Where to start with Data Mining and Data Science

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 to learn data mining (many of these steps you can do in parallel:

1. Learn R and Python
2. Read 1-2 introductory books
3. Take 1-2 introductory courses and watch some webinars
4. Learn data mining software suites
5. Check available data resources and find something there
6. Participate in data mining competitions
7. Interact with other data scientists, via social network, groups, and meetings

Also, don't forget to subscribe to KDnuggets News bi-weekly email and follow @kdnuggets - voted Top Big Data Twitter - for latest news on Analytics, Big Data, Data Mining, and Data Science.

1. Learning Languages

 
There are many great resources, but the most popular languages for data mining are R, Python, and SQL.

There are many resources for each, for example

• Free e-book 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. 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 Science for Business, by Foster Provost and Tom Fawcett, "What you need to know about data mining and data-analytic thinking".
• 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

3. Data Mining and Data Science Tools

 
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, IBM SPSS, Microsoft SQL Server, Tableau, IBM SPSS Modeler, and Rattle.

4. Courses and Webinars

 
There are 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 Abu-Mostafa,
• Open Online Course in Applied Data Science, from Syracuse iSchool
• check also free online slides from my Data Mining Course, a semester-long introductory course in Data Mining.

There are also many free webinars and webcasts on latest topics in Analytics, Big Data, 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.

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 a community for Analytics and Data Science.

You can attend 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