Data Science: Introduction
Data science is an academic field that is related to machine learning and big data which uses scientific methods, algorithms, and processes to extract insights and business intelligence from a variety of structured and unstructured data.
The data science workflow involves a complex set of processes such as data warehousing, data cleansing, data ingestion, data delivery, data processing, data modeling, data clustering, and insight aggregation. Once insights are gained, data scientists perform probing, regression, text mining, predictive analytics, and qualitative analytics. Then the insights are communicated through data visualization. This helps make profitable decisions for the business. Go for certification courses online to know more about data science and get a Great Learning data science certificate online.
Data Science: Facts
Whether it is statistics, machine learning, data science, analytics, etc., this domain has grown in the last quarter of this century. This is mainly due to the improved data acquisition capabilities and the dramatic increase in computing power. If you are an IT professional or data analyst, you will be asked for data insights to make good or bad decisions based on your data. If your data provides valuable information, you may invest more money based on performance.
The operation of such an important amount of information has evolved data analysis into another scientific discipline, data science. This new field is currently under construction. It is inconsistent and provokes debate about purpose, subject matter, the form of existence, and even proper naming. However, due to their convenience, there is less uncertainty.
But as an analytical career progresses, some truths become apparent. And while none of them are groundbreaking, they often surprise newcomers to the field. Therefore, it is useful to know some of the absolute facts of data science:
1. Digital data is the most demanding field at present
With so much data out there, there are endless combinations of real-world applications in any discipline or industry, including healthcare, commerce, agriculture, telecommunications, and banking. Students will team up with concrete teams during the last two weeks of the Data Science Boot Camp to work on selected projects. Data science has endless use cases!
2. Data is never clean
An analysis without actual data is just a collection of hypotheses and theories. The data will help you test them and find the right one for each end-use. However, in the real world, the data is never clean. Data is not clean, even for organizations that have a data science center that has been established for decades. Aside from missing or incorrect values, one of the biggest problems is merging multiple records into a cohesive whole. The join keys may be inconsistent or incorrect in particle size or format. And that's not intentional. Data storage companies are tightly integrated with the front-end software and users that generate the data and are often created independently.
3. Data analysts, data scientists, and data engineers are not the same
Data analysts are the people closest to the company. They process the raw data and clean it up for use by data scientists. You are very familiar with Excel, data visualization, statistics, SQL, and Python. Data scientists are responsible for the mathematical analysis of the data. They use probability theory and algorithms to predict outcomes from trends in the data found and require experience in the sector or industry they work to relate to. Data engineers have a very technical role, and they are compiled and guarantee the integrity of the data. Deploy your application to properly receive and store data and grant access to those who need it. Depending on your pre-Bootcamp level, you can also start as a junior data engineer.
4. Data science isn't just for large organizations
Many companies believe that data science is only for large organizations with world-class infrastructure. This belief stems from a misunderstanding in data science. Data science does not consist of machines, heavy tools, or the size of the workforce. It could be made up of big data, statistics, analytics, programming, presentations, and smart people who know how to get the most out of your data and add value to your organization. It has nothing to do with big or small organizations. Data Scientists need to come to a conclusion that benefits the company. And no one really cares which tools or techniques were used to achieve that result. As far as the infrastructure is concerned, all you need is a few tools to support the life cycle of computing devices, the Internet, and data science. There are several open-source tools available online and available for download to roll the ball.
5. Data science salaries are much higher than other technology development professionals
The role of data is very diverse, and working in data science requires a complex range of skills. In fact, most data engineers were the first back-end developers. Many data science students attended a web development boot camp or had programming and math skills before attending this course. These roles also require the ability to work in teams and know how to communicate with non-technical staff through data science models.
6. More than 90% of tasks do not require deep learning
90% is clearly a structured number, but the idea is that most real problems do not require advanced analytical skills. Solving real-world problems requires understanding the real-world problem domains, decision-makers, and end-users, rather than understanding the latest and greatest discoveries in statistics. Those that move the needle fast are far more valuable than those that are rigorous and pure. That doesn't mean there is no place in a complex model. In fact, depending on your cash flow, a 0.1% improvement in forecast accuracy is worth millions of dollars.
7. Most of the time is spent cleaning and preparing the data
As a corollary to this, most of the time is spent cleaning up and processing model consumption data. This usually bothers people unfamiliar with the industry. It seems like a waste of talent and time to spend three-quarters of the time just scrambling data with a great mind full of sophisticated machine learning techniques. Often this leads to dissatisfaction and lack of attention-a mistakes that even the most sophisticated algorithms can suffer. If you don't do this calmly and can't focus on the big picture, it's a good idea to aim for statistical research rather than a data science career.
Conclusion
The explosive growth of data has made data science inevitable in almost every area. It offers a good career opportunity. Thinking of data science as a career option is a wise decision for anyone who enjoys problem-solving and empathizes with data. Enroll in a Great Learning data analyst course online as it has great potential for both businesses and job seekers. As data science grows in popularity, we may see some myths related to data science, along with some interesting facts. However, we recommend that you verify all details and not be fooled by any false information that may arise.
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