Difference Between Data Science and Data Analytics
Talk to Expert
Data Science and Data Analytics are highly demanded fields. Analysts discuss "What happened?" using structured data to find insights. Scientists focus on "What will happen?" by building complex predictive models and using advanced coding and math.
In today’s world, Data Analytics and Data Science are two words that are on everyone’s lips. Everything runs on data. It starts from the videos you watch to the products you buy online. This data is so important that companies need professionals to understand it. That's where Data Science and Data Analytics come in the picture. These are two of the most popular and highest paying jobs right now!
Data is something that every business runs on. Every business requires some kind of data for its initiation, survival, and expansion. It is easy to get confused. For a beginner, it may sound so similar. You might be wondering, "Should I study Data Science or Data Analytics?"
Yes, it is true that both fields work with numbers and information. They ask different questions and use different tools to reach their objective. This guide will clearly explain the difference between Data Science vs Data Analytics so you can pick the path that is perfect for your skills and career goals.
- Understanding the Core Difference
- What Does Data Analyst Do?
- What Does a Data Scientist Do?
- Whether to choose Data Analytics or Data Science?
- Data Analytics vs Data Science
- Data Science v/s Date Analysis: Earning Potential
- Data Science v/s Date Analysis: Final Verdict
Understanding the Core Difference
Think of it this way: Data Science is the whole house, and Data Analytics is just one room inside it. The simplest difference is the kind of question they try to answer:
| Field | Question They Answer | Analogy |
|---|---|---|
| Data Analytics | "What happened?" (Descriptive) | Looking at a car accident and figuring out which driver was speeding. |
| Data Science | "What will happen next?" (Predictive) | Building a smart driving system that predicts and prevents accidents before they happen. |
What Does Data Analyst Do?
A Data Analyst acts like a detective. Their job is to look at past information (structured data and or website traffic) and find patterns to help the business right now.
-
Main Goal: To present big lists of numbers into clear and actionable insights. So, business team can understand it easily.
-
What they use: They primarily use tools like Excel, SQL, and simple visualization software to clean. Organize, and show data in charts and reports.
-
Key Skills: Strong analytical thinking, good communication, and the ability to find a story in the numbers.
Check out universities to pursue MS abroad programs.
What Does a Data Scientist Do?
Consider a Data Scientist as an advanced builder. Their job is to design new tools and algorithms that can predict the future or automate decisions.
-
Main Goal: To create predictive models and use Machine Learning (ML) to answer questions the business hasn't even thought of yet.
-
What they use: They use advanced coding (like Python), specialized software (like TensorFlow), and complex math to handle both organized and messy (unstructured) data.
-
Key Skills: Expert-level coding, strong knowledge of statistics and math, and the ability to build and train complex computer models.
Explore top 10 universities in the world for Data science course.
Predict your IELTS, TOEFL, and PTE in just 4 steps!
Whether to choose Data Analytics or Data Science?
Many students and new professionals, like yourself, start their career in this field but get confused after a certain point when it comes to choosing a particular domain: be it for studying a masters in abroad or choosing a career path. There is no straight answer to this dilemma. The decision to study a Master’s in Data Science or MS in Data Analysis depends on your:
- Interest
- Career goals
- Skills
- Work experience
- Competencies
In more ways than one, you have to analyze your career aspirations and interests to choose the correct path. Now that is clear, you should know what each of these career paths has to offer. Let us explore Data Analysis and Data Science a bit further to clear your confusion.
Data Analytics vs Data Science
To put it in simple words, the difference between Data Analytics and Data Science is that if we consider Data Science as a home for all the methodologies and tools, then Data Analytics is just a small room in that home. In short, Data Analytics is more specific and concentrated than Data Science. Here are some functional differences between a Data Scientist and a Data Analyst:
| Data Analyst |
Data Scientist |
|---|---|
| Works with structured data to solve tangible business problems |
Deals with the unknown by using more advanced data techniques to make predictions |
| Tools like SQL, R or Python programming languages, data visualization software, and statistical analysis are used |
Automate your own machine learning algorithms or design predictive modeling processes to handle both structured and unstructured data |
| A more specialized role and job function |
More advanced version of a Data Analyst role |
| Job role
|
Job role
|
| Skills
|
Skills
|
Check How does Shiksha Data Science model calculate the chances of admission?
Data Science v/s Date Analysis: Earning Potential
Data Analyst applies their analytical thinking skills to help solve business problems. It is one of the highly sought-after roles and needless to say that it is one of the well-compensated job roles as well. As per the Robert Half Salary Guide 2020, Data Analysts in the US made between USD 83,750 and USD 142,500, depending on years of experience. On the other hand, Data Scientists earned more than Analysts, which was between USD 105,750 and USD 180,250. Data Scientists with a specialization in Big Data Engineering or AI can expect an increased compensation package.
You may also read: Data Science v/s Machine Learning
Choose Data Science only if you love heavy math, coding, and building complex software. Choose Data Analytics if you prefer looking at numbers to find patterns and help companies make decisions right now. First, know your interest.
Generally, Data Scientists earn more. They build complex computer models and require advanced coding skills. Their role is considered a "level up" from analytics. Average salary of a data analyst falls between USD 84,000 to 1.42 lakhs. For Data Scientists range is USD 1.06 Lacs to 1.80 lakhs.
Data Science v/s Date Analysis: Final Verdict
Choosing one of these is majorly a choice depending on your preference. If you are someone who is mathematically inclined and enjoys the technical aspects of coding and modeling, a Data Science degree might just be your calling. For someone who loves working with numbers, making meaning out of huge chunks of data, churning out insights, and influencing business decisions, they should opt for a degree in Data Analytics.
Statement of Purpose
Letter of Recommendation
Student Visa Application
Admission Essay Writing
Student Curriculum Vitae
Think of Data Science as a whole house. Data Analytics is just one room inside it. Analytics looks at past data to solve current problems. On the other hand, Data Science uses complex tools to predict the future.