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Programming for Data Science

Certificate Program Tuition and Fees
As a candidate, you are eligible for academic  tuition - type "PASS" when prompted for your academic affiliation during online registration to receive a reduced tuition per course.  If you register online for three courses, an automatic $200 reduction is applied.  

Please note that payment of the tuition does not include the purchase of any required texts or software. 

The total cost of the program varies depending on the electives chosen. Actual fees may differ from the estimate below. Fees are subject to change without prior notice.

Application Fee: $75
Program Registration Fee:$695
Estimated Required Course Tuition: $3852
Estimated Electives Course Tuition: $1473
Estimated Material Cost: $770
Estimated Total Cost: $6865

Programming for Data Science Certificate Program

Program Content

The Programming for Data Science certificate program consists of courses offered completely online at Statistics.com. There is a group of required courses, and a selection of electives.

This program is for you if:

  • You are a programmer and want to learn how to harness predictive models, machine learning algorithms, and statistical methods in your work
  • You are an IT or database professional and looking to learn the programming tools for analytics
  • You are a statistician and need to expand your skill set in to the Big Data programming realm.

Planning my Program

Most courses are four weeks long, and are scheduled either once or twice a year. Courses start on specific dates, but do not require you to be online at any particular time of the day. Since various courses are available throughout the year, the Program provides flexibility in scheduling.

Course ListExplore elective options, including suggested concentration strands

Thanks

TimelineView a typical sequence of courses


FULL PROGRAM LIST

Required Courses (8)

  • Applied Predictive Analytics A predictive modeling practicum Tuition: $549 (5.0 CEUs) New available dates:
    July 03, 2015 to July 31, 2015January 01, 2016 to January 29, 2016July 01, 2016 to July 29, 2016January 06, 2017 to February 03, 2017June 30, 2017 to July 28, 2017January 05, 2018 to February 02, 2018June 29, 2018 to July 27, 2018
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  • Data Mining - R The main goal of this course is to teach users how to perform data mining tasks using R. Tuition: $469 (5.0 CEUs) New available dates:
    February 06, 2015 to March 06, 2015June 26, 2015 to July 24, 2015October 09, 2015 to November 06, 2015February 03, 2016 to March 02, 2016June 23, 2016 to July 21, 2016October 13, 2016 to November 10, 2016January 06, 2017 to February 03, 2017June 23, 2017 to July 21, 2017October 13, 2017 to November 10, 2017January 05, 2018 to February 02, 2018June 22, 2018 to July 20, 2018October 12, 2018 to November 09, 2018
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  • Introduction to Analytics using Hadoop This online class, "Introduction to Analytics using Hadoop" will introduce analytics professionals to Hadoop, and provide an exemplar workflow for using Hadoop, writing MapReduce jobs, and finally leveraging Hadoop Streaming to conclude work in an analytics programming language such as R. Tuition: $469 (5.0 CEUs) New available dates:
    March 27, 2015 to April 24, 2015October 30, 2015 to November 27, 2015March 25, 2016 to April 22, 2016October 28, 2016 to November 25, 2016March 24, 2017 to April 21, 2017October 27, 2017 to November 24, 2017March 23, 2018 to April 20, 2018October 26, 2018 to November 23, 2018
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  • Introduction to Python for Analytics In this course you'll learn basic Python skills and data structures, move on to how to load data from different sources, rearrange and aggregate it, and finally how to analyze and visualize it to create high-quality products. Tuition: $469 (5.0 CEUs) New available dates:
    January 09, 2015 to February 06, 2015May 15, 2015 to June 12, 2015September 11, 2015 to October 09, 2015January 08, 2016 to February 05, 2016May 13, 2016 to June 10, 2016September 09, 2016 to October 07, 2016January 13, 2017 to February 10, 2017May 12, 2017 to June 09, 2017September 08, 2017 to October 06, 2017January 12, 2018 to February 09, 2018May 11, 2018 to June 08, 2018September 07, 2018 to October 05, 2018
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  • Introduction to SAS Programming for Analytics This course covers the basic programming and data handling capabilities of SAS. Tuition: $469 (5.0 CEUs) New available dates:
    January 16, 2015 to February 13, 2015April 17, 2015 to May 15, 2015October 19, 2015 to November 16, 2015January 15, 2016 to February 12, 2016April 15, 2016 to May 13, 2016October 07, 2016 to November 04, 2016January 13, 2017 to February 10, 2017April 14, 2017 to May 12, 2017October 06, 2017 to November 03, 2017
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  • R Programming - Intermediate

    This course is intended for experienced data analysts looking to unlock the power of R.  It provides a systematic overview of R as a programming language, emphasizing good programming practices and the development of clear, concise code.  After completing the course, students should be able to manipulate data programmatically using R functions of their own design.

    Tuition: $489 (5.0 CEUs) New available dates:
    April 03, 2015 to May 01, 2015September 25, 2015 to October 23, 2015April 01, 2016 to April 29, 2016September 23, 2016 to October 21, 2016March 31, 2017 to April 28, 2017September 22, 2017 to October 20, 2017March 30, 2018 to April 27, 2018September 21, 2018 to October 19, 2018
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  • SQL and R - Introduction to Database Queries The purpose of this course is to teach you how to extract data from a relational database using SQL, and merge it into a single file in R, so that you can perform statistical operations. Tuition: $469 (5.0 CEUs) New available dates:
    March 20, 2015 to April 17, 2015August 07, 2015 to September 04, 2015November 13, 2015 to December 11, 2015March 18, 2016 to April 15, 2016August 05, 2016 to September 02, 2016November 11, 2016 to December 09, 2016March 17, 2017 to April 14, 2017August 04, 2017 to September 01, 2017November 10, 2017 to December 08, 2017March 16, 2018 to April 13, 2018August 03, 2018 to August 31, 2018November 09, 2018 to December 07, 2018
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  • Text Mining This course will introduce the essential techniques of text mining, understood here as the extension of data mining's standard predictive methods to unstructured text. Tuition: $469 (5.0 CEUs) New available dates:
    February 06, 2015 to March 06, 2015June 05, 2015 to July 03, 2015February 05, 2016 to March 04, 2016June 10, 2016 to July 08, 2016February 03, 2017 to March 03, 2017June 09, 2017 to July 07, 2017February 02, 2018 to March 02, 2018June 08, 2018 to July 06, 2018
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Elective Courses (3 required)

  • Advanced Analytics and Machine Learning with Hadoop This course will introduce statisticians and data analysts to higher-order tools in the Hadoop Ecosystem. Tuition: $469 (5.0 CEUs) New available dates:
    January 23, 2015 to February 20, 2015May 29, 2015 to June 26, 2015January 22, 2016 to February 19, 2016May 27, 2016 to June 24, 2016January 20, 2017 to February 17, 2017May 26, 2017 to June 23, 2017December 01, 2017 to December 29, 2017June 01, 2018 to June 29, 2018November 30, 2018 to December 28, 2018
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  • Graphics in R The aim of this course is to teach you how to produce statistical plots of data using the R language and environment for statistical computing and graphics. The creation of standard plots such as scatterplots, bar charts, histograms, and boxplots will be covered and time will be spent on the underlying model used to produce plots in R so that you can extensively customize these plots. Tuition: $469 (5.0 CEUs) New available dates:
    May 01, 2015 to May 29, 2015October 09, 2015 to November 06, 2015April 29, 2016 to May 27, 2016October 07, 2016 to November 04, 2016May 05, 2017 to June 02, 2017October 06, 2017 to November 03, 2017May 04, 2018 to June 01, 2018October 05, 2018 to November 02, 2018
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  • Mapping in R

    R provides a very flexible environment for statistical analysis, including a number of facilities to manipulate, visualise and analyse spatial information. Here, the emphasis is on visualising spatial data. A key advantage of mapping in R is that it is very easy to integrate the results of data analysis with the map creation process. In this course, you will discover how to use R as a tool for creating maps, identifying its strengths and weaknesses. the course will explain how spatial data may be written/read and visualized in R, and show how publication quality maps may be produced in R, based on the GISTools package, as well as providing a review of a number of other diverse methods for visually representing geographical information in R.

    Tuition: $549 (5.0 CEUs) New available dates:
    May 01, 2015 to May 29, 2015November 20, 2015 to December 18, 2015May 06, 2016 to June 03, 2016November 18, 2016 to December 16, 2016May 05, 2017 to June 02, 2017November 17, 2017 to December 15, 2017May 04, 2018 to June 01, 2018November 16, 2018 to December 14, 2018
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  • Natural Language Processing This course is designed to give you an introduction to the algorithms, techniques and software used in natural language processing (NLP). Tuition: $469 (5.0 CEUs) New available dates:
    March 06, 2015 to April 03, 2015July 17, 2015 to August 14, 2015March 04, 2016 to April 01, 2016July 15, 2016 to August 12, 2016March 03, 2017 to March 31, 2017July 14, 2017 to August 11, 2017March 02, 2018 to March 30, 2018July 13, 2018 to August 10, 2018
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  • Natural Language Processing Using NLTK Tuition: $589 (5.0 CEUs) New available dates:
    April 10, 2015 to May 08, 2015September 18, 2015 to October 16, 2015April 08, 2016 to May 06, 2016September 16, 2016 to October 14, 2016April 07, 2017 to May 05, 2017September 15, 2017 to October 13, 2017April 06, 2018 to May 04, 2018September 14, 2018 to October 12, 2018
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  • R Programming - Advanced

    This course covers key concepts for writing advanced R code, emphasizing the design of functional and efficient code.  It will set students down the road to mastering the intricacies of R.  After completing the course, students should be able to read, understand, modify, and create complex functions to perform a variety of tasks.

    Tuition: $489 (5.0 CEUs) New available dates:
    January 09, 2015 to February 06, 2015May 22, 2015 to June 19, 2015January 08, 2016 to February 05, 2016May 20, 2016 to June 17, 2016January 06, 2017 to February 03, 2017May 19, 2017 to June 16, 2017January 05, 2018 to February 02, 2018May 18, 2018 to June 15, 2018
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  • Sentiment Analysis Sentiment Analysis refers to the process of identifying, extracting and classifying opinions in text segments. With the rise of social media and the ability of end-users to express and share their personal views easily, the need to automatically gauge user-sentiment has become increasingly important for CRM, online advertising and brand analysis. Tuition: $389 (3.75 CEUs) New available dates:
    May 08, 2015 to May 29, 2015August 21, 2015 to September 11, 2015May 06, 2016 to May 27, 2016August 19, 2016 to September 09, 2016May 05, 2017 to May 26, 2017August 18, 2017 to September 08, 2017May 04, 2018 to May 25, 2018August 17, 2018 to September 07, 2018
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  • Social Data Mining With Python
    Tuition: $469 (5.0 CEUs) New available dates:
    March 20, 2015 to April 17, 2015September 18, 2015 to October 16, 2015March 18, 2016 to April 15, 2016September 23, 2016 to October 21, 2016March 17, 2017 to April 14, 2017September 22, 2017 to October 20, 2017March 16, 2018 to April 13, 2018September 21, 2018 to October 19, 2018
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  • Spatial Analysis Techniques in R This course will teach users how to implement spatial statistical analysis procedures using R software. Tuition: $549 (5.0 CEUs) New available dates:
    December 12, 2014 to January 16, 2015December 11, 2015 to January 15, 2016December 09, 2016 to January 13, 2017December 08, 2017 to January 12, 2018December 07, 2018 to January 11, 2019
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  • Visualization in R with ggplot2

    ggplot, created by Hadley Wickham, is an implementation of the grammar of graphics in R.  It combines the advantages of both base and lattice graphics: conditioning and shared axes are handled automatically, while maintaining the ability to build up a plot step by step from multiple data sources. It also implements a more sophisticated multidimensional conditioning system and a consistent interface to map data to aesthetic attributes.

    ggplot won the 2006 John Chambers Award for Statistical Computing.

    Tuition: $469 (5.0 CEUs) New available dates:
    January 16, 2015 to February 13, 2015July 24, 2015 to August 21, 2015July 22, 2016 to August 19, 2016January 13, 2017 to February 10, 2017January 12, 2018 to February 09, 2018July 20, 2018 to August 17, 2018
    show more dates >> more >>

Earn a Certificate

When To Apply

Applications are accepted and students are enrolled year round on a rolling basis. You pay an application fee and submit it with your application form. You may pay the fee online here, or enclose a check (USD drawn on a US bank) with your application. NOTE: The application is your opportunity to specifically outline your past education and experience, and provide any other information you want us to consider during our review. The value of this background and experience is the ability to place the statistical methods covered in the Program in an appropriate application context. Once we have accepted your application, we will be happy to assist you with more specific questions about PASS.


Admission Criteria

  1. Bachelor's degree from an accredited college or university.
  2. 1+ years of programming using R, or
    And do tell us if your programming expertise warrants exemption from any of the programming course requirements - we don't want you wasting your time!
  3. The successful recent completion of an introductory statistics course equivalent to Statistics.com's Statistics 1 and Statistics 2,* (please provide proof in your PASS application).

*If you do not meet the introductory statistics requirement, you may still enroll in PASS. Upon review of your application, we will determine whether you need to take Statistics.com's Statistics 1 and 2. If you do require these foundational courses, we will register you for them and waive the entire tuition fee for these two courses

Procedure for Enrollment and Matriculation in PASS

  1. Complete and submit the application, your photograph, and the payment of the application fee. Notification of approval is sent usually within one week.
  2. After your application is approved, enroll in the PASS of your choice here and pay the enrollment fee.
  3. If you have NOT already met the introductory statistics requirement, you will be registered for the Institute's Statistics 1 and 2 courses. Full tuition is waived for matriculated PASS candidates.
  4. After you submit your application and receive approval, pay the enrollment fee, and meet the introductory statistics requirement, you will be matriculated into the program and are considered a PASS candidate. You may now register for classes as a PASS candidate and receive our academic tuition deduction - specify PASS as your academic affiliation during the online registration process.

Rules and Policies

  • Once you are matriculated in PASS, you are considered a student at the Institute for Statistics Education at Statistics.com.
  • You must be a fully-matriculated PASS candidate to be eligible to receive the academic tuition deduction provided during online registration, and to receive PASS credit for courses.
  • The Program enrollment fee is a required one-time charge in addition to tuition for individual courses.
  • You are expected to complete all the courses in your Program within 5 years of your matriculation into the program. Any exceptions must be approved in writing by the Institute prior to that 5-year deadline.
  • Upon completion of your PASS, you will receive a Certificate and your official transcript.
  • Up to three Institute courses may be taken prior to enrolling in PASS; credit for these courses may be applied to your PASS transcript once you are a matriculated PASS candidate, provided that adequate marks are earned.

Note:  In the Hadoop course, you will need to have some familiarity with command line Linux operations - see the course description for more information and a tutorial reference.  This is not a requirement for admission.

 

Programming for Data Science Certificate Program

Description

Since its founding in 2002, the Institute has been the global pioneer and leader in statistics and analytics education.  The Institute now offers 100+ online courses in statistics and analytics, plus four certificate programs. 

The Programming in Data Science certificate program focuses on courses relevant to learning how to use R, Python, SQL and Hadoop for analytics and statistical analysis.  Most courses are 4 weeks long, do not require you to be online at specific times during the week, and offer continuing education credits.  The workload for the entire program is the equivalent of 16.5 credits in the U.S. academic system.

The courses are taught by recognized authorities with whom you share a private discussion forum for the entire course period.

Most candidates choose to take one course at a time.  With courses starting every week of the year, there is considerable scheduling flexibility.  Admission applications are accepted on a rolling basis throughout the year, so you can start when it fits your schedule.

Program Objectives

The Programming in Data Science certificate program consists of eleven 4-week courses.  You will learn how to

  • Program in R (4 course sequence, with exam)
  • Use Python for data analytics
  • Do text mining with Python and nltk
  • Query databases with SQL
  • Write and deploy MapReduce jobs for Hadoop
  • Use R for data mining
  • Extract, clean, prepare, and mine real data in a practicum, culminating the prototyping of a predictive model
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From a Student Profile:

Traditionally, reports are designed to summarize data, but they can only tell you what happened. I'm applying data mining algorithms I've learned in my Statistics.com coursework to ask why something happened."

Susan Stranburg
Software Developer

see the complete student profile
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