What is Data Analytics? Types, Processes, and Career Opportunities

What is Data Analytics? Types, Processes, and Career Opportunities | Data Analytics | Emeritus

In a world driven by digital innovation, a single question dominates conversations: What is data analytics? Every click, swipe, or tap leaves a trail of data, weaving a web of information about our behaviors, preferences, and needs. From online shopping to seeking medical care, each transaction generates data, contributing to a staggering 149 zettabytes in 2024. This figure is predicted to balloon to an extraordinary 394 zettabytes by 2028(1). However, this ocean of information holds little value without organization, interpretation, and actionable insights. This is where data analytics, or data analysis, comes into play, empowering businesses, governments, and individuals to make informed decisions. As data analytics rapidly becomes the backbone of modern businesses, and as India marches towards becoming a global data hub, data analytics figures as a promising career path (2). 

What is Data Analytics?

Product Data ManagementData analytics involves a structured process of examining, refining, transforming, and interpreting raw information to extract valuable insights and actionable knowledge. Transforming vast volumes of data into actionable knowledge empowers businesses, governments, and individuals to make data-driven decisions. In essence, data analytics serves as the connective tissue between raw data and impactful outcomes, paving the way for innovation and efficiency across industries.



Today, data analytics touches every facet of our lives, enabling smarter decisions and shaping the future. For instance, this process is indispensable in sectors such as retail, where it enhances inventory management and customer targeting. It’s also pivotal to the operations of E-commerce giants who use it to personalize user experiences, while policymakers rely on it to craft strategies based on population surveys. 

Steps in Data Analysis

To truly understand what is data analytics, we must break down its systematic methodology into distinct stages:

1. Defining the Problem

Every effective analysis starts with a clear definition of the problem or goal. Why? Because, without clarity, the process risks veering off course. Thus, analysts work closely with stakeholders to identify the key questions that need answers. For example, in retail, the objective might involve determining which products sell best during specific seasons. In this case, analysts would craft questions that would focus on measurable outcomes and align data collection efforts with these goals. 

2. Collecting Data

Once the problem is defined, data must be gathered from appropriate sources. Now, these sources can include databases, IoT devices, customer feedback forms, web scraping tools, and surveys. For instance, a healthcare provider might use electronic health records to study patient recovery trends. The reliability and consistency of collected data significantly impact the insights derived. This task of validating sources, and ensuring accuracy and credibility before proceeding is one of the key steps in what is data analytics.

ALSO READ: 5 Ways Big Data Analytics is Reshaping the Business Landscape in India

3. Preparing and Processing Data

This is a critical, but time-consuming, stage in what is data analytics. Raw data is rarely analysis-ready—it may contain missing values, inconsistencies, or duplicates; hence, analysts must process this raw, unstructured data to work with it. For this purpose, analysts often use tools like Python, R, or specialized data-cleaning software to address these issues. Proper structuring allows for smooth downstream analysis, ensuring reliability.

4. Analyzing the Data

This phase involves applying statistical techniques, Exploratory Data Analysis (EDA), and advanced algorithms. For example, an e-commerce company might analyze sales data to discover that promotions lead to higher conversions on weekdays. These insights fuel decision-making by transforming raw numbers into meaningful knowledge.

5. Interpreting and Visualizing Results

Insights are only as powerful as their presentation. Analysts employ various visualization tools (such as Tableau, Power BI, or Excel) to make complex data digestible. This is highly important step in what is data analytics, as clear visuals help stakeholders understand trends, anomalies, and actionable insights quickly. Put simply, a well-visualized report doesn’t just display data; it tells a compelling story that drives action.

Different Types of Data Analytics

Understanding what is data analytics requires exploring its different types, as each serves unique purposes. These methods help businesses uncover insights, solve problems, and predict trends. Let’s examine the four primary categories of data analytics and their applications.

1. Descriptive Analytics

Descriptive analytics centers on summarizing historical data to uncover patterns, trends, and anomalies within datasets. Analyzing past performance provides a clear understanding of data behavior over time, enabling businesses to identify valuable insights and make informed decisions. For instance, e-commerce platforms rely on descriptive analytics to evaluate seasonal sales performance or customer purchase behavior. Similarly, healthcare providers use it to analyze patient demographics and past treatments. Descriptive analytics forms the foundation of data analyst roles and responsibilities, enabling professionals to present clear, fact-based reports to stakeholders.

2. Diagnostic Analytics

In essence, diagnostic analytics aims at uncovering the root causes of observed trends or issues through correlation analysis, regression techniques, and other statistical methods. For example, a diagnostic analysis in retail may reveal that poor weather significantly impacted foot traffic during specific months.

3. Predictive Analytics

Predictive analytics works with historical data in order to identify future trends, behaviors, or events. Retailers frequently leverage predictive analytics to anticipate customer preferences, optimize inventory, or plan marketing campaigns. Machine learning models often enhance these predictions.

4. Prescriptive Analytics

Prescriptive analytics advances even further by recommending actions to achieve desired outcomes. By combining machine learning algorithms, optimization techniques, and data models, prescriptive analytics identifies the best course of action. For example, logistics companies use it to streamline delivery routes, saving time and resources. Professionals with expertise in prescriptive analytics can often command higher data analyst salary levels due to their ability to directly impact business strategy.

The variety in types of data analysis highlight the varied data analyst roles and responsibilities across industries, irrespective of the sector. Businesses increasingly prioritize data-driven decision-making, mastering different types and techniques of data analysis positions you for a thriving data analyst career.

ALSO READ: Break Down Data Silos and Watch Your Business Soar

How to Become a Data Analyst

If you’re wondering how to become a data analyst, the answer lies in a combination of education, certifications, and hands-on experience. This field offers immense opportunities for growth, with competitive data analyst salary packages and an ever-expanding demand for skilled professionals.

1. Education

Education forms the backbone of a successful data analyst career. Hence, begin by strengthening your foundation in mathematics, statistics, and computer science during high school. Afterwards, during higher education, consider pursuing undergraduate degrees in relevant disciplines such as data science, computer engineering, or statistics. These programs typically cover essential subjects like data structures, algorithms, and statistical modeling.

Additionally, specialized data analyst courses can refine your expertise in niche areas. Programs in business analytics or applied statistics offer focused learning paths tailored to industry needs. Also, if you’re transitioning from another field, these online data analyst courses provide flexible and cost-effective options to build your foundation.

2. Certifications

Certifications validate your expertise and enhance your employability, and employers often prioritize candidates with industry-recognized credentials. Certifications such as the Google Data Analytics Professional Certificate or the Microsoft Certified: Data Analyst Associate demonstrate your proficiency in analytical methodologies and data analyst skills. Also, credentials like IBM Data Analyst Professional Certificate and SAS Certified Data Scientist add further credibility to your skillset. These certifications reflect your commitment to professional development, opening doors to prestigious data analyst jobs.

3. Practical Experience

Practical knowledge bridges the gap between theory and application. Hence, it’s important to work on real-world datasets to understand the nuances of data cleaning, analysis, and visualization. Portals like Kaggle or Google dataset search hosts a huge number of datasets which are free to access for data enthusiasts (3,4).

4. Building a Portfolio

If you have been pondering over how to become a data analyst, you should take into account the fact that a strong portfolio can boost your CV considerably. So, to advance/build your career in data analysis, try showcasing your projects, visualizations, and problem-solving abilities. Also, you can think of highlighting case studies that demonstrate your proficiency in tools like Python, SQL, and Tableau. Remember, a well-curated portfolio distinguishes you from other candidates, especially in competitive data analyst jobs.

5. Internships

Last, but not the least, internships are crucial if you’re wondering how to become a data analyst. Why? Because they provide invaluable exposure in the industry. They allow you to gain hands-on experience, network with professionals, and understand real-world data analyst roles and responsibilities. Many companies offer internships as stepping stones to permanent positions, making this an essential part of your career journey.

However, if you’re transitioning into analytics without a formal degree, don’t fret. A combination of relevant data analyst courses, certifications, and a robust portfolio can establish your credibility. With growing demand and lucrative opportunities in this field, now is the perfect time to embark on a data analyst career.

ALSO READ: A Deep Dive Into Data Lakes: 7 Best Practices for Data Management

Data Analyst Skills Required for a Successful Data Analyst Career

Data-scienceThe path to excelling in a data analyst career requires mastering a combination of technical expertise and soft skills. Each skill adds a layer of proficiency, making you a well-rounded professional capable of tackling complex data challenges.

1. Proficiency in Statistics and Mathematics

A strong foundation in mathematics, particularly calculus, linear algebra, and statistics, is fundamental to understanding what is data analytics. While you don’t need to be a maths prodigy to have a successful data analyst career, a solid grasp of these concepts is essential for solving problems and interpreting data accurately.

2. Expertise in Data Visualization

Sharing and presenting your analytical findings in a compelling manner is just as important as the analysis itself. Tools like Tableau, Power BI, and Python’s Matplotlib enable analysts to create compelling visuals that help stakeholders understand complex findings. Mastery of these tools is among the core data analyst skills, ensuring clarity in data-driven decisions.

3. Mastery of Data Cleaning and Preparation

Data cleaning consumes a significant portion of an analyst’s time. For instance, identifying and fixing errors, removing duplicates, and handling missing values ensures the data’s accuracy and reliability. This skill directly impacts the credibility of analysis, a critical aspect of data analyst roles and responsibilities.

4. Knowledge of Programming Languages

Proficiency in Python, R, and SQL is indispensable. These languages empower analysts to handle large datasets, automate processes, and perform advanced computations. Python’s Pandas library, for example, makes data manipulation much simpler, while SQL figures as essential for the purpose of database management.

5. Understanding Machine Learning Techniques

Familiarity with machine learning enriches your analytical toolkit. For instance, techniques like clustering and classification allow you to uncover hidden trends, enhancing your value in advanced data analyst jobs.

6. Importance of Econometrics

Econometrics, which blends economics with statistical analysis, is a niche but valuable skill, especially in finance. Hence, understanding econometrics positions you as a specialist in roles like financial or business analytics, paving the way for lucrative data analyst salary prospects.

7. Critical Thinking and Analytical Reasoning

The ability to think critically allows analysts to approach problems logically and craft innovative solutions. This skill is crucial for addressing diverse challenges that often emerge in various data analyst roles and responsibilities.

9. Excellent Communication Skills

Articulating complex findings in an understandable manner bridges the gap between data and decision-making. Thus, clear communication, both verbal and visual, is an indispensable soft skill for building trust with stakeholders and excelling in a data analyst career.

Data Analyst Career Options

Now that you have familiarized yourself with what is data analytics, what data analyst skills are required for a successful career in this field, and the varied ways for building those skill sets, it’s time to consider the career options. A career in data analytics offers diverse opportunities, ranging from entry-level roles to advanced positions. Typically the average annual data analyst salary in India ranges from ₹5,00,000 to ₹18,00,000(5). Apart from location, and organization, one of the key determining factors is your experience. 

Now, here’s a breakdown of some of the most sought-after data analyst jobs, along with their respective average annual salaries in India:

1. Entry Level Positions

  • Junior Data Analyst: Assists in cleaning, organizing, and interpreting raw data. Salary: ₹ 4,36,000(6)
  • Data Technician: Supports data collection and maintenance across systems. Salary: ₹4,08,500(7)

2. Advanced Positions

  • Advanced Analytics Specialist: Develops advanced statistical models for in-depth insights. Salary: ₹8,05,000 (8)
  • Data Visualization Specialist: Crafts dashboards and visual tools to present data effectively. Salary: ₹10,00,000(9)
  • Data Quality Manager: Oversees the quality and integrity of organizational data. Salary: ₹25,35,000(10)

ALSO READ: What is Data Mining and How to Make a Good Career in It

If you’re eager to dive deeper into what is data analytics, build critical data analyst skills, and explore the endless possibilities in this thriving field, the path forward is clear. Data is the future, and now is your moment to step into a world where every data point tells a story, every insight sparks action, and every day presents new opportunities to innovate.

To help you excel in this journey, consider Emeritus’ online data analytics courses. Offered by globally reputed universities, designed with industry-aligned modules, and brought to you by Emeritus, these courses are the perfect choice to strengthen your expertise, build a strong foundation, and distinguish yourself in the competitive job market. 

By Sanmit Chatterjee

Write to us at content@emeritus.org 

Sources: 

  1. Volume of data/information created, captured, copied, and consumed worldwide from 2010 to 2023, with forecasts from 2024 to 2028┃Statista
  2. The strategic role of data centres in empowering India’s digital revolution┃PwC
  3. Datasets┃Kaggle
  4. Dataset Search┃Google
  5. Data Analyst salaries in India┃Glassdoor
  6. Junior Data Analyst salaries in India┃Glassdoor
  7. Data Technicians salaries in India┃Glasssdoor
  8. Advanced Analyst salaries in India┃Glassdoor
  9. Data Visualization Specialist salaries in India┃Glassdoor
  10. Data Quality Manager salaries in India┃Glassdoor

 

About the Author


Senior Content Contributor, Emeritus Blog
Varun, a seasoned content creator with over 8 years of diverse experience, excels in crafting engaging content for various geographies and categories. Leveraging this expertise, he seamlessly translates complex concepts into enriching educational content for the EdTech domain. His keen understanding of research and life experiences helps him resonate with students and create fact-based content. He finds solace and inspiration in music, nurturing his creativity for content creation.
Read More About the Author

Learn more about building skills for the future. Sign up for our latest newsletter

Get insights from expert blogs, bite-sized videos, course updates & more with the Emeritus Newsletter.

Courses on Data Analytics Category

IND +918068842089
IND +918068842089
article
data-analytics