The rapid evolution of the data industry has resulted in new and exciting roles that may have you interested because you already have some of the skills listed!
However, in order to land a job as a data scientist in the data industry, you must be trained and qualified in all the skills and technologies required to perform the role of a data scientist. You guessed it, although a traditional data analyst may have some of the skills a data scientist requires, you may not have all of the necessary skill set.
If you are interested in changing careers to data science but are struggling to understand the difference between the role of a data analyst vs a data analyst with data science qualifications, let’s find out.
1. What is a data scientist?
A data scientist is a new power profession that enables businesses to extract valuable data-driven insights that directly impact and inform business decisions and solutions.
Data scientists possess a strong analytical problem solving mindset and are certified and trained in leading big data technologies and techniques. This enables them to analyze, build and research structured and unstructured data sets to examine, organize and extract insights, trends and patterns using advanced predictive analytics, programming, machine learning /AI, data modelling and visualization techniques.
You are in a strategic position to shift your career to data science if you are currently trained in any of the following areas: IT, software engineering, mathematics, business management, database management, data mining, finance, accounting or science and related fields of research and testing.
2. What is the job description of a data analyst without data science qualifications?
Did you know the job description of a data analyst without data science qualifications can also be the job description of a business analyst? This is because both of these job descriptions usually require a professional that is able to analyze data from a business perspective and help stakeholders with their business decisions.
A traditional data analyst role will require you to focus on cleaning up data to retrieve business insights using database systems and some statistical, mathematical and programming knowledge to present findings. This would be classified as a data-related role but not a data science role.
3. What does a data analyst with data science qualifications do on the job?
A data scientist or a data analyst with data science qualifications is required to do all of the above and more.
In fact, you will be qualified to apply to these 5 jobs with data science qualifications:
That’s right – data analyst is an official job title for professionals with data science qualifications and for professionals without data science qualifications.
This is where professionals can struggle with identifying the purpose of upskilling to data science. To put it simply, data science has transformed and advanced the way industry sectors across the globe approach, handle and apply data-driven insights and so, true data professionals that can assist businesses in these data-centric initiatives are in high demand.
Therefore, the biggest difference between a data analyst and a data analyst with data science qualifications is the way similar tools and techniques are used to achieve specific business outcomes. Upskilling to data science will strengthen and add to your existing data analyst skills – enabling you to become a multi-faceted data professional with a sought after skill set and increased job security.
A data analyst role requiring a data scientist’s skill set will expect you to wrangle complex structured and unstructured data sets to identify the most strategic business solutions for the company at any given time using predictive data analytics, mathematics, statistics, databases systems, programming, machine learning, AI and visualization tools and techniques.
4. Technologies a data analyst with data science qualifications uses on the job
As a data analyst with data science qualifications, you will be trained in the tools and techniques necessary to complete daily data related tasks and skills that you can build on to meet the demands of the data industry as it continues to evolve.
The following are some of the vital technologies and methods you will learn to use and apply during a data science education program designed to equip you with the practical skills needed for a job in the data industry:
Python, SQL, R, Hadoop, Spark
Mathematics & Statistics
Databases & APIs
EDA, Pandas & SciPy
NLP & Machine Learning
Principal Component Analysis, Classification & Clustering
Logistic + Linear Regression & Ensemble Methods
5. The 3 main career benefits of upskilling to data science in the next 3 years
New Job Prospects – becoming trained and certified in practical data science tools and techniques will make you a sought-after data professional as employers and recruiters in Asia Pacific are struggling to find data professionals with the right skills to perform on the job. Here’s what happened when this professional took the leap and upskilled to data science. You could too, transform your career and land a new job opportunity this year.
Exciting Earning Potential – the earning potential of data science professionals can be upwards of 6 figures, depending on your experience and location. This is an exciting time to reap the rewards of a career in data because employers are actively seeking to hire, promote and retain professionals with data science skills crucial to their business progression.
Increase in Employee Value – one of the overlooked perks of a career in data science is the increase in perceived and genuine value as an in-demand data professional. The global skills shortage in data science, analytics and AI means better job prospects and bigger salaries but you will also become a valued employee in a job market where employers are struggling to fulfil their data-driven initiatives. This is a powerful position to be in and a career change to data science from data analytics will be a strategic career move to make.
The first step to shifting careers to data science is education. Now that you understand the differences, benefits and prospects of a career change to data science from data analytics – you are ready to explore education pathways to accelerate your data career in this growth industry.