Data Science
The simplest way to understand data science is the extraction of actionable insights from raw data. Data science involves a plethora of disciplines and expertise areas to produce a holistic, thorough and refined look into raw data. Data scientists must be skilled in everything from data engineering, math, statistics, advanced computing and visualizations to be able to effectively sift through muddled masses of information and communicate only the most vital bits that will help drive innovation and efficiency.
Data scientists also rely heavily on artificial intelligence, especially its subfields of machine learning and deep learning, to create models and make predictions using algorithms and other techniques.
Data science generally has a five-stage life cycle that consists of:
Capture: Data acquisition, data entry, signal reception, data extraction
Maintain: Data warehousing, data cleansing, data staging, data processing, data architecture
Process: Data mining, clustering/classification, data modeling, data summarization
Communicate: Data reporting, data visualization, business intelligence, decision making
Analyze: Exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis
Technical tools & skills utilized by Data Scientists:
R
Python
Apache Hadoop
Apache Spark
Apache Pig
MapReduce
NoSQL databases
Cloud Computing
D3
Tableau
iPython Notebooks
GitHub