COURSE CATALOG

COURSE CATALOG

About the ODSA

About the Omaha Data Science Academy

As data scientists, the founders of the Omaha Data Science Academy (ODSA) have been part of the data science community since D.J. Patil and Jeff Hammerbacher coined the term in 2008. In the beginning, individuals and companies struggled; the field was so new there wasn’t structure or clear leadership around how to do projects, who should do them, how to add this capability to companies, or even why capability should be added.

There was little talent and even fewer who could help companies understand how to measure talent. There were no peers to support new data scientists or mentors to lead those entering the field. Data scientists were islands in their own companies, trying to find knowledge the best they could.

In 2016, a group of data scientists in Omaha decided to build an institution to give direction to the data science community and to help Omaha’s companies compete nationally and globally. They formed the ODSA to generate the peers, mentors, and professionals–in short, the data science community–Omaha needs.

 

Mission:

For the data science community to succeed long term, the community itself has to be sustainable. The ODSA was created to aid with that mission. It ensures that:

  • Companies with data science teams understand how to use and implement data science into their organizations successfully;
  • Individual data scientists’ training is relevant, useable from the start, and continuously developed over time; and
  • The data science community is sustained and grown through peer and mentor networks, meetups, conferences, and career development opportunities such as job boards, internships, and placement assistance.

Only by having companies, individuals, and the wider community work together can data science become a thriving, lucrative field that will help our city compete globally.

The ODSA’s mission to ensure data science is accessible means there is a path into the field for any interested person who is willing to put in the time and effort to train. The academy also is committed to growing the data science community within the greater Omaha metropolitan area so individuals are supported during the whole of their careers.    To ensure the ODSA is relevant in the mission, the ODSA:
  • Makes sure students learn from actual data science practitioners. 

The best way to learn is directly from practicing data scientists. Many times our professors have said things like, “Let me show you a model I used for a client earlier today…” The knowledge you’ll gain is relevant; our professors are working with local and national companies right now. The tools you’ll learn on are tools being used by companies in Omaha currently. This makes you instantly more qualified than those who are studying data science theories.

  • Not only launches your career, but helps guide it over time. 

It’s not enough to gain the skills you need to land your first job as a data scientist; you also need to stay relevant and satisfied with your work to advance in your career. In short, you need a supportive data science community.

OSDA helps you achieve this through:

  • Opportunities for more training
  • Quarterly meetups
  • Yearly conference
  • An online community channel
  • Real world mentors
  • Peer support
  • We help the companies you might work for be successful in their data science endeavors. 

Because the OSDA’s for-profit partner, Contemporary Analysis (CAN), helps companies build out their data science capabilities and provides the OSDA with instructors who are practicing in their field, CAN ensures the continued viability of Omaha company’s data science capabilities both now and for the future.

The ODSA is invested in your short-term and long-term success. That’s why we provide:
  • Functional Knowledge, taught by practicing professionals, in the areas of:
    • Data Science Programming
    • Data Manipulation and Management
    • Data Visualization
    • Data Science Modeling including Machine Learning and AI
    • Data Engineering
    • Data Science Management
  • Understanding that a job in data is a continual learning process, including:
    • How to continue to learn as a data scientist
    • How to do data science when traditional knowledge doesn’t yield good results
  • Mentor Network
    • Contemporary Analysis
    • Professors
    • Presenters
    • Community-focused activities (meetups, conferences, etc.)
  • Peer Network
    • Classmates
    • Alumni Network
    • Practicing Professionals
    • Omaha Data Scientists Users Group
  • Career Guidance–while we don’t guarantee employment, we do offer:
    • Continuing Education Opportunities
    • Local Job Board Access
    • Resume and LinkedIn update assistance

Entry-Level Certificates

  • Data Visualization Certificate
    • Tableau 4 weeks – Feb 5 – Mar 6
    • Tableau 4 weeks – June 8 – June 18
    • Tableau 4 weeks – Sept 16 – Oct 9
    • Power BI 4 weeks – TBD
  • Business Intelligence Certificate
    • 12 weeks – Feb 5 – May 8
    • 12 weeks – June 3 – July 30
    • 12 weeks – Sept 16 – Dec 18
  • Machine Learning/AI Certificate    
    • 12 weeks – Feb 5 – May 8
    • 12 weeks – Sept 16 – Dec 18
  • Data Engineering Certificate
    • 8 weeks – On Demand
  • Fundamentals of Data Science Certificate (2 trimesters) 
    • Feb 5 – May 8 
      OR
    • June 3 – July 30 AND Sept 16 – Dec 18

Advanced Certificates

  • Data Science Management (DS Mgmt 505)
    • 2 days–On-Demand

Individual Skill Modules

  • Fundamentals of Data Visualization (Tableau 101)
    • February 5 – March 6
    • June 8 – June 18
    • September 16 – Oct 9
  • Basic Model Building (Model 202)
    • February 5 – March 6
    • September 16 – Oct 9
  • Data Manipulation and Management (SQL101)
    • March 11 – April 3
    • June 24 – July 30
    • October 21 – November 18
  • Power BI (PBI 101)
    • TBD
  • Mathematics of Model Evaluation (Eval 202)
    • March 11 – April 3
    • October 21 – November 18
  • Introduction to Python (Python 101)
    • April 15 – May 8
    • July 15 – July 30
    • November 25 – December 18
  • Data Engineering (DBA 101)
    • TBD
  • Advanced Modeling in Python (Python 202)
    • TBD
  • API & Cloud Database (Data 202)
    • April 15 – May 8
    • November 25 – December 18

Entry-Level Certificates Price
  • Data Visualization Certificate $1,650
  • Business Intelligence Certificate$4,650
  • Machine Learning/AI Certificate$4,650
  • Data Engineering Certificate$3,000
  • Fundamentals of Data Science Certificate$9,400
Advanced Certificates
  • Data Science Management$2,500
Individual Skill Modules
  • Fundamentals of Data Visualization (Tableau 101)$1,650
  • Basic Model Building (Model 202)$1,650
  • How to Use Excel (Excel 101)$1,650
  • Data Manipulation and Management (SQL101)$1,650
  • Power BI (PBI 101)$1,650
  • Mathematics of Model Evaluation (Eval 202)$1,650
  • Introduction to Python (Python 101)$1,650
  • Data Engineering (DBA 101)$1,650
  • Advanced Modeling in Python (Python 202)$1,650

Certificates and Courses

Job Title Readiness Certificates

The ODSA prepares a person for a specific job by teaching specific individual skill modules in combinations reflecting common duties of a specific job title. We do this by using practicing professionals who teach you the skills they use every day. Below are Job Titles and their corresponding certificates. 

A Data Visualist translates complex statistics and data so that business users can better understand them and make data-driven decisions by looking at them.

Common Job Title(s): Data Visualist

Common Job Duties:

  • Manage datasets
  • Be proficient with data visualization software
  • Understand the data’s audience and purpose
  • Choose the right visualization
  • Make visualization easy to read

Required Completed Certifications to Enroll:

  • None

Skill Modules learned in this Certificate:

  • Fundamentals of data visualization (Tableau 101) or Power BI (PBI 101)

Duration: 24 hours. Taught 3 hours per night, 2 times per week for 4 weeks

Cost: $1,650.

Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.

Common Job Title(s): Data Analyst, Business Intelligence Analyst, Customer Analyst, Research Analyst, or Marketing Analyst

Common Job Duties:

  • Find and collect data using programming (Python or equivalent)
  • Store, clean, and prepare data using SQL
  • Analyze data for insights
  • Create data visualization
  • Present findings to stakeholders

Required Completed Certifications to Enroll:

  • Data Visualization

Skill Modules learned in this Certificate:

  • Introduction to Python (Python 101)
  • Data manipulation and management (SQL101)

Duration: 72 hours. Taught 3 hours per night, 2 times per week for 14 weeks (1 week breaks between modules).

Cost: $4,650 – $3,000 plus Data Visualization Certificate Cost ($1,650)

A database engineer creates and manages databases for an organization. This involves designing, building, and configuring the database. This also involves monitoring security, setup and maintaining software, and products related to data movement and usage, as well as being the administer of usage rights to the database. 

Common Job Title(s): Data Engineer

Common Job Duties:

  • Understand and install Physical Schema designs for a given Database.
  • Database install, patching, high-availability & disaster recovery design, interaction with network and system administrators.
  • Debugging and optimization skills
  • Extract, Transform, and Load Data

Required Completed Certifications to Enroll:

  • None

Skill Modules learned in this Certificate:

  • Data Manipulation and Management (SQL101)
  • Data Engineering (DBA 101)

Duration: 48 hours. Taught 3 hours per night, 2 times per week for 9 weeks (1 week break between modules). 

Cost: $3,000 

A Data Scientist builds analytics tools that utilize a company’s data to provide proactive insights into customer acquisition, operational efficiency, and other key business performance metrics.

Common Job Title(s): Data Scientist, ML and or AI Programmer/Specialist/Engineer

Common Job Duties:
  • Work with product owners, management staff, and/or customers to understand problems they are having
  • Work with data engineers, data analysts, programmers, and data visualists to help find and maintain data to be used in modeling
  • Build data models including model selection, model interpretation, and model management
  • Present models to staff, management, and users
  • Help implement and maintain those models in the enterprise
Required Completed Certifications to Enroll:
  • None; however, a working knowledge of business intelligence (programming and SQL) is needed.
Skill Modules learned in this Certificate:
  • Basic Model Building (Model 202)
  • Mathematics of Model Evaluation (Eval 202)
  • Advanced Modeling in Python (Python 202)

Duration:  72 hours. Usually taught 3 hours per night, 2x per week; 12 weeks of training over 14 weeks.

Cost: $4,700

The Fundamentals of Data Science Certification Data Science is wholly different from many professions since it requires command of four distinct pillars of knowledge (Programming, Database, Data Visualization, and Machine Learning) each its own discipline.

This additional certification, conferred upon students when they have completed both the Business Intelligence Certificate and the ML/AI Certificate,  acknowledges the significant time and effort spent learning how these pillars of knowledge work together to solve problems only data science can solve.

Common Job Title(s): Data Scientist

Common Job Duties:
A data scientist’s job is to predict business outcomes so leadership can make proactive decisions and change the outcomes to their advantage e.g., customer churn, cross-sell, up-sell, re-sell, or forecasting.

They do this by:

  • Creating and managing repositories for data inside the organization
  • Manipulate that data to better analyze.
  • Build and manage models to predict outcomes
  • Research potential issues and insight inside the data
  • Present findings to non-technical leadership and/or users about their findings
  • Implement those models/findings into the enterprise for automation and use at scale

The Fundamentals of Data Science Certificate is conferred upon students after having completed the following three certifications:

  • Data Visualization
  • Business Intelligence
  • Machine Learning/AI

Cost: There is no additional cost for this certification. The cost to attain the Data Visualization, Business Intelligence and the ML/AI certifications is: $9,400.

*This is an advanced certification for those in Data Science who are planning to or already are managing Data Scientists.

 

A Data Science Manager manages the design and implementation of big data solutions for the organization. This person also oversees the team responsible for predictions, models, visualizations, APIs, and databases associated with the models.  Data Science Managers typically report to C-Suite leadership.

Common Job Title(s): Data Science Manager, Big Data Analytics Manager

Common Job Duties:

  • Those of a Data Scientist plus:
    • Manage Data Science team, including staff development
    • Oversee model selection, model implementation, and management
    • Present findings to C-Level executives.

Required Completed Certifications to Enroll:

  • Must be in the role of a Data Scientist (or equivalent) with aspirations of future management or has been awarded the Fundamentals of Data Science certification.

Skill Modules learned in this Certificate:

  • Data Science Management (DS Mgmt 505)

Duration: 16 hours. Taught as a 2-day, onsite class, or as 3 hours per night, 2 times per week for 3 weeks. 

Cost: $2,500

Individual Skill Modules

Individual skill module combinations are what makes up job readiness certificates. An individual skill module may be taken if the student only needs a specific skill or skills, but not a certificate.

Note: Students completing individual skill modules will not receive a job readiness certificate (minus Tableau 101 and Power BI 101 that do lead to a Data Visualization Cert)

Individual skill modules:

  • Last 3 hours per night, 2 times per week for 4 weeks (24 hours total)
  • Cost $1,650 each unless otherwise stated
  • May be reduced in cost using state aid
In this class, students will be introduced to some of the major concepts of Data Science (Python Programming, Database Management, Modeling, and Data Visualization) and some of the tools used in the profession. The tools include a crash course in the basics of programming, data structures and object oriented design, basic web development, Jupyter Notebooks, GitHub, and web scrapers, as well as functional programming concepts and key Python libraries (Numpy and Pandas). This module, taken individually, does not earn a student a certificate.
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Data science has gone from needing to know how to code to most modeling techniques having standardized libraries that can be pasted into a program. This means that one may do data science without understanding what the models mean or actually do. This class will drill into how to program models the traditional way. Students will use Word2Vec to scrape, debug, and enhance data science models. They also will learn how to use Python to solve other gaps such as calculations, other data manipulation, and random number population. This module, taken individually, does not earn a student a certificate.
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In this class, students will learn the fundamentals of expressing data visually using Tableau. We will teach you data design and how humans digest data–specifically, the fundamentals of data visualization design and construction, as well as best practices needed to implement them. This class uses Tableau, an industry-wide benchmark for quality visualization tools. 

Power BI is a major tool for most Data Analysts to use in Data visualization, especially when the data needs significant preparation prior to visualization. This class will teach how to visualize data using Power BI and how to collect, arrange, and prepare data for visualization.

This class teaches a student how to store and transform data specifically to be used in modeling. Students will learn database design, SQL queries, different schemas, data cleaning techniques, and data appending. The class also will introduce a tool called Dataiku, a data platforming tool used for easier data engineering and visual/drag and drop data science. This module, taken individually, does not earn a student a certificate.
Data Engineering is quickly becoming a highly sought skill. A Data Scientist’s best friend, a Data Engineer not only designs and manages the data sources Data Scientists use to run their algorithms, but they also extract, transform, and load the data; manage the APIs; and are usually in charge of the data and data science toolsets. This class teaches those skills as well as how to manage data flows and work with the data team to sustain modeling in the enterprise. Note: This module, taken individually, does not earn a student a certificate.
Fundamentally, data science is using statistics and economic modeling to predict what is likely to happen next. This class will teach the student the fundamentals of how to build common algorithms inside of an industry-leading data science platform called Dataiku. This will include the basics of model evaluation, choosing target variables and characteristics, and basic machine learning. This module, taken individually, does not earn a student a certificate. This module, taken individually, does not earn a student a certificate.

This class will dive into the metrics behind evaluating an analytics model’s performance using F1, Accuracy, Precision, Recall, AUC, Cost matrix, and Cumulative Lift. Students also will learn to show the steps to building, testing, evaluating, adjusting/rebuilding, re-testing, and re-evaluating a model. Finally, students will learn which model to use, avoiding the pitfalls of just using accuracy as an indicator. This module, taken individually, does not earn a student a certificate.

Cloud Infrastructure and API Integration for Data Science focuses on leveraging the power of cloud platforms for scalable data science applications. It also covers the importance of APIs for data retrieval, sharing, and integration. Students will understand how to build, deploy, and manage data science pipelines in cloud environments. This module, taken individually, does not earn a student a certificate.

Data Scientists are a new type of employee and require a new type of leadership, understanding, and management. Taught in workshop format alongside the book, “Leading a Data Driven Organization – A Practical Guide to Transforming Yourself and Your Organization to Win the Data Science Revolution,” this class walks through the concepts of data science to equip students to succeed as leaders in the field.

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OMAHA DATA SCIENCE ACADEMY

2112 N.30th Street
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