If you’re interested in pursuing a career involving data, you may be interested in two possible paths: becoming a data analyst or becoming a data scientist. Data science and analytics professionals are in high demand and enjoy salaries considerably above the national average annual salary. IBM’s study from 2017, The Quant Crunch, found that employers are looking for professionals with in demand skills including analytics, machine learning, and artificial intelligence. Data scientists and data analysts should be comfortable using some of these skills, but what are the differences between these two roles exactly?
Although there is heavy debate about the similarities and differences between data analysts and data scientists, the key differences lie in the skills they use to deal with data. Read on to learn more about the differences between data scientists and data analytics, educational backgrounds, salary breakdowns, and potential career paths.
Degree and Background Comparison for Data Analyst and Data Scientist
In order to become a data analyst or data scientist, you will need at least a bachelor’s degree in a quantitative field such as mathematics, statistics, or computer science, but some analysts may have a bachelor’s in business with a focus or concentration in analytics.
Education: IBM’s study found that 6 percent of job postings for data analysts require a master’s degree. However, data scientists and predictive analytics professionals (PAPs) are more likely to hold an advanced degree. According Burtchwork’s study – Salaries of Data Scientists & Predictive Analytics Professionals, from June 2019, at least 94 percent of data scientists hold a master’s or PhD and 86 percent of PAPs hold a master’s or PhD. The study also found that salaries amongst professionals with advanced degrees were higher than those with only a bachelor’s.
Work Experience: As these fields become more popular, coding bootcamps and masters programs in data science have allowed professionals to change their careers. That said, there may be a higher demand for professionals with work experience. According to IBM’s study, about 75% of job listings for data scientists and data analysts required at least three years of experience. Burtchworks found that about 35% of the data scientists and just under 30% of PAPs they sampled had 0-5 years of experience.
Role Responsibilities Differences Between Data Analysts and Data Scientists
A data analyst or data scientist’s role responsibilities may vary depending on where they work or their industry. Typically, a data analyst’s day to day may involve figuring out what happened, such as why sales dropped or creating dashboards that support a business’s KPIs, whereas data scientists are more concerned with what will happen or what could happen, using data modeling techniques and big data frameworks, such as Spark.
It’s important to read job descriptions carefully so you have a better understanding of a company’s expectations. In some cases, job postings for data scientists may actually involve the responsibilities of a data analyst and vice versa. To get a better idea of the differences between data analysts and data scientists, here are some of the common job responsibilities of data analysts and data scientists:
- Data querying using SQL
- Data analysis and forecasting using Excel
- Creating dashboards using business intelligence software
- Performing various types of analytics including descriptive analytics, diagnostic analytics, predictive analytics or prescriptive analytics
- Data mining using APIs or building ETL pipelines
- Data cleaning using programming languages (e.g. Python or R)
- Statistical analysis using machine learning algorithms such as natural language processing, logistic regression, kNN, Random Forest, or gradient boosting
- Creating programming and automation techniques, such as libraries, that simplify their day-to-day processes and for other members of their organization
Each role must analyze data and gain actionable insights to make business decisions. Data analysts use SQL, business intelligence software, and SAS, a statistical software, whereas data scientists use Python, JAVA, and machine learning to make sense of their data.
Skills Needed for Data Analyst vs Data Scientist
There is some overlap in analytics between data scientist skills and data analyst skills, but the main differences are that data scientists use programming languages such as Python and R, whereas data analysts may use SQL or excel to query, clean, or make sense of their data. Another difference is the techniques or tools they use to model their data, data analysts typically use Excel and data scientists use machine learning. It’s important to note that some advanced analysts may use programming languages or have familiarity with big data.
To gain a better understanding of the differences between data analysts and data scientists, here are some of the common job skills of data analysts and data scientists:
Data Analysts vs Data Scientists
|Data Analyst Skills||Data Scientist Skills|
|Data Mining||Data Mining|
|Data Warehousing||Data Warehousing|
|Math, Statistics||Math, Statistics, Computer Science|
|Tableau and Data Visualization||Tableau and Data Visualization/Storytelling|
|SQL||Python, R, JAVA, Scala, SQL, Matlab, Pig|
|Advanced Excel skills||Machine Learning|
Data Analyst vs Data Scientist Salary Differences
A data analyst or data scientist’s salary may vary depending on their industry and the company they work for. Data scientists can typically expect to earn a higher average starting salary than data analysts. According to IBM’s study, a data analyst with at least three years of experience may earn a salary between $67,396-$99,970. Entry level data analysts may earn a salary at the bottom of the range or lower and senior data analyst may earn a salary at the top of the range or higher.
Burtchworks found that budding data scientists with 0-3 years of experience, typically earn a starting salary of $95,000 on average. Experienced data scientists with nine or more years of experience may earn an average salary of $167,000.
Career Path Comparison
A data analyst with less than three years of experience may start out in an entry level role where their main responsibilities are reporting and creating dashboards. The next step, after five years may be to take on a role that involves strategy or advanced analytics techniques, such as a senior financial analyst. Taking it a step further, an advanced analyst may be interested in a managerial role and become an analytics manager after working for over nine years. In some cases, a data analyst will continue their education and sharpen their skills to become a data scientist.
A data scientist’s value increases as they gain more experience. There is currently a skills gap in data science where the majority of data scientists have less than five years of experience, but companies are looking for seasoned professionals with 10 years or more. Their title may not change, but after working for 10 years, a data scientist may either continue their education and earn a PhD or take a role as director of data science.