Data science is a very common term used in the technological world today. It is a multidisciplinary entity that processes data in both structured and unstructured ways. It uses the scientific method and mathematics to process data and extract knowledge from it. It works the same as big data and data mining. It requires powerful hardware as well as efficient algorithm and software programming to solve data problems or process data to gain valuable knowledge.
Current trends in information provide us with 80% of unstructured data, while the remaining 20% is provided in a structured format for quick analysis. Unstructured or semi-structured details need to be handled to make it useful in today’s entrepreneurial environment. Typically, these information or details are generated from various sources, such as text files, financial logs, instruments and sensors, and multimedia tables. Deriving meaningful and valuable insights from this information requires advanced algorithms and tools. This science presents a value proposition for this purpose, which makes it a valuable science in today’s technological world.
How does data science get insights from data?
1. For example, online websites today are maintaining a large amount of detailed information or information about their customer base. Now, online stores want to recommend products to each customer based on their past activity. The store gets all the information of the customer, such as past purchase history, browsing history of products, income, age, etc. Here, science can help a lot by coming up with train models using existing details, and stores can regularly recommend precise products to their customer base. Processing information for this purpose is a complex activity, but science can do wonders for this purpose.
2. Let’s look at another technological breakthrough where this science can help a lot. Self-driving cars are the best example here. Real-time details or information from sensors, radar, lasers and cameras often create a map of the environment for self-driving cars. The car uses this information to decide where to go fast, where to go slow, and when to overtake other vehicles. For this, data science uses advanced machine learning algorithms. This is another prime example of how science can use available details or information to help decision making.
3. Weather forecasting is another area where this science plays an important role. Here, the science is used for predictive analytics. Details or information or facts or data collected from radars, ships, satellites and aircraft used to analyze and build weather forecast models. Models developed using science help predict the weather and accurately predict the occurrence of natural disasters. Without science, the data collected would be completely futile.
Data Science Lifecycle
• Capture: Science begins with data acquisition, data entry, data extraction, and signal reception.
• Processing: This science uses data mining, data clustering and classification, data modeling, and data aggregation to efficiently process acquired data.
• Maintenance: The Science maintains processed data using data warehousing, data cleansing, data segmentation and data schema.
• Communication: This science uses data reporting, data visualization, business intelligence, and decision-making models to communicate or provide data.
• Analytics: This science uses exploratory or confirmatory processes, predictive analytics, regression, text mining, and qualitative analysis to analyze data.