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Course Outline
Introduction
- The Data Science Lifecycle
- Roles and responsibilities of a Data Scientist
Setting Up the Development Environment
- Libraries, frameworks, languages, and tools
- Local development setup
- Collaborative web-based development
Data Acquisition
-
Types of Data
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Structured
- Local databases
- Database connectors
- Common formats: xlxs, XML, Json, csv, ...
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Unstructured
- Clicks, sensors, smartphones
- APIs
- Internet of Things (IoT)
- Documents, pictures, videos, sounds
-
Structured
- Case study: Continuously collecting large volumes of unstructured data
Data Storage Solutions
- Relational databases
- Non-relational databases
- Hadoop: Distributed File System (HDFS)
- Spark: Resilient Distributed Dataset (RDD)
- Cloud storage
Data Preparation
- Ingestion, selection, cleansing, and transformation
- Ensuring data quality - correctness, meaningfulness, and security
- Exception handling and reporting
Languages for Preparation, Processing, and Analysis
-
R language
- Introduction to R
- Data manipulation, calculation, and graphical display
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Python
- Introduction to Python
- Manipulating, processing, cleaning, and crunching data
Data Analytics
-
Exploratory analysis
- Basic statistics
- Draft visualizations
- Gaining insights from data
- Causality
- Features and transformations
-
Machine Learning
- Supervised vs unsupervised learning
- Selecting the appropriate model
- Natural Language Processing (NLP)
Data Visualization
- Best practices
- Choosing the right chart for the data
- Color palettes
-
Enhancing visualization
- 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
