📊 Data Technology (Analytics ⮕ Science ⮕ Engineer)

• Data Analytics: Answers the question, "What happened?" and "Why did it happen?" Focus on business intelligence, dashboards, and descriptive statistics.
• Data Science: Answers the question, "What will happen?" and "How can we make it happen?" Focus on predictive modeling, machine learning, and advanced statistics.
• Data Engineering: Answers the question, "How do we get the data ready and in one place?" Focus on data pipelines, infrastructure, and data warehousing.

Career Platforms / Roles

• Data Analyst
• Data Scientist
• Data Engineer

✨ Practical Use of Data Techniques

0. Understanding Data (The Why?)

• Basic Terminology
• Purpose & Goals
• Forms of Data:
  • Structured & Unstructured
  • Nominal, Ordinal, Interval, Ratio
  • & more

1. Data Gathering & Extraction

• Scraping / Web Scraping
• Monitoring

2. Data Transformation

• Cleaning
• Formatting
• Assuming / Imputing Missing Data
• Converting (Data Types, Structures)

3. Data Analytics & Data Science

• Extract insights from data
• Aid in decision making
• Make predictions
• Visualize data
• & more.

🛠️ Tools

⚙️ Technologies Used

• Developer Tools & Techniques

  • Python, R, and Libraries
  • Jupyter Notebook
  • AI Analysis

• Business Intelligence / Visualization Tools

  • PowerBI, Tableau
  • Automatic AI Tools for Insights

📦 Data Sources & Storage

• Non-Database

  • Unstructured & Semi-Structured: Text files, logs
  • Structured: CSV, XML, JSON, etc.

• Business Databases

  • Spreadsheet Tools: Excel
  • No-Code Database Tools: Airtable

• Application Databases

  • SQL Databases: MySQL, PostgreSQL
  • NoSQL: MongoDB

✍️ Endnote

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