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

View original web page at www.datasciencecentral.com