Data Analytics vs Data Science - A Detailed Guide

Data analytics vs data science is a very common conflict when choosing the field. Data analytics and data science are two different disciplines, but they do overlap. Talking about the difference between data science and data analytics, Data analytics is a relatively new field that revolves around examining raw data to glean insights. Data science, on the other hand, is a broader field that often involves predictive modeling and advanced statistics. Data analytics may be a better fit for those looking to perform simple tasks with minimal statistical knowledge.

However, those who want to work in a field where they can make more complex decisions should consider studying data science. Those who want to make a career in data analytics or data science can opt for online courses and certifications provided by various online platforms or companies. Read on, to get an in-depth understanding of data analytics vs data science, what is difference between data science and data analytics and much more.

Data Analytics vs Data Science

Data Analytics is the process of analyzing data. Data science is an emerging field that uses computational thinking to extract knowledge and insights from data. So what exactly is the difference between data science and data analytics? Data analytics can be used to analyze anything, but it’s typically used for marketing purposes. It includes things like benchmarking, customer segmentation, predictive modeling, and optimization. But data science is more than just analyzing data; it's the process of extracting knowledge and insights from data using computational thinking. This includes natural language processing (NLP), artificial intelligence (AI), machine learning, statistical sciences, visualization techniques, spatial analysis, and text mining.

So what does this mean for you? Both fields, data science and data analytics are rapidly growing in both demand and prestige, but here are some key differences between them. However, those who want to work in a field where they can make more complex decisions should consider studying data science. Those who want to make a career in data analytics can opt for online data analytics certification courses provided by platforms such as Coursera, Udemy, to name a few. So how do these two fields differ? And who should you choose to work with when starting a new business or project? Let’s find out by discussing similarities and difference between data science and data analytics below:

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Difference Between Data Analytics and Data Science

If we talk about data analytics vs data science - data analytics is a subset of data science. Data analytics focuses on extracting insights from historical data sets, while data science involves collecting and interpreting real-time data sets. Here are some key difference between data science and data analytics:

Data analytics:

• Is a subset of data science.

• Focuses on extracting insights from historical data sets (for example, analyzing sales trends to predict future outcomes).

• Career fields include: business intelligence, predictive modeling and marketing analysis

Data Science:

• Involves collecting and interpreting real-time data sets (for example, by processing a stream of tweets to find patterns or topics with high levels of engagement).

• Career fields include: data scientist, machine learning analyst and biostatistician.

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Difference Between Data Analysts and Data Scientists

Both professions are growing in demand and prestige, but here are some key differences between data analyst and data scientists. The difference is that while data analysts work on past information to make predictions about future outcomes, data scientists work with live information to analyze patterns and topics with high levels of engagement.

This means that if you want to work in one of these fields, the main difference between data analyst and data scientist will be how much time they spend working with historical or current information respectively. Data analysts tend to spend more time working with historical data sets, whereas data scientists may spend more time working with current ones.

For example, data scientists generally have more complex work with broader applications. Data analytics might include looking at how your business is performing in different regions or determining the optimal price point for a specific product. On the other hand, a data scientist would analyze customer behavior in order to recommend new products they might be interested in purchasing. To explain it further in detail, here are few key differences between data analyst and data scientists:

• Data analytics is generally less complicated than its counterpart because it focuses on analyzing past information

• Data scientists generally have more complex work with broader applications

• Data analysts can earn certifications whereas data scientists must earn an undergraduate degree

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Core Skills Required By Data Scientist and Data Analyst

Data science and data analytics have a lot in common, but they also have some important differences. Generally speaking, a data analyst has a broader skill set than a data scientist, because he or she can work closely with other fields like marketing or customer service. A data scientist is more of an all-in-one field that requires expertise in multiple disciplines.

In addition to having different strengths and skillsets, the two professions require different core skills. Data scientists need technical skills for analyzing large datasets or extracting information from massive datasets that may not be accessible in traditional databases. Data analysts need statistical knowledge and skills for understanding the trends and patterns in the information they're using to make predictions about future events. These skillsets can overlap but each discipline focuses on one over the other.

Data analysts and data scientists: What do they do?

Data analysts and data scientists have many differences in what they do, but one of the most important distinctions is the type of data they work with. Data analysts primarily work with existing data sets to find insights from historical events – for example, analyzing sales trends or predicting future outcomes for a business. Meanwhile, data scientists primarily work with real-time data sets – for example, processing a stream of tweets to find patterns or topics with high levels of engagement.

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Job Roles of Data Scientist and Data Analyst

A data scientist looks at past data to predict future events, while a data analyst uses historical information to make more general conclusions about their company’s performance. Data scientists are responsible for advanced modeling, the extraction of insights from multiple sources of data, and interpreting complex relationships. Data analysts are responsible for more straightforward tasks, such as analyzing customer behavior and making improvements based on the results.

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Data Science vs. Data Analytics: Career Perspective

The field of data science has sprung in popularity in the last few years. And, it’s easy to see why. Data scientists are in demand and can be found in many industries like healthcare, media, finance, and government agencies. The Bureau of Labor Statistics predicts that employment opportunities for data analysts will increase by 11 percent between 2016-2022.

Data science is a growing field that's predicted to have 12 million job openings in the next five years. Data science professionals are also predicted to have salaries of over US$114,000 by 2026. The difference between data analytics and data science has a lot to do with what type of career you want to pursue. Data analytics is for those who want to make predictions based on historical data. Data scientists, however, work with more complex decisions, like analyzing social media trends or making recommendations based on customer shopping patterns.

The demand for data scientists is much higher than the demand for data analysts. Data scientists are in high-demand and can be used to provide insight into complex issues such as fraud detection, predictive maintenance, and business intelligence. However, when it comes to data analysis, there are many more jobs available.

Data analytics jobs generally require a lower educational qualification and don’t typically need a degree in computer science or statistics. This means you could find a job with data analytics within your industry of expertise. Generally, these types of jobs involve analyzing historical data sets to make predictions about future outcomes – for example, analyzing past sales trends to predict future outcomes or looking at customer retention rates to determine how best to attract new clients.

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Education Paths in Data Science and Data Analytics.

When it comes to educational requirements, data science requires a bachelor’s or master’s degree in computer science or statistics. Data analytics, on the other hand, is typically more of a niche specialization within the marketing field. It can be completed through an online course or through data analytics certification courses offered by organizations like Google Analytics Academy.

Data science is considered to be more complex than data analytics, so data scientists are typically paid more than data analysts. Data analysts commonly earn average salaries of US$64,000 per year. Meanwhile, data scientists earn significantly higher on average with median salaries of US$103,000 per year. A study by Glassdoor finds that while some data analyst positions pay as high as US$105,500 annually, most continue to fall into the range of $64-$104K. Data analytics is more accessible and flexible than data science without sacrificing too much prestige

Those who want to make a career in data analytics can opt for online data analytics certifications courses. It's important to note that it's possible to specialize within either field - one might decide to become an expert at big-data visualization while another might decide on machine learning algorithms.

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