It’s no longer news that data-related jobs are taking over the job market. Forbes, Harvard Business Review, The World Economic Forum, and some other tech giants all listed data-related roles as the most in-demand roles of the century. Some of these roles include data science, data analysis, data engineering, statistics, etc.
Two often misconstrued roles are data science and data analysis. While they are both related to data, they have different roles, training, and descriptions.
If you’re looking to take either, it is essential to understand their fundamental differences before deciding. While data science requires heavy coding and mathematical modeling expertise, data analysis does not. It involves a vast array of visual storytelling.
See the best schools to study data science in Canada.
While they both deal in data, their significant difference is in what they do with the data. This article explores these two worlds, so you’ll understand which fits your interest, background, and career objectives.
A Deep Dive into Data Science
A data scientist designs models and creates algorithms for working with data. He’s intuitive, asks questions, and answers them in the form of models.
Data science is more advanced than data analysis in that data science is like a blank piece of paper. Data science fills it up based on his experience.
He creates automating tools, data frameworks, and models upon which a data analyst works. Basically, a data scientist writes code to build systems.To perform his job, he works closely with stakeholders, managers, and CEOs to understand their business goals and then design how data can be used, manipulated, and framed to achieve the goals.
Therefore, a data scientist needs to be creative, intuitive, and insightful because he typically builds around the skeletal ideas that managers and CEOs feed him.
Because data science is more technical than data analysis, a data scientist is often a senior to a data analyst. Typically, a data scientist can do a data analyst job and has a higher level of experience working with data.
In a typical company, the data scientist usually heads the data team, making decisions related to the designs of databases, models, and frameworks. The position of a data analyst is considered an entry-level role. In-between a data scientist and an analyst are both data engineers responsible for preparing data for analytical purposes.
Training and Educational Requirement of a Data Scientist
Due the highly technical nature of data science, a data scientist is expected to have a master’s degree. According to KDnuggets, 88% of data scientists have a master’s degree, and 46% a Ph.D.
In addition, to become a data scientist, you need to have a background in mathematics, statistics, or computer science. KDnuggets also found that 32% of data scientists come from mathematics and statistics background, 19% from computer science, and 16% from engineering.
Here are some requirements to become a data scientist.
- A background in computer science, statistics, or mathematics with a minimum of master’s or Ph.D.
- Experience manipulating data and building statistical models and frameworks for 2-3 years.
- Experience writing codes in computer languages like Python, R, SQL, etc.
- Experience in Random Forest, data mining techniques, model/regression, boosting, mining, trees, etc.
- Experience with web services like Digital Ocean, Spark, Redshift, S3, etc.
- Experience with machine learning techniques like Artificial Neural Network, Clustering, Machine Learning, Decision Tree Learning, etc.
- Experience manipulating data from third-party systems like Google Analytics, AdWords, Facebook Insights, Coremetrics, and Crimson Hexagon.
- Experience with using advanced statistical tools and concepts like Statistical Test, Regression, and Properties of Distribution.
Job Description of a Data Scientist
A data scientist spends most of his time scrubbing, cleaning, mining, and building models for data analysis. He is responsible for mathematically envisioning the organization’s goals and building a system to achieve the goals.
Here are some of the essential job responsibilities of a data scientist:
- Ask questions to understand the organization’s goals and how they can be modeled.
- Performs data cleaning using programming languages like Python, SQL.
- Writes programs and creates automating tools like libraries and APIs to simplify the day-to-day processes.
- Develops custom data models, frameworks, and algorithms.
- Designs systems for integrating and storing data.
- Designs frameworks for the collection and acquisition of data.
- Writes programs to automate data collection and processing.
- Assess the accuracy and effectiveness of the organization’s data sources and gathering techniques.
- Develops extensive data infrastructure using tools like Hadoop, Spark, and Hive.
- Uses predictive models to optimize user experience, marketing, sales, and other areas of the organization.
A Deep Dive into Data Analysis
In simple words, a data analyst interprets data. He uses different analytical and visual tools to draw meanings out of data and then explain the insights. He is responsible for telling what stories data suggest.
A data scientist can communicate numbers and figures in a non-technical way to managers, colleagues, and clients. A data analyst can go by a range of titles: database analyst, market research analyst, price analyst, advertising analyst, international strategy analyst, financial analyst, business analyst, customer success analyst, sales analyst, and operation analyst.
A data analyst typically works with models and frameworks designed by the data scientist. He understands what each turning point and flat line says. Therefore, while a data analyst is less concerned with codes, he can interface and interpret most of the tools used in the data world.
He can use data to explain why sales dropped, why click-through rates increased, why the campaign failed or succeeded. He’s also able to advise on which marketing strategy would best suit the next product launch, based on data.
That’s the work of a data analyst. To make sense out of data.
However, data analysis is considered an entry-level career. Many data analysts often have the goal of becoming data scientists once they gather enough experience analyzing data. Well, that’s the hierarchy.
Training and Educational Requirements of a Data Analyst
Since data analyst is considered an entry-level career, it does not mandate a master’s degree. Applicants should have a background in science, mathematics, statistics, programming, business, and predictive analytics.
Here are some requirements to become a data analyst:
- A degree in mathematics, business, statistics, or computer science with a focus on analytics.
- Must have strong verbal and written communication skills.
- Must be confident working with programming languages like Python, R, and SQL.
- Exceptionally fast and intuitive working with Excel and Office.
- Must be sound in working with statistical analysis, reporting, data analysis, and database management.
Job Description of a Data Analyst
The roles of a data analyst vary according to industry, but generally, it involves analyzing and interpreting data. It is not as evolving as data science; however, it requires good insight.
Some essential job responsibilities of a data scientist:
- Looking out for trends and patterns in a data set.
- Translate data into figures, tables, charts and tell stories with them.
- Extract actionable insights from a set of databases.
- Write a vast number of queries to extract data from databases.
- Perform various types of analytics, which include prescriptive, diagnostic, and descriptive analytics.
- Work with departments like finance, engineering, marketing, and HR to collect new data.
- Design and create simplified data reports using reporting tools like Excel, Tableau, and SAS.
Data Science Vs. Data Analysis: An Overview
There’s an overlap in both fields. First, both deals with manipulating and creating meanings out of what could be described as chaotic data. Their significant differences stem from specialization.
While data science is code and mathematics-intensive, data analysis is not. Data science deals with building models and frameworks; data analysis deals with studying and analyzing the frameworks. This gap in what they do with data accounts for their significant differences in training and job roles.
Here’s a quick overview:
|Data analyst||Data scientist|
|Mathematics||Statistics, foundational math||Predictive analytics, advanced statistics|
|Programming||Basic fluency in R, Python, SQL||Advanced object-oriented programming|
|Software and tools||SAS, Excel, business intelligence software||Hadoop, MySQL, TensorFlow, Spark|
|Other skills||Data visualization, analytical thinking||Data Modelling, machine learning|
- A data scientist formulates his questions based on current and past events; a data analyst uses data to find solutions to a set of questions.
- A data scientist can predict what may happen in the future based on records; a data analyst is limited to what data is currently saying.
- A data scientist has multiple sources for examining data and can design his own sources; a data analyst usually examines data from a source, mostly from a CRM, a database, or a sheet.
Read more about statistics in relation to data science.
Which is Right for You?
While both are in high demand in the market, it’s best to understand their differences to evaluate the best fit for you. To decide, you have to consider your educational background, interest, and career path.
Data science is much more technical and mathematically involved than data analysis; it also involves a higher level of coding and higher pay. However, it all depends on your career goals.