly capable of understanding and applying stats concepts at a high level. In addition, these people have a lot of experience with doing scientific research, so shouldn't they be even more qualified to call themselves "data scientists"? Can you give an example of something that clearly distinguishes a "data scientist" from say a physicist who learned regression from a stats textbook?
  - markovbling 10y ago
    there are a lot of gotchas that don't seem like errors but completely invalidate analysis when done without a thorough understanding of a technique. For example you can learn regression from a stats textbook but unless you've gone through a thorough (and painful) graduate-level stats course, you probably haven't seen the edge cases that invalidate assumptions and necessitate a more complex regression e.g. your regression may suggest there is no effect but when you look at the residuals, you may find systematic bias that you can model using a subject-specific random effect or some transformation as a generalized linear model... That isn't to say you need a graduate level stats degree but applying statistics without understanding the pitfalls can lead to seriously wrong conclusions.
    - ibab 10y ago
      That's a good point, but I suspect that a lot of serious gotchas that a data scientist might encounter in the wild are not taught as part of a graduate-level statistics course. Being able to think critically and quickly adapting to the problem at hand might end up being more important than previous experience in stats (which is still very valuable, of course).
- taeric 10y ago
  Does the word scientist mean something different if it is in all caps? I mean, I get it. You would like it for the word to remain some pure version of meaning that it actually never had. Similar to getting upset that people using the word literally in a very figurative sense. The relevant question on this style of article is not about word smithing. That said, I'm in a field where we often through the title engineer on people, but we don't know why.
  - WhitneyLand 10y ago
    Get your point, but actually "literally" did actually used to have a different meaning. Only recently have dictionaries accepted the nonliteral use as a form of emphasis. In fact Cambridge still disallows it for formal speech.
    - taeric 10y ago
      Define recently. :) http://www.merriam-webster.com/words-at-play/misuse-of-literally http://www.merriam-webster.com/words-at-play/misuse-of-liter... Now... I deserve to be literally roasted for some of the other mistakes above. I should know throw versus through... Not even pronounced the same...
  - Obi_Juan_Kenobi 10y ago
    I don't really want to weigh in on a semantic argument, but I was considering what would be a good definition for a 'scientist' vocation. To me, a scientist is someone who engages in research with generalizable results. That would exclude someone who does experiments and analysis, but only applies established methods to a particular problem. Call them an analyst, perhaps, but science is an ongoing dialog that they are not participating in. I think that's part of why many consider it really presumptuous for 'data scientists' to call themselves such. Some of them are certainly developing new methods and engaging in a kind of dialog that is definitely science. Others are addressing business needs with a new sort of analysis, drawing on the field but not giving back to it. There is absolutely nothing wrong with that, but it does seem like that pursuit is different enough to bother calling it something else.
- tomrod 10y ago
  I appreciate that you hedged by saying "are very likely not" instead of just saying "not". See, here's the deal. Numerical literacy (numeracy) comes about through many processes, one of which is obtaining a statistics degree. However, not many fresh statisticians could tell you or me the expected runtime of a taking a numerical field's median on a large database. Why? Because the backgrounds are different. Yet, for a solid data science team, you'd like at least someone who can give insight as to whether your project will take minutes or decades to complete. This (contrived but important) example suggests that while a BS in statistics is an important and necessary component of a data science team, it not sufficient.
  - moseandre 10y ago
    It was a statistics class where I learned a linear time median algorithm. But that was pretty lucky, I admit. 95% of undergraduate statistics education is focused on formal inference. Data science, in my experience, involves a lot more exploratory data analysis [1] than formal inference (frequentist or Bayesian). The extreme focus on inference and the hypothesis testing step in the scientific method is something people with a formal statistics education have to overcome to be productive data scientists. Or applied statisticians, really! It is more important to understand the data, organize it creatively, and find unexpected structure. [1] https://en.wikipedia.org/wiki/Exploratory_data_analysis https://en.wikipedia.org/wiki/Exploratory_data_analysis
- deleted 10y ago
  [deleted]
- Fiahil 10y ago
  Saying you can't be a data scientist without a Bachelor degree in statistics is like saying you can't be a software engineer without a Master's degree in software engineering. Your educational system is not really a good environment for learning, so I'm pretty sure someone can become a data scientist with a French "License d'Histoire Moderne" and a lot of work.
  - darkhorn 10y ago
    Oh yeah, there was a miss calculation of a financial information and I guess because of this stocks have fell. Why? Because some smart guys used Excel. Or, then I can become a doctor right? By studying hard at home? You should not employ a self thought software engineer or statistician or a doctor in critical positions such as health, finance, engineering, and science. Some might be okay but 99.9% of them will be incompetent for the work.
- MichaelGG 10y ago
  Being a scientist is a mindset and someone that follows a certain approach. Existing institutions do not have a monopoly on who is a scientist.
curiousgal 10y ago
"You raise a valid point that is interesting to discuss but it contradicts with my emotions so I'll downvote you." -People downvoting you.
- minimaxir 10y ago
  It is valid to downvote on the manner in which something is said if it's not civil. In this case, not only is ALL CAPS utilized, it hits the No True Scotsman fallacy.
  - curiousgal 10y ago
    Unless someone uses pejorative adjectives I don't see how we can consider a comment uncivil, had it been in real life then yes the tone of saying would've given that up but I rather focus on the point raised than dig into the text.
    - minimaxir 10y ago
      Invoking the No True Scotsman fallacy is not a good defense for the No True Scotsman fallacy.
      - curiousgal 10y ago
        ah huh.
- dang 10y ago
  We detached this subthread from https://news.ycombinator.com/item?id=12243816 https://news.ycombinator.com/item?id=12243816 and marked it off-topic.
tomrod 10y ago
I liked this series and this part. I think it's important for people using data science in the industry to continue giving insight into best practices, feedback to academic programs, and occasional insights into the problem applications. In my mind, this ultimately improves the quality, education, and marketability of data science.
- iamjeff 10y ago
  I discovered the series earlier today on HN and the discovery could not have been any timely-er. I am just about to embark on the first six to eight months of a learning journey and see immediate utility in insightful series such as this one. I also came across a really helpful post that gives recommendations on progress markers for the self-taught developer [A Better Way to Learn Programming? Notes on The Odin Project;http://everydayutilitarian.com/essays/notes-on-the-odin-project/ http://everydayutilitarian.com/essays/notes-on-the-odin-proj...]. Guides like these, while they take a lot of time to write and refine, are complete lifesavers for entry-level professionals and prospective practitioners (and especially if they come from professionals that have been "tried and tested").
  - tomrod 10y ago
    Guides like these inspire me to quit being lazy and get back to writing one!
    - iamjeff 10y ago
      You are a data wrangler? Perhaps a guide would do; noobs like me have no perspective on what to learn and how to learn it. I mean, it wasn't even a year ago that I was convinced that I could go from 0 to 100 data science-wise in under a year: I wanted to learn it all. It took me the better part of a year to realize that I had wasted innumerable hours devising a curriculum and timelines that were plain dumb. A practical guide could have spared me a lot of hurt and while I cannot at this moment compensate you (or the community for that matter), I am sure that opportunities will certainly arise for me to pay my debt. Would love to see a guide from you- it would come with the added advantage that you would be accessible to the brilliant HN community. I would give away a limb to see such a discussion go down: what to learn and where to learn it from (a lot of folks, I imagine, would not mind recommendations for openly accessible material; I know I wouldn't mind that)? how fast should you expect to go/move/learn? time commitments? tools and frameworks? motivation hacks? where would I go to find remote jobs? what level of proficiency should I achieve in the first sprint?
apathy 10y ago
COI: author is a "data science" recruiter and the field has not coalesced down to a static definition. Caveat lector
stevehiehn 10y ago
I don't like the term 'Data Scientist' at all. I think its far to loaded. Its a bad thing. Many developers already building sophisticated analytics and predictive systems will avoid identifying with what they are really doing in fear of being challenged as to whether they are a 'real' scientist or not.
- jupiter90000 10y ago
  I think now alot of companies are using that term for what is basically a data analyst or perhaps statistical analyst role. Because it sounds impressive it's a way to attract people because we have egos and would like a title that is externally fascinating.. 'look dad, I am a scientist!' Not to say there aren't some doing impressive things or that there is not valid science done with data. What I often have seen is more like BI roles marketed with a new title though.
- dredmorbius 10y ago
  "A data scientist is a statistician who lives in San Francisco." -- Josh Wills.
- Obi_Juan_Kenobi 10y ago
  Yes, scientists do research, and even if you are doing experiments and analysis, if your results are not generalizable, then it's difficult to argue that's a 'research' pursuit. Applying established methods to a particular case is perhaps closer to engineering, or a more general term of 'analyst'. Consider that all scientists are working with data, often very large and complex datasets, the term comes off like a joke.
- manigandham 10y ago
  Yes. All scientific disciplines involve data, and while there is a field of studying "data" as a concept itself, this is most certainly not what these people at these companies do. The accurate title is Data Analyst, something that has already existed for a long time and works just fine.
  - gaius 10y ago
mastazi 10y ago
In part 1/3, the author writes that there are 2 branches of data science: > Data science for people (Type A), i.e. analytics to support evidence-based decision making > Data science for software (Type B), for example: recommender systems as we see in Netflix and Spotify Isn't "type A" business intelligence, and isn't "type B" machine learning? Why doesn't he use those more widely known terms? Or maybe he is referring to something else?
- preygel 10y ago
  The distinction is being made on end outputs -- is it more like a Powerpoint deck, or more like a production data pipeline -- not on techniques. "Machine learning" includes plenty of activities that can be used to provide evidence for one-off human decision making (e.g., using a model to produce forecasts or to understand sensitivities).
  - mastazi 10y ago
    Mmmmh yes, often ML produces data which supports human decision making; I see what you mean. In fact, now that you mention it, in my company the BI team is starting to use ML techniques to recognise some particular patterns.
- SatvikBeri 10y ago
  Both business intelligence and machine learning are narrower terms with a more specific meaning. Business intelligence has the connotation of a certain set of techniques (e.g. reporting, SQL, querying, PowerPoint presentations) while machine learning is a fairly specific set of tools that is much narrower than what most software data scientists do. E.g. I'm a software data scientist and I spend much more time on descriptive statistics than machine learning.
  - mastazi 10y ago
    Yes, I agree that both ML and BI are narrower definitions compared to that of data science.
- 10y ago
mrjaeger 10y ago
In the article Alec mentions it is important to be able to read academic papers properly. Does anyone have good resources for this? I've read some papers before but do not have a research/academic background where I really had to dig deeply into them.
- Obi_Juan_Kenobi 10y ago
  I'm not sure there's a good answer to this. The best I can say is to go to grad school. That's a terrible answer, but it's perhaps the only realistic one. It's in that situation, or one very similar, where you're exposed to loads of criticism and discussion. Basically any paper that was competently written (even if it wasn't competent work) is going to sound convincing to the naive. After hearing a few papers get torn down, you'll see the cracks in weak arguments, the poorly supported conclusions, and the seemingly boring stuff that's absolutely brilliant. Very generally, the best sign of good work is a 'masochistic' author. What I mean by this is an author that writes as though every result they get is deeply suspect and needs to be corroborated in multiple ways. When it's almost exhausting to read because it feels like they're just beating themselves up, you're probably reading something really special. Likely the most difficult thing to do as an 'outsider' is to get a sense of how 'trustworthy' certain results are. Some methods are almost binary in that you either get no result, or a great result. If an author shows this, there might be very little reason to doubt it, and thus independent lines of evidence not really necessary, especially if there's context that supports / is consistent with that result. Other methods are notoriously terrible and need a great deal of careful controls and analysis to even be considered, and then only as one angle of attack. Sometimes you can find reviews that discuss methods like this, which would be an invaluable resource. Reviews are generally a great way to start reading a field, anyhow. _______ As a practical guide, a well written paper can just be read start to finish. Then reflect on it to see if you understand it. Could you explain the paper to someone else? That's a good sign of whether you understand it. After that, think of critiques. Could the results be interpreted different ways? Was the analysis appropriate for the data? Are the methods reliable? All papers have weakness; we live in a world of finite time and resources. All papers could be better, so think about what could be done. After that, consider what would be reasonable to do. Did the authors skip something conspicuous? That's a good sign that there was some difficulty there were avoiding. That might be fine, but it also might mean there's data that doesn't fit with their conclusions, which would be a very big issue indeed. That latter part is the most important, but also the most difficult to do. It requires reading dozens, really hundreds, of papers so that you learn about some 'unknown unknowns'. Hearing talks really helps with this, too, as many people will give a sort of history of their work that includes some of the twists and dead ends. _______ That all said, anyone can read a paper. It's not 'magic' that lets you do it. You'll miss some of the nuance, and occasionally be lead astray, but peer review works reasonably well enough that papers are mostly quite good with the devil held to the details. Like most things, it likely follows the Pareto Principle, with a little effort bearing outsize results.

184 points by iamjeff 10y ago

BS in Statistics? If you don't have one you are very likely not a data SCIENTIST. You will be data guy. I know that some meteorologist have used normality in forcasting. This is an example of why you cannot become a data SCIENTIST. Another example; applying regression to your data. If you think that regression is as simple as its formula then you need at least 4 years to understand what I mean.

ibab 10y ago
Why should not having a BS in statistics prevent anyone from properly learning and applying statistics in a rigorous scientific fashion? There are a lot of people with a rigorous mathematical background (mathematicians, physicists, biologists, computer scientists, ...) who are perfectly capable of understanding and applying stats concepts at a high level. In addition, these people have a lot of experience with doing scientific research, so shouldn't they be even more qualified to call themselves "data scientists"? Can you give an example of something that clearly distinguishes a "data scientist" from say a physicist who learned regression from a stats textbook?
- markovbling 10y ago
  there are a lot of gotchas that don't seem like errors but completely invalidate analysis when done without a thorough understanding of a technique. For example you can learn regression from a stats textbook but unless you've gone through a thorough (and painful) graduate-level stats course, you probably haven't seen the edge cases that invalidate assumptions and necessitate a more complex regression e.g. your regression may suggest there is no effect but when you look at the residuals, you may find systematic bias that you can model using a subject-specific random effect or some transformation as a generalized linear model... That isn't to say you need a graduate level stats degree but applying statistics without understanding the pitfalls can lead to seriously wrong conclusions.
  - ibab 10y ago
    That's a good point, but I suspect that a lot of serious gotchas that a data scientist might encounter in the wild are not taught as part of a graduate-level statistics course. Being able to think critically and quickly adapting to the problem at hand might end up being more important than previous experience in stats (which is still very valuable, of course).
taeric 10y ago
Does the word scientist mean something different if it is in all caps? I mean, I get it. You would like it for the word to remain some pure version of meaning that it actually never had. Similar to getting upset that people using the word literally in a very figurative sense. The relevant question on this style of article is not about word smithing. That said, I'm in a field where we often through the title engineer on people, but we don't know why.
- WhitneyLand 10y ago
  Get your point, but actually "literally" did actually used to have a different meaning. Only recently have dictionaries accepted the nonliteral use as a form of emphasis. In fact Cambridge still disallows it for formal speech.
  - taeric 10y ago
    Define recently. :) http://www.merriam-webster.com/words-at-play/misuse-of-literally http://www.merriam-webster.com/words-at-play/misuse-of-liter... Now... I deserve to be literally roasted for some of the other mistakes above. I should know throw versus through... Not even pronounced the same...
- Obi_Juan_Kenobi 10y ago
  I don't really want to weigh in on a semantic argument, but I was considering what would be a good definition for a 'scientist' vocation. To me, a scientist is someone who engages in research with generalizable results. That would exclude someone who does experiments and analysis, but only applies established methods to a particular problem. Call them an analyst, perhaps, but science is an ongoing dialog that they are not participating in. I think that's part of why many consider it really presumptuous for 'data scientists' to call themselves such. Some of them are certainly developing new methods and engaging in a kind of dialog that is definitely science. Others are addressing business needs with a new sort of analysis, drawing on the field but not giving back to it. There is absolutely nothing wrong with that, but it does seem like that pursuit is different enough to bother calling it something else.
tomrod 10y ago
I appreciate that you hedged by saying "are very likely not" instead of just saying "not". See, here's the deal. Numerical literacy (numeracy) comes about through many processes, one of which is obtaining a statistics degree. However, not many fresh statisticians could tell you or me the expected runtime of a taking a numerical field's median on a large database. Why? Because the backgrounds are different. Yet, for a solid data science team, you'd like at least someone who can give insight as to whether your project will take minutes or decades to complete. This (contrived but important) example suggests that while a BS in statistics is an important and necessary component of a data science team, it not sufficient.
- moseandre 10y ago
  It was a statistics class where I learned a linear time median algorithm. But that was pretty lucky, I admit. 95% of undergraduate statistics education is focused on formal inference. Data science, in my experience, involves a lot more exploratory data analysis [1] than formal inference (frequentist or Bayesian). The extreme focus on inference and the hypothesis testing step in the scientific method is something people with a formal statistics education have to overcome to be productive data scientists. Or applied statisticians, really! It is more important to understand the data, organize it creatively, and find unexpected structure. [1] https://en.wikipedia.org/wiki/Exploratory_data_analysis https://en.wikipedia.org/wiki/Exploratory_data_analysis
deleted 10y ago
[deleted]
Fiahil 10y ago
Saying you can't be a data scientist without a Bachelor degree in statistics is like saying you can't be a software engineer without a Master's degree in software engineering. Your educational system is not really a good environment for learning, so I'm pretty sure someone can become a data scientist with a French "License d'Histoire Moderne" and a lot of work.
- darkhorn 10y ago
  Oh yeah, there was a miss calculation of a financial information and I guess because of this stocks have fell. Why? Because some smart guys used Excel. Or, then I can become a doctor right? By studying hard at home? You should not employ a self thought software engineer or statistician or a doctor in critical positions such as health, finance, engineering, and science. Some might be okay but 99.9% of them will be incompetent for the work.
MichaelGG 10y ago
Being a scientist is a mindset and someone that follows a certain approach. Existing institutions do not have a monopoly on who is a scientist.

curiousgal 10y ago

"You raise a valid point that is interesting to discuss but it contradicts with my emotions so I'll downvote you." -People downvoting you.

minimaxir 10y ago
It is valid to downvote on the manner in which something is said if it's not civil. In this case, not only is ALL CAPS utilized, it hits the No True Scotsman fallacy.
- curiousgal 10y ago
  Unless someone uses pejorative adjectives I don't see how we can consider a comment uncivil, had it been in real life then yes the tone of saying would've given that up but I rather focus on the point raised than dig into the text.
  - minimaxir 10y ago
    Invoking the No True Scotsman fallacy is not a good defense for the No True Scotsman fallacy.
    - curiousgal 10y ago
      ah huh.
dang 10y ago
We detached this subthread from https://news.ycombinator.com/item?id=12243816 https://news.ycombinator.com/item?id=12243816 and marked it off-topic.

tomrod 10y ago

I liked this series and this part. I think it's important for people using data science in the industry to continue giving insight into best practices, feedback to academic programs, and occasional insights into the problem applications. In my mind, this ultimately improves the quality, education, and marketability of data science.

iamjeff 10y ago
I discovered the series earlier today on HN and the discovery could not have been any timely-er. I am just about to embark on the first six to eight months of a learning journey and see immediate utility in insightful series such as this one. I also came across a really helpful post that gives recommendations on progress markers for the self-taught developer [A Better Way to Learn Programming? Notes on The Odin Project;http://everydayutilitarian.com/essays/notes-on-the-odin-project/ http://everydayutilitarian.com/essays/notes-on-the-odin-proj...]. Guides like these, while they take a lot of time to write and refine, are complete lifesavers for entry-level professionals and prospective practitioners (and especially if they come from professionals that have been "tried and tested").
- tomrod 10y ago
  Guides like these inspire me to quit being lazy and get back to writing one!
  - iamjeff 10y ago
    You are a data wrangler? Perhaps a guide would do; noobs like me have no perspective on what to learn and how to learn it. I mean, it wasn't even a year ago that I was convinced that I could go from 0 to 100 data science-wise in under a year: I wanted to learn it all. It took me the better part of a year to realize that I had wasted innumerable hours devising a curriculum and timelines that were plain dumb. A practical guide could have spared me a lot of hurt and while I cannot at this moment compensate you (or the community for that matter), I am sure that opportunities will certainly arise for me to pay my debt. Would love to see a guide from you- it would come with the added advantage that you would be accessible to the brilliant HN community. I would give away a limb to see such a discussion go down: what to learn and where to learn it from (a lot of folks, I imagine, would not mind recommendations for openly accessible material; I know I wouldn't mind that)? how fast should you expect to go/move/learn? time commitments? tools and frameworks? motivation hacks? where would I go to find remote jobs? what level of proficiency should I achieve in the first sprint?

apathy 10y ago

COI: author is a "data science" recruiter and the field has not coalesced down to a static definition. Caveat lector

stevehiehn 10y ago

I don't like the term 'Data Scientist' at all. I think its far to loaded. Its a bad thing. Many developers already building sophisticated analytics and predictive systems will avoid identifying with what they are really doing in fear of being challenged as to whether they are a 'real' scientist or not.

jupiter90000 10y ago
I think now alot of companies are using that term for what is basically a data analyst or perhaps statistical analyst role. Because it sounds impressive it's a way to attract people because we have egos and would like a title that is externally fascinating.. 'look dad, I am a scientist!' Not to say there aren't some doing impressive things or that there is not valid science done with data. What I often have seen is more like BI roles marketed with a new title though.
dredmorbius 10y ago
"A data scientist is a statistician who lives in San Francisco." -- Josh Wills.
Obi_Juan_Kenobi 10y ago
Yes, scientists do research, and even if you are doing experiments and analysis, if your results are not generalizable, then it's difficult to argue that's a 'research' pursuit. Applying established methods to a particular case is perhaps closer to engineering, or a more general term of 'analyst'. Consider that all scientists are working with data, often very large and complex datasets, the term comes off like a joke.
manigandham 10y ago
Yes. All scientific disciplines involve data, and while there is a field of studying "data" as a concept itself, this is most certainly not what these people at these companies do. The accurate title is Data Analyst, something that has already existed for a long time and works just fine.
- gaius 10y ago

mastazi 10y ago

In part 1/3, the author writes that there are 2 branches of data science: > Data science for people (Type A), i.e. analytics to support evidence-based decision making > Data science for software (Type B), for example: recommender systems as we see in Netflix and Spotify Isn't "type A" business intelligence, and isn't "type B" machine learning? Why doesn't he use those more widely known terms? Or maybe he is referring to something else?

preygel 10y ago
The distinction is being made on end outputs -- is it more like a Powerpoint deck, or more like a production data pipeline -- not on techniques. "Machine learning" includes plenty of activities that can be used to provide evidence for one-off human decision making (e.g., using a model to produce forecasts or to understand sensitivities).
- mastazi 10y ago
  Mmmmh yes, often ML produces data which supports human decision making; I see what you mean. In fact, now that you mention it, in my company the BI team is starting to use ML techniques to recognise some particular patterns.
SatvikBeri 10y ago
Both business intelligence and machine learning are narrower terms with a more specific meaning. Business intelligence has the connotation of a certain set of techniques (e.g. reporting, SQL, querying, PowerPoint presentations) while machine learning is a fairly specific set of tools that is much narrower than what most software data scientists do. E.g. I'm a software data scientist and I spend much more time on descriptive statistics than machine learning.
- mastazi 10y ago
  Yes, I agree that both ML and BI are narrower definitions compared to that of data science.
10y ago

mrjaeger 10y ago

In the article Alec mentions it is important to be able to read academic papers properly. Does anyone have good resources for this? I've read some papers before but do not have a research/academic background where I really had to dig deeply into them.

Obi_Juan_Kenobi 10y ago
I'm not sure there's a good answer to this. The best I can say is to go to grad school. That's a terrible answer, but it's perhaps the only realistic one. It's in that situation, or one very similar, where you're exposed to loads of criticism and discussion. Basically any paper that was competently written (even if it wasn't competent work) is going to sound convincing to the naive. After hearing a few papers get torn down, you'll see the cracks in weak arguments, the poorly supported conclusions, and the seemingly boring stuff that's absolutely brilliant. Very generally, the best sign of good work is a 'masochistic' author. What I mean by this is an author that writes as though every result they get is deeply suspect and needs to be corroborated in multiple ways. When it's almost exhausting to read because it feels like they're just beating themselves up, you're probably reading something really special. Likely the most difficult thing to do as an 'outsider' is to get a sense of how 'trustworthy' certain results are. Some methods are almost binary in that you either get no result, or a great result. If an author shows this, there might be very little reason to doubt it, and thus independent lines of evidence not really necessary, especially if there's context that supports / is consistent with that result. Other methods are notoriously terrible and need a great deal of careful controls and analysis to even be considered, and then only as one angle of attack. Sometimes you can find reviews that discuss methods like this, which would be an invaluable resource. Reviews are generally a great way to start reading a field, anyhow. _______ As a practical guide, a well written paper can just be read start to finish. Then reflect on it to see if you understand it. Could you explain the paper to someone else? That's a good sign of whether you understand it. After that, think of critiques. Could the results be interpreted different ways? Was the analysis appropriate for the data? Are the methods reliable? All papers have weakness; we live in a world of finite time and resources. All papers could be better, so think about what could be done. After that, consider what would be reasonable to do. Did the authors skip something conspicuous? That's a good sign that there was some difficulty there were avoiding. That might be fine, but it also might mean there's data that doesn't fit with their conclusions, which would be a very big issue indeed. That latter part is the most important, but also the most difficult to do. It requires reading dozens, really hundreds, of papers so that you learn about some 'unknown unknowns'. Hearing talks really helps with this, too, as many people will give a sort of history of their work that includes some of the twists and dead ends. _______ That all said, anyone can read a paper. It's not 'magic' that lets you do it. You'll miss some of the nuance, and occasionally be lead astray, but peer review works reasonably well enough that papers are mostly quite good with the devil held to the details. Like most things, it likely follows the Pareto Principle, with a little effort bearing outsize results.