How to Become a Data Scientist

 

In a nutshell, a data scientist gathers and analyzes large quantities of organized and unstructured data. This requires technical skills to handle complex challenges. There are many ways to describe what a data scientist does, but you have to be familiar with the goal of data science to understand the role of a data scientist.

Oracle offers one of the simplest and best summations of data science: preparing data for analysis, including cleansing, aggregating, and manipulating the data to perform advanced data analysis. Ultimately, the goal of this interdisciplinary field of data science is to help businesses and organizations make informed decisions using actionable insights gained from analyzing a labyrinth of data.

What Does a Data Scientist Do?  

These days, data scientists are present in almost all organizations that are serious about success and impact. However, what exactly a data scientist does depends on the industry they work in and their job responsibilities. Nonetheless, all data scientists have specific tasks, including the three below:

Data Analysis & Interpretation

Data scientists have the technical skill to analyze, sort through, and understand the implication of/ or meaning of data. They are often concerned with establishing connections between variables. Their finding can then serve as evidence of why something has happened, is happening, or will happen. They do this using a broad range of analytical tools cutting across visualization, tabulation, and statistical analysis. Excel is probably the most used tool by data scientists.

Data Collection

What is the importance of data collection in data science? Data is information, and information is crucial for research and data analysis to identify problems, predict outcomes, measure success, answer questions, predict trends, and much more.

Data scientists gather large quantities of structured and unstructured data from disparate sources using data analytical tools or simple business intelligence tools. While this heterogeneous data can come from known or unknown sources such as basic customer records and historical stock price information, an increasing number of businesses are turning to Big Data.   

Effective Data Communication

Just as essential as collecting, analyzing, and interpreting data is the scientist's ability to communicate their findings effectively to their audience or stakeholders. Considering that data-speak is highly esoteric, a data expert must have the ability to dilute their often complex findings in a language that their non-technical audience understands. For this reason, data scientists often utilize data visualization tools such as Excel, QlikView, and Zoho Analytics to communicate data effectively.

While the three above are some of the uniform roles expected of data scientists, what they actually do depends on their specific job responsibilities. Not all data scientists have to be experts in machine learning, for example, but all data scientists must possess critical thinking, communication, and problem-solving skills. And, of course, data scientists earn a living too.

Data Scientists Salaries and Job Outlook

They say money is not the most important thing in life, but it certainly is an important consideration when choosing a career or a job. Fortunately for future data scientists like you, you are poised to take home more than the median worker in America annually.

According to Glassdoor, the estimated total pay for a Data Scientist is $120,988 per year in the United States. This figure does not include additional pay, such as bonuses and commissions, that when added, increases the sum by more than $16,000. If you work at Meta as a data scientist today, you could take home more than $150,000 a year.

Data scientists are among the top earners in the industry today because they are in demand and will remain so for the foreseeable future. The U.S. Bureau of Labor Statistics predicts a 22% growth in the data science field from 2020 to 2030.  

How to Become a Data Scientist: A Step by Step Guide

 

If you are inspired by the idea of becoming a data scientist, you at some point wonder if you have what it takes to become one. Whether you already know it or not, the answer is a big "Yes," you can become not just a data scientist but a successful one too. This is not just a motivational speech. The truth is that you can develop the requisite data skills that you lack while building on the ones you already possess to become a kick-ass data expert. Below are three steps you can take right now to become a data scientist:

Learn & Develop the Right Skills

As with all professions, there are prerequisite skills necessary to become a data scientist. Granted, data science is not an easy industry to get into as it requires strong prerequisite skills in multiple areas, ranging from business to data mining, hacking, data visualization, programming, machine learning, Big Data, etc. However, having a love for data is a big step in the right direction. To become a data scientist, start by learning the requisite skills such as the ones mentioned above.  

Gain Practical Experience

The old saying that experience is the best teacher equally applies to those preparing to enter the data science field. While you learn about the fundamentals of data science, programming languages, data wrangling, statistical analysis, etc., you need to apply your newly-developed skills to build confidence through hands-on experience. You can do this with Kaggle; an online community of data scientists where you can hone your skills and even enter competitions to solve data science problems.

Start Working On Projects

After you have learned the necessary data science skills and gained confidence through a few hands-on projects, it is time to start working with real-world projects- a lot. Despite your familiarity with the data science field at this point, working on a real-world project is like taking an aptitude test. 

Are you a student asking- how to become a data scientist?

Your path to achieving this goal is comparatively straightforward: pursue an undergraduate degree in computer science, math, business, IT, or any related field; Follow this up with a Master's degree in data or any related field; gain practical experience in the industry you intend to work in (ex: telecoms, healthcare, retail). Students also can apply for data science internships at small and big companies like Amazon.

Should You Become a Data Scientist in 2022?

 

If you are still wondering if you should consider investing the time and resources to become a data scientist in 2022, I will bet your hesitation is due to the fact that data scientist is a fairly new job category that was not nearly as popular just a decade ago. This fact, including AI such as AutoML, could make one skeptical of the future and trajectory of data scientists and data science.

The role of AI in data science is likely to continue to grow, but it can never eclipse data scientist jobs for the simple reason that human intelligence is needed to modify algorithms. No wonder LinkedIn described data scientist as "the most promising career" of the future. Plus, becoming a data scientist makes you one of the most sought-after few with data science skills.

Data Scientist FAQ

What are the Top 3 Skills for Data Scientists?

Mathematics

Statistical analysis and computing

Data Visualization

Do Data Scientists Work Long Hours?

Data scientist is like any other job requiring full-time hours and an upward of 40 hours a week. 

 Is There an Age Limit to Becoming a Data Scientist?

No, there is no age limit to becoming a data scientist or pursuing a career in data science as long as you have the skillset and a relevant college degree.