Data Science Tools and Techniques Quiz

Course Overview

• The course covers data science tools and environments, including libraries, packages, and datasets for machine learning and big data
• Students will work with languages like Python, R, and SQL
• Essential tools such as Jupyter notebooks, RStudio, and GitHub are important for development, project management, and collaboration

Data Science Task Categories

• Data Management: Efficient collection, storage, and retrieval of data
• Data Integration & Transformation (ETL): Extracting data from various sources, restructuring it, and loading it into data warehouses
• Data Visualization: Creating charts, maps, etc. to understand data insights
• Model Building: Training machine learning models to identify patterns
• Model Deployment: Deploying models into production environments for data-driven decisions
• Model Monitoring: Ensuring accuracy, fairness, and robustness of models through continuous monitoring

Tools Categories

• Code Asset Management: Tools like GitHub for version control and collaborative coding
• Data Asset Management: Platforms for organizing and managing data, supporting versioning and collaboration
• Development Environments: IDEs like Jupyter Notebooks, RStudio, and Apache Zeppelin for coding and testing
• Execution Environments: Cloud-based tools like Apache Spark and Flink for code execution
• Fully Integrated Visual Tools: IBM Watson Studio for handling all data tasks from data handling to model building

Open-Source Tools

• Data Management: MySQL, PostgreSQL, MongoDB, Hadoop
• Data Integration: Apache Airflow, SparkSQL
• Data Visualization: PixieDust, Kibana, Apache Superset
• Model Building: TensorFlow, Kubernetes, Seldon
• Monitoring: IBM Al Fairness 360 ensures model accuracy, fairness, and explainability

Commercial Tools

• Data Management: Oracle Database, Microsoft SQL Server, IBM Db2
• Data Integration: IBM InfoSphere, Microsoft Integration
• Data Visualization: Tableau, Microsoft Power BI, IBM Cognos Analytics
• Model Building & Deployment: SPSS Modeler, SAS Enterprise Miner, SPSS Collaboration

Cloud-Based Tools

• Fully Integrated Tools: Watson Studio, Microsoft Azure Machine Learning
• Cloud Data Management: AWS DynamoDB, IBM Db2 Cloud
• Cloud Data Visualization: Datameer, IBM Cognos
• Model Building & Deployment: Watson Machine Learning, Amazon SageMaker

Key Takeaways

• Data science involves managing, modelling, and deploying data using different tools
• Open-source and commercial tools are both essential for professional data science work
• Cloud platforms are crucial for scalable data science solutions

Python Libraries for Data Science

• Scientific Computing Libraries: Pandas for data manipulation and structure (DataFrames)
• Visualization Libraries: Matplotlib for charts and graphs, and Seaborn for complex visualizations
• Machine Learning Libraries: Scikit-learn for regression, classification, and clustering; Keras and TensorFlow for deep learning
• Other Languages & Libraries: Apache Spark for large-scale data processing, using Python, R, Scala, and SQL

APIs (Application Programming Interfaces)

• Enable communication between software components
• REST APIs handle requests and responses, often using JSON format
• Examples include Watson APIs (Text to Speech, Language Translator)
• They abstract backend complexity

Data Sets in Data Science

• Structured collections of information (tabular, hierarchical, raw)
• Sources include open data platforms like Kaggle
• Licensing includes CDLA (Community Data License Agreement) with sharing and permissive licenses

IBM Data Asset eXchange (DAX)

• IBM's open data repository for high-quality data sets
• Includes tutorials, notebooks, and tools for integrating data into projects
• Data like weather data is available for exploration

Machine Learning Models

• Types of Learning: Supervised (regression, classification), Unsupervised (clustering, anomaly detection), Reinforcement
• Deep Learning: Emulates the human brain for tasks like NLP and image analysis
• Requires large datasets, specialized hardware, and frameworks like TensorFlow, PyTorch

Key Tools for Deep Learning

• Frameworks: TensorFlow, PyTorch, Keras for model building and training
• Pre-trained Models: Access to pre-built models from repositories for faster development
• Custom Models: Steps to prepare data, label, build, train, and deploy custom models.

###Key Takeaways (General Summary)

• Modern data science relies on strong libraries like Pandas, Scikit-learn, and TensorFlow for powerful manipulation, visualization, enabling communication between various modules.
• Open-source and commercial data tools are indispensable for professionals.
• Cloud services like AWS and IBM have become indispensable in tackling scalable data science tasks and for leveraging services for large scale machine learning solutions.