Data Analysis v/s Data Science. What is right for you?
In this article, we’ll answer 5 important questions about these 2 fields.
In this article, we’ll answer 5 important questions about these 2 fields:
What is Data Analysis? v/s What is Data Science?
What does a Data Analyst do? v/s What does a Data Scientist do?
Skills required to become a Data Analyst? v/s Skills required to become a Data Scientist?
Is coding required to become a Data Analyst? v/s Is coding required to become a Data Scientist?
How much do Data Analysts earn? v/s How much do Data Scientists earn?
A lot of people including myself a few years ago, don’t understand the difference between Data Analysis and Data Science. So, the goal of this article is that you won’t have to ask this question again. So, let’s dive right in.
#1: Data Analysis v/s Data Science — Definition
Data analysis is the practice of working with data to extract useful information, which can then be used to make informed decisions. Let’s take an example for more clarity, you work as a Data Analyst for a home automation company, now based on the previous several year’s data you noticed that a certain X product has had its best sales during the months of September and October each year, now using this information you obviously are going to predict a similar high point during the upcoming year.
Data Science on the other hand is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. Data science uses more sophisticated and complex machine learning algorithms to build predictive models. Ex — All search engines such as Google, Bing, Yahoo, etc. leverage data science algorithms to come up with the best results for the searched query within a fraction of a second.
#2: Data Analysis v/s Data Science — Job Description
They both work with data but in different ways. While a data scientist is expected to forecast the future based on past patterns, data analysts extract meaningful insights from various data sources.
So on a day-to-day basis, a Data Analyst would:
Collect data then clean and reorganize it for data analysis
Analyse data sets to spot trends and patterns that can be translated into actionable insights
Presenting findings in an easy-to-understand way to inform data-driven decisions
Whereas a Data Scientist would:
Gather, clean, and process raw data
Design predictive models and machine learning algorithms to mine big data sets
Develop tools and processes to monitor and analyze data accuracy
Build data visualization tools, dashboards, and reports
Write programs to automate data collection and processing
#3: Data Analysis v/s Data Science — Skills Required
So, a Data Analyst would need skills like:
Foundational Mathematics and Statistics
Certain widely used languages for Data Analysis such as R/Python and basic SQL skills(SQL is a database query language, used to extract and store data in a database).
Analytical Thinking and Data Visualization Skills, you can either use Python with the right Data Visualization libraries such as pandas, matplotlib, etc., or Excel as a data analytics and business intelligence tool.
Now, a Data Scientist on the other hand would need the following skills:
More advanced Mathematical and Statistical skills,
Analytical and Problem Solving, one would also need to attain advanced programming skills as they are expected to develop sophisticated machine learning algorithms for predictive analysis. One should work on advanced concepts like Algorithms, Data Structures, Object Oriented Programming, among others.
It’s good to have knowledge of big data tools and techniques, such as Hadoop, TensorFlow, Spark by Apache, etc.
I’d also like to stress the fact that machine learning, data modeling, and artificial intelligence to an extent play a vital role in Data Science.
#4: Data Analysis v/s Data Science — Coding
Well, I think by now you might’ve already guessed the answer to this question, especially after looking at the Skills Required for each of these fields.
You might’ve often heard people saying that Data Analysts don’t code or coding isn’t typically required if you want to be a Data Analysts, however, the truth of the matter is you will be expected to have fluency in at least one language, as a recommendation I’d suggest Python as it’s a dynamically typed, beginner-friendly language, which makes it easy to learn.
Now, for Data Scientists, yes they have to code, absolutely, and we have already discussed the required concepts and skills for this so I won’t go into the details again.
#5: Data Analysis v/s Data Science — Salary
Data Analysts earn an average of US$80,000 per annum, for an entry-level job, this of course can vary depending on your expertise and negotiation skills.
Data Scientists earn an average of US$120,000 per annum again for an entry-level job and needless I say, it will increase with your experience.