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Course Outline
Introduction
- The Data Science Process
- Roles and responsibilities of a Data Scientist
Preparing the Development Environment
- Libraries, frameworks, languages, and tools
- Local development setups
- Collaborative web-based development environments
Data Collection
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Types of Data
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Structured Data
- Local databases
- Database connectors
- Common formats: xlsx, XML, JSON, CSV, ...
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Unstructured Data
- Clicks, sensors, smartphones
- APIs
- Internet of Things (IoT)
- Documents, images, videos, audio
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Structured Data
- Case Study: Continuously collecting large volumes of unstructured data
Data Storage
- Relational databases
- Non-relational databases
- Hadoop: Distributed File System (HDFS)
- Spark: Resilient Distributed Dataset (RDD)
- Cloud storage solutions
Data Preparation
- Ingestion, selection, cleansing, and transformation
- Ensuring data quality: accuracy, relevance, and security
- Exception reporting
Languages Used for Preparation, Processing, and Analysis
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R Language
- Introduction to R
- Data manipulation, calculations, and graphical displays
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Python
- Introduction to Python
- Manipulating, processing, cleaning, and analyzing data
Data Analytics
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Exploratory Analysis
- Basic statistics
- Draft visualizations
- Gaining data understanding
- Causality
- Feature engineering and transformations
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Machine Learning
- Supervised vs. unsupervised learning
- Model selection criteria
- Natural Language Processing (NLP)
Data Visualization
- Best practices
- Selecting the appropriate chart for the data
- Color palettes
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Advancing visualization techniques
- Dashboards
- Interactive visualizations
- Data storytelling
Summary and Conclusion
Requirements
- A general understanding of database concepts
- A basic understanding of statistics
35 Hours
Testimonials (1)
workshops, practical examples
