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Course Outline
Introduction
- The Data Science Lifecycle
- Roles and responsibilities within Data Science
Setting Up the Development Environment
- Libraries, frameworks, programming languages, and tools
- Local development setups
- Collaborative, web-based development environments
Data Collection
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Various Data Types
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Structured Data
- Local databases
- Database connectors
- Standard formats: xlsx, XML, JSON, CSV, etc.
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Unstructured Data
- Clickstream data, sensors, smartphones
- Application Programming Interfaces (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-based storage solutions
Data Preparation
- Ingestion, selection, cleansing, and transformation processes
- Ensuring data quality—focusing on accuracy, relevance, and security
- Generating exception reports
Languages for Preparation, Processing, and Analysis
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R Language
- Introduction to R
- Data manipulation, calculations, and graphical representation
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Python
- Introduction to Python
- Manipulating, processing, cleaning, and analyzing data
Data Analytics
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Exploratory Data Analysis
- Fundamental statistics
- Creating preliminary visualizations
- Gaining deeper understanding of the dataset
- Establishing causality
- Feature engineering and transformations
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Machine Learning
- Supervised vs. unsupervised learning
- Guidelines for selecting the appropriate model
- Natural Language Processing (NLP)
Data Visualization
- Industry best practices
- Matching the right chart type to the data
- Effective use of color palettes
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Advancing visualization techniques
- Interactive dashboards
- Dynamic visualizations
- Data storytelling
Summary and Conclusion
Requirements
- A foundational understanding of database concepts
- Basic knowledge of statistics
35 Hours
Testimonials (1)
workshops, practical examples
