What Is the Difference between Data Science and Data Analytics?

The unprecedented growth of big data is directly influencing two of the biggest domains in all the industries, namely Data Science and Data Analytics. According to a report published by Fortune Business Insights , the size of the global big data analytics market is expected to grow worth USD 549.73 billion by 2028.

Moreover, according to the World Economic Forum , the daily global data generation will amount to 463 exabytes of data by 2025. Fascinating right? The data generated regularly each day is so massive and complex that it is not possible for traditional data processing or analysis systems to handle it. And this is where data science and data analytics play a huge role. These two terms are often used interchangeably, and they are considered the two sides of a single coin.?

However, sometimes confusion arises due to the fact that both data science and data analytics work to address big data. In this guide, we will take a look at the differences between data science and data analytics.?

Core skills: Data Science Vs Data Analytics

Data science skills

To work in the data science domain, a data scientist must have the following skills:

• Proficient in mathematics and statistics.?
• Expertise in programming languages- Python, R, SQL, etc.?
• Good knowledge of predictive modeling, machine learning, database management, and data wrangling.?
• Skilled at using big data platforms such as Apache Spark, Hadoop, etc.?
• Expertise in using SQL and NoSQL databases like Cassandra and MongoDB
• Experience with using data visualization tools like QlikView, D3.js, and Tableau.
• Problem-solving skills
• Critical thinking skills
• Communication skills

Data Analytics skills

A data analyst must possess the following core skills.?

• Proficient in using excel and SQL databases.
• Adept in programming languages- R, or Python.
• Proficient in using BI tools such as Power BI for reporting purposes.?
• Well-versed in using tools such as SAS, Tableau, Power BI, etc.?
• Predictive analytical skills.?

Job responsibilities: Data Scientist vs Data analyst?

Both data scientists and data analysts use data, but the way they utilize the data is different. For instance, a data scientist uses a complete blend of mathematical, statistical, and machine learning techniques for cleaning, processing, and interpreting the data to extract valuable information from it.?

While a data analyst is responsible for examining the data sets for identifying trends, patterns, and figures to draw conclusions. Data analysts play a huge role in the data collection, organization, and analysis process to identify relevant data patterns and identify the findings through data visualization methods.?

To get a better idea about the job responsibilities of both a data scientist and data analyst, refer below:

Data scientist: Job responsibilities

• Clean, process, and validate the integrity of data sets.
• Performing exploratory data analysis on complex and large datasets.
• Creating ETL pipelines and performing data mining.
• Utilizing ML algorithms such as logistic regression, KNN, Random Forest, decision trees, etc. for performing statistical analysis.?
• Writing code for automation and building resourceful ML libraries.
• Identifying trends and patterns in datasets for business predictions.?

Data analysts: Job responsibilities?

• Collecting and interpreting the data.?
• Identifying relevant patterns in datasets.?
• Using SQL for performing data queries.?
• Experimenting with different analytical tools such as predictive analytics, prescriptive analytics, descriptive analytics, and diagnostic analytics.?
• Using data visualization tools such as Tableau, IBM Cognos Analytics, etc. for presenting the datasets for better decision making.?

Difference between data science and data analytics?

While the two terms-Data science and data analytics are often used interchangeably, they are both unique fields, with the scope being a major difference. Data science is an umbrella term and is considered a multidisciplinary field for extracting or mining large raw, unstructured datasets. In contrast, data analytics focuses on existing data sets and helps in processing and performing statistical analysis on data.?

Another difference between the two is that data science is more focused on finding the right questions, while data analytics finds the solutions to those questions in mind based on the existing data. It means that data science is concerned about asking and finding the right questions, and data analytics is concerned about finding actionable data to work on for achieving better insights and conclusions.?

We can say that both data science and data analytics are different sides of the same coin as their functions may be interconnected. Data science is known to lay the foundation of datasets and create initial observations, trends, and potential insights from the data. On the other side of the coin, data analytics works on the foundational existing datasets laid down by data scientists.?

Which one is right for you: Data Science vs Data Analytics??

There are several factors that you should consider before choosing between data science or data analytics as it will help you align your professional and personal goals with your work segment. Here are the three most crucial factors to consider before choosing between the two domains.?

Education background?

The first crucial factor to consider before choosing between data science or data analytics is your educational background. Since data science places the focus on the design and construction of new processes for production and data modeling, a master’s degree in data science is usually good for gaining an in-depth knowledge of data science processes and for professional development.?

While data analytics places the focus on examining datasets and identifying trends and patterns to visualize them using data visualization tools. And to help achieve expertise for these tasks, data analysts usually pursue an undergraduate degree in technology, science, engineering, or math.?

Therefore, before you select a domain, consider your educational background as it will help you incorporate your study practically in your chosen domain.?

Your past experience and interest?

The second factor to consider is your past experience and interest in the field. If you have a knack for math, programming, computer science, and as well as an interest in the business world, then you should consider the data science domain as it is the wise pick for you. On the other hand, if you love numbers, statistics, and programming along with a comprehensive understanding of the industry you are working in, then you may go for data analytics. Nevertheless, your choice depends on your interest in the specific domain and your past experience if any.?

Salary and job opportunities

The third crucial factor that many candidates consider before choosing their preferred domain is the salary perks and job opportunities. Here is the salary structure for both data scientists and data analysts to give you an idea.?

• Data science- A data scientist can potentially earn an average salary of $97,140 as stated by Payscale.?
• Data analytics- A data analyst can earn an average salary of $66,138 as stated by Indeed.?

However, there is potential growth for both of these segments as stated above and with experience and additional certifications, one can earn an even higher salary range.?

Conclusion

Both data science and data analytics are working with data. And there is no obligation when it comes to becoming a data scientist if you are a data analyst or vice-versa. You can select either of the fields as they are largely interconnected. However, you must be clear with the concepts, tools, techniques, programming languages, and other segments necessary for both domains. Moreover, getting a professional certification can help boost your data analytics or data science career ever further. You can explore the data analytics fundamentals course or the data analytics course with excel offered by Techcanvass.?