1. Data Architects
1.1. Creates blueprints for data management systems to integrate, centralize, protect and maintain data sources
1.2. Needs to be on top of every new innovation in the industry
1.3. Skills & Talents:
1.3.1. Data warehousing solutions
1.3.2. In-depth knowledge of database architecture
1.3.3. Extraction Transformation and Load (ETL), spreadsheet and BI tools
1.3.4. Data Modeling
1.3.5. Systems Development
1.4. Mindset
1.4.1. Inquiring ninja with a love for data architecture design patterns
1.5. Languages
1.5.1. SQL
1.5.2. XML
1.5.3. Hive
1.5.4. Pig
1.5.5. Spark
2. Statisticians
2.1. Mindset
2.1.1. Logical and enthusiastic stats genius
2.2. Role
2.2.1. Collects, analyzes and interprets-qualitative as well as quantitive data with statistical theories and methods
2.3. Languages
2.3.1. R, SQL
2.3.2. SAS
2.3.3. SPSS
2.3.4. Matlab
2.3.5. Stata
2.3.6. Python
2.3.7. Perl
2.3.8. Hive
2.3.9. Pig
2.3.10. Spark
2.4. Skills and talents
2.4.1. Statistical theories & methodology
2.4.2. Data mining & machine learning
2.4.3. Hadoop
2.4.4. SQL and NO SQL
2.4.5. Cloud Tools
3. Data Analyst
3.1. Typical duties
3.1.1. performing initial analysis to assess the quality of the data
3.1.2. performing further analysis to determine the meaning of the data
3.1.3. preparing reports based on analysis and presenting to management
3.2. Skills
3.2.1. A high level of mathematical ability
3.2.2. Programming languages, such as SQL, Oracle and Python
3.2.3. The ability to analyse, model and interpret data
3.2.4. A methodical and logical approach
3.2.5. The ability to plan work and meet deadlines
3.2.6. Written and verbal communication skills
4. Data Engineer
4.1. Data between servers and applications
4.2. Engineering Backgrounds
4.3. Data Structures and architectures
4.4. Core DE Skills
4.4.1. Basic Language Requirement: Python
4.4.2. Basic Machine Learning Familiarity
4.4.3. Solid Knowledge of Operating Systems
4.4.4. Production coding
4.4.4.1. Data Warehousing
4.4.4.2. Heavy, In-Depth Database Knowledge – SQL and NoSQL
4.4.4.3. Database design
4.4.4.4. Data Collection
4.4.4.5. Data transformation
4.5. Responsabilities
4.5.1. Data Ingestion
4.5.2. Pipeline for data collection and storage
4.5.3. Processing
4.5.4. Design, construct, install, test and maintain highly scalable data managment systems
4.5.5. Guidelines and standards
4.5.6. Integrate new data management technologies and software engineering tools into existing structures
4.6. Tools
4.6.1. Hadoop
4.6.2. MapReduce
4.6.3. Apache Spark
4.6.4. JS
4.6.5. HIVE
4.6.6. Kafka
4.7. Career Path
4.7.1. Data Engineer
4.7.2. Senior Data Engineer
4.7.3. BI Architect
4.7.4. Data Architect
4.8. Soft Skills
5. Data Scientist
5.1. Build Models
5.1.1. Statistics
5.1.2. Mathematics
5.1.3. Machine Learning
5.2. Responsabilities
5.2.1. Develop and plan required analytic projects in response to business needs
5.2.2. Contribute to data mining architectures
5.2.3. Modeling standards
5.2.4. Reporting
5.2.5. Data Analysis methodologies
5.2.6. Collaborate with stakeholders to integrate data mining results
5.3. Skills
5.3.1. Programming
5.3.2. Mathematics
5.3.3. Business Understanding
5.3.4. Statistics
5.3.5. Data Visualizacion
5.3.6. Machine learning
5.3.7. Attention to detail
5.4. Tools
5.4.1. Python
5.4.2. Spark
5.4.3. tableau
5.5. Career Path
5.5.1. Junior Data Scientist
5.5.2. Data Scientist
5.5.3. Senior Data Scientist
5.5.4. Chief Data Scientist