Data science
What is data science for ?
1. Descriptive analysis
Descriptive analytics examines data to better understand what was or is happening in the data environment. It is characterized by data visualization such as pie chart, bar chart, line chart, table or generated story. For example, a flight booking service might record data such as the number of tickets booked per day. Descriptive analytics will reveal peak bookings, booking reductions, and best performing months for that service.
2. Diagnostic analysis
Diagnostic analysis is an in-depth or detailed examination of data to understand why something is happening. It is characterized by techniques such as exploration, data discovery, data mining, and correlations. Several data manipulations and transformations can be performed on a given data set to uncover unique patterns in each of these techniques. For example, the flight department might discover a particularly strong month to better understand the peak in bookings. This can lead to the discovery that many customers visit a particular city to attend a monthly sporting event.
3. Predictive analytics
Predictive analytics uses historical data to make accurate predictions about possible future data patterns. It is characterized by techniques such as machine learning, forecasting, model matching, and predictive modeling. In each of these techniques, the computer is trained to unravel causal links in the data. For example, a flight services team can use data science to predict next year’s flight booking patterns at the beginning of each year. A computer program or algorithm can look at past data and predict peak bookings for certain destinations in May. Anticipating a customer’s future travel needs, the company could start targeting these cities from February.
4. Regulatory analysis
Prescriptive analytics takes predictive data to the next level. It not only predicts what is likely to happen, but also suggests an optimal response to that outcome. He can analyze the potential effects of different options and recommend the best course of action. It uses graph analysis, simulation, complex event processing, neural networks, and recommendation engine from machine learning.
Going back to the airline booking example, regulatory analytics can look at historical marketing campaigns to maximize the benefits of an upcoming booking spike. A data scientist can predict booking results for different levels of marketing spend across different marketing channels. These data forecasts will give the airline booking company more confidence in its marketing decisions.