One of the most frequently asked questions by budding data scientists is what is the difference between data science and data analytics. In this article, we will be doing a comprehensive data analytics vs data science comparison. Amongst others, we will answer the questions:
- What is data analytics?
- What is data science?
- Difference data analytics vs data science?
- What does a data analyst do?
- What does a data scientist do?
Let’s begin with some background.
The internet and fast computer processors have left us with an enormous amount of data in our hands, making ‘data’ one of the buzzwords of the last decade. But big data is like a goldmine not explored – it is as good as nothing. Companies are actively seeking professionals that can sift through messy data to make informed decisions. This accounts for the enormous demand for data scientists and data analysts in virtually all industries, whether health, technology, business, politics, law, you name it. No wonder Harvard Business School calls it the sexiest job of the 21st century.
Apparently, the fields of data science and data analytics. which were once relegated to the four walls of the classroom have now become a powerful tool for business insights and decision making. Data science and data analytics are however two terms that can be confusing to differentiate. Sometimes, they are thrown around interchangeably. But there’s a sharp difference between their approach and ultimately, their result.
If you’re looking to start a career in the data niche, it is vital to be able to answer fundamental questions such as: What is data analysis? What is data science? Difference data analytics vs data science? What does a data analyst do? What does a data scientist do?
In addition, you should be able to differentiate data science vs data analytics. By the end of the article, you’d be able to answer these questions and get started in this ‘sexy’ data niche.
Let’s begin with data analytics.
What is Data Analytics?
Data analytics is the process of parsing data to find hidden patterns, create visualizations and ultimately, draw insights from the data. Any individual who does this is called a data analyst. The major responsibility of a data analyst is to unearth trends and help to make strategic business decisions. A data analyst answers questions such as,
Why is the product not getting as many sales as it’s counterpart?
When is the best time to run an ad?
What requires more investment for better returns, etc.
What does a Data Analyst do?
The primary responsibility of a data analyst is to draw insights from data and solve business problems. The field of data analytics has subfields with a range of titles that include marketing research analyst, business analyst, sales analyst, database analyst, pricing analyst, operation analyst etc.
A data analyst must be able to communicate technical insights and quantitative discoveries to nontechnical colleagues through appealing visualizations and storytelling.
Typical Background and Skills of a Data Analyst
A data analyst must have a solid understanding of mathematical and statistical concepts. Having a degree in mathematics or statistics can be a higher advantage.
Microsoft Excel, Power BI, Tableau, SPSS are tools a data analyst must be familiar with. Data analytics involves database management and reporting as well. Consequently, a data analyst must be conversant with SQL and any other database management software. It is as well important for a data analyst to know how to communicate findings through reports and presentations. In other words, a good written and communication skill is fundamental.
Furthermore, emerging technologies for data mining and big data such as MapReduce, HBase, Hive, Cassandra and so on are required.
Now let’s delve into Data Science.
What is Data Science?
Data science is the process of collecting, cleaning, analyzing and drawing insights from messy data by building algorithms and models. A data scientist is anyone who carries out the process described above. The major difference between a data scientist and an analyst is the massive coding skills required of the data scientist.
Data science is the intersection between programming skills, statistics and some domain knowledge. A data scientist needs all three ingredients to get actionable insights from data and build machine learning models that make predictions.
What does a Data Scientist do?
A data scientist does the data cleaning, data processing, data visualization and exploratory data analysis that a data analyst does. He however takes a step further to build models and train them for making predictions, sentiment analysis, binary classification, recommendation, anomaly detection, optimization etc.
Data scientist looks into the data critically and comes up with questions that opens up new avenues to study. He is focused on asking the right questions: questions that are most times maiden. Because a data scientist is charting new paths, he’d need to experiment with various models, tweak parameters and play around with various algorithms in a bid to identify the best performing one.
Typical Background and Skills of a Data Scientist
A data scientist must have a strong background in statistics, programming and a substantive domain knowledge. Having a degree in computer science or statistics is a great deal of advantage.
A data scientist should be skilled in data mining, web scraping, database management, exploratory data analysis, data visualization, machine learning, object oriented programming and other related fields.
- For programming, you’d need to learn software like R, Python, Scala.
- For machine learning, you’d need to learn Natural Language Processing, deep learning, classification, ensemble methods etc
- Data visualization, you’d need to learn to use Power BI, Tableau, Power BI, seaborn, matplotlib, plotly
- For data management, you’d need SQL, MongoDB
- For big data, you’d need Amazon Web Services, Google Cloud Platform, Microsoft Azure, Hadoop, Cassandra, Bhive
It is critical for a data scientist to have good verbal and nonverbal communication skills to share results of the data with colleagues in an unequivocal manner.
For emphasis, let’s highlight the key differences between data science and data analytics.
Data Science vs. Data Analytics: The Key differences.
- While a data analyst finds answers to already existing questions, a data scientist creates new questions and provides answers to them.
- A data analyst carries out data cleaning and visualization and other exploratory data techniques. To find hidden patterns and informs a pragmatic decision-making process. A data scientist, on the other hand, does exploratory data analysis but also build models that can predict future happenings based on past events.
- Due to the higher responsibility, the pay of a data scientist edges that of a data analyst. According to Glassdoor, a data analyst in the US makes an average salary of 43,000 USD to 95,000 USD in a year while a data scientist has an average salary of 83,000 USD to 154,000 USD per annum.
Which should you go for: Data Science or Data Analytics?
A data scientist and data analysts position may have almost similar job titles, but the roles and responsibilities are however different. Before choosing your career path, you must consider factors such as your educational background, personal interest as well as your desired salary. Lastly, it’s also advisable to go for a job in a niche where you have some domain knowledge.