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Become a Data Scientist by Doing Data ScienceThe best way to become a data scientist is to learn - and do - data science. There are a many excellent courses and tools available online that can help you get there.Here is an incredible list of resources compiled by Jonathan Dinu, Co-founder of Zipfian Academy, which trains data scientists and data engineers in San Francisco via immersive programs, fellowships, and workshops.EDIT: I've had several requests for a permalink to this answer. See here: A Practical Intro to Data Science from Zipfian AcademyEDIT2: See also: "How to Become a Data Scientist" on SlideShare: http://www.slideshare.net/ryanorban/how-to-become-a-data-scientistEnvironmentPython is a great programming language of choice for aspiring data scientists due to its general purpose applicability, a gentle (or firm) learning curve, and — perhaps the most compelling reason — the rich ecosystem of resources and libraries actively used by the scientific community.DevelopmentWhen learning a new language in a new domain, it helps immensely to have an interactive environment to explore and to receive immediate feedback. IPython provides an interactive REPL which also allows you to integrate a wide variety of frameworks (including R) into your Python programs.STATISTICSData scientists are better at software engineering than statisticians and better at statistics than any software engineer. As such, statistical inference underpins much of the theory behind data analysis and a solid foundation of statistical methods and probability serves as a stepping stone into the world of data science.CoursesedX: Introduction to Statistics: Descriptive Statistics: A basic introductory statistics course.Coursera Statistics, Making Sense of Data: A applied Statistics course that teaches the complete pipeline of statistical analysisMIT: Statistical Thinking and Data Analysis: Introduction to probability, sampling, regression, common distributions, and inference.While R is the de facto standard for performing statistical analysis, it has quite a high learning curve and there are other areas of data science for which it is not well suited. To avoid learning a new language for a specific problem domain, we recommend trying to perform the exercises of these courses with Python and its numerous statistical libraries. You will find that much of the functionality of R can be replicated with NumPy, @SciPy, @Matplotlib, and @Python Data Analysis LibraryBooksWell-written books can be a great reference (and supplement) to these courses, and also provide a more independent learning experience. These may be useful if you already have some knowledge of the subject or just need to fill in some gaps in your understanding:O'Reilly Think Stats: An Introduction to Probability and Statistics for Python programmersIntroduction to Probability: Textbook for Berkeley’s Stats 134 class, an introductory treatment of probability with complementary exercises.Berkeley Lecture Notes, Introduction to Probability: Compiled lecture notes of above textbook, complete with exercises.OpenIntro: Statistics: Introductory text book with supplementary exercises and labs in an online portal.Think Bayes: An simple introduction to Bayesian Statistics with Python code examples.MACHINE LEARNING/ALGORITHMSA solid base of Computer Science and algorithms is essential for an aspiring data scientist. Luckily there are a wealth of great resources online, and machine learning is one of the more lucrative (and advanced) skills of a data scientist.CoursesCoursera Machine Learning: Stanford’s famous machine learning course taught by Andrew Ng.Coursera: Computational Methods for Data Analysis: Statistical methods and data analysis applied to physical, engineering, and biological sciences.MIT Data Mining: An introduction to the techniques of data mining and how to apply ML algorithms to garner insights.Edx: Introduction to Artificial Intelligence: Introduction to Artificial Intelligence: The first half of Berkeley’s popular AI course that teaches you to build autonomous agents to efficiently make decisions in stochastic and adversarial settings.Introduction to Computer Science and Programming: MIT’s introductory course to the theory and application of Computer Science.BooksUCI: A First Encounter with Machine Learning: An introduction to machine learning concepts focusing on the intuition and explanation behind why they work.A Programmer's Guide to Data Mining: A web based book complete with code samples (in Python) and exercises.Data Structures and Algorithms with Object-Oriented Design Patterns in Python: An introduction to computer science with code examples in Python — covers algorithm analysis, data structures, sorting algorithms, and object oriented design.An Introduction to Data Mining: An interactive Decision Tree guide (with hyperlinked lectures) to learning data mining and ML.Elements of Statistical Learning: One of the most comprehensive treatments of data mining and ML, often used as a university textbook.Stanford: An Introduction to Information Retrieval: Textbook from a Stanford course on NLP and information retrieval with sections on text classification, clustering, indexing, and web crawling.DATA INGESTION AND CLEANINGOne of the most under-appreciated aspects of data science is the cleaning and munging of data that often represents the most significant time sink during analysis. While there is never a silver bullet for such a problem, knowing the right tools, techniques, and approaches can help minimize time spent wrangling data.CoursesSchool of Data: A Gentle Introduction to Cleaning Data: A hands on approach to learning to clean data, with plenty of exercises and web resources.TutorialsPredictive Analytics: Data Preparation: An introduction to the concepts and techniques of sampling data, accounting for erroneous values, and manipulating the data to transform it into acceptable formats.ToolsOpenRefine (formerly Google Refine): A powerful tool for working with messy data, cleaning, transforming, extending it with web services, and linking to databases. Think Excel on steroids.Data Wrangler: Stanford research project that provides an interactive tool for data cleaning and transformation.sed - an Introduction and Tutorial: “The ultimate stream editor,” used to process files with regular expressions often used for substitution.awk - An Introduction and Tutorial: “Another cornerstone of UNIX shell programming” — used for processing rows and columns of information.VISUALIZATIONThe most insightful data analysis is useless unless you can effectively communicate your results. The art of visualization has a long history, and while being one of the most qualitative aspects of data science its methods and tools are well documented.CoursesUC Berkeley Visualization: Graduate class on the techniques and algorithms for creating effective visualizations.Rice University Data Visualization: A treatment of data visualization and how to meaningfully present information from the perspective of Statistics.Harvard University Introduction to Computing, Modeling, and Visualization: Connects the concepts of computing with data to the process of interactively visualizing results.BooksTufte: The Visual Display of Quantitative Information: Not freely available, but perhaps the most influential text for the subject of data visualization. A classic that defined the field.TutorialsSchool of Data: From Data to Diagrams: A gentle introduction to plotting and charting data, with exercises.Predictive Analytics: Overview and Data Visualization: An introduction to the process of predictive modeling, and a treatment of the visualization of its results.ToolsD3.js: Data-Driven Documents — Declarative manipulation of DOM elements with data dependent functions (with Python port).Vega: A visualization grammar built on top of D3 for declarative visualizations in JSON. Released by the dream team at Trifacta, it provides a higher level abstraction than D3 for creating “ or SVG based graphics.Rickshaw: A charting library built on top of D3 with a focus on interactive time series graphs.Modest Maps: A lightweight library with a simple interface for working with maps in the browser (with ports to multiple languages).Chart.js: Very simple (only six charts) HTML5 “ based plotting library with beautiful styling and animation.COMPUTING AT SCALEWhen you start operating with data at the scale of the web (or greater), the fundamental approach and process of analysis must change. To combat the ever increasing amount of data, Google developed the MapReduce paradigm. This programming model has become the de facto standard for large scale batch processing since the release of Apache Hadoop in 2007, the open-source MapReduce framework.CoursesUC Berkeley: Analyzing Big Data with Twitter: A course — taught in close collaboration with Twitter — that focuses on the tools and algorithms for data analysis as applied to Twitter microblog data (with project based curriculum).Coursera: Web Intelligence and Big Data: An introduction to dealing with large quantities of data from the web; how the tools and techniques for acquiring, manipulating, querying, and analyzing data change at scale.CMU: Machine Learning with Large Datasets: A course on scaling machine learning algorithms on Hadoop to handle massive datasets.U of Chicago: Large Scale Learning: A treatment of handling large datasets through dimensionality reduction, classification, feature parametrization, and efficient data structures.UC Berkeley: Scalable Machine Learning: A broad introduction to the systems, algorithms, models, and optimizations necessary at scale.BooksMining Massive Datasets: Stanford course resources on large scale machine learning and MapReduce with accompanying book.Data-Intensive Text Processing with MapReduce: An introduction to algorithms for the indexing and processing of text that teaches you to “think in MapReduce.”Hadoop: The Definitive Guide: The most thorough treatment of the Hadoop framework, a great tutorial and reference alike.Programming Pig: An introduction to the Pig framework for programming data flows on Hadoop.PUTTING IT ALL TOGETHERData Science is an inherently multidisciplinary field that requires a myriad of skills to be a proficient practitioner. The necessary curriculum has not fit into traditional course offerings, but as awareness of the need for individuals who have such abilities is growing, we are seeing universities and private companies creating custom classes.CoursesUC Berkeley: Introduction to Data Science: A course taught by Jeff Hammerbacher and Mike Franklin that highlights each of the varied skills that a Data Scientist must be proficient with.How to Process, Analyze, and Visualize Data: A lab oriented course that teaches you the entire pipeline of data science; from acquiring datasets and analyzing them at scale to effectively visualizing the results.Coursera: Introduction to Data Science: A tour of the basic techniques for Data Science including SQL and NoSQL databases, MapReduce on Hadoop, ML algorithms, and data visualization.Columbia: Introduction to Data Science: A very comprehensive course that covers all aspects of data science, with an humanistic treatment of the field.Columbia: Applied Data Science (with book): Another Columbia course — teaches applied software development fundamentals using real data, targeted towards people with mathematical backgrounds.Coursera: Data Analysis (with notes and lectures): An applied statistics course that covers algorithms and techniques for analyzing data and interpreting the results to communicate your findings.BooksAn Introduction to Data Science: The companion textbook to Syracuse University’s flagship course for their new Data Science program.TutorialsKaggle: Getting Started With Python For Data Science: A guided tour of setting up a development environment, an introduction to making your first competition submission, and validating your results.CONCLUSIONData science is infinitely complex field and this is just the beginning.If you want to get your hands dirty and gain experience working with these tools in a collaborative environment, check out our programs at http://zipfianacademy.com.There's also a great SlideShare summarizing these skills: How to Become a Data ScientistYou're also invited to connect with us on Twitter @zipfianacademy and let us know if you want to learn more about any of these topics.

Without valid training examples you are pretty much stuck using a heuristic-based approach. Learning models require training examples. While much of cutting-edge research in ML and AI is focused on reducing the amount of training required to fit an adequate model, the fact remains: learning models need data for both training and validation.A human example to help make this clear might be learning how to pick the M&M’s out of trail mix. I love trail mix, but it takes a lot of willpower to keep from preferentially eating the M&M’s—so this is a rather personal example for me.If you were from another planet and I told you to pick M&M’s out, you would most definitely require some guidance. This guidance would probably take one of two forms: supervised or heuristic. If we told an alien to look for round/oblong candies with the letter “M” painted on them, the alien could probably perform quite well (provided they already understood the English alphabet and could successfully identify sweet flavors). The alien could probably learn to perform quite well with guided practice—such as providing them with a head nod for every M&M selected and a shake for every other type of object. But how could an alien ever learn to perform this task if they receive neither supervision nor useful heuristics? Humans and computers are no different.Without data for training you need to be aware of what insurance fraud looks like so you can build some rules (heuristics) for your program. Some tell-tale signs are[1]:Claimant has submitted a lot of claimsClaimant adds coverage immediately prior to claimThe claimant is treated or represented by medical practitioners or lawyers who are involved in a disproportionately high amount of frivolous claimsClaimants appear well, or otherwise give themselves away, in recent social media postsWhile this list is very short, you can imagine that a very long list of plausible tell-tales like this could be used to identify fraudulent claims. Heuristic-based programs have been in use since the beginning of time but remain very effective in situations where the data simply aren’t available to fit a superior model.Best of luck!Footnotes[1] 10 Ways Insurance Agents Spot Fraudulent Claims

The biggest edge chatbots have over human agents is that they are not human and don't make behavioural errors like frustration, being mean or judgmental. But this very same edge is also the biggest negative - chatbots can't assess the situation of conversation (though we can make rules based on the user level data, but an evolved ML and AI engine is still far off) and alter the course/ tone of the conversation.(eg. Responses of a human agent could be different for a user who is repeatedly coming to check the status of his/her order , whereas a bot might respond in the same empathetic way - and if we able to solve this problem it is going to be big win for Chatbots)Keeping the above stated problem aside, we usually try to apply Pareto Principle (80:20 rule) while developing and testing a bot . The idea is to get a decently working bot out for 20% of the highly repetitive use cases that are more than 80% of the total volume. If a user comes and ask 'How to fly a kite?' from a Movie Booking/ Review Bot, we don't intend to answer it, we can say a polite Sorry and try to guide the conversation towards primary intents that bot handles (A sample response: 'Sorry, I am not able to help you with your request right now. But I can help you with Booking tickets for a movie or with its review') The overall idea is to serve the serious customers with their routine problem statements.Based on my experience, here are a few use cases that we usually try to cover while developing, testing or approving a bot:- Proper Introduction: Bot should be able to introduce itself properly as to what all it can do etc..- Basic Salutation: Bot should be able to respond to Hi, Hello, Good Morning, Thank you etc..- Common variations: Bot should be able to handle common variations of Yes, No, Male, Female etc. if they are part of the Bot Flow- No cyclic loop: Bot should not get stuck in a cyclic look if a condition fails (repeatedly)- Fail gracefully: If bot is not able to handle/ understand a request fail gracefully and try to guide the conversation towards possible intents- Basic NLP (if possible): Bot shall able to comprehend basic natural language at least for single entities (e.g. Name can be entered as Jon or my name is Jon or I’m Jon.) If bot doesn’t support NLP, mention the pattern in the question itself e.g. Enter your Name (eg. Jon Snow)- Test Dates and Days: Different users have different ways to enter dates e.g. 20 June, June 20, today etc. (If accepting input as text input and not from pre populated list, if bot doesn’t support these variations, mention the pattern in the question itself e.g. Enter your Date of Birth (eg. 12 May 2011))- Typos: Bot should able to handle basic typos so that bot flow don't break abruptly (eg. California vs Califonria)- Hop Intents: A good bot should allow users to hop between intents (eg. If user is booking tickets for a movie and now wants to read its review)- Latency: It's very important as chat is very fast by nature and users ended up typing if they don't get the reply from bot quickly- Multiple Input: If bot is asking ‘What’s your age?’ and user has entered ‘33’ and then corrected his age ‘31’ before bot could have validated 33 as user age input. These multiple inputs situation shall be handled gracefully. The situation aggravates due to a poor latency.- Proper support and validation: Bot should be able to validate Email, Age, Mobile and other patterns. It should also render the Links, Images etc. properly on UI- Text Length Limit: Bot should able to handle the char length in accordance with the length allowed in db. If bot is for Facebook, the length of text of items is very important otherwise partial text is shown on Card layouts and other elements.(I will add to the list if I recall more use cases.)Hope the list of the use cases will help you build a bot that we all can proud of.