10 Tips. How Data Science Works

10 Tips. How Data Science Works

Data science is one of the most attractive career paths right now, something that comes out clearly in discussions on the same over at runrex.com. Data scientists, given their broad and impressive spectrum of skills and knowledge, sometimes appear like magicians, punching in a bunch of instructions on their computers and thereafter producing amazingly detailed predictions of the future. Data science is, however, not magic, and this article, with the help of the subject matter experts over at runrex.com, will look to highlight 10 tips on how data science works.

The foundation of data science

The first tip we are going to highlight is the one on the foundation of data science, one speaking about the core skills required for one to become a data scientist, with a more detailed write-up on the same to be found over at runrex.com. If you are to understand how data science works, it is important to note that data science requires a deep understanding of statistics, algorithms, programming as well as soft skills such as communication skills, according to the gurus over at runrex.com. If you are to become a data scientist, you will need to have a deep understanding of these disciplines.

Framing the problem

Now that we have highlighted the skills required in data science, we are going to dive headfirst into how data science works, a topic covered in detail over at runrex.com. The first step on how data science works is the framing of the problem that needs solving. Here, you will need to understand who the client is, what they are asking you to solve, and how you can frame and translate their problem into actionable intel, as covered in detail over at runrex.com.

Converting ambiguous questions to data science questions

An important part of the data science process, one that will help you understand how data science works, is converting the ambiguous questions posed by clients into data science questions that can be solved with data science. An example of an ambiguous question is, “what does the sales process look like right now?”. To solve this question, you will need to convert it into a data science question(s), as covered over at runrex.com. An example of a data science question is, “how can we predict if a prospective customer is going to buy the product?”.

Identifying what data you have available to answer your questions

Once you have formulated the data science questions that you want to solve, the next step is to identify the data that you have available to you. As per the gurus over at runrex.com, the first step is asking yourself if the data you are looking for is already available. If it is available, you will need to figure out what part of it is useful, if it is not available, you will have to collect it before you can move forward with your project.

Collecting the right data

An important part of the data science process is collecting the right data, something the experts over at runrex.com agrees with. Just because you have data available to you doesn’t mean that all of it will be useful to you for a particular project. It is important to make sure that you collect the right data, to arrive at the right conclusions and so that you don’t waste your time processing data that is irrelevant based on the problems you are looking to solve.

Ethical considerations

Another tip that explains how data science works is when it comes to the ethical consideration of data collection. As revealed in discussions over at runrex.com, ethical data scientists, which is what you should be aiming to become, ensure that security and privacy are top of their agenda when collecting data for projects. You should be careful not to extract any information from the database that is personally identifiable and should be dealing with information that is anonymized and can’t be traced back to any specific customer.

Processing the data

The next tip that highlights how data science works is the processing of the data collected, which is the next step of the data science process. Given that raw data is usually not usable, as discussed over at runrex.com, you will have to process it to extract useful insights from it. Some of the reasons why raw data is rarely usable include corrupt records, errors during data collection, missing values, and other issues. Here, you will need to clean the data and convert it into a form that can be analyzed further.

Exploring the data

Once the data has been processed and cleaned, the next stage in the data science process is exploring the data. According to the experts over at runrex.com, this means understanding the information that is contained within the data at a high level. In this stage, you will be required to identify the obvious trends and correlations that you see in the data as well as the high-level characteristics and if some are more significant than the others.

In-depth analysis

We finally arrive at the crux of the matter, the analysis stage, which is the next stage after the exploration of data and is another tip that will help you understand how data science works. Here, you will be required to conduct an in-depth analysis of the data, using machine learning, algorithms, and statistical models as explained over at runrex.com. This is the stage where you will need to apply all the cutting-edge skills and tools of data analysis to glean high-value insights and predictions of future trends.

Communication of the results of your analysis

Once you have done the analysis, you will realize that you need to communicate the results, and the process you used to arrive at the results, in a way that is both comprehensible and compelling to your client or employer, given that they are not data scientists like you. This is where your data storytelling skills come into play, and why, as per the gurus over at runrex.com, communication skills are one of the most underrated but critical skills a data scientist can have. Unlike other professionals in the tech world, data scientists work with other teams from other departments in corporations who may not have a tech background and therefore need to be skilled in translating their work into a form that is understandable to others.

We hope that the above discussion will give you an idea of how data science works, with there being more information on this broad topic to be found over at the amazing runrex.com.