Data Science Beginner Guidance

What is data science?

It is common to use the term data science, but what does it mean? skills required to become a Data Scientist? What's the Difference Between BI and Data Science? How are decisions and assumptions made in data science? These will be answered some questions later.

First, let's look at what data science is all about. Data science is a combination of many tools, algorithms and machine learning principles aimed at finding hidden patterns from raw data. How is this different from what statisticians have been doing for years?
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Data Analyst v / s Data Science

As you can see in the image above, the data analyst usually describes what happens when processing data history. On the other hand, the data scientist not only performs exploratory analysis to find their perspectives, but also uses a number of advanced machine learning algorithms to detect the occurrence of a particular event in the future. The Data Scientist examines data from many angles, and sometimes the angles are previously unknown.

Therefore, data science is mainly used to make decisions and assumptions using ic attendant causal analysis, prescriptive analysis (predictive plus decision science) and machine learning.

Predictive Causal Analysis: If you want a model that can predict the likelihood of a particular event in the future, you must apply causal analysis. For example, if you are offering money on credit, you may be concerned that customers may make future credit payments in a timely manner. Here, you can create a model that can perform attendance analyzes on the customer's payment history to determine whether future payments will be made on time.

Prescriptive Analysis: If you want a model that has the intelligence and dynamic parameters to make its own decisions, you definitely need prescriptive analysis. This new field is associated with advising. In other words, it refers not only to the prescribed actions but also to the assessment of the associated outcomes.

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A good example of this is the Google car, which I talked about earlier. The data collected by the vehicles can be used to train cars on their own. You can implement algorithms on this data to make you aware. This allows you to make decisions about when to turn your car, what road to take, when to slow down or when to accelerate.

Automated learning to make predictions: If you have transaction data from a financial institution and you need to build a model to determine future trends, then machine learning algorithms are the best option. This follows the pattern of supervised learning. This will be monitored as you already have data that can train your machines.

Machine Learning for Pattern Discovery: If you don't have parameters that can make predictions, you need to find hidden patterns in the data you set to create meaningful predictions. This is an unsupervised model because the group does not have predefined labels. Clustering is the most common algorithm used for pattern discovery.

Say you are working in a telephone company and you need to set up a network by placing towers in an area. Then, you can use the grouping method to find the locations of the towers, which ensures that all users receive the correct signal strength.

Let’s look at how the ratio of approaches described above for data analysis and data science is different. As you can see in the image below, data analysis has some degree of detailed analysis and assumptions. Data Science, on the other hand, is more about Predictive Causal Analysis and Machine Learning.