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The most common and basic mistake which I saw people with poor knowledge of statistics make is to apply different ML tools on data without understanding the importance of dimensionality constant and data’s probability distribution.1. Dimensionality constant and the Curse of Dimensionality: Dimensionality constant gives a metric to understand if enough data is available to apply a certain statistical algorithm. It is basically related to the size of the data matrix. If there are 100 features (say 100 stocks from S &P) and 500 samples (500 daily return values of each stock), then the size of the data matrix will be 100 x 500 and dimensionality constant will be 100/500 = 1/5. This also means that in given 100-dimensional feature space, each basis feature has 500 samples located in that 100-dimensional space. The value of dimensionality constant can greatly affect the performance of any statistical tool. Most of the derived multivariate asymptotic results in statistics assume that dimensionality constant is close to zero (i.e. both number of features and number of samples are asymptotically large but the number of samples is much larger than the number of features). The most widely used methods like multivariate regression also perform well only under this condition. This simply means that it is important to have enough samples for each feature to accurately understand the role of that feature in regression or any other statistical setup.If the dimensionality constant is too large, it means that there are more features but samples per features are very less. In this case, commonly applied methods like covariance analysis, PCA, linear regression, etc. can give highly inaccurate results. There is a whole field dedicated to deriving results when enough samples per feature are not available. In fact, as the number of features (or dimensions) increases, the total number of samples required increases exponentially. This is also studied under the topic called the Curse of Dimensionality! This can be easily seen from the above image[1]which shows that as the dimension increasesl from 1D to 3D, more samples are required to explain the basic useful geometrical information.The best theoretical case will be that dimensionality constant is close to zero but this is not always good for real-world applications. For example, in case of financial data, if we will take many years of daily return (i.e. a large number of samples for each stock) then it can give misleading correlations and information. Many financial applications require to only consider recent data to avoid including bias from history. In such a case, dimensionality constant approaching to one can be better than zero. So, it is crucial to decide what is the desired number of samples per feature for the given application. So, sometimes right number (range) of samples are required to correctly model correlations among multivariate data (not too many and not too less).2. Normality of data: Most of the widely used ML algorithms including most famous ones like linear regression requires error (or noise) distribution to be Gaussian distributed. Have a look at following three histograms which represent the same data. The data[2] is the noise samples from underwater measurement in an urban water supply system pipeline (it was collected by me).The first image shows the histogram of noise samples with both x and y-axis in linear scale. At first glance, the data looks nearly gaussian. In the second image, I overlapped a Gaussian density curve (dotted red line) with the same mean and variance as the noise data. Now, it can be seen that data histogram is not perfectly Gaussian and there is a little deviation from gaussianity (blue curve different from the red curve). In the third image, I changed y-axis from linear to log scale. Now, it can be seen that there is a huge difference in the shape of the noise data histogram and gaussian distribution. The tails of the data are significantly diverging from the gaussian nature. It turns out that a much complex class of distributions known as alpha-stable distribution perfectly fits this noise histogram[3]. This is the case of heavy-tailed data distribution which is looking almost gaussian in linear scale but after superimposing gaussian curve and changing from linear to log scale, it turns out to be much different from a gaussian distribution. In fact, the heaviness of tails in the above case is quite huge and collecting more samples shows that the tails of the data distribution decay much slower than the Gaussian distribution even for very large values of noise amplitudes. This means that the probability of seeing outliers (outside gaussian curve) is significant even at very large noise amplitudes.Applying standard signal processing or ML algorithms which are optimally derived for gaussian nature of data can be suboptimal in this case. In fact, for heavy-tailed distributions, the most commonly used tools like covariance analysis, PCA, etc are ineffective because the data with heavy-tailed distribution (like Cauchy, alpha-stable, etc) have extremely high variance (theoretically infinite variance) which leads to undefined or highly unstable covariance.So, the common mistake here is to interpret any bell-like curve as gaussian which might be far from reality. The first step should be to use standard tests like Kolmogorov–Smirnov test to check the nature of the distribution. If the sample size is too small then even these tests are not reliable and if the sample size is too big (like in the above image, there are around 10 million samples) then these tests might take hours to give results. In such scenarios, small tricks like overlapping standard density curves on the empirical distribution or changing scales of the y-axis might give a better and quick approximation.3. Histogram, normalized histogram and probability density function (pdf): In any programming language, there are multiple options to select the “type” in the argument of histogram function. This is important. In the default setting, the y-axis of histogram simply represents “relative frequency” or “normalized count”, i.e. y-axis value for each bin represents “discrete probability mass” (occurrence of certain event in that bin width/total occurrences). This means that the y-axis values of all bins should give one on summation. This is also called relative frequency histogram. Another type of histogram is called probability density histogram in which the y-axis value of each bin gives “density” value rather than probability mass. This means y-axis value of each bin is equal to (discrete probability mass or normalized count)/bin width. For this type of histogram, the total area of all the histogram bars should sum to one. Finally, the last type is called probability density function (pdf) which is a continuous function and provide density at each value of a random variable (RV). The pdf usually have an analytical expression with few parameters to control its shape and properties. The total area under this function curve should integrate to be one.To sum up, the first type of histogram simply tells us how many incidences of event occurred inside a bin and this count is divided by total incidences to get normalized count. The second type goes one step further and divide the normalized count by bin width to get the probability density instead of probability mass. The last one is a theoretical continuous function and completely describes a RV (few exceptions).The biggest mistake made by the beginners is to overlap theoretical pdf over relative frequency histogram. An empirical histogram can be compared to the theoretical pdf only if the histogram is of “density” type rather than the normalized count type.Some other common mistakes are:4. Ignoring sampling error[4]5. Choosing the wrong Loss function[5]6. Correlation vs Causation[6]Footnotes[1] Escaping the Curse of Dimensionality[2] Measurement and Characterization of Acoustic Noise in Water Pipeline Channels[3] Measurement and Characterization of Acoustic Noise in Water Pipeline Channels[4] http://www.cs.cmu.edu/~tom/10601_sp08/slides/evaluation-2-13.pdf[5] 5 Regression Loss Functions All Machine Learners Should Know[6] Correlation is not causation

Hello friends,I will first begin with introducing myself.I am currently pursuing M.Sc in Applied Statistics & Informatics from IIT Bombay.I have completed my B.Sc in session 2016.Let me begin with opportunity in Statistics.Friends,Statistics is very good subject if you want to pursue your post graduation in Statistics.There are lot of opportunities in corporate ,teaching and research sector for Statistics students.Your chance of success will increase manyfold if you will get into premier institutes like IIT,IISC,IISER.Then an important question arise that how to prepare for IIT-JAM with Mathematical Statistics? What are the important books for preparation? Let’s begin with syllabus.Syllabus — Mathematical Statistics (MS)The Mathematical Statistics (MS) test paper comprises of Mathematics (40% weightage) and Statistics (60%weightage).MathematicsSequences and Series: Convergence of sequences of real numbers, Comparison, root and ratio tests for convergence of series of real numbers.Differential Calculus: Limits, continuity and differentiability of functions of one and two variables. Rolle’s theorem, mean value theorems, Taylor’s theorem, indeterminate forms, maxima and minima of functions of one and two variables.Integral Calculus: Fundamental theorems of integral calculus. Double and triple integrals, applications of definite integrals, arc lengths, areas and volumes.Matrices: Rank, inverse of a matrix. Systems of linear equations. Linear transformations, eigenvalues and eigenvectors. Cayley-Hamilton theorem, symmetric, skew-symmetric and orthogonal matrices.Differential Equations: Ordinary differential equations of the first order of the form y’ = f(x,y). Linear differential equations of the second order with constant coefficients.StatisticsProbability: Axiomatic definition of probability and properties, conditional probability, multiplication rule. Theorem of total probability. Bayes’ theorem and independence of events.Random Variables: Probability mass function, probability density function and cumulative distribution functions, distribution of a function of a random variable. Mathematical expectation, moments and moment generating function. Chebyshev’s inequality.Standard Distributions: Binomial, negative binomial, geometric, Poisson, hypergeometric, uniform, exponential, gamma, beta and normal distributions. Poisson and normal approximations of a binomial distribution.Joint Distributions: Joint, marginal and conditional distributions. Distribution of functions of random variables. Product moments, correlation, simple linear regression. Independence of random variables.Sampling distributions: Chi-square, t and F distributions, and their properties.Limit Theorems: Weak law of large numbers. Central limit theorem (i.i.d. with finite variance case only).Estimation: Unbiasedness, consistency and efficiency of estimators, method of moments and method of maximum likelihood. Sufficiency, factorization theorem. Completeness, Rao-Blackwell and Lehmann-Scheffe theorems, uniformly minimum variance unbiased estimators. Rao-Cramer inequality. Confidence intervals for the parameters of univariate normal, two independent normal, and one parameter exponential distributions.Testing of Hypotheses: Basic concepts, applications of Neyman-Pearson Lemma for testing simple and composite hypotheses. Likelihood ratio tests for parameters of univariate normal distribution.IMPORTANT BOOKS(TOPIC WISE)-MathematicsSequences and Series: N.N Bhattacharya’s book ,Class notes or Shanti Narayan’s Real Analysis BookDifferential Calculus: Read differential calculus book of Goarakh Prasad,Class Note(Don’t underestimate it)Integral Calculus: Again Gorakh Prasad’s bookMatrices: Schaum series,N.N Bhattacharya’s Book,class NotesDifferential Equations: Gorakh Prasad’s Book,B Rai,D.P Chaudhary’s book,class notesStatisticsIn statistics these are the topic mentioned in syllabus.I had read only class notes ,Gupta-Kapoor and V.K Rohtagi.Probability: Introduction to probability theory, by Hoel, Port and Stone,or Gupta Kapoor,or An introduction to probability and statistics, by Rohatgi and Saleh + Class notesRandom Variables: Introduction to probability theory, by Hoel, Port and Stone,or Gupta-Kapoor,or An introduction to probability and statistics, by Rohatgi and Saleh + Class notesStandard Distributions: Introduction to probability theory, by Hoel, Port and Stone,or Gupta-Kapoor,or An introduction to probability and statistics, by Rohatgi and Saleh + Class notesStandard Distributions: Introduction to probability theory, by Hoel, Port and Stone,or Gupta-Kapoor,or Rohatgi and Saleh + Class notesSampling distributions: Introduction to probability theory, by Hoel, Port and Stone,or Gupta-Kapoor,or Rohatgi and Saleh + Class notesLimit Theorems: Introduction to probability theory, by Hoel, Port and Stone,or Gupta-Kapoor,or An introduction to probability and statistics, by Rohatgi and Saleh + Class notesTesting of Hypotheses- Casella berger’s Statistical inference,Gupta-Kapoor,V.K Rohtagi + Class notesIf you like you can also download the pdf of A First course in Probability, by Sheldon Ross.Most important thing in any exam is practice with previous year papers.Believe me if you have solved 100 books but you have not practiced with previous year papers, your chance of success is limited(Exception exist).So Go to the website,download previous year papers of JAM-MS and start preparing.Don’t skip a single question of previous year paper.Previous year papers are like Brahmastra for aspirants.IMPORTANT TOPICS-Although every topic is important ,focus more on Distribution theory,Bayes theorem ,Maximum Likelihhod Estimator,Unbiasedness,Consistency etc.Believe me if you will follow above mentioned advice,no one can stop you from getting good JAM Rank.Forgive me for any grammatical mistake.All the best!

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