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Johnson: http://pj.freefaculty.org/R/Rtips.htmlRabbit: Introduction to R webbookR Programming References and LinksData Science plus - R tutorialsSwirl: Learn R, in RData Science with R TutorialsRelated Professional GroupsASA North Texas Chapter: http://smu.edu/asant/DFW INFORMS Chapter: http://www3.informs.org/site/ChapterDFW/KDNuggets: http://www.kdnuggets.com/DFW Big Data meetup groupDFW Data Science meetupStatistics, Data Science and Data Mining ResourcesIntroduction to Statistical LearningFree statistics e-books: http://www.r-statistics.com/2009/10/free-statistics-e-books-for-download/Statistical Data Mining Tutorials: http://www.autonlab.org/tutorials/The Elements of Statistical Learning: http://www-stat.stanford.edu/~tibs/ElemStatLearn/Forecasting principles and practice: http://otexts.com/fpp/Machine Learning Video Library - CalTech: http://work.caltech.edu/library/DataTau - data mining news aggregatorData SourcesUCI Machine Learning Repository: http://archive.ics.uci.edu/ml/Infochimps: http://www.infochimps.com/World Bank Data: http://data.worldbank.org/Windows Azure Marketplace: https://datamarket.azure.com/Freebase: http://www.freebase.com/Amazon Public Data Sets: http://aws.amazon.com/publicdatasets/Yahoo Query Language: http://developer.yahoo.com/yql/DBpedia: http://dbpedia.org/...US Census FactFinder Data: http://factfinder.census.gov/servlet/DatasetMainPageServlet?_program=DEC&_submenuId=datasets_0&_lang=enGoogle Public Data Explorer: http://www.google.com/publicdata/directoryUS Government Data: http://www.data.gov/KDnuggets Data Sets: http://www.kdnuggets.com/datasets/index.htmlAggData: http://www.aggdata.com/Baseball Reference: http://www.baseball-reference.com/Pro Football Reference: http://www.pro-football-reference.com/Data ManipulationGoogle Refine: http://code.google.com/p/google-refine/Data VisualizationGGobi: http://www.ggobi.org/

A Complete Tutorial to Learn Data Science with Python from Scratch

After working on SAS for more than 5 years, I decided to move out of my comfort zone. Being a data scientist, my hunt for other useful tools was ON!

But, over the years, with strong community support, this language got dedicated library for data analysis and predictive modeling.

Due to lack of resource on python for data science, I decided to create this tutorial to help many others to learn python faster. In this tutorial, we will take bite sized information about how to use Python for Data Analysis, chew it till we are comfortable and practice it at our own end.

There are 2 approaches to install Python: Second method provides a hassle free installation and hence I’ll recommend that to beginners.

The imitation of this approach is you have to wait for the entire package to be upgraded, even if you are interested in the latest version of a single library.

It provides a lot of good features for documenting while writing the code itself and you can choose to run the code in blocks (rather than the line by line execution) We will use iPython environment for this complete tutorial.

The most commonly used construct is if-else, with following syntax: For instance, if we want to print whether the number N is even or odd: Now that you are familiar with Python fundamentals, let’s take a step further.

What if you have to perform the following tasks: If you try to write code from scratch, its going to be a nightmare and you won’t stay on Python for more than 2 days!

Following are a list of libraries, you will need for any scientific computations and data analysis: Additional libraries, you might need: Now that we are familiar with Python fundamentals and additional libraries, lets take a deep dive into problem solving through Python.

We will now use Pandas to read a data set from an Analytics Vidhya competition, perform exploratory analysis and build our first basic categorization algorithm for solving this problem.

The essential difference being that column names and row numbers are known as column and row index, in case of dataframes.

To begin, start iPython interface in Inline Pylab mode by typing following on your terminal / windows command prompt: This opens up iPython notebook in pylab environment, which has a few useful libraries already imported.

You can check whether the environment has loaded correctly, by typing the following command (and getting the output as seen in the figure below):

describe() function would provide count, mean, standard deviation (std), min, quartiles and max in its output (Read this article to refresh basic statistics to understand population distribution) Here are a few inferences, you can draw by looking at the output of describe() function: Please note that we can get an idea of a possible skew in the data by comparing the mean to the median, i.e.

The frequency table can be printed by following command: Similarly, we can look at unique values of port of credit history.

Now we will look at the steps required to generate a similar insight using Python. Please refer to this article for getting a hang of the different data manipulation techniques in Pandas.

If you have not realized already, we have just created two basic classification algorithms here, one based on credit history, while other on 2 categorical variables (including gender).

Next let’s explore ApplicantIncome and LoanStatus variables further, perform data munging and create a dataset for applying various modeling techniques.

Let us look at missing values in all the variables because most of the models don’t work with missing data and even if they do, imputing them helps more often than not.

So, let us check the number of nulls / NaNs in the dataset This command should tell us the number of missing values in each column as isnull() returns 1, if the value is null.

the simplest being replacement by mean, which can be done by following code: The other extreme could be to build a supervised learning model to predict loan amount on the basis of other variables and then use age along with other variables to predict survival.

Since, the purpose now is to bring out the steps in data munging, I’ll rather take an approach, which lies some where in between these 2 extremes.

This can be done using the following code: Now, we will create a Pivot table, which provides us median values for all the groups of unique values of Self_Employed and Education features.

Next, we define a function, which returns the values of these cells and apply it to fill the missing values of loan amount: This should provide you a good way to impute missing values of loan amount.

So instead of treating them as outliers, let’s try a log transformation to nullify their effect: Looking at the histogram again:

For example, creating a column for LoanAmount/TotalIncome might make sense as it gives an idea of how well the applicant is suited to pay back his loan.

After, we have made the data useful for modeling, let’s now look at the python code to create a predictive model on our data set.

One way would be to take all the variables into the model but this might result in overfitting (don’t worry if you’re unaware of this terminology yet).

In simple words, taking all variables might result in the model understanding complex relations specific to the data and will not generalize well.

Accuracy : 80.945% Cross-Validation Score : 80.946% Accuracy : 80.945% Cross-Validation Score : 80.946% Generally we expect the accuracy to increase on adding variables.

Accuracy : 81.930% Cross-Validation Score : 76.656% Here the model based on categorical variables is unable to have an impact because Credit History is dominating over them.

Let’s try a few numerical variables: Accuracy : 92.345% Cross-Validation Score : 71.009% Here we observed that although the accuracy went up on adding variables, the cross-validation error went down.

Also, we will modify the parameters of random forest model a little bit: Accuracy : 82.899% Cross-Validation Score : 81.461% Notice that although accuracy reduced, but the cross-validation score is improving showing that the model is generalizing well.

You would have noticed that even after some basic parameter tuning on random forest, we have reached a cross-validation accuracy only slightly better than the original logistic regression model.

I am sure this not only gave you an idea about basic data analysis methods but it also showed you how to implement some of the more sophisticated techniques available today.

If you come across any difficulty while practicing Python, or you have any thoughts / suggestions / feedback on the post, please feel free to post them through comments below.

100 Free Tutorials for learning R

R language is the world's most widely used programming language for statistical analysis, predictive modeling and data science.

R programming language is getting powerful day by day as number of supported packages grows.

It is available in open source for FREE. Unlike standard R, it supports various premium features such as intelligent code completion, syntax highlighting, structured R documentation, interactive debugging tool etc.

Top Data Mining Resources: 50 Tutorials, Articles and Videos to Learn Data Mining Methods, Analysis and More

As Big Data takes center stage for business operations, data mining becomes something that salespeople, marketers, and C-level executives need to know how to do and do well.

Companies and organizations are using data mining to get the insights they need about pricing, promotions, social media, campaigns, customer experience, and a plethora of other business practices.

To help you get a better handle on data mining, we have searched for resources from Big Data and data mining experts, top marketers and data scientists, leading Big Data and data analysis software solutions providers, and other data mining thought leaders, to compile our list of the top online learning resources for data mining.

While we have listed our top data mining resources in no particular order, we have included a table of contents to make it easier for you to jump to the resources categories that are of most interest to you.

Business 2 Community contributors cover news and trends in social media, digital marketing, content marketing, social selling, and more.

Liran Malul’s Business 2 Community data mining article explains that one of the best approaches to data mining is to first identify the problem you have and how you would like to solve it, and then determine the best data mining technique to gain the insights you need.

The New York City Fire Department is using data mining to predict which buildings will erupt in fire, and their data analysts have been working since July 2014 to determine which buildings to inspect.

As part of CNN Money’s Small Business Resource Guide, Cindy Waxer shares anecdotes about small businesses using data mining to crunch customer data and increase sales while reducing customer turnover.

Waxer explains that large corporations can afford expensive servers and data scientists, but small businesses can take advantage of web-based, cost-effective data mining alternatives.

Business News Daily Assistant Editor Nicole Fallon explores the fine line customers walk between wanting companies to gather their data and not wanting companies to analyze their digital data.

Marketers and companies need to balance “being helpful and being invasive by only collecting social data from customers who follow them, and avoid anything that appears to be part of a conversation among other users.”

Erring on the side of caution is important when mining customer data because the last thing companies want to do is drive customers away when gathering the very data they want to use in order to keep them.

In this data mining article for the Marketing Research Association, Eric Wright, VP of solutions consulting at Allegiance, Inc., explores how to use text analytics and data mining to gain actionable customer insights.

His course material on data mining is a treasure trove of everything data mining and covers such topics as data mining and business value and the date mining process.

RDataMining.com is a leading resource for R and data mining, offering examples, documents, tutorials, resources, and training on data mining and analytics with R.

RDataMining.com also offers a list of free online data mining courses, covering data analysis, a data mining specialization, social network analysis, and more.

The course will help students to learn how to apply data mining principles and dissect complex data sets, including those in large databases or through web mining.

The seven-week course is available at various times throughout the year, and it is best if students have taken courses in database systems, algorithms and data structures, and multivariable calculus and linear algebra.

Data Mining includes 58 lectures and 6 hours of video and requires students to have a basic understanding of the IT industry and a knowledge of the English language.

The Online Graduate Data Mining Certificate Program is an online program for working professionals looking to acquire data mining or predictive analytics or data science skills through online courses.

In addition to the graduate certificate in business data mining, students in the program may also earn three other certificates – SAS and OSU Data Mining Certificate, SAS and OSU Predictive Analytics Certificate, or SAS and OSU Marketing Data Science Certificate – depending on which courses they take and the credentials they achieve.

publisher of science, technology, and medical reference books, textbooks, handbooks, and monographs, CRC Press offers Statistical and Machine-Learning Data Mining, a data mining eBook available for purchase or on a six-month or twelve-month rental agreement.

Their data mining eBook, Data Mining Tools and Techniques, is a robust resource that helps readers learn how to turn Big Data into actionable intelligence, especially for those in the healthcare, insurance, and finance fields.

Data Mining: Concepts and Techniques (Third Edition) is a comprehensive data mining resource offering 13 chapters on the concepts and techniques used in the data mining process.

Aggarwal, Data Mining: The Textbook is a data mining resource that discusses the fundamental methods of data mining, data types, and data mining applications.

Considered a leading introductory book to data mining, this data mining resource centers on using the latest data mining methods and techniques to solve common business challenges.

A resource appropriate for readers without strong backgrounds in computer science and statistics, Data Mining with Rattle and R focuses on the hands-on end-to-end process of data mining.

They explore the three steps of a basic process of data mining: data preprocessing, data analysis, and result interpretation, making this data mining eBook an approrpriate resource for beginners.

Covering an introduction to data mining for both predictive analytics and Big Data, Dell’s Data Mining Techniques is a useful data mining resource that also includes a video, visuals, and links to external resources.

They also offer a data mining resource, Data Mining Techniques, that covers a range of the major data mining techniques have been recently developed to address data mining projects.

With over 15 million readers reading 35 million pages per month, Tutorials Point is an authority on technical and non-technical subjects, including data mining.

In fact, the data mining tutorial from Tutorials Point is intended for computer science graduates who are seeking to understand all levels of concepts related to data mining.

This data mining resource is better suited to individuals with a basic understanding of schema, ER model, structured query language, and data warehousing.

From StatSoft, Inc., now a part of Dell, this data mining video offers an introduction to data mining and covers hands-on tutorials of data mining applications.

VideoLectures.NET, an award-winning free and open access educational video lectures repository, features lectures given by top scientists and scholars at conferences, workshops, and other events.

This 90-minute live interactive event is a vendor-neutral webinar that helps participants learn how to get started with data mining and persevere when data mining projects do not meet their full potential Three key topics we like from Data Mining: Failure to Launch – How to Get Predictive Modeling Off the Ground and Into Orbit: Cost: FREE 41.

Presenter Joseph Rickert is technical marketing manager at Revolution Analytics, and the webinar focuses on data mining as an application area and how to use a basic knowledge of data mining techniques to become productive in R.

ShowWare, a full service, real-time ticketing solution that is redefining how facilities and event planners sell tickets to patrons, offers an analytics and data mining webinar on YouTube.

The data mining webinar is a little over 60 minutes in length and explores the various approaches for tracking and predicting information in online networks.

Their data mining webinar, Google Analytics Data Mining with R (Includes 3 Real Applications), is intended for data analysts, web analysts, and digital marketing managers who are data mining beginners.

Their data mining Wiki, Data Mining Algorithms and Tools in Weka, contains links to overview information regarding the various types of learning scheme and tools included in Weka for data mining.

If you want to learn Data Science, take a few of these statistics classes

A year ago, I was a numbers geek with no coding background.

started creating my own data science master’s degree using online courses shortly afterwards, after realizing it was a better fit for me than computer science.

For this guide, I spent 15+ hours trying to identify every online intro to statistics and probability course offered as of November 2016, extracting key bits of information from their syllabi and reviews, and compiling their ratings.

We made subjective syllabus judgment calls based on three factors: William Chen, a data scientist at Quora who has a master’s in Applied Mathematics from Harvard, wrote the following in this popular Quora answer to the question: “How do I learn statistics for data science?” Since a lot of a data scientist’s statistical work is carried out with code, getting familiar with the most popular tools is beneficial.

My favorite explanation of their differences is from Stony Brook University: They explain that “probability is primarily a theoretical branch of mathematics, which studies the consequences of mathematical definitions,” while “statistics is primarily an applied branch of mathematics, which tries to make sense of observations in the real world.” Statistics is generally regarded as one of the pillars of data science.

“Foundations of Data Analysis” includes two of the top reviewed statistics courses available with a weighted average rating of 4.48 out of 5 stars over 20 reviews.

Update (December 5, 2016): Our original second recommendation, UC Berkeley’s “Stat2x: Introduction to Statistics” series, closed their enrollment a few weeks after the release of this article.

…which contains the following five courses: This five-course specialization is based on Duke’s excellent Data Analysis and Statistical Inference course, which had a 4.82-star weighted average rating over 55 reviews.

The early reviews on the new individual courses, which have a 3.6-star weighted average rating over 5 reviews, should be taken with a grain of salt due to the small sample size.

Reviews suggest that the specialization is “well worth the money.” Each course has an estimated timeline of 4–5 weeks at 5–7 hours per week.

One prominent reviewer said the following about the original course that the specialization was based upon: Consider the above MIT course if you want a deeper dive into the world of probability.

We covered programming in the first article, and the remainder of the series will cover several other data science core competencies: the data science process, data visualization, and machine learning.

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