Chapter Eight: Data Science in a Sector

Most statisticians have been stating that data science is exactly what they have been doing for the last few decades. While this may be true to an extent, you cannot agree with them. Data science is completely different and separate from traditional statistical and mathematical approaches. Data science is different from statistics in many ways. Data scientists use programming languages to improve the efficiency of their analysis. If you are a statistician, you need to know the subject in its entirety. As a data scientist, you need only to understand the basics of mathematics and statistics.

Statisticians have very limited expertise about anything apart from statistics. They need to reach out to subject matter experts if they want any help or information about other subjects. They also need some help from experts to help them determine and understand their findings. This is the only way they can learn how they can move forward.

On the other hand, a data scientist has enough information about different subjects that helps him understand and analyze the findings with ease. A data scientist generates deep insights and uses his subject matter expertise to understand what the data is trying to tell him.

The following are some ways to use data science to improve the performance of the business in specific sectors.

• Some architectural firms have begun to use machine learning algorithms to optimize the consumption of energy in modern designs
• Marketing firms and businesses use logistic regression and other statistical algorithms to predict how customers may behave in the future
• The healthcare industry uses data science to determine illnesses and personalize treatment plants. This allows doctors to predict and check for any future health problems
• A data journalist takes information from the website to identify any patterns and connections within the data set
• Law enforcement agencies and organizations use data science to prevent and predict any criminal activities

Communicate the Insights Captured Through Data

If you want to become a good data scientist, you need to have sharp written and oral communication skills. As a data scientist, you need to communicate the information you have inferred from the data. If you cannot do this, all the information in the data set means nothing to you or the business. A data scientist must find a way to explain the insights gathered from the data set so that he can develop meaningful and clear visualizations. It is important to remember that people are visual, so they learn better when they can visualize anything. As a data scientist, you need to be pragmatic and creative about the data collected and find the right way to communicate them clearly.

Leverage Cloud-Based Solutions

If you are not used to working with large volumes of data, you can use cloud-based solutions to learn more about the patterns in the data set. Most organizations and data analysts have understood the use of data analytics and big data and their importance. It is a new domain for some people and organizations, but they are working as hard as possible to prepare for the flood of big data. many cloud applications, both private and new, are dedicated to improving the way companies handle their big data.

You can also use cloud services, such as Tableau, to clean and process data sets. Most cloud-based applications are open-source and code-free. You can use these solutions to model and visualize data using various statistical tools and techniques. You need basic knowledge of mathematics and statistics before you apply any data science techniques on the data set. If you do not have knowledge about programming, you can use applications that do not require you to write any scripts or code.

Regardless of whether you use cloud-based solutions to perform data analysis or not, you need to train your employees and gather more information about what data science is. You also need to learn about the algorithms discussed earlier. It is important for you to learn more about how to process data, design a model, build the model and analyze it if you want to obtain insights about the data. You cannot expect any cloud-based platform to make it easier for anybody to work with the data set if they do not have basic knowledge of mathematics and statistics.