1. Data Acquisition
1.1. Purpose
1.1.1. Acquire new data sources and define new metrics that support current reporting and analysis needs as well as identify new elements to be incorporated to the larger Enterprise Data Warehouse
1.2. Tools
1.2.1. Hadoop / Impala / Shark
1.2.2. Python
1.2.3. R
1.2.4. Tableau
1.3. Skillsets
1.3.1. ETL
1.3.2. Stats
1.3.3. Data Architecture
2. Advanced Analytics
2.1. Purpose
2.1.1. Conduct scientific research using data to discover unknown questions previously not considered. Develop hypothesis and test theories as answers to previously unknown questions.
2.2. Tools
2.2.1. R
2.2.2. Database platforms
2.2.3. Tableau
2.3. Skillsets
2.3.1. Stats
2.3.2. Data Visualization
2.3.3. Data Science
3. Data Warehousing
3.1. Purpose
3.1.1. Develop reliable, complete, and accurate data structures for long-term analysis and business continuity
3.2. Tools
3.2.1. MySQL / PostgreSQL / Vertica
3.2.2. Python / PHP
3.2.3. Tableau
3.2.4. Data Modeling Tools
3.3. Skillsets
3.3.1. Dimensional Modeling
3.3.2. ETL
3.3.3. Physical Data Architecture
3.3.4. Query Tuning
3.3.5. Data Visualizations
4. Data Analytics
4.1. Purpose
4.1.1. Provide thorough and accurate comprehension of company objectives. Work closely with stakeholders to identify new opportunities for competitive advantage and research the potential success thereof.
4.2. Tools
4.2.1. R
4.2.2. Tableau
4.2.3. D3
4.2.4. ProcessingJS
4.2.5. Predixion
4.3. Skillsets
4.3.1. Information Display Techniques
4.3.2. Data manipulation and querying
4.3.3. Statistics (light)
4.3.4. Coding (javascript, etc.)
4.3.5. Hacker mentallity
4.3.6. Curiosity about how the world works