UniLI Curriculum Bridge Research - Data Science

1. Industry Topics

1.1. Hadoop Ecosystem

1.2. R

1.3. Strong Analytics Skills

1.4. NoSQL Databases

1.5. SAS Software Experience

1.6. Machine Learning

1.7. Unstructued Data analysis

1.8. Python programming

1.9. Hands-on Experience

1.10. Visualization

1.11. SAP Hana

1.12. SQL

1.13. Data management tools

1.14. Work with Databases

1.15. Data mining

1.16. Data handling

1.17. Communication and presentation

1.18. MapReduce

1.19. Java

1.20. Cloud

1.21. Research

1.22. Statistics

1.23. Cassandra

1.24. MatLab

1.25. Relevant Technical Skills

2. Relationships

2.1. Cloud

2.1.1. AWS Educate - https://aws.amazon.com/education/awseducate/

2.1.2. Azure Education - https://azure.microsoft.com/en-us/community/education/

2.2. Database

2.2.1. Mongo University - https://university.mongodb.com/training

2.3. Platform

2.3.1. SAS  Academy for Data Science - http://www.sas.com/en_us/learn/academy-data-science.html

2.3.2. SAP University Alliance - http://go.sap.com/training-certification/university-alliances.html#

2.3.3. Google for Education - https://www.google.com/edu/higher-education/

3. Relevant Certifications

3.1. MapR Hadoop Certifications - https://www.mapr.com/services/mapr-academy/certification

3.2. SAS Certified Data Scientist = http://www.sas.com/en_us/learn/academy-data-science/data-science-certification.html

3.3. Microsoft Professional Program Certificate in Data Science - https://academy.microsoft.com/en-us/professional-program/data-science/

4. Business Statistics

5. Decision Theory

5.1. Introduction to decision making under certainty and risk

5.2. Designing decisions on websites

5.3. Emotions in decision making

5.4. Biases in decision making

5.5. Heuristics in decision making

5.6. Measuring and modeling individual risk preferences

6. System Analysis & Design

6.1. Programming with Django (Python)

7. Data Management

7.1. Modern data management requirements

7.2. Database system architecture

7.3. Diagnosing and handling data quality problems

7.4. Relational databases (SQL)

7.5. Hands-on labs with MySQL

7.6. Concurrency control techniques

7.7. NoSQL databases (e.g., MongoDB)

7.8. Apache Hadoop (HDFS, MapReduce)

8. Data Mining & Predictive Analysis

8.1. Supervised Learning (regression and classification)

8.2. Unsupervised Learning

8.3. Text Mining, sentiment analysis

8.4. R. R-Studio