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7 Reasons Why You Should Become a Data Scientist

Reasons to Become a Data Scientist

Data scientists are hot right now. Everyone wants to be a data scientist, and there is an obvious reason why: job opportunities. Data science is expected to become one of the fastest-growing jobs in the next few years. Plus, the demand for data scientists will only continue to increase.

The coming age of artificial intelligence, machine learning, and big data will create more and more demand for skilled data scientists. In addition, companies are increasingly demanding that their employees have a solid understanding of data in order not only to make predictions but also to validate those predictions before investing resources into testing them out.

What is a data scientist?

A data scientist is a person who uses data to create value for an organization through the application of statistics and computer science to business problems. In other words, data scientists are not just statisticians or programmers who use data to find patterns or create models. They are problem solvers who can leverage data to solve a wide range of business challenges.

A data scientist is also a key member of the data-driven organization, where stakeholders across different departments use data to make informed decisions and optimize operations. They are responsible for bringing together a variety of tools and disciplines to solve business challenges.

1. Good jobs for becoming a data scientist

Although jobs come and go especially during an economic downturn, there are still plenty of job opportunities to become a data scientist. Jobs for data scientists span a wide range of industries and settings.

Some of the best jobs for becoming a data scientist include consulting. Consultants use data to improve operations for their clients and use their own data to find new ways to work. A data scientist in this role works with executives and stakeholders at their company to find ways to optimize their operations and make more informed decisions.

Other great options include startups where they work on products that help companies make better decisions. But employment is not only in the private sector as there are various ways how governments leverage data science to help them make better decisions.

2. A high-growth field, with great pay and benefits

The coming age of artificial intelligence, machine learning, and big data will create more and more demand for skilled data analysts. You can expect to find plenty of job openings for data scientists in the years ahead.

According to Glassdoor, the average salary for a data scientist in the United States is around $126,000 per year. However, salaries can vary depending on factors such as experience, education level, industry, and geographic location. As a top 10 paying job in tech, many companies also offer benefits packages to data scientists, including health insurance and flexible work arrangements.

3. There are plenty of opportunities for juniors and pros.

There are plenty of opportunities to get started as a data scientist. If you have a bachelor’s degree, you’re likely at an advantage over your peers.

Data Scientist

For junior data scientists, there are many entry-level positions available that provide opportunities to learn and develop skills on the job. These positions may involve working on smaller projects or assisting more senior team members with their work. Junior data scientists may also be able to gain experience through internships, apprenticeships, or other training programs.

Experienced data scientists can find opportunities to work on more complex and challenging projects, and may be able to take on leadership roles within their organizations. They may also be able to specialize in specific areas of data science, such as machine learning, data engineering, or data visualization.

4. Building Statistics and Machine Learning Skills

A data scientist uses a combination of statistics and machine learning to investigate and solve problems. Machine learning techniques can be applied to a wide range of data types, including structured and unstructured data, and can be used for a variety of applications, such as image recognition, natural language processing, and fraud detection.

In order to be a successful data scientist, you must have a very strong understanding of statistics. This can involve calculating descriptive statistics, such as the mean and standard deviation of a data set, as well as inferential statistics, such as hypothesis testing and regression analysis.

5. Developing AI Applications

Data scientists can develop a wide range of AI applications, including but not limited to:

  • Image and Video Recognition: AI can be trained to recognize objects, faces, and actions in images and videos. This has applications in security, entertainment, and autonomous vehicles.
  • Natural Language Processing (NLP): AI can be trained to understand and interpret human language. This has applications in virtual assistants, chatbots, and sentiment analysis.
  • Predictive Analytics: AI can be trained to analyze data and make predictions about future trends. This has applications in finance, marketing, and healthcare.
  • Recommendation Systems: AI can be trained to recommend products, services, and content based on a user’s behavior and preferences. This has applications in e-commerce, streaming services, and social media.
  • Fraud Detection: AI can be trained to detect fraudulent activity in financial transactions, insurance claims, and online purchases.
  • Autonomous Systems: AI can be used to develop autonomous vehicles, drones, and robots that can perform tasks without human intervention.

These are just a few examples of the many AI applications that data scientists can develop. The possibilities are almost limitless.

6. Data cleaning is super important, especially when it comes to ML work

When you’re doing machine learning and AI work, you need to clean your data. This means removing noise and including only the relevant information for your work. Data cleaning can be an extremely time-consuming process, but fortunately, you don’t have to do it manually. You can use tools such as Apache Spark to clean your data and prepare it for use.

This is important for many reasons, but one of the most important reasons is that it allows the model to produce accurate results. If the data is not clean enough for the model, then the model will not work properly. If the data is cleaned properly, then the model will be able to process the data correctly, and it will be able to make accurate predictions.

7. Branching out into data engineering

Data engineering is largely about extracting value from data and transforming it into actionable insights. Data science, on the other hand, is about using data to improve understanding of complex phenomena.

Both are closely related to each other because they involve the process of extracting meaning from data, transforming that data into a form that can be used by business analysts, and making that data available to the appropriate users. 

First, you need to build the infrastructure, such as the database, to store the data. You also need to build tools to help you transform and prepare the data. And finally, you need to build tools to help you get the data into the infrastructure, such as a data pipeline.

Data Science Roles Mind Map


Data scientists are in high demand, have great pay and benefits, and can have a huge impact on the organizations that hire them. To become a data scientist, you often need to have a bachelor’s degree in statistics or computer science and strong programming skills. If you want to get started, you should consider starting with a master’s in a data science program.

Data science is a field that is expected to grow quickly in the next few years. There is a high demand for data scientists, and the pay and benefits of this profession are also very appealing. You must be aware of the skills that are needed for this profession, and you must develop them in order to be successful in this field.

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