The simplest definition of Data Science is that it extracts actionable insights from raw data. In the digital age, we consume a large amount of data, and the trend isn’t slowing down anytime soon. The Data Science field is growing very rapidly and is responsible for bringing a revolution in many industries. Generally, Data Science involves the extraction of information from raw data to formulate actionable insights.
Data Science Life cycle
From Data Engineering, Statistics, Advanced Computing to Math, a Data Scientist needs to have all the skills to effectively ride through heaps of information and data and communicate only those that will help in innovation and efficiency.
Data Scientists also relies on Artificial Intelligence, Deep Learning and Machine Learning to create models, use algorithms and make predictions.
Data Science has a five-step life cycle:
Capture
This step involves Data Entry, Signal Reception, Data Extraction, Data Acquisition etc.
Maintain
This stage includes Data Architecture, Data Cleansing, Data Staging, Data Processing, Data Warehousing etc.
Process
This step includes Data Modeling, Data Summarization, Clustering/Classification, Data Mining etc.
Communicate
This stage includes Data Visualization, Business Intelligence, Decision Making, Data Reporting etc.
Analyze
This step includes Predictive Analysis, Qualitative Analysis, Exploratory/Confirmatory, Regression, Text Mining etc.
These 5 steps require different skillsets, techniques and programs.
Real-life Uses of Data Science
Data Science has solved many problems that required a lot of time or energy. Here are the uses of Data Science in real life:
Healthcare
The advent of Data Science
has led to a lot of breakthroughs in the healthcare sector. From EMRs to
clinical databases, medical professionals and Data Scientists are finding new
ways to incorporate Data Science in the healthcare industry. Data Science can
also help find ways of understanding diseases, diagnose diseases, and explore
new treatment options.
Logistics
Many logistics based companies use Data Science-backed statistical modelling and algorithms, which also helps the company to save millions of gallons of fuel. Using Data Science helps in maximizing the efficiency of the industry.
Finance
Data Science is the reason why the finance sector has been able to save millions of dollars and unquantifiable amounts of time. For example - a task that would typically take 360,000 labour hours to complete can be finished in a few hours through the use of Data Science.
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