Even people who have some basic knowledge of data science have confused the data scientist and data analyst roles. So, what’s the difference between a data scientist and a data analyst? Both work with data, but the key difference is what they do with this data.
Data analysts sift through data and seek to identify trends. What stories do the numbers tell? What business decisions can be made based on these insights? They may also create visual representations, such as charts and graphs to better showcase what the data reveals.
Data scientists are pros at interpreting data, but also tend to have coding and mathematical modelling expertise. Most data scientists hold an advanced degree, and many actually went from data analyst to data scientist. They can do the work of a data analyst, but are also hands-on in machine learning, skilled with advanced programming, and can create new processes for data modelling. They can work with algorithms, predictive models, and more.
Data analysts sift through data and provide reports and visualizations to explain what insights the data is hiding. When somebody helps people from across the company understand specific queries with charts, they are filling the data analyst role. In some ways, you can think of them as junior data scientists, or the first step on the way to a data science job.
At their core, most required:
- Degree in mathematics, statistics, or business, with an analytics focus
- Experience working with languages such as SQL/CQL, R, Python
- A strong combination of analytical skills, intellectual curiosity, and reporting acumen
- A solid understanding of data mining techniques, emerging technologies (MapReduce, Spark, large-scale data frameworks, machine learning, neural networks) and a proactive approach, with an ability to manage multiple priorities simultaneously.
- Familiarity with agile development methodology
- Exceptional facility with Excel and Office
- Strong written and verbal communication skills
A crucial part is exploratory data analysis, which combines visualization and data sense. They’ll find patterns, build models, and algorithms some with the intention of understanding product usage and the overall health of the product, and others to serve as prototypes that ultimately get baked back into the product. She may design experiments, and she is a critical part of data-driven decision making. will communicate with team members, engineers, and leadership.
So, not only must a data scientist know how to collect and clean data, but they must also know how to build algorithms, find patterns, design experiments, and share the results of the data with team members in an easily digestible format.
qualifications for a data scientist:
- Great grip on statistics, mathematics, or computer science
- Experience using statistical computer languages such as R, Python, SQL, etc.
- Experience in statistical and data mining techniques, including generalized linear model/regression, random forest, boosting, trees, text mining, social network analysis
- Experience working with and creating data architectures
- Knowledge of machine learning techniques such as clustering, decision tree learning, and artificial neural networks
- Knowledge of advanced statistical techniques and concepts, including regression, properties of distributions, and statistical tests
- 5-7 years of experience manipulating data sets and building statistical models
- Experience using web services: Redshift, S3, Spark, DigitalOcean, etc.
- Experience analyzing data from third-party providers, including Google Analytics, Site Catalyst, Coremetrics, AdWords, Crimson Hexagon, Facebook Insights, etc.
- Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.
- Experience visualizing/presenting data for stakeholders using: Periscope, Business Objects, D3, ggplot, etc.
More work goes into becoming a data scientist than a data analyst, but the reward is a lot greater as well. If you excel in math, statistics, and programming and have an advanced degree in one of those fields, then it sounds like you’d be a perfect candidate for a career in data science.
However, if you are early in your career and are great with numbers but still need to hone your data modeling and coding skills, then you’d be better suited for a job as a data analyst. You can think of a data analyst as a stepping stone to becoming a data scientist, if that is your final goal.