It is until recently that the US government recognized data science as a job.
Companies now have a green light to hire data scientists and to build their job descriptions. People have become interested in this emerging science, but what does it mean?
What is Data Science?
Well, data science is an interdisciplinary field that combines different statistical and mathematical processes and algorithms to extract knowledge from structured or unstructured data. Data science is a concept that emerged to unify independent domains like machine learning, statistics, and data analysis.
Machine learning means studying how computer systems work to solve different algorithms, while statistics helps in collecting and organizing data based on the search for patterns in a variety of input data. Data analysis is a small part of statistics but it can exist as an independent domain that inspects and transforms modelling data.
All these small domains are united under the data science concepts to analyze and use data to extract knowledge and support the decision-making process.
Data science is a helpful and useful domain that can help people make informed decisions while discovering important information. Data scientists are the ones that can help a business extract valuable knowledge from their data.
The technologies are changing from day to day and many more dependent domains need to evolve too. Data analysis has become more and more complex and the tasks are more and more demanding.
Although in the last years it was programmers who tried to analyze data, now data scientist exists as an independent job.
Yes, it is useful to have a data scientist to help you with the decision-making process. Data analysis can provide some useful information about your business and the ecosystem within so that you can improve your business model and processes. But who needs to hire data scientists today?
1. Retail Companies
Retail companies have a huge amount of data that needs to be analyzed. Here is where data scientists can help them analyze their costs and profits and plan better for the future.
Data scientists can help retail companies improve customer experience by analyzing data and providing information about their behaviour. By analyzing the customer data, data scientists can recommend engines to improve the customer experience.
Also, they provide data about fraud, price optimization, and inventory management. They can also predict marketing trends and provide information to build a personalized marketing plan.
Using data science and analytics financial institutions allow you to stand out from the competition and take a fresh look at doing business.
2. Content Companies
Most people would think that only big and important companies need data scientists. Indeed, it emerged firstly in the IT and Artificial Intelligence domains, but it is not limited to them. Data science can be used in the media industry to keep up with the latest technological improvement.
The media domain has become more and more dynamic in recent years, with newer and newer possibilities for sending messages emerging. You can use photos, videos, GIFs, images, texts, and so on. So, the need to develop a deep learning process and data analysis is essential for content companies.
The data they collect is mostly unstructured, but if it is analyzed, it can provide valuable insights into customer behaviours. And like this, data science can help analyze and extract knowledge to build targeted media campaigns.
It is also important to monitor the coverage. It shows the number of people who at least once visited the blog or saw the post in the feed, or commented on some post. It is considered separately if there is an intermediate task to compare the effectiveness of several posts. Organic coverage is influenced by content posting frequency and social media ranking algorithms
Data scientists can help content companies understand why their audience likes or dislikes a specific content. This kind of information helps companies build successful media campaigns and improve their decision-making processes within the company.
3. Tech Companies
Tech companies are the ones that constantly develop new products and apps, and all decisions regarding them need to be supported by data. When you have a tech company or a start-up that wants to bring a new technology or service to the market, data science is the key to success.
All changes in features or products are supported by innovations in data management and machine learning. Data scientists working in tech companies have the task of grabbing all data and analyzing it, providing useful information, and supporting feature changes.
4. Healthcare Companies
What would humanity do without doctors, medications, and hospitals? The healthcare industry developed to ease doctors’ jobs but to also maintain the connection with the patients easier. Nowadays, new healthcare companies emerge and offer medical solutions for hospitals or healthcare tech services.
Companies that are active in the medical domain need data scientists to make the healthcare industry more organized, efficient, synchronized, and productive. Data insights can help doctors prescribe the best medical treatment, having the risks minimized.
They can help build plans to adapt to any emergency and deliver services on time. Data scientists help healthcare companies store and compile medical records efficiently and identify gaps in patients’ medical records.
5. Financial Companies
Financial companies and banks need to use data science to make predictions about the spending behaviour of their clients. They have all the data about their income, purchase patterns, and powers.
The benefits of data science in the financial sector are obvious especially when a company or a bank needs to decide whether to give or not credit cards.
The most important resource for financial companies is information. Effective data management is the key to business success.
Today, there is a huge array of financial data that is diverse in structure and volume: from the activity on social networks and instant messengers to market data or transaction details. Often, financial professionals have to work with completely unstructured data; manually processing such an array is not an easy task.
For most companies, it is obvious to integrate machine learning methods into management processes to extract optimized information from a dataset.
Financial companies need to make the best decisions to minimize loss. To give or not a credit card, they need to take into consideration important aspects of the client requesting it.
Or, they can develop algorithms based on machine learning and big data to extract important information from contracts. Because banks and financial institutions sign contracts daily, it would be difficult to do this manually.
How does Data Science Drive Innovation?
Data science is an important science that has emerged in recent years. It combines knowledge from statistics, machine learning, and data analysis to provide valuable information and knowledge. This knowledge helps content companies shape better media campaigns and understand the behaviour of their audience.
It helps healthcare companies ensure a healthy workflow and medical treatment administration. This valuable information data scientists provide helps financial companies predict the behaviour of their clients.
It helps tech companies support changes in features or the development of new products. It helps retail companies analyze their costs and profits and make better plans.
Data science is important because technology is constantly evolving. And if you want to be successful in your industry, you need to invest in data analysis to better understand your current status.
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