Data Science And Its Life Cycle

As the world moved into the era of big data, the need for storage also boomed.

Until 2010, it was the main concern and challenge for corporates with the main focus on building solutions and frameworks to store these data. Now, when frameworks successfully solved this issue of storage, the challenge now goes into processing these collected data.

This is where data science comes in.

In this article, you will learn data science and its life cycle.

Defining Data Science.

Data is drawn from various sectors, platforms, and channels including social media, cell phones, healthcare surveys, internet searches, and e-commerce sites. The boost in the amount of available data has opened a door to a new field of study based on the big data.

However, the ever-increasing data is complex and unstructured and needs parsing for effective decision making. The process is time-consuming and complex for companies, and thus, emerged data science.

In a nutshell, data science is the multidisciplinary mixture of data inference, technology, and algorithm development to solve systematically complex problems. At its core, of course, is data.

A treasure chest of raw information, collected and streamed and then stores in corporate data warehouses. By mining it, corporates can learn so many things from it and those with advanced capabilities can build something with it.

And data science is the ultimate weapon that allows businesses to use this data in unlimited creative ways and generating business value.

Data Science Life Cycle.

In any Data Science Project, there are 5 stages.

1. Capturing.

Data collection or acquisition is the first step in a Data Science project. So, how is data captured?

• Signal Reception.

One way to capture data is via data devices like smartphones and computers, but typically in control systems.

• Data Entry.

Data can also be created with new data values for the company by devices or human operators.

• Data Extraction.

This is the process that involves retrieving data from different resources including online repositories, logs, databases and web servers.

2. Maintaining.

After data capturing, the question now is, what happens to the collected data?

• Data Warehousing.

This process emphasizes that capturing and storing data from various sources for analysis and access. This is the repository of all collected data by an organization.

• Data Cleansing.

This is the process of identifying then correcting or removing inaccurate records from a table, database or dataset. It recognizes unreliable, unfinished, duplicate, missing and inaccurate values and then re-modeled, restored ore removed.

• Data Staging.

After cleaning, intermediate storage is then used for the processing of data during the ETL process. This process sits between the data source and data targets, which are often data marts, data warehouses, and other data repositories.

• Data Processing.

Here the data is interpreted and done by using machine learning algorithms, although the process may vary depending on the data source being processed and its intended use.

• Data Architecture.

This is a framework built in order to transfer data efficiently. It is full of rules and models that govern what data should be collected. This also controls how the acquired data should be arranged, stores and put to use.

3. Processing.

Now that the data is acquired and stored, it is time to process the data. Here are things you can do with clean data.

• Data Mining.

This is about finding the trends in a certain data set and used to identify future patterns.

• Data Modeling.

The process of producing a diagram of relationships between several information stored in a database.

• Clustering and Classification.

This is the process of classifying or dividing data points into several groups. In short, it aims to segregate groups with similar traits and assigning them to clusters.

• Data Summarization.

This involves finding a general description of the dataset. It is a short conclusion after the analysis of a large dataset.

4. Analysis.

After you modeled and classified your data, then the next thing to do is to analyze the data. So, how do you do that?

• Predictive Analysis.

This is the process of utilizing data analytics in order to make predictions based on the data. It uses the data along with statistics, analysis, and machine learning techniques in order to create a model for forecasting future happenings.

This type of analysis is mainly used in determining customer purchases and responses, helping businesses to retain attract and grow profitable clients and manage inventories.

• Confirmatory/Exploratory.

Data analysis typically falls into 2 phases — confirmatory and exploratory — operating side-by-side for effective results. Confirmatory analysis is the process of evaluating evidence while exploratory is the gathering of evidence.

• Regression.

Another form of predictive modeling strategy, regression investigates the relationship between an independent and dependent variable. It is most commonly used for tome series modeling, forecasting and finding causal effect between variables.

• Qualitative analysis.

Data is harder to understand if it is not in the form of numbers. In such a case, qualitative analysis is needed. It is simply the process of examining qualitative data in order to derive an explanation. It also gives you some basic understanding of the research objective by revealing themes and patterns in the data.

• Text Mining.

This type of analysis use data mining strategies in order to discover useful patterns from the texts. Text mining can result in unstructured data and the relations and information are hidden into the language structure.

5. Communicating.

After you analyzed the data, how will you display your findings and the result?

• Data Visualization.

This is a graphical representation of data and information. By using visual elements such as maps, graphs, charts, and other tools, you provide an easy to understand outliers, trends, and patterns in the data.

• Data Reporting

Data reports communicate information that has been compiled as a result of data analysis and research. It can cover a broad range of topics but it typically focuses on transmitting info with a clear purpose to a particular audience. Additionally, good reports are clean documents that are objective, accurate and complete.

• Business Intelligence

A crucial part of Data Science, BI is a simpler version of data science. In order to make predictive analysis, first, you need to know what’s wrong and BI provides you this insight.

• Decision Making

This lies in continual growth and consistency. It allows organizations to create new business opportunities, predict future trends, generate more revenue, produce actionable insights and optimize current operational efforts.

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