Difference Between Data Science and Data Analytics

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30 September 2022 by AECC Global

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Difference Between Data Science and Data Analytics: A Complete Guide 

Ever wondered how Amazon or Netflix suggests the right picks for you? Today, almost all big businesses are utilizing the potential of data science and data analytics to analyse customer behaviour and offer personalised services. Though the fields of data science and data analytics have many similarities, they are different in many ways too. This blog will give you a comprehensive overview of what the fields of data science and data analytics entail, the difference between data science and data analytics, the various career opportunities in each field and the best courses available. 

What is Data Science? 

Data science is the study of extracting valuable information and insights from data using various algorithms, processes, and scientific methods. It is related to big data, one of today's most popular and in-demand skills. Data science comprises mathematics, computations, statistics, programming, etc., to gain meaningful insights from large amounts of data in various formats.

Know more: What is data science, and what are the best universities to study abroad?

What is Data Analytics?

Data analytics involves looking for trends in large datasets to understand what they mean for a particular business area. This information can then improve or take action on specific problems. By understanding the patterns hidden in their data, businesses can make better decisions that lead to success. This process of extracting insights from data is known as data analytics, forming an important part of business intelligence.

Data Science vs. Data Analytics

Though there is a common misconception that these two terms can be used interchangeably, the is a big difference between data science and big data analytics. Data science is the umbrella term for a group of fields that deal with mining large datasets. On the other hand, data analytics is more focused and can even be considered part of the more extensive process. Analytics is about finding actionable insights that can be implemented immediately based on existing queries.

Difference Between Data Science and Data Analytics

Another difference between data science and data analytics is that data science is more about asking questions and understanding trends than finding specific answers.

The field is focused on using existing data to establish potential trends and identify better ways to analyse and model that data. This makes data science an essential tool for businesses and organisations who want to make the most of their data.

Here is a table to help you better understand the difference between data science and data analytics:

 

Features of difference

Data Science

Data Analytics

Coding Language

Python is a commonly used language along with C++, Java, Perl, etc.

Knowledge in Python and R Language is essential 

Programming Skills

In-depth knowledge of programming is required 

Basic Programming skills is necessary

Other Skills

Makes use of data mining activities to get meaningful insights

Hadoop Based analysis is used to draw conclusions from raw data.

Scope

The scope of data science is large

The Scope of data analysis is limited

Data Type

Data Science mostly deals with unstructured data

Data Analytics deals with structured data

Who is a Data Scientist?

Data scientists play a vital role in understanding and processing large data sets, both structured and unstructured. By combining computer science, statistics, and mathematics, data scientists can create actionable plans that companies and other organisations can use based on their findings.

As data becomes increasingly complex, businesses require experts who can analyse and manage it effectively. Data scientists use technology and social science skills to find trends and develop solutions to business challenges. They can uncover innovative new ways to improve business operations by applying industry knowledge and contextual understanding. A data scientist can earn a salary ranging from $90,000 to $2,50,000 annually. 

Responsibilities of a Data Scientist 

As a data scientist, you are expected to carry out the following responsibilities: 

  • Perform data collection, preprocessing and analysis 
  • Identify data sources and automate the process of data retrieval from them 
  • Process, clean, and validate the integrity of data to be used for analysis
  • Examine the accuracy of new data sources and data gathering techniques
  • Analyze data systems for efficiency, security, and optimisation opportunities
  • Use statistical modelling and predictive analytics techniques by building predictive models to forecast data trend 
  • Develop company A/B testing framework and test model quality.

Top Skills Needed for a Data Scientist 

Though data science is a growing field, recruiters are actively looking for skilled professionals to fill in the posts. In the field of data science, the more skills you possess, the better your chances of getting higher compensation packages. Some of the technical skills that a data scientist requires include 

  • Statistics and Probability
  • Machine Learning and AI
  • Big Data 
  • Programming 
  • Data Wrangling 
  • Data Visualization 

Job Opportunities for a Data Scientist 

As data becomes increasingly central to our economy and society, those with the skills to analyse and interpret it will be in high demand.

Data science careers offer competitive salaries and strong job prospects in various industries. This is an industry with immense potential, so if you have the right education and qualifications, plenty of jobs are waiting for you—both now and in the future. Some of the industries in which you can excel as a data scientist are

Finance: 

Data scientists play a vital role in the finance industry. Their skills are utilised to improve the companies' ability to assess creditworthiness, identify potential defaulters, and detect different types of fraud. Experienced data scientists and actuarial analysts are in high demand in this field, as their work can help finance companies stay ahead of the competition.

Tech:

As data becomes increasingly important, those with the skills to analyse and make sense of it are in high demand. Big tech companies are a natural home for data scientists, who often work with machine learning models to automate data analysis. In addition to their analytical skills, data scientists at tech companies use their programming abilities to help build better products and study customer behaviour.

Healthcare:

A healthcare data scientist plays a crucial role in informing the decisions made in public health and medicine. By analysing performance data of various drugs and their effects, the data scientists can help ensure that these vital services are always operating at their best. 

Entertainment:

Data scientists are the ones hired to use their skills to quantify consumer interest in different media properties, build recommendation engines, and analyse how various marketing methods translate to box office performance or views online.

Who is a Data Analyst?

A data analyst studies and interprets data so the organisation can make better decisions and function more effectively. They use critical and analytical thinking skills to determine what questions need to be answered, locate the data sets that can provide those answers, prepare and interpret the data, and then communicate the findings. 

The data analyst is responsible for ensuring that an organisation's data is accessible and understandable to stakeholders so they can use it to make informed strategic decisions. This highly technical role requires advanced analytics, computer modelling, science, or mathematics training. A data analyst can earn anything from $60,000 to $1,40,000 annually.

Responsibilities of a Data Analyst

A data analyst is expected to carry out the following responsibilities:

  • Extract relevant data from primary and secondary sources
  • Convert raw data into structured data by eliminating duplicates, errors, redundancies, etc
  • Verify data quality to ensure accurate analysis and reporting.
  • Design and maintain database systems 
  • Find patterns or trends in data that would be valuable for predictive analysis
  • Communicating results of findings through visualisations  

Top Skills needed for a Data Analyst 

Data analytics is a rewarding career, but it requires dedication and hard work to succeed. Data analysts need to have specific technical skills, as well as soft skills, to be successful in their field. Here are some of the most sought-after skills in a data analyst: 

  • SQL
  • Machine Learning 
  • Data Cleaning
  • Statistics and Probability
  • Statistical Programming 
  • Data Management 
  • Statistical Visualization

Job Opportunities for a Data Analyst 

There has never been a better time to pursue a career in data analytics. With opportunities spanning multiple industries and the potential for flexible schedules and even self-employment, there is something to suit everyone's needs. Here are some of the career options you can pursue after studying data analytics: 

Quantitative Analyst:

Quantitative analysts play a vital role in financial firms, using data analytics to identify potential investment opportunities and risk management problems. They may also develop trading models to predict the prices of stocks, commodities and exchange rates. With their in-depth knowledge of financial markets, quantitative analysts are highly sought-after professionals.

Data Analytics Consultant:

An analytics consultant is responsible for providing insights and data-driven recommendations to organisations in order to help them make more informed decisions.

While analytics consultants may specialise in any particular industry or area of research, they may also work with multiple companies at a time, focusing on specific projects with well-defined goals. This type of position is ideal for individuals who thrive in changeable environments and have a keen interest in a particular area of study.

Operations Analyst:

Operations analysts play a vital role in large companies, acting as consultants and focusing on internal business processes. These professionals use their technical knowledge and business savvy to streamline systems and improve efficiency from product manufacturing and distribution to general business operations. Operations analysts are essential for keeping businesses running smoothly in every type of business, from postal service providers to the military.

Marketing Analyst:

As digital marketing increasingly relies on data analytics, marketers are turning to tools like Google Analytics and custom reporting tools to help decide how best to allocate their resources. With so much money at stake, marketing professionals must access analysts who can provide insight into how campaigns are performing and where improvements can be made.

IT Systems Analyst:

Systems analysts are responsible for designing and implementing systems that solve problems in information technology. They need to have a strong understanding of technical and business issues to succeed. Systems analysts often use existing third-party tools to test software within a company, but they may also develop new proprietary tools by combining data analytics and business operations.

Courses in Data Science and Data Analytics 

Despite the difference between data analytics and data science, these degrees are becoming increasingly popular. These programs typically combine computer science, statistics and mathematics coursework to allow students to analyse large data sets and draw insights from them. Undergraduate data science degrees are available in some universities, though most students choose to study at the graduate level, with a broader range of programs. Master's degrees in data science are offered by many institutions around the world and usually take two years of full-time study to complete. Although there is a difference between data analytics and data science, careers in this field are rising, offering graduates a promising future. 

Read more: Statement of Purpose for Data Science