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Data Analytics and Data Science: A Comprehensive Guide

Data is an asset for every organization as it helps to identify problems, provide solutions, make confident business decisions, analyze trends, increase sales, and grow the overall business when handled effectively. It necessitates the demand for data professionals and opens the door to more data-driven jobs with high salaries and promising careers. Data Analytics and Data Science are two domains in the data world. Both are connected to one another in numerous ways and are the primary paths to kick-start a career in the data field.

A comprehensive guide to data analytics and data science

On an outlook, they appear similar, yet they are different, and each has its pros and cons. If you wish to become a data analyst or a data scientist and establish yourself in the data world, this article is for you. Read the article to get more insights about the key points to consider in deciding between Data Analytics and Data Science careers.

You should know the most important Data Analyst Interview Questions

Data Analytics

Data Analytics is a subset of data science that processes and analyzes a large amount of data to give meaningful insights and solutions for organizational problems. It begins with data collection, cleaning, statistical data analysis, interpretation, and sharing of results. The outcomes of data analytics are reliable and exhibit immediate progress in the business.

Data Analytics has four levels of analytics listed below.

  1. Descriptive Analytics – It gives insights based on historical business data about market trends and patterns.
  2. Diagnostic Analytics – This level is to identify the root cause of events that halt the growth of a business.
  3. Predictive Analytics – This level uses statistics to predict the future of any business
  4. Prescriptive Analytics – It analyzes past data and makes proposals to proceed further in the industry.

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Data Science

Data Science is the umbrella term that includes data analytics along with data engineering, data mining, and other related disciplines. It deals with large volumes of data using machine-learning algorithms or predictive modeling processes to give valuable insights. Data Science is constantly changing and developing. However, Data science relies on the following concepts, which are the pillars of data science.

  1. Domain Knowledge – This indicates the knowledge about the business, product, or service and its customer base.
  2. Math & Statistic Skills – These skills are necessary to analyze and interpret data to get insights and valuable solutions.
  3. Computer Science – Computer science is the foundation for all the other pillars of data science. It gives the algorithms, software, and systems to collect and interpret data.
  4. Communication and Visualization – Communication is essential to share the results visually with graphs, charts, and diagrams.

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10 Key Points To Choose Between Data Analytics and Data Science


1. Scope

As we know, Data Science is a broad field with different disciplines like machine learning, statistics, and programming, whereas Analytics is a subset of data science. Data science has more scope compared to data analytics. Moreover, data science is a research-oriented field, while analytics is a practical-oriented one.

Data scientists work on large data sets to give new conclusions and better tools for analyzing data. Data Analytics is a more focused domain in which data analysts work on the same data sets to offer actionable insights to the organization. Data Science does not give answers to any specific question.

Instead, it guides the organization with the insight to frame the questions needed for the business to grow. Data Analytics always works with questions in hand. Data Analysts work to discover the answers to the questions asked.

If you are interested in a more practical-oriented job, you can choose data analytics. Most data scientists start their work as data analysts and then upgrade themselves.


2. Data Types in Data Analytics and Data Science

Data Science handles structured and unstructured data, while data analytics deals only with structured data. Unstructured data has no specific format. It represents the bulk of business data in its native form with no predefined model and is not stored in a structured database.

Unstructured data is collected, stored, and managed quickly. It includes images, videos, emails, social media posts, reports, web pages, and presentations. Specialized tools like  NLP and data science experts are mandatory for analyzing unstructured data.

Structured data is well-organized data with a persistent order and conforms to a predefined model. It includes data generated at colleges, universities, banks,  companies, etc., and is stored using SQL, MySQL, Oracle DB, and SQLite. It can be handled by data scientists as well as data analysts.

As unstructured data is in its native form and supports massive storage, the importance of unstructured data is increasing. Many businesses prefer this data type for their data management as it gives a deeper understanding of customer behavior. This increases the demand for data scientists compared to data analysts.

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3. Requirement of Statistical Skills

Both need knowledge of statistics to analyze data. Statistics is the basis of machine learning algorithms to capture data patterns and translate them into meaningful insights. It also helps with data preprocessing and feature engineering.

Statistics aids data scientists and analysts in visualizing numbers to figure out the trends in quantitative data. Statistics are mathematical analyses of big data and are broadly classified into two types.

1. Descriptive Statistics – Summary Statistics

This type gives an overview of an organization’s data. It helps to review and summarize data understandably. It does not provide insights into decision-making and the growth of a business.

2. Inferential Statistics

This type of statistics interprets data, identifies patterns, and helps to build predictions for decision-making. It suggests valuable insights to achieve organizational success.

Statistical skills help data scientists and analysts confirm the validity of the analysis and also to avoid logical errors. The level of expertise in statistics may vary depending on the job role and the data handled. However, Statistical skills are mandatory for data scientists and analysts. If you are not comfortable and confident in statistics, then it is of no meaning to pursue a career in the data field.

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4. Programming Skills

Both Data Analytics and Data Science require programming skills to clean, process, and transform raw data into valuable insights. A data scientist with programming skills or a software background is a more relevant candidate. Their expertise in software makes them not depend on other outside resources.

Advanced object-oriented programming is the most important skill needed by data scientists as they have to handle large data sets, write SQL queries to a database, understand black box tools, and do data cleaning. A Data Analyst also needs programming skills but only a basic level of expertise is sufficient.

The main responsibilities of a data analyst are focused on data crafting and presenting. However, practicing analyzing tools and algorithms, requires programming skills. This skill can also help them to perform advanced data analysis without the support of a programming expert. Moreover, programming expertise is very much needed for data analysts who aspire to be data scientists in the future.


5. Coding Languages

Data Analysis uses coding languages like Python and R for data analysis. Recruiters expect expertise in any one of the two languages even from an entry-level data analyst. Python is a general-purpose language, which is predominantly preferred for data analysis in the business world.

R is a language for data analysis and visualization. It is mostly preferred by academia for research and the finance industry. In addition, data analysts also require knowledge of SQL, RDBMS, and Oracle to extract data from databases and perform data wrangling and presentation.

Data Science uses advanced object-oriented programming languages like C++, Java, Javascript, SAS (Statistical Analysis Stem), and Scala. However, Python is the preferred language, as it easily integrates with SQL and TensorFlow.

The coding language is selected based on the data set provided by the industry. For example, in the retail sector, python is preferred to build trust by providing valuable suggestions to customers. IoT applications prefer languages like C and C++.

So, to kick start a career in data analytics and data science, knowledge in any one of the above-mentioned programming languages is mandatory. Online certification in any one of the coding languages will make you shine from your competitors.


6. Career Growth

In the digital era, data is everywhere, and every business, regardless of its size,  searches for people who can handle data effectively and devise constructive ways to develop the company. Data Analytics and Data Science are two different career paths in the data field.

When you choose Data Analytics, you start your career as an entry-level analyst in any industry and after a few years, you can become a specialist in that industry and can take up roles like financial analyst, health analyst, and so on. Moving on the ladder, you can expect yourself to be a senior analyst or an analyst manager in the data industry.


You also have the opportunity to move to the data science field with strong expertise in data analytics. A Data Scientist with more than three years of experience can become a senior data scientist with a reasonable salary and later on, with further experience and expertise can be the director of analytics.

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7. Job Description

Data Analyst

Data Analysts are responsible for analyzing data using statistical tools and converting raw data into information, information into valuable insights, and insights into progressive business decisions. Their day-to-day duties cover the following.

  • Data interpretation, statistical analysis of interpreted data, and provision of ongoing reports
  • Create and deploy databases, data collection methods, data analytics, and other measures to improve statistical accuracy and efficiency.
  • Maintain databases and data systems and gather data from primary or secondary sources.
  • Finding, analyzing, and interpreting patterns or trends in large, complicated data sets
  • Review computer outputs, printouts, and performance indicators to clean and filter data, and find and fix coding issues.
  • Prioritize business and information needs in collaboration with management
  • Discover possibilities for process improvement

Data Scientist

Data Scientists handle large amounts of structured and unstructured data to trace patterns and trends to offer business insights and predict the future growth of the business.

  • Find useful data sources and automate the processes for gathering them
  • Create machine learning algorithms and predictive models.
  • Combine various models.
  • Using data visualization approaches presents information.
  • Build strategies and solutions for organizational problems.
  • Work along with the product development team.


It is evident from the job descriptions that, Data science is for persons with a passion for research while data analytics is for people who like to work for practical business.


8. Additional Job Roles

Data is the lifeblood of any business in today’s digital world as it gives tremendous growth and profit. An ocean of opportunities is there for an expert data professional. All businesses are recruiting data analysts and scientists. Besides data analyst and scientist, there are few other job roles available for data professionals.


Additional Job roles

  • Business Intelligence Analyst – Analyzes the company’s data to extract valuable insights for the successful growth of the business.
  • Data Engineer – Focuses on large data sets to optimize database formats and data collection processes.
  • Quantitative Analyst – Analyzes data of financial organizations to propose investment plans.
  • Data Analytics Consultant – It is similar to data analysis but for different businesses over a short duration. It is more like a freelancing job.
  • Operations Analyst – Focuses on internal business processes like product manufacturing and distribution.
  • Marketing Analyst – Work along with marketing professionals to help improve the results of campaigns and in turn sales of the company.
  • IT Systems Analyst – Analyse data and propose plans to address issues in information technology.

Additional Job roles

  • Data Architect – Design the process and flow of data management. They create new databases or enhance existing databases.
  • Machine Learning Engineer – develop machine learning models, algorithms, and systems to process organizational data.
  • Database Administrator – Maintains the organization’s database and ensures safe data storage with data recovery solutions.
  • Clinical Data Managers – Analyze data collected for clinical research and predict the future of the medical industry.


9. Salary in the Field

DomainJob RoleAverage Salary



Data Science

Data Scientist10-12 LPA
Data Architect13-15 LPA
Machine Learning Engineer8-10 LPA
Database Administrator13-15 LPA
Clinical Data Managers6-8 LPA






Data Analytics

Data Analyst3-5 LPA
Business Intelligence Analyst4-6 LPA
Data Engineer8-10 LPA
Quantitative Analyst2-4 LPA
Data Consultant10-12 LPA
Operations Analyst5-7 LPA
Marketing Analyst8-10 LPA
IT Systems Analyst7-10 LPA


The salaries given are average values and may vary with your expertise, experience, and the size and reputation of the organization you work for. All the job roles provide a handsome salary.


10. Courses

Data Analytics

As we have seen in the above key points, the following skills are required to become a data analyst

  • Mathematics and Statistical Skills
  • Basic level programming skills in any one programming language like Python or R.
  • SQL
  • Microsoft Excel
  • Data Visualization
  • Critical thinking
  • Presentation skills

To acquire the above skills, you can enroll in an online course from a renowned institution. Most of the courses come with the following modules in the curriculum.

  • Business Statistics
  • Advanced Excel
  • SQL
  • Tools – Tableau and BI
  • Python Basics

Anyone with a graduate degree can pursue an analytics course. It costs around 10,000 INR to earn a short-term certification course.


Data Scientist

The skills required for a data scientist are listed below,

  • Statistical skills
  • Advanced programming skills
  • Machine Learning
  • Problem-solving skills
  • Data Analysis

You can develop the above skills by completing an online short-term certification course with the following modules.

  • Mathematics
  • Probability and Statistics
  • Data Visualization and Analysis Tools
  • Machine Learning
  • Programming with Python

Anyone with a graduate degree with a STEM background (Science, Technology, Engineering, and Mathematics) is eligible for a data science course. The curriculum, duration, and fees rely on the level of the course. Depending on your requirements and budget, you can do a short-term certification course, diploma course, undergraduate course, or postgraduate course.

Frequently Asked Questions

1. Which is easy data analytics or data science?

Both are comfortable for persons who love to work with numbers. Interest in mathematics, statistics, and programming makes it easy to learn. Data Science requires a depth of knowledge in machine learning and advanced object-oriented programming. So Data Science looks not as easy as data analytics. But given the time, effort, and patience, you can easily learn it.  Initially, you can get analytics skills, start as a data analyst, and then upgrade your programming and machine learning skills to become a data scientist.

2. Which pays more?

Any profession related to the data world fetches an average salary of 4 Lakhs per Annum at the entry-level. Data Analyst at the front level earns an average of 2 lakhs per annum and it reaches 12 lakhs per annum within a span of 5 years of expertise in the same industry. In contrast, a data scientist at the initial stage of their career gets up to 3.5 lakhs per annum, and around 8 years of dedication in the industry can earn him 26 lakhs per annum. It is clear that data science pays better than data analytics.

3. Is Python enough for Data Science?

Python is a high-level language predominantly used in data science. It is feasible to work in the data science field with proficient knowledge of Python. However, in addition it, expertise in SQL, statistics, mathematics, critical thinking, and great communication skills to interact with all departments of a business are essential to secure a job in data science. Besides, other programming languages like R, C, C++, JAVA, and JavaScript are also desired, based on the industry standard to progress in the data science career.

4. How long does it take to learn Data Analytics?

Most institutions promise to offer courses in 3 to 6 months. But practically it is very hard to learn all the modules from statistics to programming, all in one within a short span. Short-term certification courses can be completed in 3 months but only the basic knowledge of the domain can be gained.

To acquire expertness and master the field a graduate or post-graduate course with a duration of around 1 year is necessary. The post-graduate courses offered by renowned universities just don’t teach you the knowledge but give hands-on training in handling data via real-time projects with the aid of their industry partners.

5. Is a job in data science stressful?

Many data analysts and data scientists all over the world feel data science is a stressful job due to factors like continuous learning, no help of textbook knowledge, just a degree or course is not enough, doing difficult tasks on your own, relationships with professionals from multiple departments and a keen interest in the business. Nevertheless, no job is stressful when you love the job. It relies fully on the person’s passion for the job.


Every year the demand for data science professionals increases. From business analyst to Data Engineer, to data architect and machine learning engineer, the opportunities are endless in the data field. This growth is expected to continue in the next decade too. The platforms to learn these courses are also enormous.

From online to offline institutions, from short-term certification to post-graduate level courses, from project training to internship, a lot of gateways are open to enter the data world. Despite that, a massive gap exists between the demand and supply of professionals with the right skills. A proper understanding of the field and being a leader in the skills needed for the profession can open your doors to the data world. All the Best.

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