Ultimate Guide to Understanding the Difference Between Effective Data Analytics vs Data Science

 

Are you struggling to figure out the concept of effective data analytics vs data science? In this guide, we will break down the meaning behind these two concepts in order to get a better understanding of each one before taking a closer look at their differences in career roles and responsibilities. Later, we will also discuss how to decide which career option between the two is the best for you. Read more below.

 

You might also enjoy our guides on big data and data automation.

 

The similarities between data analytics and data science are often confused by most people across the globe. In order to distinguish between the two, the best option would be to take a closer look at their differences in order to find what makes each concept their own. Some people would argue that in order to become a data scientist, it is required to be a data analyst. Data scientists usually have a higher degree than that of a data analyst. But what are data science and data analytics all about?

 

What is Data Science?

Data science is a field where scientific methods, processors, and algorithms are used to try and gather information from structured and unstructured data.

 

In order to fully understand this concept, we need to look deeper into the terms structured and unstructured data. Structured data is seen as the type of data that is organized. All of the data that is grouped into this category is clearly defined data types. These data types usually display patterns that are easy to find. One of the most common examples of structured data would have to be a case where the data has been successfully formatted into dedicated fields, i.e. credit card numbers and addresses.

 

All of the other data types that cannot be sorted into this category is known as unstructured data. Unstructured data can be textual, non-textual or even machine-generated. One of the main differences between these two types of data is how easy it is to analyse structured and unstructured data.

 

Now that we understand the concept of structured and unstructured data, we can take a closer look at data science.

 

As we are now in an era where data is being transmitted almost every second of the day, the need to sort and store data has become important now more than ever. Data science is seen as the key to storing data.

 

A data scientist is a person who practices data science. A data scientist works to find solutions to some of the most complex data problems. He /she will use mathematics, computer science, and even various statistics in order to achieve their goal.

 

Working as a Data Scientists

Most often you would find that a company identifies a person who analysis data as a scientist. Having these types of skills can really boost your career. In order to become a data scientist, a person needs to study and obtain a degree or masters in data science.

 

One of the main advantages of working as a data scientist is that you have the freedom to follow your own ideas to find patterns and trends in the data.

 

Even if you have some experience working in the field a data scientist, it is really important that you have some the following skills in order to be successful in your line of work.

 

These skills include, but are not limited to:

  • Understanding statistics and the different techniques that need to be used as a data scientist.
  • Know different machine learning methods.
  • Be able to visualize and your findings from the data.
  • Creating algorithms and models to gather information to solve the problem.

 

Understanding Data Analytics

When we talk about data analytics, we are referring to the process in which raw data is analysed in order to reach an informed decision. Data analytics techniques are used to help find patterns and trends within the raw datasets. Different techniques are used on the raw data to help to make sense of the information.

 

For example, a corporate company would record the running time of their machines and then analyse the data at a later stage, to create a more effective working plan for the machines to operate to their full capacity.

 

This is not the only way in which data analytics can be used. Some companies which work with website content also use data analytics to keep you engaged in their content by watching and clicking on it in order for their company to receive the most amount of views, clicks, and traffic to their website.

 

Working in the Data Analytics Field

Working in the data analytics field requires a person to sift through large quantities of data in order to find trends and gather the information that will help a business make more informed decisions at work.

 

A data analyst can work in different kinds of businesses, some of which include private firms, insurance companies, and technologically advanced corporations.

Companies such as Facebook and Google analyse large quantities of data to such a degree that they choose to employ many data analysts for reasons such as internal marketing and even advertising analysis.

A typical example taken from Google would be their free analytics service which collects data that is used for advertising purposes. The data is then compiled into statistic reports that measure the potential effectiveness of an ad campaign.

One of the ways in which data analysis examine the datasets, is through the data analysis process.

 

The Five-Step Data Analysis Process

In order to successfully analyse data, there are five different steps that need to be carried out to ensure that the most information is generated from each dataset.

 

The five-step data analysis process involves the following:

 

  • Step 1: Find the purpose of your data analysis

The first, and most important step in the data analysis process is to figure out why are you undergoing this process. In most cases, the reason for conducting a data analysis process would be derived from a problem or question faced by a business.

 

A typical example of a possible question that a business may wish to analyse could be, “ Are my customers satisfied with our products?”

During this time, it is also best advised to try to find possible sources where the data could be collected from.

 

  • Step 2: Gathering data

After you have figured out the main reason behind conducting this data analysis process, the next step involves collecting data for your analysis.

 

There are two distinct types of sources in which data can be found, this includes internal and external sources. Internal sources hold valuable information about the business finances, customers, and even potential gaps in the market which the company can maximize on. External sources, on the other hand, is all of the other unstructured data that can be collected from other places.

 

Once you have gathered all of the data you would need to make an analysis, it is then time to sort through the data.

 

  • Step 3: Sorting and cleaning the data

This is an important step in the data analysis process. One of the key factors to keep in mind is that not all data is going to be considered good to use in your analysis. In order to get the best results, a data scientist needs to comb through all of the datasets in order to make sure there are no duplicates or any other interferences that could potentially skew the analysis.

 

  • Step 4: Analysing the data

After all of the data has been sorted through, it is then time to take a closer look and examine each dataset. This can be done by using various data mining techniques.

 

  • Step 5: Explain your results

The last and final step involves interpreting your results found during the data analysis process. It is during this step where the analyst can check their reasoning behind the purpose of conducting the analysis process in the first place.

 

Difference Between Data Science and Data Analytics?

Today, data can provide some of the most crucial insights as to how a business is performing in the market.

 

But, all of the hard work and effort that goes into gaining this useful information is either performed by a data scientist or data analyst. These two jobs are known to be some of the most popular in the field of data.

 

It can become a little confusing when trying to figure out the difference between data science and data analytics. The main differences between the two include the fact that a data analyst tries to identify trends and patterns within a dataset, whereas data scientists interpret the data by creating different algorithms.

 

Each job comes with its own set of roles and responsibilities. As a data analyst, you are required to complete an undergraduate degree in one of the STEM (science, technology, engineering, or mathematics) subjects. Not only does a data analyst have to have an educational degree, but they also are required to have strong skills in predictive analytics.

 

It is no surprise that you would also have to be very educated in order to become a data scientist too. As a data scientist, you would have to hold a master’s degree in mathematics, science, computer programming, or engineering. Data scientists also have to be comfortable with the idea of presenting their findings to major company stakeholders.

 

Responsibilities of a Data Analyst vs a Data Scientist

The type of responsibilities that a data analyst has will differ depending on the industry that they work in however, the most common responsibility within all these fields involves analysing and interpreting data.

 

The responsibilities of a data analyst include, but are not limited to:

  • Coming up with data systems that can help to solve and manage data related problems
  • Providing the best quality control over the data
  • Providing technical information such as data mining, storage, and cleaning
  • Using different tools to interpret datasets by taking a closer look at the different patterns and trends within them

 

As a data scientist, the type of responsibilities you will have will differ in some aspects from that of a data analyst. Data scientists usually work closely with companies in order to find out more about their goals and assist them with ways in which their goals can be achieved.

 

It is the responsibility of a data scientist to ask the most appropriate questions which will help them begin their discovery process. Once this has been taken care of, other responsibilities such as applying data science techniques, measuring and improving on the results, and even reporting back to business stakeholders will shortly follow after.

 

From this, it is quite clear that the responsibilities of a data analyst and data scientist are completely different from each other. Due to the technical nature of the role of a data scientist, this career is often viewed to be more superior to that of a data analyst. But, both of these jobs do require that a person has the correct educational background in order to work as a professional in their chosen field of work.

 

Common Job Titles in the Data Science and Data Analytics Field

Data science job opportunities exist in almost every field, not just technology. Companies such as Microsoft and Amazon are always on the lookout for people who possess these skills near them. There are many different careers that one could branch off into the data science field. Below we discuss some of these options:

 

Data Engineer

A data engineer is someone who designs, builds, and manages data. They work towards developing systems that analyse and process the data in a way that is the most suitable for an organization. It is the duty of the data engineer to ensure that these systems run perfectly.

 

This career path is unique in the sense that its focus is on the systems and hardware that are used to make these processers easier for a company’s data activities.

This job is seen as a more senior role and requires the data engineer to be responsible for carrying out tasks such as providing valuable data solutions.

 

Data Scientists

One of the most common job titles in the data science field is that of a data scientist. As a data scientist, you will be responsible for designing and creating data modelling processors that will be used in algorithms and predictive models.

 

A data scientist has the skills such as data mining, statistical skills, and machine learning abilities that will help them to work with coding and different algorithms.

As a data scientist, you will be required to use large amounts of data and create a different hypothesis and identify relationships between the datasets. The demand for data scientist skills has grown significantly over the years due to the fact that major companies are all looking to allocate people with the task of analysing large quantities of structured and unstructured data that are produced by the company.

 

Another great career to look into is that of data analytics. There has never been a better time to learn data analytics and enter the field of work as a professional data analyst. With the rapid improvements of technology occurring every single day and people having immediate access to the internet, there is no reason for data analytics to not become one of the most in-demand jobs out there.

 

Here are some of the most common jobs in the data analytics field:

Data Analyst

It is not a surprise that the first common job title within the data analytics field would be that of a data analyst. A data analyst takes all of the data and examines it in order to find patterns or trends within the datasets.

 

Some roles as a data analyst would have more industry-specific names such as “professional healthcare data analyst” or “marketing analyst” but one thing that all of these jobs have in common is that it all comes down to the task of analysing data.

 

Data Analytics Consultant

The role of a data analytics consultant is to provide companies with feedback in order to help them grow. One of the main differences between a data analytics consultant and a data analyst is the fact that a data analytics consultant specializes in a specific industry, whereas data analysts usually work for different companies in a small period of time.

 

The role of a data analytics consultant is more aimed at those people who like change and can also work remotely.

 

These are just a handful of common job titles that one can branch into when working in the field of data science or data analytics. Whether it is your goal to get hired for a specific job or in a new industry, these two career paths could help to prepare you for that opportunity. But it is important to keep in mind that these jobs cannot be attained without some sort of higher educational background first.

 

Data Analyst or Data Scientists – Which is the Best Career for You?

Now that we have a better understanding between data science and data analytics, you can now start figuring out which career path is best for you.

 

Even though both of these fields are very similar in their line of work, they also have their differences when it comes to educational requirements and the way that each field functions.

Two important factors to consider when deciding between these two careers include:

 

  • Your personal interest

To determine which career is best for you, it would be best to take a look at your own interest and see how they align with either data science or data analytics.

If you are someone who is very interested in numbers, you may want to consider working to find trends and patterns within datasets.

 

The type of interest you have could ultimately assist you in finding out which option between the two is best for you.

 

  • The amount of salary you would like to earn

One of the most important factors that will help you to determine which career is better for you is the amount of money you would like to earn.

Both fields have different amounts that you can earn, but depending on the amount of experience that you have will ultimately determine the amount of money you could potentially be earning. More experience means more pay.

 

As a data analyst, you could increase the amount you earn by learning extra programming skills such as Python.

 

Data scientists usually hold a degree, but have little to none experience. Although this could be a setback in the amount of money they receive, a data scientist has many opportunities to advance into much higher roles in a company, resulting in more pay.

 

Once you have considered these factors, you would be able to make an informed decision about which career is best for you.

 

Advantages of Choosing a Career in Data Science and Data Analytics

Getting to work in the field of data analytics or data science can be great. But this is not an ideal career option for everyone. Here are some advantages which you can look forward to when working in these two fields:

 

  • People who choose to follow a career in data science or data analytics usually get paid a high salary every month. This is because of the fact that it is such an in-demand job and required specialized skills in order to be done correctly.
  • These careers are not tedious in the sense that it’s mainly focused on technologically advanced work. These fields have a good balance of both technical and non-technical activities that need to be carried out in order to achieve their goal.

 

Data analytics and data science have similar job roles, but at the same time, both of these careers are so vastly different from one another in areas such as education as well as the job roles and responsibilities. We hope that this guide helped you to distinguish between effective data analytics vs data science