Section 2: Data Analysis and Presentation - DEEPSEEK

BigQuery SQL Queries

Writing and executing SQL statements in BigQuery to generate reports and extract insights.

Jupyter Notebooks

Interactive environments (e.g., Colab Enterprise) for analyzing and visualizing data.

Business Insights Analysis

Using data to answer business questions and drive decision-making.

Looker Dashboards

Visual tools in Looker for creating, modifying, and sharing analytics dashboards.

Looker vs. Looker Studio

Looker (enterprise BI) vs. Looker Studio (self-service) for different analytics needs.

LookML Parameters

Elements in Looker's modeling language to define data models and metrics.

BigQuery ML

A tool for creating, training, and evaluating machine learning models using SQL.

AutoML

Google Cloud service for automated model training without coding (e.g., Vision, Tables).

Pretrained LLMs in BigQuery

Using Google’s large language models (e.g., PaLM) via remote connections in BigQuery.

ML Project Lifecycle

Phases: data collection, model training, evaluation, and prediction deployment.

SQL Model Creation

Writing SQL queries in BigQuery to define and train ML models.

Model Inference

Using trained ML models to generate predictions on new data.

Model Registry

A centralized repository in Google Cloud for organizing and managing ML models.

Data Visualization in Jupyter

Creating charts/graphs in Jupyter notebooks (e.g., Matplotlib, Colab).

Report Generation

Designing structured summaries of data insights using SQL or BI tools.

Key Insights Extraction

Identifying critical trends or patterns from analyzed data.

Dashboard Customization

Tailoring Looker dashboards to meet specific business requirements.

Analytics Use Case Comparison

Choosing between Looker and Looker Studio based on scalability or user needs.

Data Model Customization

Modifying LookML schemas to adjust dimensions, measures, or relationships.

ML Use Case Identification

Determining scenarios where ML adds value (e.g., forecasting, classification).

Pretrained Model Integration

Leveraging Google’s pre-built models (e.g., Vision API) in analytics workflows.

Model Training in SQL

Using BigQuery ML syntax (e.g., CREATE MODEL) to train models directly in BigQuery.

Inference Execution

Running predictions via SQL (e.g., ML.PREDICT) on BigQuery ML models.

Model Organization

Storing and versioning models in the Model Registry for reproducibility.

Colab Enterprise

Managed Jupyter notebooks with enterprise security for collaborative analytics.

LookML Adjustments

Editing LookML code to refine calculated fields, joins, or access controls.

Machine Learning Lifecycle

End-to-end process: data prep, training, evaluation, deployment, monitoring.

Model Evaluation Metrics

Assessing model performance using metrics like accuracy, precision, or ROC curves.

Prediction Deployment

Operationalizing ML models to serve real-time or batch predictions.

Business Data Analysis

Translating raw data into actionable insights aligned with business goals.