Becoming an expert in data science follows the same path as becoming a data scientist. The path you take to get there isn’t as important as the skills you have when you arrive.
To be an expert data scientist, you will need both hard and soft skills because your job is to find business insights from massive amounts of data. In a way, you are responsible for organizing the chaos of big data so companies can make data-driven decisions for business growth.
Some employers say that finding an expert in data science is like finding a unicorn.
They are almost impossible to find because experts need more than just coding, math, or engineering skills. They need to understand the how and the why of their results.
In other words, an expert in data science needs to understand how to translate business objectives into computer models that can solve problems. They can effectively communicate the results so all stakeholders can understand, and they can defend their modeling methodology when questioned.
What Does a Data Scientist Do?
At the heart of every data scientist position is data collection. An expert’s job is to extract meaning from a mass of data. That may involve:
- Collecting and cleaning data from multiple sources
- Integrating structured and unstructured data
- Creating computer models to deliver meaningful insights
An expert presents research results, so decision-makers can use those data insights to solve business problems.
Unlike data analysts, data scientists are part of predictive analytics. A data analyst reports on what has happened while a scientist works to predict what will happen.
What Skills Must an Expert Have?
The value of a data science expert is in the unusual mix of skills – skills that cannot be acquired following a predetermined set of steps. Instead, an individual becomes an expert over time by continually learning and honing the needed skills. So what are the crucial skills to becoming an expert in data science?
Critical thinking
At the top of the list is critical thinking. Data scientists must analyze data as objectively as possible to eliminate biases in the outcome. They must be open to all possible solutions and not look for predetermined answers.
Rather, data scientists use data to create models that can help solve business problems or predict outcomes. Data scientists with a background in machine learning or artificial intelligence are in demand as the technology continues to advance.
Coding
The data science community disagrees about the need for programming skills for a data scientist. Some believe that programming is a mandatory requirement, while others contend that a basic understanding of programming concepts is all that is needed.
You may not need developer-level programming skills, but knowing a programming language is recommended. As the field becomes more established, programming skills will become expected.
To be successful, an expert in data science will need programming skills in computation and modeling.
Computational skills are needed to process raw data, sometimes in real-time. Statistical modeling is applied once the data has been cleaned.
Mathematics
Data scientists are often asked to develop complex business models that require an understanding of statistics.
Due to the volume of data involved, expertise in mathematics is essential to creating valid statistical models. If you’re not a math person, data science is probably not for you.
Experts not only apply mathematical concepts, but they also understand these concepts well enough to communicate the pertinent details to non-technical audiences.
Business executives want to understand the assumptions used to produce the outcomes to feel comfortable with the results. It’s a data scientist’s responsibility to communicate the validity of a model in terms a decision-maker can understand.
How to Become a Data Scientist
As with many of the newer technology roles such as a DevOps engineer, the data scientists job begins with a road map. Making a list of the needed skills helps identify where to focus your efforts. In addition to critical thinking, coding, and mathematics skills, a data scientist also needs:
- Exposure to machine learning, deep learning, and artificial intelligence
- Risk analysis and management training
- Problem-solving skills and business acumen
Once you have the list, you can identify where you need more training and experience.
A data scientist needs experience to become an expert in the field. Whether it’s assisting on projects or leading smaller projects, working as a data scientist helps develop the skills you need to become an expert in data science.
How Much Does an Expert Earn?
What can you expect to earn as an expert data scientist?
After all, a lot of work goes into becoming one. A 2019 Burtch Works study on Data Scientist and Predictive Analyst found that salaries of data scientists were 19% to 34% higher than other predictive analytics professionals.
For data scientists, median base salaries for individual contributors range from $95,000 to $167,000. For managers, median base salaries ranged from $146,000 to $250,000. The most significant increase occurred in the top 25% of data scientists. A data scientist’s salary depends on years of experience, skillset, education, and location.
The average salary for a data scientist is $118,000 per year. A senior or elite data earns an average salary of $171,000. Employers place a higher value on data scientists with specialized skills, such as machine learning or artificial intelligence. Data scientists working on the West Coast earn the highest average salary. Entry-level data scientists can expect to earn at least $90,000.
Where to Begin?
You are in control of your life and your career in data science.
Maybe you need to brush up on your computer programming skills or take a seminar on risk management.
Have you taken the time to understand data architecture? Whatever you need to advance or start your career, you control when and where you acquire the required skills.
Take control of your life and invest in your future by contacting us to find out about a Woz U powered program in data science.
Sources
https://www.datasciencecentral.com/profiles/blogs/can-you-be-a-data-scientist-without-coding-a-meta-analysis
https://www.infoworld.com/article/3429185/stop-searching-for-that-data-science-unicorn.html
https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm
https://www.burtchworks.com/wp-content/uploads/2019/06/Burtch-Works-Study_DS-PAP-2019.pdf
https://towardsdatascience.com/how-to-become-a-data-scientist-3f8d6e75482f
