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How To Utilize The Power Of Data Science For Problem-Solving?

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Introduction

Since its introduction, Data science has given the business arena a new way to make decisions. It uses mathematical functions and computer algorithms to process a set of data, and give us results that can help in determining the consequences of making a decision.

It may seem like a tedious process due to the amount of number crunching and data churning, but it teaches us the discipline to follow a process.

A business arena is a competitive place; a new opportunity for one firm could be a threat to the other. Data science allows the firm to find a solution to the problem that has a mathematical success rate.

Let’s consider the COVID-19 situation and its ramifications on the industries. A lot of companies have come up with contingency plans or continuity plans by estimating numbers that they’ve obtained via data analysis.

Let’s find out how we can use data science for problem-solving:

Define The Problem

To solve a problem using the principles of data science, we must identify its dynamic and define it in numerical terms; this allows the problem to translate in machine codes. It is important to define the problem in a measurable form because that decides it falls under the data science category. If we are unable to determine whether or not a problem is a part of data science, we clear the confusion by asking a series of questions.

  • We will start by understanding the business vision.
  • We will try to convert the problem into business terms or numeric terms.
  • We will work on the difficulties faced by the business.
  • We will find about the resources at our disposal.
  • We will weigh the outcomes of problem-solving and decide whether to pursue the problem or not.
  • We will work on the machine learning tools required to solve the problem.

Setting The Goals

Once we have defined the problem, we will work on creating the goals for solving the problem. We will perform the following steps for it.

  • Try to answer descriptively for the five W’s – Who, What, Where, When, and Why.
  • We will do risk calculations and find whether we must put our efforts into solving the problem.
  • Determine and inform which benefits are realistic.
  • Finally, a set of realistic benefits becomes our achievable goals.

Preparing Data For The Problem-Solving

In this step, we are aware of the problem that we have to solve to achieve our goals. Now, we will work on collecting the required information for it. We will follow the following steps for it.

  • We will list the variables of concern.
  • We will find out the sources of data.
  • We will gather the data from them on a daily, weekly, monthly, quarterly, and yearly basis; based on the duration in the problem.
  • We will find out the dependent and independent variables of the data.
  • We will apply cleaning techniques on our data to rule out missing values, remove duplicates, remove outliers, convert qualitative data into quantitative, etc.

Perform The Analysis

Once we have gathered the required data, we perform various types of analysis on the data to gather insights for using machine learning algorithms on it.

  • We will perform an analysis of the data. Starting with descriptive analytics to diagnostic analysis, predictive analysis, and prescriptive analysis.
  • We will find out the patterns amongst the data and show correlations among the features.
  • We will find out the type of problem we are facing based on whether the input data is labeled or not.
  • We will apply the appropriate machine learning algorithm like regression analysis or classification on our problem to get the solution.
  • We will train the model based on the algorithm and test the model. We will make the predictions and fine-tune the model to give more accurate predictions.

Interpret The Results

We will interpret the results of the algorithm and showcase the results in the form of beautiful dashboards. We can help the business decision-makers make the interpretations from these results and guide them about future decisions.

For Example, Regression Model helps in understanding the importance of variables on the outcome and stimulate these variables and see the results accordingly.

Conclusion

Data science can help you in solving problems at an early stage and save you the trouble of dealing with a threat later. Through this blog, we’ve tried to highlight the steps that can help you in using data analysis to make decisions about a problem and not let it disturb your business cycle.

In such dubious times, data analysis is our ticket out of the shredder. Crunch those numbers and churn that data to find out the obstacles that are hampering the continuity of your business cycle.

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