Data Engineering Specialisation

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Data Engineering Specialisation создатель Mind Map: Data Engineering Specialisation

1. Use Cases

1.1. Business Intelligence

1.2. Forecasting

1.3. Dashboards

1.4. Real-time Fraud Detection

1.5. Clickstream Analytics

1.6. Text M;ining

1.7. Process Mining

1.8. Recommender Systems

1.9. Sentiment Analysis

1.10. NOGO Areas

1.10.1. Image Processing

1.10.2. Speech Processing

1.10.3. Physical Models

1.10.4. Sensor Programming

2. Tools

2.1. Analytics Clients

2.1.1. Jupyter

2.1.2. RapidMiner

2.1.3. R Studio

2.1.4. Power BI

2.1.5. Disco

2.1.6. ProM

2.2. Server Tools

2.2.1. Pentaho Kettle

2.2.2. MS SQL Server

2.2.3. MS Analytics Servcies

2.2.4. Talend

2.2.5. etc. etc.

3. Programming Languages

3.1. Python

3.2. R

3.3. Scala

4. 6x 4 Credit Modules

4.1. DE 1 - Morning

4.1.1. Infrastructure 1

4.1.1.1. Formats and Interfaces

4.1.1.2. Data Profiling

4.1.1.3. ETL Processes

4.1.2. Infrastructure 2

4.2. DE 1 - Noon

4.2.1. Methods 1

4.2.2. Programming 1: Python Ecosystem

4.2.3. Analytics Tools 1

4.3. DE 2 - Morning

4.3.1. Data Sources 1

4.3.1.1. Web

4.3.1.2. IOT

4.3.1.3. Text

4.3.1.4. Image

4.3.2. Large Use Cases 1

4.4. DE 2 - Noon

4.4.1. Methods 2

4.4.2. Analytics Tools 2

4.4.3. Programming 2: Scala

4.5. DE 3 - Morning

4.5.1. Data Sources 2

4.5.2. Large Use Cases 2

4.6. DE 3 - Noon

4.6.1. Methods 3

4.6.2. Analytics Tools 3

5. Competencies

5.1. BSc

5.1.1. Knowledge about relevant Computing Infrastructure

5.1.2. Operation of Computing Infrastructure

5.1.3. Basic Analysis Skills

5.1.4. Usage of Formats and Interfaces

5.2. MSc

5.2.1. Advanced Analysis Skills

5.2.2. Definition of Formats and Interfaces

6. Other Languages

6.1. SQL

6.2. GraphQL

6.3. OpenAPI

7. Models and Algorithms

7.1. Neural Networks

7.2. ARMA

7.3. Backprogagation

7.4. Gradient Methods

7.5. etc.

7.6. etc.

7.7. etc.