CSCI946 - Big Data Analytics Week 2

Introduction

In this post, we’ll delve into the data lifecycle, focusing on the different stages, key roles, and common pitfalls associated with managing big data projects. Understanding these elements is crucial for successful data science endeavors.

Key Stages of the Data Lifecycle

1. Discovery
   • Domain Understanding: Comprehend the industry and project objectives.
   • Criteria of Success/Failure: Define what success looks like and identify potential challenges.
   • Interviews: Engage with sponsors and stakeholders to gather initial insights.
   • Initial Hypotheses: Formulate preliminary hypotheses based on the gathered information.
2. Data Source Identification
   • Identify and gather relevant data sources from various departments and warehouses.
3. Data Preparation
   • Sandbox Environment: Set up a sandbox for data preparation.
   • ETLT (Extract, Transform, Load, Transform): Perform data extraction, transformation, and loading. Note that ELT can be slow for large datasets, so ETLT is often preferred.
   • Data Conditioning: Assess the quality of data, addressing noise, outliers, and missing values.
   • Survey and Visualization: Use plots such as scatter plots, histograms, and heat maps to understand data distributions and correlations.
   • Scaling and Normalization: Apply techniques like Z-normalization to standardize data.
4. Model Planning
   • Identify Candidate Models: Select models based on hypotheses and literature review.
   • Variable Selection: Choose the relevant variables for modeling.
   • Tools and Languages: Decide on the tools and programming languages for model development.
5. Model Building
   • Training and Testing: Separate data for training and testing if necessary to validate model performance.
6. Communication of Results
   • Comparison: Evaluate the results against success criteria.
   • Reporting: Clearly communicate the insights and findings.
7. Operationization
   • Implementation: Finalize and communicate the benefits of the model.
   • Pilot Project: Test the model in a pilot project to ensure its effectiveness.

Common Mistakes in Data Science Projects

• Rushing into Data Collection and Analysis: Prematurely jumping into analysis without proper planning can lead to suboptimal results.
• Insufficient Planning: Not spending adequate time on planning can result in missing critical insights and inefficiencies.

Key Roles in Data Science Projects

• Business User: Provides context and requirements.
• Project Sponsor: Supports and funds the project.
• Project Manager: Oversees project execution and ensures timelines are met.
• Business Intelligence Analyst: Analyzes business data to inform decisions.
• Database Administrator: Manages and maintains database systems.
• Data Engineer: Develops and maintains data pipelines.
• Data Scientist: Designs, implements, and deploys models to derive actionable insights.

Conclusion

Understanding and managing the data lifecycle is essential for successful data science projects. By following a structured approach and avoiding common mistakes, you can ensure that your data projects are effective and deliver valuable insights.

Lecture 2