Corporations have dramatically increased investments in their "digital enterprise" in the past few years. It has been estimated that by 2020, IT departments will be monitoring 50 times more data than they are today. This tidal wave of data is driving unprecedented demand for those with the skills required to manage and leverage these very large data sets into a competitive advantage.
Curriculum is designed to help meet the expanding needs for data scientists who are skilled in the utilization of a unique blend of science, art and business. These professionals understand how to automate methods of collecting and analyzing data and utilize techniques to discover previously hidden insights that can profoundly impact the success of any business.
Understand the skills needed to effectively collect and manage big data, perform data-driven discovery and prediction, and extract value and competitive intelligence for your organization. This program provides the skills required to become a data scientist and provides existing data analysts with opportunities to broaden skills.
Learn topics such as: utilizing concepts in on- and off-cloud; scalable data engineering (inspecting, cleaning, transforming, and modeling data), unstructured data and NoSQL; computational statistics; pattern recognition; data mining /predictive analytics; machine learning; data visualization; and high performance software and hardware.
Who Should Enroll
This program is intended for professionals in a variety of industries and job functions who are looking to help their organization understand and leverage the massive amounts of diverse data they collect. Others who would benefit from this program include: data engineers, data analysts, computer scientists, business analysts, database administrators, researchers, and statisticians.
Occupational summary for software developers, applications in the United States.
- Jobs: 801,667 (2016)
- Projected Growth: 18.40% (2017-2027)
- Annual Salary: $88k-$141k (25th-75th Percentile)
- Learn from industry experts how to utilize a combination of science, art & business techniques to deliver new insights and competitive intelligence
- Describe the phases of the analytics lifecycle
- Utilize a variety of statistical and computer science tools and techniques to analyze data
- Describe and use the typical tools and technologies required to model and analyze large (big) datasets
- Explain the use of typical tools to explore data (R, STATISTICA, Hadoop, etc.)
- Utilize an inquisitive "hacker" mentality to uncover new meaning from existing data
- Effectively design, model and manage databases
- Describe and utilize unstructured and structured data sets leveraging text analytics tools.
- Define requirements, develop an architecture, and implement a data warehouse plan
- Introduction to Data Science (3 units)
- Data Exploration, Analytics and Visualization (3 units)
Elective Courses (Minimum 9 units)
- Math Review for Data Science and Analytics (2.5 units)
- Business Intelligence/Data Warehouse (3 units)
- Introduction to Big Data (2 units)
- Big Data Analytics (2 units)
- Effective Data Preparation (2 units)
- Introduction to Programming with Python (2 units)
- Introduction to Python for Data Analysis (1.5 units)
- Data Modeling (2.5 units)
- Data Management (2.5 units)
- Tools and Techniques for Machine Learning (2 units)
- R Programming (2 units)