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Great question, and since you mentioned you’re getting started, I’ll start with a very basic setup for a lab(s). Red labs or Threat labs can get very complex including 100’s of virtual machines running various flavors of Windows, Linux, OSX, and other popular OS’s and software.First things first, most of everything you need to do will require access to the internet. You do not want to use your home internet IP for this. Find and utilize a commercial VPN service that fits your needs and tunnel all traffic thru it. Also make sure that the terms of service for that VPN provider accommodate the white hat security professional.For a malware lab:- 1 large server that’s able to accommodate at least 10 virtual machines with a minimum of 3 concurrent running systems. Don’t slack on the memory, you’ll need lots of it.- A segregated network from your home network with very little or no connectivity between the two. You don’t want to unleash malware that ends up compromising every machine you own. You can accomplish this with networking hardware and some very basic understanding of networks.- Virtual machines, I highly suggest that you start with Windows XP, Windows 7 and Windows 10. Some malware is very specific to which OS it will run on, it’s nice to have all them at hand for such a case. I tend to use Windows 7 unpatched and find a good amount of success executing malware on it. You’ll also need to get software to track what exactly the malware is doing to the machine. This includes wireshark for network capture, sysinternals tools for registry/file tracking and also process tracking.- You’ll need a machine to do the reversing and exploring on, I suggest Remnux Linux it has all the basic and more advanced tools you’ll need to start pulling malware apart and understanding it. From here you’ll progress to more advanced tools/techniques but this is a solid starting platform.- I highly suggest you utilize sandboxing technologies, they are incredibly useful for rapidly triaging malware and understanding what it’s doing at high level. Cuckoo sandbox is an excellent (free) choice here and utilized by sites like Malware Analysis by Cuckoo Sandbox- Lastly, you’ll need access to malware, which there are several options to choose from and even commercial offerings like Free Online Virus, Malware and URL Scanner- Protip: take regular snapshots of all your VM’s so you can roll them back to a previous clean state after infecting them.- Protip: some malware is virtual machine aware, meaning they detect you’re running in a VM or sandbox and refuse to run. There are many anti-sandbox detection techniques you can utilize to trick the malware into executing.For a penetration testing lab:- Everything that’s above :)- You’ll need something to do with the penetration testing with, I highly suggest Kali Linux. There are other options out there, but I’m a die-hard backtrack Linux fan so Kali is my choice.- You’ll need vulnerable machines which the previous Windows machines above can be a great resource for. The key is to make sure the OS is unpatched, what’s the fun of attempting exploits if it’s not vulnerable. Don’t forget Linux either, finding an older version of popular Linux OS’s is just a Google search away.Hopefully this gives a good basic understanding of what’s needed, allowing a lot to be discovered along the way. Happy threat hunting!

My experience on Quora over the years has been little different from what other women report: random harrassing or just plain dumb inbox messages, sexual remarks along the lines of what someone would want to "do" to me posted publicly (and anonymously) in the body of an answer; direct physical threats (where the poster said that it was easy to find where I live and implied hurting me (ironically, in a comment to my answer to a question about when romantic persistence crosses the line into stalking).I've had my name included on lists outside of Quora, as part of the vast Quora Feminist troublemaker conspiracy because of my participation on The Fourth Wave 1 (I am deeply honored to have appeared on this list, btw). (With respect to the mention of The Fourth Wave blog, I had to enter into the search bar on the top of this page, because it didn't come up by using the "@ mention" - although the question Is there a "Fourth Wave" to deal with men issues on Quora?" did...).I've been downvoted and collapsed into oblivion for questions where I was an expert on the topic, but wasn't part of the boys club, so my answer relating to an industry that I have 20+ years experience in, (as well as being an attorney) got collapsed and overruled by someone fresh out of college with an undergraduate degree in economics and the words "startup founder" in his profile, who amassed upvotes like swarms of flies. (That kind of thing is not just about women on Quora, of course; although I'm sure that community attitudes towards women played a significant role in the number of downvotes I'd received.) I feel that his friends would still have upvoted his (bad and irresponsible) answer; but they would have been far less likely to downvote, challenge, and collapse mine, if I were male.Like most other women on this site, I've been dismissed, disrespected, dishonored; objectified, ogled, offended. I've been patronized and the recipient of condescending and ad hominem attacks, and unsolicited sexual advances. Most of that behavior slowed to a crawl after I changed my profile picture to one that revealed less of my face (and a few more wrinkles) and as more women, especially young women, with South Asian names became active on the site, and drew away many of the worst individual offenders like bees to better quality, fresher honey.I'm not sure if if I no longer frequent topic areas that generate the most offensive commentary as much as I once did, or if offensive content has diminished and shifted or is more restricted in distribution, and if the tools that Quora's added since I've started ("mute", "block", "report") and the admins have become more efficient at weeding it all out; but I don't feel as offended, intimidated, and harassed as I once did.I have to thank the Quora community, and specifically it's amazing vocal female minority, as well as the many supportive and vocal men, who call the crap and the crap-flingers out on their idiot behavior. I've learned a great deal about feminism and about human behavior from Quora. Quora has delivered on its promise to me of shining a light on knowledge, especially on this particular topic. US Supreme Court Justice Louis Brandeis famously said: "Sunlight is the best disinfectant". For me, Quora has shown me just how much discriminatory gender filth still exists and needs disinfecting. Because of what I've seen on Quora, I've become a fiercer feminist. Thank you all, for that.Online communities like Quora that have existed for barely a decade, with their attributes like anonymity, open forum (specifically with respect to a reach beyond geographic/nation-state borders), rapid scale, and text-based communication, should consider precedent. The philosophy, structure, and body of laws, both American and international, regarding sexual discrimination and harrassment provide a good framework for inquiry and analysis.Women using Quora experience sexual discrimination and harassment in a very similar way to how they experience sexism in the real world. This article from Wikipedia is definitely problemmatic, but it does provide some helpful information on the nature of sexism, and its pervasiveness: Sexism.I group the issues that women face when writing on Quora in 2014 into 2 broad categories:"Bad Actors": Individuals who harrass and troll.Community Culture: Attitudes and mores of the global community as reflected in questions, answers, and incentives for behaviors (upvotes, downvotes, page rankings, credits & distribution (including topic-management, etc).Solutions to deal with Bad Actors are relatively easy. Consistent enforcement of the rules is critical. The tools that Quora has implemented over time to reduce the "bad actor" category (with respect to all kinds of bad behavior, not only sexual harrassment) like "mute" and "block" and "report" are great: but the problem, as I see through this thread, is the lack of education for users about those available tools.But first, get rid of the bad actors! Stop them and knock them out cold. In some cases, Quora might want to report them to law enforcement, or specifically request if victims of abusive behavior want to prosecute, and provide them with the support they might need in the form of evidence and documentation. Some individuals have recently been successfully prosecuted and sued for their bad behavior on social media in the US, the UK, and other nations; bullying, harassment, libel, intentional interference with business, intentional infliction of emotional distress may all be legitimate causes of action. People who troll and bully ought to know that their behavior won't be tolerated and will be prosecuted with Quora's support. Often, harrassment that might start or be facilitated by use of the Quora platform can spill over into other contexts, both into other sites and in real life, and there is very real harm that can result. Quora needs to make it clear and unequivocable that such behavior will not be tolerated.Quora also needs to provide more education for its users, on the tools and resources it has available to stop this behavior. This goes to a deeper challenge for the site, as it relates to on-boarding of new users, and communication of changes to policy, and introduction of new product features. There's been a great deal of discussion as to how to achieve a better on-boarding and education process, and I know Quora has been considering various options within the platform architecture to alleviate some of the difficulties that new users encounter.I would urge Quora to consider providing additional information and navigation tools within site design. Maybe there should be a "report" or "admin chat" button on the left-side of the page, for instant access, actively manned by a real person who can triage and prioritize reports, and who can respond in real time and immediately to those that are most urgent and disturbing. How Quora chooses to staff those positions and roles with either paid employees/contractors or rotating volunteers is Quora's choice; however, consistency in enforcement and institutional memory is best reinforced by people who are committed and valued through financial reward. Personally, I think Quora needs to hire and provide institutionalized guidance and performance measurement for at least a few admins who do this sort of work, and can continue to supplement with volunteers. Unlike Wikipedia, Quora is a private, ultimately for-profit company; the stakes and objectives are different. (they could look to a "B Corporation" model, which is a hybrid for-profit, not-for-profit model). Quora's community is its lifeblood, and its primary asset for developing and building content; the actual text (Q&A&posts), information, and "knowledge" is its library, and hard evidence documentation and a form of account representing its primary asset. Quora is a hybrid of community and information; with community being both producer and consumer, needing constant reinforcement, building, encouragement. In order to preserve its most precious asset, Quora ought to make more of a financial commitment to that community, by showing how deeply it values its moderators and admins by creating market-value compensated roles for them.Setting community standards is a far more difficult task. Quora, as well as any international platform, has to grapple with how it wants to set its boundaries, and how it wants to deal with its international customers (or users). For a long time, online communities wanted to believe that they were apolitical; that they merely provided tools for people to use as they wanted, to express themselves and provide their opinion. Every one of us, and every online platform and community, has built-in cultural preferences and biases that reflect those of its makers and those of its most vocal community. It has to take a position with respect to how its addresses its minorities, cliques, and mini-communities, whether those are defined by geography, gender, faith, or political persuasion.Some of those minorities represent positions that are anathema to what Quora as a company might support; however, I believe people should have the freedom to express themselves, unless they rise to the level of hostility that would turn them into a "bad actor" (e.g. hostile, threatening to other or specific individuals, extremely offensive or pornographic, etc. per Quora Company standards). There are people in the world who have ideas that are offensive within Western culture; however, unless they're able to state their positions and have them challenged, those positions will not be "disinfected". Conflict is inevitable where there is a difference of opinion; we should not fear it.Quora is in English, reflecting the culture of English-speakers. Quora has a voting system, which assesses both popularity and quality of opinions and writing expressed, and complex algorithms associated with PeopleRank. It has a credit system, where people are awarded credits based on their participation and popularity within the community. The way that Quora chooses to calibrate and weigh both credits, upvotes, and downvotes can serve to incent certain behaviors.For instance, providing a downvote "rationale" with instant feedback to the writer would make a difference, and in certain cases should be fed straight into the triage line, to prevent abuses. Better metrics and readily available histories should be provided to users. A metric along the lines of downvotes/upvotes on aggregate answers in a particular topic area could perhaps be published on a user's profile page. Simple notification to people who transgress or fail to adhere to community standards would help them to learn. Perhaps upvoting could also be better calibrated, so the weight assigned might better reflect not only a person's popularity or volume of answers, but also to reflect their particular expertise in a subject. For instance, if I register as from India, the weight accorded my answer, terms of ranking and position on a question about India would be more than the weight accorded a non-Indian user. Or a man answering a question about men would be given slightly more weight than a woman answering the same question. Although it could be unwieldy and probably difficult to implement, upvotes could also have drop-downs, for why someone might be upvoting and answer: e.g. it's funny, I like the writing style, I agree with the position, I like the writer. Or perhaps upvotes should be rated on a 1-5 scale, instead of a binary upvote or no vote choice. Any kind of calibration of upvotes or downvotes could be connected to the credit system; some upvotes might receive more credits than others; similarly, downvotes could act as demerits, and take away credits. I'm not an expert on crunching data, but I know that there are better ways to capture and calibrate the information that's being collected; not just about us in order to provide better information about the population to increase the potential for Quora site monetization and advertising, but also in order to improve our experience.I don't have an issue with anonymity. I think if other processes work as they should, anonymous trolls will go away. I do think that people should be able to block certain anonymous users; and that anonymity with respect to egregious answers should be revoked and revealed. If Anon knows that if he makes inappropriate comments about my body parts, I should be able to ask Quora and know who he is, so that his name can be revealed publicly, and block him. It'd be like a perp walk, or when johns arrested in prostitution stings are revealed on local television, or their names are published in the newspaper. Shame is powerful.Many of the issues that create problems with respect to everyone's experience, whatever their gender, are related to topics and topic ontologies. I believe that topic assignments require more proactive review and standards. Quora users could benefit from a Quora topic clean-up project, allowing humans to conduct a thorough quality control review. Topic assignments have a lot to do with distribution. Quora ought to survey and review the existing framework holistically, be clear on how questions fit into various categories, and publish that ontology, so people understand with clear topic descriptions just what they're tagging material with - and not be allowed to freely create new topics unless approved. This process should be systematized and formalized, and published for comment. I understand that there are millions of questions on the site; however, this is no different than a transaction analysis and clean-up, akin to a process review or audit, in other industries. It might take a while, but it would result in a system and platform that's leaner, more efficient, and more capable of spotting problems. Putting more structure around quality control with respect to the tools Quora controls and supplies for its users would improve the overall content quality and user experience, without interfering with the flexibility and freedom of expression that Quora affords its users.I don't think Quora can change the baggage we each bring with us from the real world; but I do think they can change the way it's managed on the site, through education and better tools. This requires Quora to understand that it is taking a political position through how it allocates its resources. Changing how women are treated on the site (as well as attracting and diversifying the community) and maintaining or even improving the quality of content requires a resource commitment and prioritization on content management and user experience, in the sense of the term that transcends a mere user interface.Without making that commitment, Quora risks testing Burke's admonition: "All that is necessary for the triumph of evil is that good men do nothing." In this case, all that is necessary for the triumph of bad behavior and the end of quality content is for Quora to fail to set standards, fail to promulgate them and fail to enforce its rules consistently, and to fail to actively set standards of acceptable behavior on the site. The best way, I think, to achieve the goals of fixing "bad" behavior, whether immature or hostile, is by focussing on increasing transparency and assigning accountability and responsibility, through better communication, cleaner, more professional organization, and better metrics.

Here are some amazing and completely free resources online that you can use to teach yourself data science.Besides this page, I would highly recommend following the Quora Data Science topic if you haven't already to get updates on new questions and answers!Step 1. Fulfill your prerequisitesBefore you begin, you need Multivariable Calculus, Linear Algebra, and Python. If your math background is up to multivariable calculus and linear algebra, you'll have enough background to understand almost all of the probability / statistics / machine learning for the job.Multivariate Calculus: What are the best resources for mastering multivariable calculus?Numerical Linear Algebra / Computational Linear Algebra / Matrix Algebra: Linear Algebra, Introduction to Linear Models and Matrix Algebra. Avoid linear algebra classes that are too theoretical, you need a linear algebra class that works with real matrices.Multivariate calculus is useful for some parts of machine learning and a lot of probability. Linear / Matrix algebra is absolutely necessary for a lot of concepts in machine learning.You also need some programming background to begin, preferably in Python. Most other things on this guide can be learned on the job (like random forests, pandas, A/B testing), but you can't get away without knowing how to program!Python is the most important language for a data scientist to learn. To learn to code, more about Python, and why Python is so important, check outHow do I learn to code?How do I learn Python?Why is Python a language of choice for data scientists?Is Python the most important programming language to learn for aspiring data scientists and data miners?R is the second most important language for a data scientist to learn. I’m saying this as someone with a statistics background and who went through undergrad mainly only using R. While R is powerful for dedicated statistical tasks, Python is more versatile as it will connect you more to production-level work.If you're currently in school, take statistics and computer science classes. Check out What classes should I take if I want to become a data scientist?Step 2. Plug Yourself Into the CommunityCheck out Meetup to find some that interest you! Attend an interesting talk, learn about data science live, and meet data scientists and other aspirational data scientists. Start reading data science blogs and following influential data scientists:What are the best, insightful blogs about data, including how businesses are using data?What is your source of machine learning and data science news? Why?What are some best data science accounts to follow on Twitter, Facebook, G+, and LinkedIn?What are the best Twitter accounts about data?Step 3. Setup and Learn to use your toolsPythonInstall Python, iPython, and related libraries (guide)How do I learn Python?RInstall R and RStudio (It's good to know both Python and R)Learn R with swirlSublime TextInstall Sublime TextWhat's the best way to learn to use Sublime Text?SQLHow do I learn SQL? What are some good online resources, like websites, blogs, or videos? (You can practice it using the sqlite package in Python)Step 4. Learn Probability and StatisticsBe sure to go through a course that involves heavy application in R or Python. Knowing probability and statistics will only really be helpful if you can implement what you learn.Python Application: Think Stats (free pdf) (Python focus)R Applications: An Introduction to Statistical Learning (free pdf)(MOOC) (R focus)Print out a copy of Probability CheatsheetStep 5. Complete Harvard's Data Science CourseAs of Fall 2015, the course is currently in its third year and strives to be as applicable and helpful as possible for students who are interested in becoming data scientists. An example of how is this happening is the introduction of Spark and SQL starting this year.I'd recommend doing the labs and lectures from 2015 and the homeworks from 2013 (2015 homeworks are not available to the public, and the 2014 homeworks are written under a different instructor than the original instructors).This course is developed in part by a fellow Quora user, Professor Joe Blitzstein. Here are all of the materials!Intro to the classWhat is it like to design a data science class? In particular, what was it like to design Harvard's new data science class, taught by professors Joe Blitzstein and Hanspeter Pfister?What is it like to take CS 109/Statistics 121 (Data Science) at Harvard?Course MaterialsClass main page: CS109 Data ScienceLectures, Slides, and Labs: Class MaterialAssignmentsIntro to Python, Numpy, Matplotlib (Homework 0) (Solutions)Poll Aggregation, Web Scraping, Plotting, Model Evaluation, and Forecasting (Homework 1) (Solutions)Data Prediction, Manipulation, and Evaluation (Homework 2) (Solutions)Predictive Modeling, Model Calibration, Sentiment Analysis (Homework 3) (Solutions)Recommendation Engines, Using Mapreduce (Homework 4) (Solutions)Network Visualization and Analysis (Homework 5) (Solutions)Labs(these are the 2013 labs. For the 2015 labs, check out Class Material)Lab 2: Web ScrapingLab 3: EDA, Pandas, MatplotlibLab 4: Scikit-Learn, Regression, PCALab 5: Bias, Variance, Cross-ValidationLab 6: Bayes, Linear Regression, and Metropolis SamplingLab 7: Gibbs SamplingLab 8: MapReduceLab 9: NetworksLab 10: Support Vector MachinesStep 6. Do all of Kaggle's Getting Started and Playground CompetitionsI would NOT recommend doing any of the prize-money competitions. They usually have datasets that are too large, complicated, or annoying, and are not good for learning. The competitions are available at Competitions | KaggleStart by learning scikit-learn, playing around, reading through tutorials and forums on the competitions that you’re doing. Next, play around some more and check out the tutorials for Titanic: Machine Learning from Disaster for a binary classification task (with categorical variables, missing values, etc.)Afterwards, try some multi-class classification with Forest Cover Type Prediction. Now, try a regression task House Prices: Advanced Regression Techniques. Try out some natural language processing with Quora Question Pairs | Kaggle. Finally, try out any of the other knowledge-based competitions that interest you!Step 7. Learn Some Data Science ElectivesData science is an incredibly large and interdisciplinary field, and different jobs will require different skillsets. Here are some of the more common ones:Product Metrics will teach you about what companies track, what metrics they find important, and how companies measure their success: The 27 Metrics in Pinterest’s Internal Growth DashboardMachine Learning How do I learn machine learning? This is an extremely rich area with massive amounts of potential, and likely the “sexiest” area of data science today. Andrew Ng's Machine Learning course on Coursera is one of the most popular MOOCs, and a great way to start! Andrew Ng's Machine Learning MOOCA/B Testing is incredibly important to help inform product decisions for consumer applications. Learn more about A/B testing here: How do I learn about A/B testing?Visualization - I would recommend picking up ggplot2 in R to make simple yet beautiful graphics and just browsing DataIsBeautiful • /r/dataisbeautiful and FlowingData for ideas and inspiration.User Behavior - This set of blogs posts looks useful and interesting - This Explains Everything " User BehaviorFeature Engineering - Check out What are some best practices in Feature Engineering? and this great example: http://nbviewer.ipython.org/github/aguschin/kaggle/blob/master/forestCoverType_featuresEngineering.ipynbBig Data Technologies - These are tools and frameworks developed specifically to deal with massive amounts of data. How do I learn big data technologies?Optimization will help you with understanding statistics and machine learning: Convex Optimization - Boyd and VandenbergheNatural Language Processing - This is the practice of turning text data into numerical data whilst still preserving the "meaning". Learning this will let you analyze new, exciting forms of data. How do I learn Natural Language Processing (NLP)?Time Series Analysis - How do I learn about time series analysis?Step 8. Do a Capstone Product / Side ProjectUse your new data science and software engineering skills to build something that will make other people say wow! This can be a website, new way of looking at a dataset, cool visualization, or anything!What are some good toy problems (can be done over a weekend by a single coder) in data science? I'm studying machine learning and statistics, and looking for something socially relevant using publicly available datasets/APIs.How can I start building a recommendation engine? Where can I find an interesting data set? What tools/technologies/algorithms are best to build the engine with? How do I check the effectiveness of recommendations?What are some ideas for a quick weekend Python project? I am looking to gain some experience.What is a good measure of the influence of a Twitter user?Where can I find large datasets open to the public?What are some good algorithms for a prioritized inbox?What are some good data science projects?Create public github repositories, make a blog, and post your work, side projects, Kaggle solutions, insights, and thoughts! This helps you gain visibility, build a portfolio for your resume, and connect with other people working on the same tasks.Step 9. Get a Data Science Internship or JobHow do I prepare for a data scientist interview?How should I prepare for statistics questions for a data science interviewWhat kind of A/B testing questions should I expect in a data scientist interview and how should I prepare for such questions?What companies have data science internships for undergraduates?What are some tips to choose whether I want to apply for a Data Science or Software Engineering internship?When is the best time to apply for data science summer internships?Check out The Official Quora Data Science FAQ for more discussion on internships, jobs, and data science interview processes! The data science FAQ also links to more specific versions of this question, like How do I become a data scientist without a PhD? or the counterpart, How do I become a data scientist as a PhD student?Step 10. Share your Wisdom Back with the Data Science CommunityIf you’ve made it this far, congratulations on becoming a data scientist! I’d encourage you to share your knowledge and what you’ve learned back with the data science community. Data Science as a nascent field depends on knowledge-sharing!Think like a Data ScientistIn addition to the concrete steps I listed above to develop the skill set of a data scientist, I include seven challenges below so you can learn to think like a data scientist and develop the right attitude to become one.(1) Satiate your curiosity through dataAs a data scientist you write your own questions and answers. Data scientists are naturally curious about the data that they're looking at, and are creative with ways to approach and solve whatever problem needs to be solved.Much of data science is not the analysis itself, but discovering an interesting question and figuring out how to answer it.Here are two great examples:Hilary: the most poisoned baby name in US historyA Look at Fire Response DataChallenge: Think of a problem or topic you're interested in and answer it with data!(2) Read news with a skeptical eyeMuch of the contribution of a data scientist (and why it's really hard to replace a data scientist with a machine), is that a data scientist will tell you what's important and what's spurious. This persistent skepticism is healthy in all sciences, and is especially necessarily in a fast-paced environment where it's too easy to let a spurious result be misinterpreted.You can adopt this mindset yourself by reading news with a critical eye. Many news articles have inherently flawed main premises. Try these two articles. Sample answers are available in the comments.Easier: You Love Your iPhone. Literally.Harder: Who predicted Russia’s military intervention?Challenge: Do this every day when you encounter a news article. Comment on the article and point out the flaws.(3) See data as a tool to improve consumer productsVisit a consumer internet product (probably that you know doesn't do extensive A/B testing already), and then think about their main funnel. Do they have a checkout funnel? Do they have a signup funnel? Do they have a virility mechanism? Do they have an engagement funnel?Go through the funnel multiple times and hypothesize about different ways it could do better to increase a core metric (conversion rate, shares, signups, etc.). Design an experiment to verify if your suggested change can actually change the core metric.Challenge: Share it with the feedback email for the consumer internet site!(4) Think like a BayesianTo think like a Bayesian, avoid the Base rate fallacy. This means to form new beliefs you must incorporate both newly observed information AND prior information formed through intuition and experience.Checking your dashboard, user engagement numbers are significantly down today. Which of the following is most likely?1. Users are suddenly less engaged2. Feature of site broke3. Logging feature brokeEven though explanation #1 completely explains the drop, #2 and #3 should be more likely because they have a much higher prior probability.You're in senior management at Tesla, and five of Tesla's Model S's have caught fire in the last five months. Which is more likely?1. Manufacturing quality has decreased and Teslas should now be deemed unsafe.2. Safety has not changed and fires in Tesla Model S's are still much rarer than their counterparts in gasoline cars.While #1 is an easy explanation (and great for media coverage), your prior should be strong on #2 because of your regular quality testing. However, you should still be seeking information that can update your beliefs on #1 versus #2 (and still find ways to improve safety). Question for thought: what information should you seek?Challenge: Identify the last time you committed the Base Rate Fallacy. Avoid committing the fallacy from now on.(5) Know the limitations of your tools“Knowledge is knowing that a tomato is a fruit, wisdom is not putting it in a fruit salad.” - Miles KingtonKnowledge is knowing how to perform a ordinary linear regression, wisdom is realizing how rare it applies cleanly in practice.Knowledge is knowing five different variations of K-means clustering, wisdom is realizing how rarely actual data can be cleanly clustered, and how poorly K-means clustering can work with too many features.Knowledge is knowing a vast range of sophisticated techniques, but wisdom is being able to choose the one that will provide the most amount of impact for the company in a reasonable amount of time.You may develop a vast range of tools while you go through your Coursera or EdX courses, but your toolbox is not useful until you know which tools to use.Challenge: Apply several tools to a real dataset and discover the tradeoffs and limitations of each tools. Which tools worked best, and can you figure out why?(6) Teach a complicated conceptHow does Richard Feynman distinguish which concepts he understands and which concepts he doesn't?Feynman was a truly great teacher. He prided himself on being able to devise ways to explain even the most profound ideas to beginning students. Once, I said to him, "Dick, explain to me, so that I can understand it, why spin one-half particles obey Fermi-Dirac statistics." Sizing up his audience perfectly, Feynman said, "I'll prepare a freshman lecture on it." But he came back a few days later to say, "I couldn't do it. I couldn't reduce it to the freshman level. That means we don't really understand it." - David L. Goodstein, Feynman's Lost Lecture: The Motion of Planets Around the SunWhat distinguished Richard Feynman was his ability to distill complex concepts into comprehendible ideas. Similarly, what distinguishes top data scientists is their ability to cogently share their ideas and explain their analyses.Check out https://www.quora.com/Edwin-Chen-1/answers for examples of cogently-explained technical concepts.Challenge: Teach a technical concept to a friend or on a public forum, like Quora or YouTube.(7) Convince others about what's importantPerhaps even more important than a data scientist's ability to explain their analysis is their ability to communicate the value and potential impact of the actionable insights.Certain tasks of data science will be commoditized as data science tools become better and better. New tools will make obsolete certain tasks such as writing dashboards, unnecessary data wrangling, and even specific kinds of predictive modeling.However, the need for a data scientist to extract out and communicate what's important will never be made obsolete. With increasing amounts of data and potential insights, companies will always need data scientists (or people in data science-like roles), to triage all that can be done and prioritize tasks based on impact.The data scientist's role in the company is the serve as the ambassador between the data and the company. The success of a data scientist is measured by how well he/she can tell a story and make an impact. Every other skill is amplified by this ability.Challenge: Tell a story with statistics. Communicate the important findings in a dataset. Make a convincing presentation that your audience cares about.Good luck and best wishes on your journey to becoming a data scientist! For more resources check out Quora’s official Quora Data Science FAQ