7 Steps for Learning Data Mining and Data Science
[http likes 823] How to learn data mining and data science? I outline seven steps and point you to resources for becoming a data scientist.
I am frequently asked  how to learn Data Mining and Data Science?
Here is my summary. Let me know what I missed and add your comments below.
You can best learn data mining and data science by doing, so start analyzing data as soon as you can! However, don't forget to learn the theory, since you need a good statistical and machine learning foundation to understand what you are doing and to find real nuggets of value in the noise of Big Data.
Here are 7 steps for learning data mining and data science. Although they are numbered, you can do them in parallel or in a different order.
 Languages: Learn R, Python, and SQL
 Tools: Learn how to use data mining and visualization tools
 Textbooks: Read introductory textbooks to understand the fundamentals
 Education: watch webinars, take courses, and consider a certificate or a degree in data science
 Data: Check available data resources and find something there
 Competitions: Participate in data mining competitions
 Interact with other data scientists, via social networks, groups, and meetings
Also, don't forget to subscribe to KDnuggets News biweekly email and follow @kdnuggets  voted Top Big Data Twitter  for latest news on Analytics, Big Data, Data Mining, and Data Science.
Here I use Data Mining and Data Science interchangeably  see my presentation Analytics Industry Overview, where I look at evolution and popularity of different terms like Statistics, Knowledge Discovery, Data Mining, Predictive Analytics, Data Science, and Big Data.
1. Learning Languages
Recent KDnuggets Poll found that the most popular languages for data mining are R, Python, and SQL.
There are many resources for each, for example
 Free ebook on Data Science with R
 Getting Started With Python For Data Science
 Python for Data Analysis: Agile Tools for Real World Data
 An indispensable Python : Data sourcing to Data science.
 W3 Schools Learning SQL
2. Tools: Data Mining, Data Science, and Visualization Software
There are many data mining tools for different tasks, but it is best to learn using a data mining suite which supports the entire process of data analysis.
You can start with open source (free) tools such as KNIME, RapidMiner, and Weka.
However, for many analytics jobs you need to know SAS, which is the leading commercial tool and widely used.
Other popular Analytics and Data Mining Software include MATLAB, StatSoft STATISTICA, Microsoft SQL Server, Tableau, IBM SPSS Modeler, and Rattle.
Visualization is an essential part of any data analysis  learn how to use Microsoft Excel (good for many simpler tasks), R graphics, (especially ggplot2), and also Tableau  an excellent package for visualization. Other good visualization tools include TIBCO Spotfire and Miner3D.
3. Textbooks
There are many data mining and data science textbooks available, but you can check these
 Data Mining and Analysis: Fundamental Concepts and Algorithms, free PDF download (draft), by Mohammed Zaki and Wagner Meira Jr.
 Data Mining: Practical Machine Learning Tools and Techniques, by Ian Witten, Eibe Frank, and Mark Hall, from the authors of Weka, and using Weka extensively in examples.
 The Elements of Statistical Learning, Data Mining, Inference, and Prediction, by Trevor Hastie, Robert Tibshirani, Jerome Friedman  great introduction for mathematically oriented
 LIONbook: Learning and Intelligent Optimization, by Roberto Battiti and Mauro Brunato, freely available on the web, chapter by chapter.
 Mining of Massive Datasets Book, by A. Rajaraman, J. Ullman.
 StatSoft Electronic Statistics Textbook™ (free), includes many data mining topics
4. Education: Webinars, Courses, Certificates, and Degrees
You can start by watching some of the many free webinars and webcasts on latest topics in Analytics, Big Data, Data Mining, and Data Science.
There are also many online courses, short and long, many of them free  see KDnuggets online education directory.
Check in particular these courses:
 Machine Learning, at Coursera, taught by Andrew Ng
 Learning from Data at edX, taught by Caltech professor Yaser AbuMostafa,
 Open Online Course in Applied Data Science, from Syracuse iSchool
 Data Mining with Weka, free online course.
 check also free online slides from my Data Mining Course, a semesterlong introductory course in Data Mining.
Finally, consider getting Certificates in Data Mining, and Data Science or advanced degrees, such as MS in Data Science  see KDnuggets directory for Education in Analytics, Data Mining, and Data Science.
5. Data
You will need data to analyze  see KDnuggets directory of Datasets for Data Mining, including
 Government, Federal, State, City, Local and public data sites and portals
 Data APIs, Hubs, Marketplaces, Platforms, Portals, and Search Engines.
 Free Public Datasets
6. Competitions
Again, you will best learn by doing, so participate in Kaggle competitions  start with beginner competitions, such as Predicting Titanic Survival using Machine Learning
7. Interact: Meetings, Groups, and Social Networks
You can join many peer groups  see Top 30 LinkedIn Groups for Analytics, Big Data, Data Mining, and Data Science.
AnalyticBridge is an active community for Analytics and Data Science.
You can attend some of the many Meetings and Conferences on Analytics, Big Data, Data Mining, Data Science, & Knowledge Discovery.
Also, consider joining ACM SIGKDD, which organizes the annual KDD conference  the leading research conference in the field.
More ...
Check also other answers:
 How to Get Started in Data Science, Hortonworks
 A Practical Intro to Data Science, Zipfian Academy
 Becoming a Data Scientist  Curriculum via Metromap, a tour de force visual map of data scientist journey.
 Getting a Free Data Science Education, by Daniel Gutierrez
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