How to become a Data Scientist?

by Analytics Insight

September 6, 2021

Here’s all you need to know.

Data science is one of the most demanded careers of the 21st century. In today’s high-tech world, everyone has important questions that big data needs to answer. From businesses and nonprofits to government agencies, there is an almost limitless amount of data that can be processed, examined, and used for a variety of purposes. Read on to find out how to become a data scientist and get started on this rewarding career path.

What is a Data Scientist?

Data science is a complex and often confusing field that requires hundreds of different skills, which makes it difficult to categorize.

Essentially, a data scientist is a person who collects and analyzes data in order to reach a conclusion. They can deliver the data in a visual context, commonly referred to as “data visualization,” allowing a user to look for obvious patterns that would be missed if the data were provided in raw numbers on a spreadsheet. They frequently develop extremely sophisticated algorithms that are used to identify patterns and turn data from a jumble of numbers and statistics into something that can be of use to a business or organization. Data science is, at its most basic level, the process of finding meaning in large volumes of data.

Let’s take a look at a common scenario with a data scientist. Perhaps a large company, such as a mobile phone provider, wants to determine which of its current customers are most likely to switch to a competitor’s service. They can hire a data analyst who can examine millions of data points (or, more precisely, create an algorithm to examine millions of data points) relating to previous consumers. Consumers who consume a particular bandwidth are more likely to stop, or engaged customers aged 35 to 45 are more likely to transfer carriers. To retain and retain these consumers, the mobile phone provider can adjust its business model or marketing activities.

Every time a Netflix user logs in, they see a real-life example of managing data in motion. The video service includes software that will provide recommendations based on your taste. An algorithm uses information from your previous viewing behavior to make suggestions for shows you might like.

Steps to Become a Data Scientist

Step 1: Preparation

Future data scientists can start preparing for their careers even before setting foot on a college campus or enrolling in an online degree program. Ambitious data scientists can get off to a good start by learning the computer languages ​​most commonly used in the field, such as Python, Java, and R, as well as honing their understanding of applied mathematics and statistics. In reality, students’ learning rates often improve when they attend college with a well-developed skill set. Most importantly, an early introduction to data science knowledge needs will help you determine if a data science profession is right for you.

Step 2: Complete undergraduate studies

Statistics, computer programming, communications technology, mathematics, or data science, if available, are the most sought-after courses in data science. If you are currently enrolled in another undergraduate program and don’t want to relocate, a minor in one of the disciplines is also a good idea. Continue to learn computer languages ​​and database design, as well as add SQL / MySQL to your “data science to-do list”. Now is the time to begin to network professionally by exploring relationships within academic groups, researching internship opportunities, and seeking guidance from instructors and advisers.

Step 3: Get an entry-level job

Companies are frequently looking for someone to fill entry-level data science positions. Jobs such as Junior Data Analyst or Junior Data Scientist can be found by performing a job search. When looking for entry-level data science employment, system-specific training or qualifications in data-related disciplines (e.g. business intelligence tools, relational database systems, data visualization tools , etc.) can be beneficial.

Step 4: Earn a doctorate or master’s degree

Data science is one area where people with advanced degrees, like a master’s or doctorate, have a better chance of getting a job. Graduate degrees in data science are in high demand and include the same requirements as undergraduate degrees: computer science, data science (if available), information technology, arithmetic, and statistics. Many companies, on the other hand, accept STEM degrees like biotechnology, engineering, and physics (among others). Remember, data scientists need to know how to use enterprise data management tools and how network storage and computing (for example, Hadoop, MapReduce, and Spark) work in order to build models and build them. ” perform predictive analyzes.

Step 5: Get a promotion

Continuing education and experience are important parts of being promoted or becoming a highly sought-after data scientist. Businesses place great importance on results. When strong technical skills are combined with leadership and project management experience, it usually leads to more substantial opportunities and higher compensation.

Step 6: never stop learning

In the constantly evolving field of data science, being up to date is essential. Lifelong learning is a buffer against career market changes in this era of rapid technological innovation. This is also true for data science, which is not as well known as other fields with an analytical and technological orientation. A data scientist with a long career is constantly learning and growing with the industry. Try to network and attend boot camps and conferences to seek educational and professional opportunities.


Companies are increasingly looking for data scientists to support them in processing and understanding data. Consider the world’s most powerful and influential companies, such as Apple, Amazon, Google, and Facebook, all of which rely heavily on data-driven strategic planning. Amazon, for example, uses data analytics to power sales and marketing algorithms that deliver products to users based on previous purchases and behaviors. However, Apple selects its products based on how and when you, the consumer, use their iPhones, iPads, Macbooks, and other products and technologies.

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Sean N. Ayres