Many people new to data science might believe that this field is just about R, Python, Spark, Hadoop, SQL, traditional machine learning techniques or statistical modeling. While those technologies are a large part of the field, the answer is more nuanced than that.

Here’s a thoughtful article from Vincent Granville on Data Science Central about this very question and here is the list of resources that

**24 Articles About Core Data Science**

- Data Science Compared to 16 Analytic Disciplines
- 10 types of data scientists
- 40 Techniques Used by Data Scientists
- 50 Questions to Test True Data Science Knowledge — also read this article
- 24 Uses of Statistical Modeling
- 21 data science systems used by Amazon to operate its business
- 10 Modern Statistical Concepts Discovered by Data Scientists
- 8 Deep Data Science Articles
- 22 tips for better data science
- How to detect spurious correlations, and how to find the real ones
- High versus low-level data science
- The of curse of big data
- 4 Easy Steps to Structure Highly Unstructured Big Data
- Fast Feature Selection with New Definition of Predictive Power
- Fast clustering algorithms for massive datasets
- Building blocks of data science
- Life Cycle of Data Science Projects
- Data Scientist Shares his Growth Hacking Secrets
- Hitchhiker’s Guide to Data Science, Machine Learning, R, Python
- Data Scientist vs Statistician
- Data Scientist versus Data Engineer
- Data Scientist versus Business Analyst
- Data Scientist versus Data Architect
- Vertical vs. Horizontal Data Scientist