Tools for Data Science PDF

Tools for Data Science  2 Table of Contents 1. Course Overview 2 2. Data Science Task Categories 2 3. Tools Categories 2 4. Open-Source Tools 3 5. Comm...

Tools for Data Science  2 Table of Contents 1. Course Overview 2 2. Data Science Task Categories 2 3. Tools Categories 2 4. Open-Source Tools 3 5. Commercial Tools 3 6. Cloud-Based Tools 3 Key Takeaways 4 1. Python Libraries for Data Science 5 2. APIs (Application Programming Interfaces) 5 3. Data Sets in Data Science 6 4. IBM Data Asset eXchange (DAX) 6 5. Machine Learning Models 6 6. Key Tools for Deep Learning 7 Key Takeaways 7  3 1. Course Overview The course covers essential tools and environments for data science, including libraries, packages, and data sets for machine learning and big data. You'll work with widely used languages like Python, R, and SQL. Tools such as Jupyter notebooks, RStudio, and GitHub will be essential for coding, project management, and collaboration. 2. Data Science Task Categories Data Management: Collecting, storing, and retrieving data efficiently from sources like social media or sensors. Data Integration & Transformation (ETL): Extracting data from different sources, transforming its structure, and loading it into data warehouses. Data Visualization: Using tools to create visual representations (charts, maps, etc.) to make data insights clear. Model Building: Training machine learning models to find patterns in data. Model Deployment: Deploying models into production environments to make data-driven decisions. Model Monitoring: Ensuring model accuracy, fairness, and robustness through continuous monitoring. 3. Tools Categories Code Asset Management: Tools like GitHub for version control, enabling collaborative work on code. Data Asset Management: Platforms that organize and manage data with support for versioning and collaboration. Development Environments: IDEs like Jupyter Notebooks, RStudio, and Apache Zeppelin for writing and testing code. Execution Environments: Cloud-based tools that offer libraries for compiling and executing code, such as Apache Spark and Flink. Fully Integrated Visual Tools: Solutions like IBM Watson Studio that encompass all tasks from data handling to model building.  4 4. Open-Source Tools Data Management Tools: Examples include MySQL, PostgreSQL, MongoDB, and Hadoop. Data Integration Tools: Tools like Apache AirFlow and SparkSQL help in transforming and moving data across systems. Data Visualization Tools: Tools like PixieDust, Kibana, and Apache Superset visualize data. Model Building Tools: TensorFlow, Kubernetes, and Seldon are used to build, deploy, and monitor models. Monitoring Tools: IBM AI Fairness 360 ensures models are accurate, fair, and explainable. 5. Commercial Tools Data Management: Oracle Database, Microsoft SQL Server, and IBM Db2 are widely used. Data Integration: Tools like IBM InfoSphere and Microsoft Integration handle complex data pipelines. Data Visualization: Tableau, Microsoft Power BI, and IBM Cognos Analytics are used for generating business insights through data visualization. Model Building and Deployment: SPSS Modeler and SAS Enterprise Miner are key for model creation, while services like SPSS Collaboration handle deployment. 6. Cloud-Based Tools Fully Integrated Tools: Watson Studio and Microsoft Azure Machine Learning provide complete environments for data science projects. Cloud Data Management: Services like AWS DynamoDB and IBM Db2 Cloud store and manage data. Cloud Data Visualization: Datameer and IBM Cognos help with visualization on the cloud. Model Building and Deployment: Watson Machine Learning and Amazon SageMaker streamline model deployment and monitoring on the cloud.  5 Key Takeaways: Data science involves managing data, building models, and deploying them using a variety of tools. Open-source and commercial tools differ, but both are essential in professional data science work. Cloud platforms have become important for scalable data science solutions. 1. Python Libraries for Data Science Scientific Computing Libraries: Pandas is essential for data cleaning and manipulation, offering powerful structures like DataFrames. Visualization Libraries: ○ Matplotlib is used for customizable charts and graphs. ○ Seaborn is excellent for more complex visualizations such as heat maps and violin plots. Machine Learning Libraries: ○ Scikit-learn is a key library for regression, classification, and clustering tasks. ○ Deep learning is powered by Keras (for quick model building) and TensorFlow (for large-scale deep learning). Other Languages and Libraries: Apache Spark allows cluster computing, supporting Python, R, Scala, and SQL for large-scale data processing.  6 2. APIs (Application Programming Interfaces) Definition: APIs enable communication between software components, allowing integration and interaction between different tools and services. REST API Basics: REST APIs use HTTP methods to handle requests and responses, typically transmitting data in JSON format. Example APIs: Watson APIs like Text to Speech and Language Translator provide advanced features for transcription and translation. Client-Server Communication: APIs abstract backend complexity, making it easier for developers to use services across various languages. 3. Data Sets in Data Science Definition: Data sets are structured collections of information, including tabular, hierarchical, and raw data formats (e.g., images, audio). Data Sources: Open data comes from public entities and platforms like Kaggle, which have democratized access to data for research and development. Licensing: The Community Data License Agreement (CDLA) ensures open data sharing with two licenses: ○ CDLA-Sharing License: Requires modifications to be shared. ○ CDLA-Permissive License: Allows modification without mandatory sharing. 4. IBM Data Asset eXchange (DAX) Overview: DAX is IBM’s open data repository, providing access to high-quality data sets for enterprise-level applications. Key Features: ○ Provides tutorial and advanced notebooks for data processing, machine learning, and statistical analysis. ○ Allows users to explore, download, and integrate data sets into projects like Watson Studio. Use Cases: DAX includes data sets like weather data, which can be explored and analyzed using integrated tools in IBM Watson Studio.  7 5. Machine Learning Models Types of Learning: ○ Supervised Learning: Includes regression (predict numeric values) and classification (predict categories). ○ Unsupervised Learning: Includes clustering (grouping similar data) and anomaly detection (identifying outliers). ○ Reinforcement Learning: Involves learning from trial and error based on rewards. Deep Learning: ○ Emulates the human brain to solve complex problems such as natural language processing (NLP) and image analysis. ○ Requires large datasets and specialized hardware. ○ Tools like TensorFlow, PyTorch, and Keras are used to build and train deep learning models. 6. Key Tools for Deep Learning Frameworks: TensorFlow, PyTorch, and Keras are leading frameworks for building and training models. Pre-Trained Models: These can be accessed from repositories like TensorFlow and PyTorch for faster development. Custom Model Workflow: Steps include collecting data, preparing it, labeling, selecting or building models, training, and deploying. Key Takeaways: Data science relies heavily on powerful libraries (e.g., Pandas, Scikit-learn) for data manipulation, visualization, and machine learning. APIs are crucial for integrating various services and enabling communication between software. Open data, facilitated by initiatives like DAX, plays a significant role in supporting data science research and innovation. Machine learning and deep learning form the backbone of predictive analytics, with frameworks like TensorFlow driving advancements in the field.