C1. Introduction to Data-Analytic Thinking

C1. Introduction to Data-Analytic Thinking

1. For the las 15 years businesses have seen that data collection, thinking and analysis is vital in every area

   • Exploiting data gives them a competitive advantage
     • Now a days, computers with algorithms can connect datasets for deeper analyses
     • Widest applications: marketing (general customer relationship management), finance industry (credit score), and marketing to SCM

2. Data science: set of principles, processes and techniques that guide the extraction of knowledge from data

3. Data mining: is the extraction of knowledge from data via technologies that incorporate principles

4. Data analytic thinking: enables you to evaluate proposals for data mining projects. How can data improve performance

5. Examples: Walmart Stores predictive technology in hurricane Frances & Churn in MegaTelCo to retain customers. Target knowing shopping habits

6. Data-Driven decision-making is the practice of basing decision on the analysis of the data and not intuiton. Is not an all or nothing practice

   • Benefits: the more DDD, the more productive. Higher assets, return on equity, asset utilization, market value

7. A predictive model abstracts away most of the complexity of the world, focusing on indicators that correlate with things on interest

8. Data engineering and processing are critical to support data science (which needs access to data)

   • Data processing technologies are important to data-oriented business tasks that do not involve extracting knowldege

9. Big data: datasets that are too large for traditional data processing systems. Big data technologies are used in data engineering and are used to implement data mining techniques

10. Fundamental Principles of data Science

    • Data and the capabiliity to extract useful knowledge from data ahould be regarded as key strategic asset. (E.G.. Signet Bank)
    • From large data, IT can be used to find informative descriptive attributes en entities of interest (finding variables that correlate)
    • If you find something in a dataset, but do not generalize: overfitting a dataset
    • Finding data mining soultions involves thinking carefully about the context

11. Data science is not only the analytical skills but also se tools used: specific programming languages