Data science is popular on the internet. Companies and people are aware of how vital data science is in changing the world. Today, you notice news feeds that are based on the things that seem to interest you. This is how far data science has infiltrated. Data Science offers the best opportunities to those who are willing to work and better themselves.
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We all desire to make a better life, and data science is one of the most lucrative career paths you can take. Many people covet this field. However, the main question is, is it for everyone?
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To give a quick answer, we would say yes, everyone can study data science. However, even though anyone can learn data science, we must appreciate that you must dedicate a lot of effort and time to it. It is always a learning process in data science. This can be a good option regardless of your domain, expertise, area of interest, or nature. Study data science.
Why you should study data science
We are living in digital time. Regardless of age, using the internet is essential. With such skills, life becomes less frustrating. Imagine an older person learning to use the internet. So many privileges come into play. The person can navigate, communicate, shop, and network efficiently. They can join group conversations, do video calls, take digital photos, and so much more without living at home. Without the internet today, seniors remain digitally disabled and barely know what is happening in the world around them.
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The same is valid with leveraging data. If a company is operating in this era but is incapable of leveraging data, it is digitally disabled. The same applies to an entrepreneur, a professional, or a student. You need to learn to work with data and make sense of it to rise higher in the ranks.
In data science, there are tools at all levels, and things keep on evolving. When you are not handling too much data, a tool such as Excel can suffice and give results. However, when there is so much more data to deal with, excel cannot be adequate. Working with such a large Excel data set can be frustrating, if not impossible. The main reasons why anyone can study data science with some efforts are:
It is a field that is industry agnostic. This is to say that regardless of your domain, data always has an edge. Leveraging your experience and knowledge becomes possible.
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Data science is a practical field. Many problems that companies face today can be handled with the right insights. These insights can only be deduced from data. Data needs to be analyzed, corrected, interpreted, cleaned, and processed to help in decision-making. Regardless of who you are or the experience you have, data science is a possible path to take.
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Data science roles
There are different roles in data science. The ones taken up usually depend on the needs of the company or business. It is important for the stakeholders to find the best path to improve their businesses. To understand data science better, think of the following steps:
- Orchestrate Data: this involves retrieving data from different sources. Data can be on the cloud, local database, or Excel.
- Standardizing and cleaning data: once data has been retrieved, it has to be standardized to have a uniform format. In addition, the data must be organized and cleaned to develop a master data set.
- Analytics and modeling: after a master data set has been created, analytics needs to be performed, especially for the bottom and top performers. This helps with optimization. This is achieved using different methods, including:
- Heuristic model: This model has rules to tell us whether something like a product is performing poorly or well. You would need code to write business rules that can then be applied to the master data set.
- Statistical model: there are seasonal products. A good example is umbrellas. This kind of product may only sell well during the rainy seasons but have poor sales during other seasons. It does not make the product a poor performer. Data science can help a business realize that the product peaks during certain periods and performs poorly during others. With such data, seasonal sales may be inferred by learning more about the statistical distribution.
- AI/Machine Learning model: machine learning models are also an important approach. In this model, the best and worst performers are used, but instead of statistics or a heuristic approach, we use historical data to make conclusions. It is with such data that we can know the best commodities.
This is how data science works. You need to learn and practice these things during your data science career path. The roles in data science that you can consider include the following:
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- Data Engineer: data comes from different sources. Data engineers make pipelines to tap into as many sources as possible.
- Machine Learning Engineer: machine learning engineers work closely with a business team to understand different requirements and with the engineering team to access data. This role can be filled by those who love solving problems.
- Business Analyst: This is another critical area in data science. Business analysts use presentations and dashboards to help quantify and make sense of data. There are many skill sets needed here.
- Data Scientist: this is one of the most coveted roles anyone can study. You must put in much work and effort to qualify as a data scientist. Data scientists need to have a combination of the above skills.
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Conclusion
We can comfortably say that learning data science is possible for everyone. There are many areas to pick from. First, you should consider your interests, experience, and ambitions. Find out what it takes to take up a role in data science, and then decide what path to take. Learn more about the demands in data science and possible obstacles to face along the way. Then, if you take the data science path, you should always be ready to learn.
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