Data Analytics Course Overview

Course Information

• Course title: Transform data into insights
• Date: Saturday, February 1, 2025
• Time: 12:55 PM
• Topics covered include definitions, lecture notes, summary, and study questions related to data transformation into insights.
• Resources included Coursera Google Data Analytics material.

Data Analytics in Everyday Life

• Data analysis: The collection, transformation, and organization of data to draw conclusions, make predictions, and drive informed decision-making.
• Analysts: Converted raw data into actionable insights.
• Everyday use: Data analysis is used in daily activities like sleep patterns, food choices and workouts.
• Business use: Businesses collect and utilize data to improve processes, identify trends, launch products and enhance customer experiences.

New Data Perspectives

• Data Analysis Process: Ask, Prepare, Process, Analyze, Share, and Act.
• Employee Retention: The ability of a company to retain its employees and reduce turnover rates.
• People Analytics: Using data to improve employee experiences and business efficiency.

How Data Analysts Approach Tasks

• Six Phases of Data Analysis: Ask, Prepare, Process, Analyze, Share, and Act.
• Case Study: Geo-Flow Inc. faced employee retention issues with new hires. Data analysts used the six phases to identify the cause of high turnover in new hires (a complex hiring process). They presented their findings. Hiring and evaluation standardization improved retention rates.

How Data Analysts Approach Tasks: Data Analysis Frameworks

• Data analysis processes evolve from ancient recording on papyrus to modern models including Google's six-step approach and variations from EMC, SAS, project-based, and big data analytics.

Understanding the Data Ecosystem

• Data ecosystem: The interconnected system of hardware, software tools, and people producing, managing, storing, analyzing, and sharing data.
• Real-world examples: Retail data usage, HR analytics, agricultural data, and conservation tracking.

Data and Gut Instinct

• Data-driven decision-making: Using facts and data as a guide for business strategies and selections.
• Gut instinct: An intuitive understanding or feeling about something with little or no apparent data.
• Importance of context: Data should be understood within a particular context.
• Bias: A pre-conceived notion that can influence data analysis or decisions.
• Combining data with human knowledge & experience: The best analysis involves data, contextual insight and a combination of intuition.

Data Drives Successful Outcomes

• Data helps with business planning instead of relying on assumptions or relying on gut feelings.
• Real-world examples: An example of data-driven decisions in HR and another in nonprofit journalism are provided.

Witness Data Magic

• Data-driven decision-making: The use of data, facts, and insights to guide business and strategic decisions.
• Analytics and success: Data analysis case studies show success in corporate and nonprofit environments.

Analytical Thinking for Effective Outcomes

• Importance of analytical thinking: Using data and logic, and step-by-step analysis for better business outcomes.
• Key Aspects:
  • Structure
  • Data visualization
  • Strategy
  • Problem orientation
  • Correlations and causation
  • Big-picture and detail-oriented thinking.

Core Analytical Skills

• Aspects of analytical thinking (visualization, strategy, problem-orientation, correlation, big-picture/detail-oriented thinking).
• Five Whys: Repeated "why" questions to discover root causes of a problem.
• Gap Analysis: evaluating current process to an improved future state.

Use Five Whys for Root Cause Analysis

• Using the method of asking "why?" repeatedly to uncover root causes leading to a problem.
• Example: a case of a grocery store receiving customer service complaints, and an example of an irrigation company facing increased defects in their water pumps.