How to Become a First-class Data Scientist - Best Advice

Data is powerful to those who know how to make sense of it.

Data science and AI (Artificial Intelligence) has not been for long; however, the concept of pulling out value from data has been around for a long time.

This article introduces you to Data Science and how it helps define the future of businesses that want to excel in their various industries.

Before we talk about becoming a Data Scientist, I will explain the various things you can do with it and how it affects our day-to-day lives.

## 1. What is Data Science? Data Science is used across several industries to create better and more efficient solutions in a simple term.

Data Science is the occupation of acquiring data, discovering insights, and transmitting discoveries in all this data. The amount of information is constantly increasing along with its complexity. Data Scientists systemise it and make sense of it.

## 2. Who is a Data Scientist? They are the people who do the Data science job. A data scientist gathers and analyses information and looks for possible patterns. Data scientists can do this through a variety of techniques.

They might present the data in a visual context or data visualisation, observing clear directions that would not have been noticeable if the data was presented in a usual spreadsheet, complex numbers and tables.

## 3. Example of a Data Scientist job Looking at a real-life scenario where a data scientist could help society. Let us consider the health sector;

While it is tough to place a price tag on the global economic burden of diseases like asthma, the most reliable way out is to increase the awareness of the possible course and prevent people from contracting the illness in the first place.

Over 2 million people will end up in an emergency ward in the US alone due to preventable attacks.

Fifty billion dollars ($50,000,000,000) is spent yearly on asthma patient visits to the hospital, making it the most significant contributor.

So what if we could identify the patterns behind these attacks? What if we could prevent unnecessary suffering from victims and save billions of dollars?

This is where data scientists come in. Their responsibility is to pay attention to occurrences, ask relevant questions, and develop a model to save asthmatic patients from unnecessary attacks.

Meet Kola, a data scientist whose job is to help solve these problems.

Formulating hypotheses

Kola is studying the trends in the field of biomedical research. In addition, he is has been working on a project involving weather data and pollen, a known asthmatic trigger.

Over the weekend, Kola got a report about increasing asthma patients from the hospital. Kola wonders if the weekend attacks were triggered and if the weather could help predict when patients are at higher risk of an attack.

Now, Kola just came up with a hypothesis; the next step is for him to prove it. Considering that Kola's theory has not been validated, however, if he finds a link, it could be a breakthrough for patients and related health care businesses.

Data Mining

Kola decides to go further with his research, and he approaches his engineer Matthew to determine ways to proceed.

For Kola's theory to produce possible results, they will need a combination of data from previous records. Mainly, this can be achieved through electronic medical records.

They will need records on asthmatic related emergency visits to the hospital. The documents are considered privileged and have to be extracted and kept private.

Kola and his engineering team will also need to consider external sources such as pollution and weather data that are publicly available.

Data wrangling

With a suitable dataset secured by Kalo and his team, the next step for Kola is to clean up the data. The cleanup process is called data wrangling; this involves removing irrelevant data points and putting the correct information into a format so it can be easily analysed.

Data exploration

The next step is data exploration; Kola looks for the appropriate location and time-specific correlation.

For instance, Kola compares wind partners with past asthma records to see if pollen spread correlated to a high outbreak.

Kola's findings support his hypothesis, so he begins feature and model selections to create predictive models.

Based on the correlations, he selects features in the data that are the most useful and relevant to predicting asthma outbreaks.

Validating model

Now that the model is set, Kola starts to cross-validate by dividing his data and conducting multiple tests for accuracy. Then, if those tests function well, Kola runs a test on an entirely new set of data the model hasn't seen.

After successfully testing with new data sets, Kola has quantified specific weather conditions strongly tied to the onset of severe asthma attacks.

Deploying model

And now that Kola has created a model to predict those conditions accurately, he can develop it to create an alert application that will benefit patients and businesses.

With support from Matthew (the head of the engineering team) and his crew, turns Kola's model into an accessible application programming interface (API) that will service the alert application.

Selling the solution

Now it is period to earn some money. Mary, the offering manager, starts telling her clients about the new application and how it can be of use to them and their businesses.

• For an insurance company, fewer ER (Emergency Room) visits when their patients are prepared.
• For hospital administrators, it is cost savings and more innovative staffing of specialists.
• For pharmacies, it is higher customers loyalty and engagement. They alert customers to get their prescriptions on time.
• Finally, for the asthmatic patients, it is enjoying the seasonal weather.

## 4. How to become a data scientist Now that you've seen the practical example of the role of a data scientist in an organisation, the question to ask now is, "am I ready to become one of them?"

If your answer is yes, then let us continue.

• There are three possible ways to become a data scientist:
• Acquire a bachelor's degree in IT-related courses (business, computer science, math, or any other related field);
• Gain a master's degree in the data-related field;
• Hands-on experience in the area you intend to work in (ex: business, healthcare, physics).

## 5. The traits of a good Data Scientist - A data scientist must be curious; this pushes them to learn more things constantly. Data is broad and massive; to accurately analyse this cumbersome information, data scientists must possess an inherent curiousness that drives their need to find explanations. - The ability to organise information is also crucial. Most times, other people need to make meaning out of their findings. There are millions of data points, making sure information is organised is critical.

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