Difference Between Analytics, Data Science, Machine Learning, and AI

watch full video here

Introduction

The video and blog explains the differences between analytics, data science, machine learning (ML), artificial intelligence (AI), and related fields. It also covers their historical evolution, overlaps, and interactions.

1. Evolution of Analytics and Data Science

• The data revolution started with ERP and CRM systems, leading to massive data accumulation.
• Big Data emerged with challenges related to volume, velocity, variety, and veracity.
• The need to extract insights from data led to analytics, which evolved into data science.

2. The Analytical Hierarchy

• Descriptive Analytics: Reports, dashboards, KPIs (e.g., sales trends).
• Diagnostic Analytics: Root cause analysis (why something happened).
• Predictive Analytics: Forecasting future trends (e.g., demand prediction).
• Prescriptive Analytics: Decision optimization (e.g., personalized recommendations).

Data analysts focus on the first two, while data scientists specialize in predictive and prescriptive analytics.

3. Difference Between Data Science and Machine Learning

• Data Scientists: Identify a problem first, then collect and analyze data using statistics and mathematics.
• ML Engineers: Start with available data, apply algorithms, and derive insights for automation.

Advancements in storage and computing power have accelerated ML adoption.

4. Machine Learning and AI

• AI: Broad field where machines mimic human intelligence.
• ML: A subset of AI where models learn patterns from data.
• Deep Learning (DL): Subset of ML using neural networks with multiple layers.
• Generative AI: AI models capable of generating text, images, videos, and speech.

5. Overview of Generative AI

• Examples:
  • ChatGPT (text generation)
  • DALL·E (image generation)
  • Sora (video generation)
• Uni-Modal AI: Works with a single data type (text-to-text, image-to-image).
• Multi-Modal AI: Can process multiple data types (e.g., text-to-image).

6. Career and Learning Path

• Key Differences:
  • Business Intelligence (BI): Focus on reporting and dashboards.
  • Data Science: Includes inferencing, hypothesis testing, and predictive modeling.
  • Machine Learning: Focus on algorithms and automation.
  • AI: Encompasses all of the above, aiming to replicate human intelligence.

Conclusion

The video clarifies the distinctions and overlaps between analytics, data science, ML, and AI. Viewers are encouraged to like, subscribe, and join future sessions to continue learning.