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Develop the computer science, mathematics and analytical skills needed to enter the field of data science. Use data science techniques to analyze and extract meaning from extremely large data sets, or big data. Become familiar with relational and non-relational databases. Apply statistics, machine learning, text retrieval and natural language processing to analyze data and interpret results. Learn to apply data science in fields such as marketing, business intelligence, scientific research and more.

Key Outcome

You will be equipped with the fundamental tools, techniques and practical experience to acquire valuable insights from data sets at any scale – from gigabytes to petabytes.

Introduction to Data Science

Downtown Seattle, Classroom, Autumn 2014

Instructor: Ernst Henle

Tu, 10/7 - 12/16, 2014, 6-9 p.m.

Cost: $1,089 | 3 CEUs

This course is designed to introduce students to the data management, storage and manipulation tools common in data science and will apply those tools to real scenarios. An overview of different SQL and No-SQL database technologies is presented and the course finishes with a discussion of choosing the appropriate tool to get the job done.

Topics include:

  • Introduction to data (data types, data movement, terminology, etc.)
  • Storage and Concurrency Preliminaries
  • Files and File-based data systems
  • Relational Database Management Systems
  • Hadoop Introduction
  • NoSQL - MapReduce vs. Parallel RDBMS

Methods for Data Analysis

Downtown Seattle, Classroom, Winter 2015

Instructor: Oliver Downs

Tu, 1/6 - 3/10, 2015, 6-9 p.m.

Cost: $1,089 | 3 CEUs

This course is designed to develop an understanding of core statistical and machine learning techniques which provide the theoretical tools data scientists use to generate knowledge. Students will be asked to apply the course content to real-life scenarios and think creatively as well as critically through issues. By the end of the class, students will be able to apply statistics and machine learning techniques to data, and interpret and communicate their results. Feedback from guest speakers or program graduates about the current state of data science and the job market.

  • Entity Resolution
  • Inferential Statistics
  • Gaussian Distributions, Other Distributions and The Central Limit Theorem
  • Testing and Experimental Design
  • Bayesian vs. Classical Statistics
  • Probabilistic Interpretation of Linear Regression, and Maximum Likelihood
  • Graph Algorithms
  • Raw Data to Inference Model
Open for Single Course Enrollment

This course is part of a certificate program. You can enroll in this course on a space available basis even though you are not a certificate student. Courses taken when you are not a certificate student don't automatically count toward earning a certificate.

You will pay a course fee when you are notified of your eligibility to enroll.

How to Apply

Step 1: Complete the on-line qualifications assessment quiz

  • Once you begin the on line assessment you must complete it. You have 90 minutes. The assessment is 30 multiple-choice questions divided into three sections: Stats and Linear Algebra, Programming, Databases and SQL. We typically expect applicants to receive a total score of at least 18/30, as well as a minimum score of 6/10 in each section. Once you submit your survey you will be able to see your total point score (section scores are not available). Please use the same Email address on both your assessment and application.
  • This assessment, together with your application, will be used to evaluate your acceptance into the program. The application will not be considered complete until you take the on-line assessment.
  • Take the Data Science qualifications assessment quiz.

Step 2: Gather the following materials

  • A typed letter of application (250-word maximum) describing:
  • Relevant work experience and volunteer experience
  • Transferable skills, knowledge of the field and commitment to professional growth
  • A resume listing education and applicable experience

Step 3: Apply

Apply online, or submit an application packet that includes:

We will let you know whether you are accepted or not accepted into the course before the first class session.

Interested in earning the certificate? Go to the Certificate in Data Science.

Deriving Knowledge from Data at Scale

Downtown Seattle, Classroom, Spring 2015

Instructor: Roger Barga

Tu, 3/31 - 6/2, 2015, 6-9 p.m.

Cost: $1,089 | 3 CEUs

This course is designed to pull together what has been learned so far about the structuring and manipulation of data and core statistical and machine learning techniques and add knowledge on the machinery used to leverage those techniques in real-world scenarios in which data scientists are asked to generate knowledge. Students will be asked to apply the course content to real-life scenarios and think creatively as well as critically through issues. By the end of the class, students will be able to attack data sciences questions impacting their business, establish robust experimental tests of data-driven hypotheses, generate meaningful and reliable findings and communicate them clearly.

Topics include:

  • Motivation & Applications of Machine Learning
  • Supervised Learning
  • Linear and Non-Linear Learning Models
  • Classification, Clustering and Dimensionality Reduction
  • Advanced Non-Linear Models
  • Collaborative Filtering and Recommendation
  • Models that are Robust
  • Data Sciences with Text and Language
  • Data Sciences with Location
  • Social Network Analysis
Open for Single Course Enrollment

This course is part of a certificate program. You can enroll in this course on a space available basis even though you are not a certificate student. Courses taken when you are not a certificate student don't automatically count toward earning a certificate.

You will pay a course fee when you are notified of your eligibility to enroll.

How to Apply

Step 1: Complete the on-line qualifications assessment quiz

  • Once you begin the on line assessment you must complete it. You have 90 minutes. The assessment is 30 multiple-choice questions divided into three sections: Stats and Linear Algebra, Programming, Databases and SQL. We typically expect applicants to receive a total score of at least 18/30, as well as a minimum score of 6/10 in each section. Once you submit your survey you will be able to see your total point score (section scores are not available). Please use the same Email address on both your assessment and application.
  • This assessment, together with your application, will be used to evaluate your acceptance into the program. The application will not be considered complete until you take the on-line assessment.
  • Take the Data Science qualifications assessment quiz.

Step 2: Gather the following materials

  • A typed letter of application (250-word maximum) describing:
  • Relevant work experience and volunteer experience
  • Transferable skills, knowledge of the field and commitment to professional growth
  • A resume listing education and applicable experience

Step 3: Apply

Apply online, or submit an application packet that includes:

We will let you know whether you are accepted or not accepted into the course before the first class session.

Interested in earning the certificate? Go to the Certificate in Data Science.

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