Data Science vs Data Analytics: A Comprehensive Guide

Data science vs Data analytics

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This article on data science vs data analytics is a comparison between two prominent fields of the tech industry that are often confused with one another owing to their similar titles and a list of workplace responsibilities that are interrelated in most aspects. However, there are also distinct differences between both roles, which will be the focus of this guide.

Data science is primarily associated with gathering various forms of data and making it presentable for different purposes. On the other hand, data analytics is an extension of the broader field of data science skills concerned with detailed analysis and study of the target data. 

Whether you are a first-time learner trying to understand which route is best for you or an established professional aspiring to upskill in your current role, the following sections are crafted to help you pick the most suitable career option for the long run!

What is data science best used for?

Data science vs Data analytics

Data science is concerned with optimising different data types, both structured and raw, to provide valuable insights for business operations and related market trends. After education and training, data science experts use information, including consumer and product data, to identify and predict problems and solutions that wouldn’t be visible otherwise.

They use analysis and comprehensive study to boost the efficiency of different operations within an organisation, making predictions about market trends and designing technical solutions better tailored to recognise and incorporate consumer behaviour.

For example, with the study of data science, companies like YouTube and Netflix can collect raw data about consumer patterns related to offered services and then create software that supplies recommendations best suited to the individual.

Skills required for working in data science

  1. Establishing and maintaining a data science career requires expertise in multiple skills, including programming, web scraping, data visualisation and analysis. Apart from this, you should also be proficient in statistics and mathematics and have a knack for problem-solving.
    1. Statistics and Mathematics: Proficiency in mathematics and statistics is always a valuable skill to have on your résumé when you apply for a job in tech, preferably in the form of a master’s degree. It is important to remember that the everyday responsibilities of data science professionals often revolve around studying mathematical data and tables and constructing improved algorithm structures. Knowledge of calculus, regression, vector models and probability distribution are some aspects of the two subjects candidates should expect to master.
    2. Being a problem solver: If you expect to work as a data scientist, developing a problem-solving outlook in the workplace is essential. You will be tasked with providing immediate and effective solutions for complicated issues. Freshly gathered data is usually always unstructured, so it is necessary to restructure it appropriately. Getting it down to the suitable form for its purpose requires working through many challenges and differing requirements. This calls for an attentive professional ready to navigate any hurdles and challenges.
    3. Proficiency in data analysis and visualisation: As discussed in the prior section, analysing data in its rawest forms and putting it into usable arrangements is the core of data science. Other professional responsibilities include revising it into a presentable format that can be reviewed and acted upon by the company executives and conveying the data to other members of the workplace.
    4. Excelling in programming languages: Before you edit data, construct practical algorithms and make accurate predictions for a business, you will need to be proficient in multiple programming languages and hone your machine-learning skills. For this reason, candidates are expected to be fluent in several computer-programming languages, including Python and Java.
    5. Experience in web scraping: As a data scientist, hunting the web for the correct data from multiple sources and verifying its authenticity is another routine task. This process is best known as web scraping, which is data extraction from various sources to find the most valuable information related to an organisation’s operations.

Career options in data science

Data science students can expect a varied career with several challenging roles once they get the hang of the core skillset. For example, being a data scientist, data engineer, and data analyst are popular choices within a similar career field. Still, it is also essential to consider the importance of the subject in related fields.

  1. Data scientist: This position requires candidates to be proficient in the syntax and application of programming languages. Programming is the core of algorithm construction and is the base of the finished software products. This role also requires excellent communication skills, as corresponding with colleagues and senior management to design the most efficient solutions using the assessed data is vital.
  2. Data analyst: A data analyst researches and arranges big data to be evaluated and reorganised for future predictions that could provide valuable insights into market patterns and changes. Professionals are also constantly involved with statistical analysis and data visualisation methods while using tools such as SQL to stimulate raw data within a business.
  3. Data Engineer: Responsibilities in this role include taking the roughest forms of data and organising it to produce realistic insights and solutions that could be useful for creating new goals for members of the workplace and the company as a whole. Professionals in this position collaborate with colleagues in similar roles, such as business analysts, to arrange data in a coherent, workable format, thus improving performance and profitability.

What is data analytics best used for? 

Data science vs Data analytics

Data analytics is best used to create techniques for gathering and assembling relevant data in response to any complications that arise within an organisation. Professionals are often tasked with evaluating technical insights that are crucial for making critical decisions and can benefit an organisation in the long run.

Data analytics is one of the many areas in the larger field of data science. It is most useful for accurately assessing data to help an organisation overcome market and workplace barriers. Possible responsibilities include identifying immediate solutions to problems affecting the business on any level and ensuring that the final presentation of the results is simplified to be communicated with other staff members.

Skills required for working in data analytics

Before taking on a role in this career route, it is essential to be proficient in data analytics fundamentals like machine learning and data cleaning. Expertise in the mathematical aspects of calculus and linear algebra and sharp critical thinking and communication skills will also help you better settle into your new role.

  1. Background in data cleaning: Before generating complicated models and algorithms, data analytics roles focus on cleaning and sorting out unstructured data till it is effectively presentable, as relying on raw data can lead to misinformed insights and the possible implementation of incorrect solutions.
  2. Experience in machine learning: Professional data analysts use various machine learning tools while working with artificial intelligence solutions. Using the proper range of tools can automate the entire process and provide more comprehensive data at an increased rate. Therefore, data analysts are expected to have efficient command over the fundamentals of machine learning.
  3. Communication skills: Professionals in data analytics require excellent interpersonal skills as they will be expected to communicate their assessment and arrangement of data to senior management. As a working professional, you must also present and discuss your recommendations with non-technical colleagues. You might even have to explain certain things to customers.
  4. Expertise in Calculus and Linear Algebra: Being adept in advanced mathematics is a core skill for every capable data analyst. This is especially true for expertise in linear algebra and calculus subfields, which support constructing various matrix and vector models. Aspirants should consider getting hands-on work experience with algorithm projects to get a proper insight into the exact places their mathematical skills are lacking.
  5. Critical Thinking Skills: Critical thinking as a data analyst involves consideration of technical data beyond mere data processing, such as potential problems and the result of implemented solutions. Candidates must exercise their critical thinking abilities before tackling real-world workplace problems. It is essential to identify the root causes of a problem before proceeding to find immediate fixes so that business operations are not delayed. 

Career options in data analytics

After gaining sufficient expertise in the base skills required to establish a solid data analytics foundation, you can apply for various positions as a data scientist, data analytics consultant and marketing analyst.

  1. Data Scientist: Professionals in this role gather and assess valuable data and make predictions that will benefit a business’s future decisions. It requires the individual to remain informed about the data and its effects. Data scientists also use various programming languages, data visualisation, and software development tools.
  2. Data Analytics Consultant: A consultant can work with multiple organisations simultaneously, focusing on each business as a project. This position requires a change-oriented disposition and the ability to analyse all sorts of data, often for unrelated markets, and provide valuable insights for the relevant organisation.
  3. Marketing Analyst: Marketing is often one of the most expenditure-prone departments in any company, and successful marketing campaigns usually require a heavy budget. This is where the expertise of a marketing analyst comes into play, as they are responsible for making predictions and identifying the most profitable trends. The position requires expertise with several analytics tools and extensive study of raw internet traffic behaviour.

Which option should you pursue?

Choosing the ideal career path from these two options comes down to the skills you possess, your ambitions, your attitude toward learning and your behaviour towards practical work in an organisation.

Suppose you are more interested in dedicating yourself to a successful career in one organisation. In that case, the broader field of data science might be an ideal option. On the other hand, positions in data analytics are more suited to professionals who are constantly looking for a change in professional responsibilities and are excellent at multitasking.
Both data analytics and data science require an extensive background in programming languages, software tools and methods of the most efficient data utilisation.

However, the critical difference is that while data analytics allows the individual to switch between similar projects, careers in data science usually branch out toward higher executive positions.

You can pursue the option you select by signing up for the detailed courses at the Institute of Data.


Check out our data science courses if you’re eager to learn more about how the Institute of Data can help you make the move to a career in data science today!

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