As is the case with applied scientists in other fields, applied data scientists probe for answers and apply what they have learned to provide practical solutions. They search and research, observe, experiment, analyze, hypothesize and predict.
The role of an applied data scientist combines numerous disciplines, such as those of a statistician, mathematician and computer scientist. Data science usually involves looking for answers to business solutions. It is also commonly applied to the medical field. Regardless of the industry in which they work, data scientists deal with large databases of historical and sometimes real-time information. Based on their findings, applied data scientists give advice, present information to stakeholders and forecast future scenarios.
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What skills do data scientists need?
In this multidisciplined environment, the data scientist needs to have a range of technical and analytical skills, a strong mathematical and statistical background, computer skills and the ability to analyze and hypothesize. They need a sound understanding of the business or organization they are working for, as well as good communication skills.
A vast range of tools, algorithms and methods assist with the analysis and extraction of data as well as the presentation of data and forecasting, so a sound knowledge of machine learning (MI), big data and artificial intelligence (AI) is a strong recommendation for someone seeking employment in the field of applied data science. Baylor University offers a data science online masters degree with a data science track, enabling you to achieve a computer science qualification and seek employment as an applied data scientist. You will also learn about data mining and data warehouses, data architecture and cloud environments, parallel programming and how to analysis and reporting tools.
What data scientists do
Here are some of the functions of a data scientist:
- Needs analysis: Based on sound knowledge of the organization, the data scientist must ascertain what the needs are for the project. Together with the stakeholders, a project specification will be drawn up. If the data is not familiar to the data scientist, they may enlist the help of the people who work with the data, namely database administrators, data miners and software specialists. These people will provide details as to how the data is stored and what information it should yield.
- Collection of data: The data then needs to be mined, collected and analyzed. The data is often in huge databases, and the format may be in a very basic, raw format, or it may be organized in more user-friendly formats. This is where your computer skills come in. The data scientist may have to create algorithms or use software tools to collect the data. Often in raw format and sometimes from many disparate sources, the data needs to be categorized and sorted for ease of use. To this end, the data scientist searches for trends and patterns in the data, as well as associated records that may shed extra insight.
- Optimization of data: The data is then “cleaned” and verified for integrity and completeness. Superfluous records are eliminated or ignored in the process, as working with large amounts of data at a time may slow the process down. It can then be further classified and sorted into subsets, where applicable, in a format that is suitable for analysis.
- Analysis of data: Using predictive tools and algorithms, the data is analyzed for current trends and future performance, and trends can be forecasted. The analyst will identify patterns in the data that are indicative of problems that should be fixed or favorable conditions that can be further enhanced.
- Reporting: Findings are communicated, either verbally or visually, by means of reports, summaries, graphs and diagrams. Recommendations are made based on findings.
In addition, the data scientist may upgrade and introduce new algorithms and analytical tools. Algorithms are used to determine which data is applicable and may be used for calculations and forecasting. Sometimes, existing algorithms need to be modified and optimized for increased processing efficiency, and part of the data scientist’s job is to enhance the diagnostic and investigative processes for the business by introducing new algorithms, analytical software and models.
How does this affect business?
The analysis of big data has become essential to the success and continued survival of businesses, large and small.
The data scientist has the necessary skills to recommend improvements and automate areas of the business operations that were previously manual, thus saving time and money and freeing up resources.
Some of the benefits of data science in business include:
- Improved customer service and customer retention.
- Accurate product recommendations and restocking levels.
- Improvement of marketing strategies.
- Efficient financial analysis.
- Minimized risk of fraud.
- Algorithmic trading.
- Credit risk management.
- Process automation and maintenance scheduling in manufacturing environments.
- Identification of e-commerce consumers, recommending products and analyzing reviews.
- Optimization of delivery routes enabled by logistical analysis.
- Enhanced security systems with voice, face and speech recognition.
Data science is also used in areas besides commerce, such as various medical environments. Numerous breakthroughs in medical science can be attributed to advances in data science. Medical research uses data science to track the efficacy of new drugs and treatments. Doctors keep track of patient data, and analysis of medical issues enables healthcare professionals to identify and monitor the stages of a disease and the extent of damage. As a result, data science can suggest an appropriate treatment.
Data science is also used in educational and scientific institutions, agriculture, transportation, engineering and governmental departments. The list goes on.
Applied data science as a career
Besides acquiring many technical skills, the applied data scientist also needs to have strong communication skills as well as good business acumen. An ability to explain highly technical information to non-technical colleagues requires exceptional presentation skills, be they verbal, written or visual.
As a career, applied data science is highly technical, but also interesting and challenging. Technology changes daily, and there is always something new to be learned. Become an integral part of your organization with your knowledge and skills, and know that you are making a difference.