What is data science? A Complete Guidance for Beginners.

As the world enters the age of great data, the warehouse is changing. The main problem and concern for companies were until 2010. The main emphasis is on creating frameworks and data storage solutions. Now that Hadoop and other frameworks have solved storage problems, the focus is on processing that data. Data science is a secret sauce here. Data science can confirm every thought you see in Hollywood science fiction movies. The destiny of data science is artificial intelligence. Therefore, it is important to understand what data science is and how it can help your business.

What is Data Science?

Data science is a combination of various tools, algorithms, and machine learning principles for identifying hidden patterns of sensitive information. What is the difference between what statisticians have seen over the years?

Data Science is widely used to make decisions and make predictions, using risks and hazards, analytical analysis perhaps, and machine learning.

Predictive Causal Analysis: If you need a model that can predict the future of a particular event, you need to apply predictive causal analysis. Let's say that when you borrow money, you are worried about the possibility that your customers will repay the loan on time in the future. Here you can create a model that allows you to perform a predictive analysis of your customers' payment history to predict whether future payments will be made on time.

Prescriptive analytics: If you need an example and wisdom to make your own decisions, as well as the ability to change and practice a strong family, then you need an editor. This new group is affiliated with advisors. In other words, it not only predicts but also provides various observed actions and related results.

The best example of this is Google, which I mentioned earlier. Driving information can be used to teach driving. You can run algorithms based on this information to give them information. This allows your car to choose when to turn, which direction to take, and when to slow down or accelerate.

Machine learning for forecasts: If you have data on a company's financial transactions and need to create a model to determine future trends, machine learning algorithms are the best choice. It is part of the guided learning paradigm. This is called monitoring because you already have data that you can use to train your machines. For example, the fraud detection model can be taught using the story of fraudulent purchases.

Identifying Machine Learning Patterns: If you don't have prediction parameters, you need to find hidden patterns in your data to make meaningful predictions. It's just an unattended model because you don't have predefined group tags. The most commonly used algorithm to detect a pattern is clustering.

Suppose you work for a telephone company and you have to build a network by placing poles nearby. You can then use cluster technology to find tower locations that provide optimal signal strength for all users.

Why data science?

Traditionally, the data available to us are mostly structured and small, which can be analyzed with simple BI tools. Unlike traditional systems, which are mostly structured, most of today’s data is unstructured or semi-structured. Let’s look at the data trend in the chart below, which shows that by 2020, more than 80% of the data will be unstructured.

That’s not the only reason data science is so popular. Let’s take a closer look at how data science is used in different fields.

What do you say to understand your customers' exact needs based on available information such as past search history, purchase history, year, and revenue? There is no doubt that you have had all this information before, but now you can teach your models more efficiently and give your product a lot of information to customers. Wouldn’t it be great to bring more work to your organization?

Let's take another scenario to understand the role of information science in decision-making. What if you want to drive home? Self-driving cars collect data from sensors such as radars, cameras, and lasers to map the environment. Based on this information, using advanced machine learning algorithms, decisions are made on when to accelerate, when to accelerate, when to move and where to turn.

Who is a Data Scientist?

More definitions are available for data scientists. Simply put, a data scientist is someone who practices the art of data science. The term "data scientist" was introduced with the idea that scientists receive a lot of information from scientific disciplines and applied programs, such as statistics and mathematics.

Let’s understand the lifecycle of Data Science.

An error that could easily cause your application to be denied is a failure. In order to ensure the smooth running of the project, it is important that all stages of data science are present throughout its life.

Lifecycle of Data Science

Discover: Before starting a project, it is important to understand the various features, needs, priorities, and budget required. You want a view to asking the proper questions. Here you can determine whether you have the resources to support people, technology, time, and data plans. At this stage, you must specify the business problem and make a preliminary assumption (IH) for testing.

Data preparation: At this stage, you need a sandbox for analysis, in which you can perform analyzes for the duration of the project. Data must be found, processed, and conditioned before modeling. You will also receive an ETLT to receive inbox data. Let's look at the flow of statistical analysis below.

You can use R to delete, modify and view data. This will help you feel the conflict and build relationships between the variables. After clearing and preparing the data, it's time to investigate.

Model Design: Information Science Model Planning describes methods and techniques for establishing relationships between variables. These relationships will form the basis of your application algorithms in the next step. Use Search Engine Optimization (EDA) using various statistical formulas and visualization tools.

Most Common Tools.

1, R has a complete set of modeling parameters and provides a good environment for building interpretive models.

2, SQL analytics services can perform analytics on databases using common data retrieval functions and basic predictive models.

3, SAS/ACCESS can be used to access data from Hadoop and can be used to generate multiple and repeating pattern charts.

While there are many tools on the market, the most commonly used tool is R.

You already understand the nature of your data and have decided which algorithm to use. In the next step, you will apply algorithms and build models.

Model building: At this point, you will collect a database for training and testing. You should consider whether you have enough tools to run the model if you need a more robust environment (such as fast, parallel processing). You will analyze different learning methods such as classification, association, and clustering to create models.

Operationalize: Data Science operationalize, and provide the latest reports, information, indicators, and technical information. Also, the test project is sometimes run in a real production environment. This gives a clear idea before production and other minor issues are fully realized.

Communicate Results: Now it's important to assess whether you've achieved the goal you set for yourself in step one. So in the last step, you identify key findings, communicate with stakeholders, and determine whether the project results were successful or not, based on the criteria developed in phase 1.