Table of Contents
- What is Data Science?
- Why do companies need data science?
- Why We Need Data Science: The Life Cycle of Data Science
- Practical applications of data science using the example of e-commerce
More than 90 percent of the data stored today in all sorts of devices and systems in the world has been generated in the last two years alone. These vast amounts of data—now called big data—can help gain insights and trends about users and their user behavior. The enormous volume of data in structured and unstructured formats is difficult to process with traditional database models and tools. Therefore, scientific methods, algorithms, and tools must be used to analyze and understand big data and the need for data science and analytics.
What is Data Science?
Data science is all about creativity. The goal of Data Science is to gain insights and trends by analyzing various data sets that give companies a competitive advantage. Data Science is a combination of mathematics, statistics and software with expertise in the applied business environment.
Another buzzword that is often misinterpreted in data science is Business Intelligence (BI). BI focuses on data analysis and reporting, but does not include predictive modeling, so BI can be considered a subset of data science. Creating predictive models is one of the most important activities in data science. Other processes in data science include business analytics, data analytics, data mining, and predictive analytics. Data Science also deals with data visualization and the presentation of results on understandable dashboards for users.
Why do companies need data science?
Companies need to use data to run and expand their business. The core goal of data science is to help companies make faster and better business decisions to gain better market share and industry leadership. It can also help them to take tactical approaches to be competitive and to survive in difficult situations. Businesses of all sizes are adapting to a data-driven approach, with advanced data analytics at the heart of change.
Here are some examples of which companies use data science:
- Streaming service Netflix analyzes viewer patterns to understand what awakens user interest and uses the information to make decisions about the next production series
- The discount chain Target, on the other hand, identifies the most important customer segments and the unique purchasing behaviour of customers in these segments. This helps them to guide different market audiences.
- The consumer goods group Proctor & Gamble uses time series models to better understand future demand and thus plan production volumes more optimally.
Why We Need Data Science: The Life Cycle of Data Science
There are five phases in the life cycle of a data science project.
1. Collection: How is the data collected?
Data collection is the very first step in a data science project. The full set of required data is never found in one place because it is distributed across line industries and systems.
The data can be created by data entry by human operators or devices with new data values for the company. It is a time-consuming process, but it is necessary in certain cases.
Another source of data collection is data devices, which are usually important in control systems, but are now more important for information systems with the invention of the “Internet of Things”.
Data extraction is a process in which data is retrieved from different sources. These can be Web servers, databases, logs, and online repositories.
2. Data maintenance: What happens to the collected data?
Data warehousing focuses on the collection and storage of data from various sources for access and analysis. It is a repository of all data collected by the organization.
Data cleansing identifies and removes (or corrects) inaccurate records from a record, table, or database. Unfinished, unreliable, inaccurate, missing and duplicate values or non-relevant parts are detected.
A cache area is used for data processing during the Extract, Transform and Load (ETL) process. Data provisioning is located between the data sources and the data targets, which are often data warehouses, data marts, or other data repositories.
In the data processing phase, the data is processed for interpretation. Processing is done using machine learning and artificial intelligence algorithms. However, the process itself may vary slightly depending on the data source to be processed and its intended use (examination of advertising patterns, medical diagnosis, data deep dives, etc.).
The data architecture is a framework that allows data to be efficiently transferred from one location to another. It is full of models and rules that govern what data to collect. It also controls how the collected data is stored, arranged, integrated, and used in an organization’s data systems. In short, the data architecture sets standards for all data systems as a vision or model for how the interactions of the data systems work.
3. Data Strategy: What happens to the information obtained?
After the data has been collected and stored, we can proceed to the next step of data processing.
Data mining is about determining the trends in a data set. These trends are used to identify future patterns. This often involves analysing the large amount of historical data that has not previously been taken into account.
Clustering and classification is the task of dividing or classifying the population or data points into multiple groups, so that data points in the same groups are more similar to other data points in the same group than in other groups. In simple terms, the goal is to separate groups with similar characteristics and divide them into clusters.
Data modeling creates a descriptive diagram of the relationships between different types of information to store in a database.
Data summary is an important data mining concept that includes techniques for finding a compact description of a data set. Data summary is a simple term for a short conclusion after analyzing a large data set. The pooling of data is of great importance for the data strategy.
4. Web analysis: How can the data be analyzed?
Data is often tested in two phases: exploratory and confirmatory analysis. The two work most effectively side by side. Exploratory data analysis is sometimes compared to detective work: it is the process of gathering evidence. An analysis of corroboative data is comparable to a court case. It is the process of evaluating evidence.
Predictive Analytics is the process of using data analytics to make predictions based on data. This process uses data along with web analytics, statistics, and machine learning techniques to create a predictive model for predicting future events. Predictive analytics are used to achieve conversion optimization and promote cross-selling opportunities. Predictive models help companies attract, retain and grow their most profitable customers. Many organizations use predictive models to predict inventory and manage resources.
Regression analysis is a form of predictive modeling technique that examines the relationship between a dependent (target) and an independent variable (predictor). This technique is used to predict, model time series, and determine the causal effect between the variables.
The text Mining refers to the use of data mining techniques to detect useful patterns from text. The text mining of the data is unstructured. Information and relationships are hidden in the language structure and not explicitly as in data mining.
When data is not available in the form of numbers, it is even more difficult to understand it. Qualitative data are defined as the data that approximate and characterize. Qualitative data can be monitored and recorded using qualitative analysis. This data type is not numeric in nature. This type of data is collected through observation methods, individual interviews, execution of focus groups and similar methods.
Qualitative data analysis only examines qualitative data in order to provide an explanation for a particular phenomenon. Qualitative data analysis gives you an understanding of your research goal by revealing patterns and topics in your data. Data scientists and their models can greatly benefit from qualitative methods.
5. Communication: How are the results displayed?
Data reporting communicates information compiled as a result of research and analysis of data and problems. Reports can cover a wide range of topics, but tend to focus on transmitting information to a specific audience with a clear purpose. Good reports are documents that are accurate, objective and complete.
Data visualization is a graphical representation of information and data. By using visual elements such as charts, graphs, and dashboards, data visualization tools provide an easily accessible way to identify and understand trends, breaks, and patterns in data.
Business Intelligence (BI) is an essential part of data science. To perform a predictive analysis first, we need to know what went wrong. Therefore, BI is a simpler version of data science.
The importance of Data Deep Dives for decision-making lies in consistency and continuous growth. It enables companies to create new business opportunities, generate more revenue, predict future trends, optimize current operational efforts, and gain actionable insights.
All five levels require different techniques, programs and, in some cases, skills.
Practical applications of data science using the example of e-commerce
Data science has proven useful in almost every industry. Online retailers already use data science to deliver business benefits. These include:
- Conversion Optimization
- Identify the Most Valuable Customers
- Identify which customers are likely to leave
- Increase sales with smart product recommendations
- Automatically extract useful information from reviews
At a time of rising costs and increasing competitive pressure, it is important to make the right decisions in the company quickly and proactively. Business intelligence is the basis for the available data. By combining data science with predictive analytics, organizations can gain detailed insights into their data and make future forecasts.
In a world of increasing data flooding, data analysis is becoming increasingly important in many companies. As a result, the data scientist is increasingly becoming the hero of the moment, as he and artificial intelligence organize and evaluate large amounts of data in a targeted and structured manner, solve long-term business problems, and discover inefficient processes.