If you’re in business, you have data. Companies generate data over time. That’s a fact of business life. Data science is the practice of taking the data you’ve generated over time, analyzing it, and using the results to improve your future decision making. To that end, performing data science and analytics should become part of the software development process. As a result, your automated solutions will generate positive results and become valuable decision-making tools.
Common data science and analytics efforts include:
- Segmenting and identifying the most profitable areas of a business
- Evaluating your historical data sets to identify predictive factors in financial success
- Reviewing third party data to enrich your data density
- Running simulations to forecast the probability of potential future events
- Analyzing performance data as part of A/B software testing
- Looking at your customer’s performance data to better cater your services to their needs
What’s data science good for?
In general, there are 3 different kinds of data. First, structured data is highly organized and generally tabular in format. For example, employee information and customer information tables in relational databases are structured data.
Second, semi-structured data has a self-describing structure in the form of tags, markers or key-value pairs that separate semantic elements and create hierarchies of records and fields among the data. Thus, semi-structured data examples include data in CSV, XML and Json formats.
Lastly, unstructured data consists of photos and videos, satellite image, radar and mobile data, and various types of unstructured textual data such as web content and social media content.
Typically, data scientists involve themselves in solving problems dealing with semi-structured and unstructured data. By employing aspects of programming, Natural Language Processing (NLP), statistics, data mining, and machine learning and visualization, as well as various techniques from other disciplines, data scientists interpret and extract meaning from complex data.
Of Course Jaroop Does Data Science
To be sure, analyzing data is expensive and time-consuming, especially if you don’t have the right resources and software. Fortunately, we’re experts at providing the right resources and software to businesses like yours. Using their many years of hands-on experience, our data science team can import robust sets of data and turn meaningful conclusions around quickly.