Data science framework with key analytical techniques

Description:

The image is a PowerPoint slide titled "Data Science Framework with Key Analytical Techniques," providing an organized overview of various concepts and methods utilized in data science. The slide is structured into several rows, each labeled with a different category, and corresponding columns filled with specific data science terms or tools.

From top to bottom, the categories and their respective terms are:

1. Application: 

Including 'Model Evolution', 'Predictive Analysis', 'Business Intelligence', and 'Decision Making'.

2. Data Making and Analytic: 

Contains 'Exploratory Data Analysis', 'Unstructured Data Processing', 'Narrative Analysis', 'Regression Analysis', and a placeholder for additional text.

3. Data Strategic Technologies: 

With terms like 'Big Data Warehousing', 'NoSQL', 'RDBMS' (Relational Database Management Systems).

4. Technical Skills: 

Highlighting 'Data Processing Tools', 'SQL/Database/Coding', 'MapReduce', and 'Data Visualization'.

Probability and Statistics, Machine Learning and AI, Quantitative Analysis, and Data Intuition are listed under a final category that seems to be incorrectly labeled as "Fun demented 
knowledge" due to a typographical error; it likely intends to mention "Fundamental knowledge" or a similar term.

The slide is a comprehensive tool for showcasing the multifaceted skills and technologies that form the basis of data science work. It indicates that it's fully editable, meaning the listed terms can be tailored to the specific focus or expertise of the presentation's audience.

Use Cases:

This framework is essential in industries that leverage data for strategic decisions, innovation, and efficiency:

1. Technology:

Use: Driving innovation through data analytics

Presenter: Data Scientist

Audience: Product Development Teams

2. Healthcare:

Use: Enhancing patient care through predictive analytics

Presenter: Healthcare Data Analyst

Audience: Clinical Researchers

3. Finance:

Use: Risk assessment and financial forecasting

Presenter: Financial Analyst

Audience: Investment Managers

4. Retail:

Use: Customer behavior analysis for market strategy

Presenter: Retail Analyst

Audience: Marketing Teams

5. E-commerce:

Use: Personalization and recommendation engine development

Presenter: Data Engineer

Audience: E-commerce Strategists

6. Manufacturing:

Use: Process optimization through data analysis

Presenter: Operations Analyst

Audience: Supply Chain Managers

7. Energy:

Use: Predictive maintenance and resource optimization

Presenter: Data Analysis Manager

Audience: Energy Sector Executives