Data Science Methodology - Fall 2024 PDF

Data Science Methodology Prof. Dr. Mohamed Kholief ([email protected]) Fall 2024  Lecture 2: Understanding the Data Science Pipeline Business Understanding, Data Acquisition, and CRISP-DM Learning Objectives  Gr...

Data Science Methodology Prof. Dr. Mohamed Kholief ([email protected]) Fall 2024  Lecture 2: Understanding the Data Science Pipeline Business Understanding, Data Acquisition, and CRISP-DM Learning Objectives  Grasp the phases of business understanding and data understanding.  Understand different data acquisition methods and data types.  Learn about the CRISP-DM methodology and its significance in data science projects.  Recognize the importance of aligning data science projects with business goals. Recap of Lecture 1  Brief overview of Data Science Methodology.  Data Science Life Cycle stages introduced.  Importance of problem formulation and the role of a data scientist. Business Understanding Phase  Definition: Translating business objectives into data science goals.  Key Activities:  Identifying business problems and opportunities.  Defining project objectives and success criteria.  Assessing the feasibility of the project.  Importance: Ensures that data science efforts are aligned with organizational goals. Data Understanding Phase  Definition: Initial exploration and analysis of data to understand its quality and relevance.  Key Activities:  Data collection from various sources.  Data exploration to identify patterns, anomalies, and relationships.  Assessing data quality and completeness.  Importance: Provides insights into the data's suitability for addressing the business problem. Data Acquisition Methods  Primary Methods:  APIs (Application Programming Interfaces): Accessing data from web services.  Web Scraping: Extracting data from websites.  Databases: Querying structured data from SQL/NoSQL databases.  Public Datasets: Utilizing available datasets from repositories (e.g., Kaggle, UCI).  Considerations:  Data accessibility and permissions.  Data format and structure.  Frequency of data updates. Structured vs. Unstructured Data  Structured Data:  Organized in fixed formats (e.g., tables, spreadsheets).  Easily searchable and analyzable.  Examples: SQL databases, CSV files.  Unstructured Data:  No predefined format or organization.  Requires more processing to extract meaningful information.  Examples: Text documents, images, videos, social media posts.  Semi-Structured Data:  Contains some organizational properties (e.g., JSON, XML). Challenges in Data Acquisition  Data Quality Issues:  Missing values  Inconsistent data formats  Duplicates and errors  Data Integration:  Combining data from multiple sources  Ensuring compatibility and consistency  Scalability:  Handling large volumes of data  Ensuring efficient data processing Introduction to CRISP-DM  Definition: Cross-Industry Standard Process for Data Mining.  Phases of CRISP-DM:  Business Understanding  Data Understanding  Data Preparation  Modeling  Evaluation  Deployment  Significance: Provides a standardized framework for managing data science projects. CRISP-DM cyclical diagram Detailed Look at CRISP-DM Phases  Business Understanding: Define objectives and requirements from a business perspective.  Data Understanding: Collect initial data and become familiar with it.  Data Preparation: Clean and transform data for modeling.  Modeling: Apply various modeling techniques to the prepared data.  Evaluation: Assess the model's performance and ensure it meets business objectives.  Deployment: Implement the model in a production environment. Benefits of Using CRISP-DM  Flexibility: Applicable across different industries and projects.  Structured Approach: Ensures all critical aspects of a project are addressed.  Reusability: Phases can be revisited as needed, supporting iterative improvements.  Communication: Provides a common language and framework for teams. Case Study: Applying CRISP-DM  Scenario: Retail company wants to optimize inventory management.  Application of CRISP-DM:  Business Understanding: Define goals to reduce stockouts and excess inventory.  Data Understanding: Collect sales data, inventory levels, supplier information.  Data Preparation: Clean data, handle missing values, create relevant features.  Modeling: Develop predictive models for demand forecasting.  Evaluation: Validate model accuracy and its impact on inventory metrics.  Deployment: Integrate the model into the inventory management system. Tools for Data Acquisition and Understanding  Data Acquisition:  APIs: Postman, Insomnia  Web Scraping: BeautifulSoup, Scrapy  Databases: SQL, MongoDB  Data Understanding:  Exploratory Tools: Pandas Profiling, Tableau, Power BI  Visualization: Matplotlib, Seaborn Best Practices in Data Acquisition  Plan Ahead: Understand data requirements based on business objectives.  Ensure Data Quality: Implement validation checks during data collection.  Automate Processes: Use scripts and tools to streamline data acquisition.  Document Sources: Keep track of data sources and acquisition methods for transparency. Common Pitfalls in Data Science Projects  Poor Problem Definition: Misalignment between business and data science goals.  Inadequate Data Quality: Making decisions based on incomplete or inaccurate data.  Overlooking Data Privacy: Failing to comply with data protection regulations.  Ignoring Iterative Processes: Not revisiting earlier phases based on new insights. Summary  Business and Data Understanding: Critical for aligning data science projects with business goals.  Data Acquisition: Involves various methods and understanding data types.  CRISP-DM: A robust methodology for managing data science projects effectively.  Best Practices: Essential for successful data acquisition and project execution. Discussion Questions  How can misalignment between business objectives and data science goals impact a project?  What are some strategies to handle unstructured data effectively?  How does CRISP-DM facilitate better project management in data science? Next Week  Topic: Data Collection and Wrangling  Techniques for collecting data from various sources.  Methods for cleaning and transforming raw data for analysis.