Data Science (DS)

Courses

DS 210   Introduction to Data Science (3 Hours)

In this course students receive an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, techniques and tools that data analysts and data scientists work with. This course provides a conceptual introduction to the ideas behind turning data into actionable knowledge and tools that will be used to analyze this data. The course will cover collecting, cleaning and sharing data. Additionally, this course will cover how to communicate results through visualizations. 3 hrs. lecture/wk.

DS 220   Data Visualization (3 Hours)

This course introduces students to key design principles and techniques for interactively visualizing data. In addition to understanding how visual representations are used in the analysis and understanding of complex data, students will acquire data visualization skills including designing effective visualizations and creating interactive visualizations using spreadsheets. 3 hrs. lecture/wk.

DS 230   SQL for Data Analysis (3 Hours)

In this course students will focus on how to apply the Structured Query Language (SQL) to data analysis tasks. Spreadsheets will be used for the visualization of data. Additionally, basic statistics will be covered. All data will be extracted from relational tables. 3 hrs. lecture/wk.

DS 240   Introduction to Statistical Programming (3 Hours)

Students in this course will use a statistical programming language to perform effective data analysis. Students will acquire programming skills including reading data, accessing statistical packages, writing functions, debugging, profiling code, organizing code and commenting code. 3 hrs. lecture/wk.

DS 250   Data Analysis (3 Hours)

In this course the student will manipulate, process, clean, analyze and visualize data in a programming language. Real world datasets will be utilized. Structured data will be emphasized. 3 hrs. lecture/wk.

DS 260   Data Mining (3 Hours)

This course will provide students with an understanding of fundamental data mining methodologies and the ability to formulate and solve problems with these methodologies. Particular attention will be paid to the process of extracting data, analyzing it from many dimensions or perspectives, then producing a summary of the information in a useful form that identifies relationships within the data. The lectures will be complemented with hands-on experience with data mining software to allow development of execution skills. 3 hrs. lecture/wk.

DS 270   Introduction to Machine Learning (3 Hours)

This introductory course gives an overview of machine learning concepts, techniques and algorithms. Supervised and unsupervised machine learning will be covered. Machine learning is an integral part of data analytics, which deals with developing data-driven insights for better designs and decisions, and gives computers the ability to learn without being explicitly programmed. 3 hrs. lecture/wk.

DS 280   Big Data Architecture (3 Hours)

This course covers emerging big data architectures that deal with large amounts of unstructured and semi-structured data. This course is designed for developers who need to create applications to analyze big data stored in distributed file systems. Topics include file architecture, data retrieval, performance and data analysis. 3 hrs. lecture/wk.

DS 210

  • Title: Introduction to Data Science
  • Number: DS 210
  • Effective Term: 2017-18
  • Credit Hours: 3
  • Contact Hours: 3
  • Lecture Hours: 3

Description:

In this course students receive an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, techniques and tools that data analysts and data scientists work with. This course provides a conceptual introduction to the ideas behind turning data into actionable knowledge and tools that will be used to analyze this data. The course will cover collecting, cleaning and sharing data. Additionally, this course will cover how to communicate results through visualizations. 3 hrs. lecture/wk.

Supplies:

Refer to the instructor's course syllabus for details about any supplies that may be required.

Objectives

  1. Describe data science in the context of big data.
  2. Discuss the ethics of big data.
  3. Describe and apply the data science life cycle.
  4. Describe data.
  5. Describe the extract, transform and load (ETL) process and why it is important.
  6. Apply programming to the ETL process.
  7. Analyze real-world problems based on data analysis techniques.
  8. Explain the solutions.

Content Outline and Competencies:

I. Data Science and Big Data

A. Describe big data.

B. Discuss examples of data science.

C. Discuss case studies.

II. Ethics of Big Data

A. Explain identity.

B. Show privacy.

C. Discuss ownership.

D. Demonstrate reputation.

III. Data Science Life Cycle

A. Identify the problem.

B. Identify available data sources.

C. Apply data analysis.

D. Solve the problem.

E. Explain the solutions.

IV. Data

A. Show types of data.

B. Demonstrate sources of data.

C. Explain storage of data.

D. Illustrate structured versus unstructured data.

E. Discover messiness of data.

V. ETL Process

A. Extract data from data sources.

B. Transform extracted data.

C. Load transformed data.

VI. ETL Process Automation

A. Apply ETL basics.

B. Employ data structures.

C. Use programming language packages.

VII. Real-World Problems

A. Formulate the problem.

B. Collect relevant data.

C. Analyze the data.

D. Apply the techniques using programming.

VIII. Solutions

A. Prepare solution documents.

B. Justify solutions orally.

C. Present visualizations.

Method of Evaluation and Competencies:

15-20%    Exams and Quizzes
15-20%    Minimum of 15 Problem Sets
30-40%    Final Project
15-20%    Final Presentation
5%           Updating ePortfolio

Total: 100%

Grade Criteria:

90 – 100% = A
80 – 89% = B
70 – 79% = C
60 – 69% = D
0 – 59% = F

Caveats:

Student Responsibilities:

Disabilities:

JCCC provides a range of services to allow persons with disabilities to participate in educational programs and activities. If you are a student with a disability and if you are in need of accommodations or services, it is your responsibility to contact Access Services and make a formal request. To schedule an appointment with an Access Advisor or for additional information, you may send an email or call Access Services at (913)469-3521. Access Services is located on the 2nd floor of the Student Center (SC 202).

DS 220

  • Title: Data Visualization
  • Number: DS 220
  • Effective Term: 2017-18
  • Credit Hours: 3
  • Contact Hours: 3
  • Lecture Hours: 3

Description:

This course introduces students to key design principles and techniques for interactively visualizing data. In addition to understanding how visual representations are used in the analysis and understanding of complex data, students will acquire data visualization skills including designing effective visualizations and creating interactive visualizations using spreadsheets. 3 hrs. lecture/wk.

Supplies:

Refer to the instructor's course syllabus for details about any supplies that may be required.

Objectives

  1. Describe data visualization in the context of big data.
  2. Recognize graphical integrity.
  3. Describe and apply key design principles.
  4. Describe techniques of data visualization.
  5. Analyze various types of data sets.
  6. Apply visualizations and basic statistics to analyze and understand data.
  7. Analyze real-world problems based on data visualization techniques.
  8. Explain the solutions.

Content Outline and Competencies:

I. Data Visualization and Big Data

A. Identify misleading graphs.

B. Illustrate stories with data.

C. Discuss the audience.

D. Discuss the story.

II. Graphical Integrity

A. Explain labeling.

B. Demonstrate sourcing.

C. Illustrate managing data relevance and density.

III. Key Design Principles

A. Express the importance of context.

B. Demonstrate exploratory and explanatory analysis.

IV. Techniques

A. Discuss choosing an effective visual.

1. Clutter

2. Decluttering

3. Audience focus

4. Accessibility

5. Aesthetics

6. Acceptance

B. Explain storytelling.

V. Data Sets

A. Use data from the web.

B. Select comma-separated value (CSV) files.

C. Analyze text files.

D. Manipulate relational tables.

VI. Visualizations

A. Use visualizations to analyze and understand data.

B. Use basic statistical formulas to analyze and understand data.

VII. Real-World Problems

A. Formulate the problem.

B. Collect relevant data.

C. Analyze the data.

D. Apply the techniques of data visualization.

VIII. Solutions

A. Prepare solution documents.

B. Justify solutions orally.

C. Present visualizations.

Method of Evaluation and Competencies:

15-20%    Exams and Quizzes
15-20%    Minimum of 15 Problem Sets
30-40%    Final Project
15-20%    Final Presentation
5%            Updating ePortfolio

Total: 100%

Grade Criteria:

90 – 100% = A
80 – 89% = B
70 – 79% = C
60 – 69% = D
0 – 59% = F

Caveats:

Student Responsibilities:

Disabilities:

JCCC provides a range of services to allow persons with disabilities to participate in educational programs and activities. If you are a student with a disability and if you are in need of accommodations or services, it is your responsibility to contact Access Services and make a formal request. To schedule an appointment with an Access Advisor or for additional information, you may send an email or call Access Services at (913)469-3521. Access Services is located on the 2nd floor of the Student Center (SC 202).

DS 230

  • Title: SQL for Data Analysis
  • Number: DS 230
  • Effective Term: 2017-18
  • Credit Hours: 3
  • Contact Hours: 3
  • Lecture Hours: 3

Description:

In this course students will focus on how to apply the Structured Query Language (SQL) to data analysis tasks. Spreadsheets will be used for the visualization of data. Additionally, basic statistics will be covered. All data will be extracted from relational tables. 3 hrs. lecture/wk.

Supplies:

Refer to the instructor's course syllabus for details about any supplies that may be required.

Objectives

  1. Describe SQL in the context of data analysis.
  2. Use SQL queries.
  3. Apply SQL to data exploration.
  4. Use basic statistics.
  5. Apply SQL to time-related data analysis.
  6. Apply SQL for business analysis.
  7. Analyze real-world problems using SQL.
  8. Explain the solutions.

Content Outline and Competencies:

I. SQL and Data Analysis

A. Discuss relevancy.

B. Illustrate Select statement examples.

II. Queries

A. Use the Select clause.

B. Employ the From clause.

C. Apply the Where clause.

D. Illustrate the Group by clause.

E. Apply the Having clause.

F. Interpret the Order by clause.

G. Use the Fetch and Offset clause.

III. Data Exploration

A. Describe data exploration.

B. Use spreadsheets for visualization.

IV. Statistics

A. Describe statistical concepts.

1. Quantitative data

2. Identifier data

3. Categorical data

4. Mean

5. Mode

6. Median

7. Outliers

8. Standard deviation

B. Analyze data using statistics.

V. Data Analysis

A. Describe concepts.

1. Distributions

2. Plots

3. Correlation

4. Causation

5. Linear regression

6. Hypothesis testing

B. Analyze time-related data.

VI. Business Analysis

A. Describe various types of business analysis.

B. Apply different SQL analysis techniques to relational data.

1. Selection

2. Filters

3. Aggregates

4. Joins

5. Sorts

VII. Real-World Problems

A. Formulate the problem.

B. Collect relevant data.

C. Analyze the data.

D. Apply analysis using SQL.

VIII. Solutions

A. Prepare solution documents.

B. Justify solutions orally.

C. Present visualizations.

Method of Evaluation and Competencies:

15-20%    Exams and Quizzes
15-20%    Minimum of 15 Problem Sets
30-40%    Final Project
15-20%    Final Presentation
5%            Updating ePortfolio

Total: 100%

Grade Criteria:

90 – 100% = A
80 – 89% = B
70 – 79% = C
60 – 69% = D
0 – 59% = F

Caveats:

Student Responsibilities:

Disabilities:

JCCC provides a range of services to allow persons with disabilities to participate in educational programs and activities. If you are a student with a disability and if you are in need of accommodations or services, it is your responsibility to contact Access Services and make a formal request. To schedule an appointment with an Access Advisor or for additional information, you may send an email or call Access Services at (913)469-3521. Access Services is located on the 2nd floor of the Student Center (SC 202).

DS 240

  • Title: Introduction to Statistical Programming
  • Number: DS 240
  • Effective Term: 2017-18
  • Credit Hours: 3
  • Contact Hours: 3
  • Lecture Hours: 3

Description:

Students in this course will use a statistical programming language to perform effective data analysis. Students will acquire programming skills including reading data, accessing statistical packages, writing functions, debugging, profiling code, organizing code and commenting code. 3 hrs. lecture/wk.

Supplies:

Refer to the instructor's course syllabus for details about any supplies that may be required.

Objectives

  1. Describe the statistical programming language in the context of big data.
  2. Describe the statistical software environment.
  3. Apply basic skills of a statistical programming language.
  4. Use the statistical programming language to access data.
  5. Construct statistical programming language functions.
  6. Use the statistical programming language advanced data structures.
  7. Analyze real-world problems using the statistical programming language.
  8. Explain the solutions.

Content Outline and Competencies:

I. Statistical Programming Language and Big Data

A. Describe the statistical programming language.

B. Describe the benefits of a statistical programming language.

C. Apply the statistical programming language to big data.

II. Statistical Programming Language Software Environment

A. Use the statistical programming language integrated development environment (IDE).

B. Explain the statistical programming language packages.

III. Statistical Programming Language

A. Apply basic math.

B. Use variables.

C. Employ data types.

D. Demonstrate vectors.

E. Construct control statements.

F. Write loops.

G. Create functions.

H. Interpret missing data.

IV. Data

A. Use comma separated value (CSV) files.

B. Employ spreadsheet data.

C. Select databases.

D. Manipulate data included with the statistical programming language.

E. Use data from websites.

V. Functions

A. Memorize syntax.

B. Use function arguments.

C. Use return values.

VI. Advanced Structures

A. Use data frames.

B. Employ lists.

C. Construct matrices.

D. Create arrays.

VII. Real-World Problems

A. Formulate the problem.

B. Collect relevant data.

C. Analyze the data.

D. Apply the statistical programming language.

VIII. Solutions

A. Prepare solution documents.

B. Justify solutions orally.

C. Present visualizations.

Method of Evaluation and Competencies:

15-20%    Exams and Quizzes
15-20%    Minimum of 15 Problem Sets
30-40%    Final Project
15-20%    Final Presentation
5%           Updating ePortfolio

Total: 100%

Grade Criteria:

90 – 100% = A
80 – 89% = B
70 – 79% = C
60 – 69% = D
0 – 59% = F

Caveats:

Student Responsibilities:

Disabilities:

JCCC provides a range of services to allow persons with disabilities to participate in educational programs and activities. If you are a student with a disability and if you are in need of accommodations or services, it is your responsibility to contact Access Services and make a formal request. To schedule an appointment with an Access Advisor or for additional information, you may send an email or call Access Services at (913)469-3521. Access Services is located on the 2nd floor of the Student Center (SC 202).

DS 250

  • Title: Data Analysis
  • Number: DS 250
  • Effective Term: 2017-18
  • Credit Hours: 3
  • Contact Hours: 3
  • Lecture Hours: 3

Description:

In this course the student will manipulate, process, clean, analyze and visualize data in a programming language. Real world datasets will be utilized. Structured data will be emphasized. 3 hrs. lecture/wk.

Supplies:

Refer to the instructor's course syllabus for details about any supplies that may be required.

Objectives

  1. Describe the fundamentals of data analysis.
  2. Establish data analysis context for programming.
  3. Describe and apply the steps in a data analysis project.
  4. Describe structured versus unstructured data.
  5. Apply the extract, transform, load (ETL) process using programming.
  6. Apply data analysis using programming.
  7. Analyze real-world problems based on data analysis techniques.
  8. Explain solutions.

Content Outline and Competencies:

I. Data Analysis Fundamentals

A. Explain descriptive analytics.

B. Describe predictive analytics.

C. Summarize prescriptive analytics.

II. Data Analysis Programming Context

A. Structure and frame the decision problem.

B. Define business objectives and constraints.

C. Apply a problem-solving approach.

D. Apply data design.

E. Apply a methodology.

III. Data Analysis Project Steps

A. Describe essential programming libraries.

B. Formulate a question.

C. Formulate measurements.

D. Collect data.

E. Summarize data.

F. Create visualization.

G. Analyze data and interpret results.

IV. Data

A. Define various types of data.

B. Analyze examples.

C. Analyze structured real-world data.

D. Analyze unstructured real-world data.

V. ETL Process

A. Apply programming to extract data.

B. Employ programming to transform data.

C. Use programming to load data.

VI. Data Analysis using Programming

A. Apply numerical manipulation algorithms.

B. Employ data analysis algorithms.

C. Use visualization algorithms.

D. Apply machine learning algorithms.

VII. Real-World Problems

A. Formulate the problem.

B. Collect relevant data.

C. Analyze the data.

D. Apply data analysis techniques using programming.

VIII. Solutions

A. Prepare solution documents.

B. Justify solutions orally.

C. Present visualizations.

Method of Evaluation and Competencies:

15-20%    Exams and Quizzes
15-20%    Minimum of 15 Problem Sets
30-40%    Final Project
15-20%    Final Presentation
5%           Updating ePortfolio

Total: 100%

Grade Criteria:

90 – 100% = A
80 – 89% = B
70 – 79% = C
60 – 69% = D
0 – 59% = F

Caveats:

Student Responsibilities:

Disabilities:

JCCC provides a range of services to allow persons with disabilities to participate in educational programs and activities. If you are a student with a disability and if you are in need of accommodations or services, it is your responsibility to contact Access Services and make a formal request. To schedule an appointment with an Access Advisor or for additional information, you may send an email or call Access Services at (913)469-3521. Access Services is located on the 2nd floor of the Student Center (SC 202).

DS 260

  • Title: Data Mining
  • Number: DS 260
  • Effective Term: 2017-18
  • Credit Hours: 3
  • Contact Hours: 3
  • Lecture Hours: 3

Description:

This course will provide students with an understanding of fundamental data mining methodologies and the ability to formulate and solve problems with these methodologies. Particular attention will be paid to the process of extracting data, analyzing it from many dimensions or perspectives, then producing a summary of the information in a useful form that identifies relationships within the data. The lectures will be complemented with hands-on experience with data mining software to allow development of execution skills. 3 hrs. lecture/wk.

Supplies:

Refer to the instructor's course syllabus for details about any supplies that may be required.

Objectives

  1. Describe data mining in the context of big data.
  2. Describe and apply a data mining methodology.
  3. Identify inputs to the data mining process.
  4. Produce outputs from data mining process.
  5. Describe basic algorithms.
  6. Describe advanced algorithms.
  7. Analyze real-world problems using data mining techniques.
  8. Explain the solutions.

Content Outline and Competencies:

I. Data Mining and Big Data

A. Explain data mining and machine learning.

B. Explain patterns.

C. Discuss examples.

D. Discuss ethics.

II. Data Mining Methodology

A. Illustrate business understanding.

B. Apply data understanding.

C. Summarize data preparation.

D. Employ modeling.

E. Discuss evaluation.

F. Demonstrate deployment.

III. Data Mining Process Inputs

A. Apply definitions.

B. Use attributes.

C. Determine sparse data.

D. Modify missing values.

E. Manipulate inaccurate values.

F. Explain data.

IV. Data Mining Process Outputs

A. Use tables.

B. Apply linear models.

C. Describe trees.

D. Produce rules.

E. Demonstrate instance-based representation.

F. Manipulate clusters.

V. Basic Algorithms

A. Interpret rudimentary rules.

B. Apply statistical modeling.

C. Produce decision trees.

D. Construct rules.

E. Use mining association rules.

VI. Advanced Algorithms

A. Use linear models.

B. Apply instance-based learning.

C. Demonstrate clustering.

D. Employ multi-instance learning.

VII. Real-World Problems

A. Formulate the problem.

B. Collect relevant data.

C. Analyze the data.

D. Apply the techniques of data mining.

VIII. Solutions

A. Prepare solution documents.

B. Justify solutions orally.

C. Present visualizations.

Method of Evaluation and Competencies:

15-20%    Exams and Quizzes
15-20%    Minimum of 15 Problem Sets
30-40%    Final Project
15-20%    Final Presentation
5%           Updating ePortfolio

Total: 100%

Grade Criteria:

90 – 100% = A
80 – 89% = B
70 – 79% = C
60 – 69% = D
0 – 59% = F

Caveats:

Student Responsibilities:

Disabilities:

JCCC provides a range of services to allow persons with disabilities to participate in educational programs and activities. If you are a student with a disability and if you are in need of accommodations or services, it is your responsibility to contact Access Services and make a formal request. To schedule an appointment with an Access Advisor or for additional information, you may send an email or call Access Services at (913)469-3521. Access Services is located on the 2nd floor of the Student Center (SC 202).

DS 270

  • Title: Introduction to Machine Learning
  • Number: DS 270
  • Effective Term: 2017-18
  • Credit Hours: 3
  • Contact Hours: 3
  • Lecture Hours: 3

Description:

This introductory course gives an overview of machine learning concepts, techniques and algorithms. Supervised and unsupervised machine learning will be covered. Machine learning is an integral part of data analytics, which deals with developing data-driven insights for better designs and decisions, and gives computers the ability to learn without being explicitly programmed. 3 hrs. lecture/wk.

Supplies:

Refer to the instructor's course syllabus for details about any supplies that may be required.

Objectives

  1. Describe machine learning in the context of big data.
  2. Discuss machine learning basics.
  3. Use structured and unstructured data.
  4. Apply the k-nearest neighbors algorithm.
  5. Apply decision trees.
  6. Apply linear regression.
  7. Apply multiple linear regression.
  8. Analyze real-world problems based on machine learning techniques.
  9. Explain the solutions.

Content Outline and Competencies:

I. Machine Learning and Big Data

A. Discuss big data.

B. Discuss artificial intelligence.

II. Machine Learning Basics

A. Define key terminology.

B. Interpret key tasks.

C. Illustrate algorithms.

D. Demonstrate steps in developing an application.

E. Employ programming libraries.

III. Structured and Unstructured Data

A. Discuss supervised learning.

B. Discuss unsupervised learning.

IV. K-Nearest Neighbors Algorithm

A. Explain the algorithm.

B. Apply to a variety of problems.

V. Decision Trees

A. Explain decision trees.

B. Apply to a variety of problems.

VI. Linear Regression

A. Explain linear regression.

B. Apply to a variety of problems.

VII. Multiple Regression

A. Explain multiple regression.

B. Apply to a variety of problems.

VIII. Real-World Problems

A. Formulate the problem.

B. Collect relevant data.

C. Analyze the data.

D. Apply the techniques of machine learning.

IX. Solutions

A. Prepare solution documents.

B. Justify solutions orally.

C. Present visualizations.

Method of Evaluation and Competencies:

15-20%    Exams and Quizzes
15-20%    Minimum of 15 Problem Sets
30-40%    Final Project
15-20%    Final Presentation
5%            Updating ePortfolio

Total: 100%

Grade Criteria:

90 – 100% = A
80 – 89% = B
70 – 79% = C
60 – 69% = D
0 – 59% = F

Caveats:

Student Responsibilities:

Disabilities:

JCCC provides a range of services to allow persons with disabilities to participate in educational programs and activities. If you are a student with a disability and if you are in need of accommodations or services, it is your responsibility to contact Access Services and make a formal request. To schedule an appointment with an Access Advisor or for additional information, you may send an email or call Access Services at (913)469-3521. Access Services is located on the 2nd floor of the Student Center (SC 202).

DS 280

  • Title: Big Data Architecture
  • Number: DS 280
  • Effective Term: 2017-18
  • Credit Hours: 3
  • Contact Hours: 3
  • Lecture Hours: 3

Description:

This course covers emerging big data architectures that deal with large amounts of unstructured and semi-structured data. This course is designed for developers who need to create applications to analyze big data stored in distributed file systems. Topics include file architecture, data retrieval, performance and data analysis. 3 hrs. lecture/wk.

Supplies:

Refer to the instructor's course syllabus for details about any supplies that may be required.

Objectives

  1. Describe the importance of big data architecture.
  2. Describe big data architectures.
  3. Identify software technologies of big data architecture platforms.
  4. Describe data-related technologies of big data architecture platforms.
  5. Describe storage technologies of big data architecture platforms.
  6. Employ big data architecture platforms for data analysis tasks.
  7. Analyze real-world problems using big data architecture platforms and associated technologies.
  8. Explain the solution.

Content Outline and Competencies:

I. Big Data Architecture

A. Illustrate storing.

B. Demonstrate transforming.

C. Describe analyzing.

II. Architectures

A. Explain data migration.

B. Describe business uses.

III. Software Technologies

A. Discuss open-source software.

1. Data division

2. Data analysis

3. Multiple users

B. Illustrate algorithms.

IV. Data-Related Technologies

A. Practice data retrieval.

B. Practice data analysis.

V. Storage Technologies

A. Show file systems.

B. Demonstrate performance issues and solutions.

C. Practice storage and retrieval of non-relational data.

D. Practice storage and retrieval of relational data.

VI. Data Analysis Using Architecture Platform

A. Use platform technologies for data analysis.

B. Solve case studies.

VII. Real-World Problems

A. Formulate the problem.

B. Collect relevant data.

C. Analyze the data.

D. Apply the techniques of big data architecture platforms and core technologies.

VIII. Solutions

A. Prepare solution documents.

B. Justify solutions orally.

C. Present visualizations.

Method of Evaluation and Competencies:

15-20%    Exams and Quizzes
15-20%    Minimum of 15 Problem Sets
30-40%    Final Project
15-20%    Final Presentation
5%            Updating ePortfolio

Total: 100%

Grade Criteria:

90 – 100% = A
80 – 89% = B
70 – 79% = C
60 – 69% = D
0 – 59% = F

Caveats:

Student Responsibilities:

Disabilities:

JCCC provides a range of services to allow persons with disabilities to participate in educational programs and activities. If you are a student with a disability and if you are in need of accommodations or services, it is your responsibility to contact Access Services and make a formal request. To schedule an appointment with an Access Advisor or for additional information, you may send an email or call Access Services at (913)469-3521. Access Services is located on the 2nd floor of the Student Center (SC 202).