Data Science vs Data Analytics- What's the Difference

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9 min read

Introduction

The buzzwords of the year are data analytics and data science. Big data and data science employment have long been a solid bet for people looking for long-term career opportunities. As AI and Machine Learning grow more incorporated into our daily lives and economy, this tendency is expected to continue. Data is the new oil for organizations today, allowing them to acquire essential insights and improve corporate performance in order to stay competitive. But who will gain knowledge? Who will process all of the raw data that has been gathered? A data analyst or a data scientist is in charge of everything. As firms around the world seek to make the most of data, these are the two most popular career titles in this field. Data Science and Data Analytics are a jumble of concepts that intertwine and overlap but are still quite distinct.

As firms around the world seek to make the most of data, these are the two most popular career titles in this field. Data Science and Data Analytics are a jumble of concepts that intertwine and overlap but are still quite distinct.

Yes, we have our ears to the ground as professionals (both beginners and transitioners). Many of you who are just starting out in a career in data analytics or data science is unsure about whether the path is suitable for you. Well, it would rely on your career objectives as well as your talents and competencies. To avoid any misunderstanding and to give you a comprehensive understanding of these two cutting-edge career choices, we'll look at Data Analysis and Data Science. This article attempts to help you better grasp the differences between the two fields so you can decide which path is best for you.

What is Data Science? Data science is a concept that combines data purification, preparation, and analysis and is used to deal with massive data. A data scientist collects data from a variety of sources and uses machine learning, predictive analytics, and sentiment analysis to extract useful information from it. They can provide accurate predictions and insights that can be used to support crucial business decisions since they understand data from a business perspective.

Skills Required to Become a Data Scientist Anyone who makes that decision for themselves in this field should focus on three areas: analytics, programming, and domain expertise. Going a step further, the abilities listed below will assist you in carving out a niche as a data scientist:

  • Strong Python, SAS, R, and Scala skills

  • Practical SQL database coding experience

  • Working with unstructured data from a variety of sources, such as video and social media

  • Recognize a variety of analytical functions

  • Machine learning expertise

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Job Roles of a Data Scientists

  • The Data Scientist Job Role entails:

  • Data processing, cleansing, and integrity verification.

  • Data Exploration and Analysis

  • Using machine learning techniques and algorithms to extract business insights.

  • Identifying new trends in data to create future forecasts.

What is Data Analytics?

A data analyst is someone who can perform basic descriptive statistics, visualize data, and communicate data points in order to reach conclusions. They must have a fundamental understanding of statistics, a thorough understanding of databases, the capacity to design new views, and the ability to visualize data. The necessary level of data science is known as data analytics.

Skills Required to Become a Data Analyst

A data analyst should be able to take a specific query or topic, discuss how the data is organized, and deliver that information to appropriate company stakeholders. If you want to work as a data analyst, you'll need to learn the following four skills:

  • Mathematical statistics knowledge

  • R and Python are both second nature to you.

  • Data manipulation

  • Recognize PIG/ HIVE

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Job Roles of Data Analyst

Job Descriptions for Data Analysts include:

  • Analyzing exploratory data

  • Cleaning up data

  • Using various statistical methods, discover new patterns.

  • Build KPIs and visuals.

Data Science vs. Data Analytics

Data science is a broad term that includes data analytics, data mining, machine learning, and a variety of other related fields. Data analysts extract useful insights from numerous data sources, whereas data scientists are expected to foresee the future based on historical patterns. A data scientist asks questions, while a data analyst looks for solutions to those already asked.

Data analysis entails responding to queries posed in order to make better business decisions. It uncovers actionable data by utilizing existing data. Data analytics concentrates on a few key areas with unique objectives. Data science, on the other hand, focuses on uncovering new questions that you may not have recognised you wanted to be answered in order to drive innovation. Unlike data analytics, which focuses on testing a hypothesis, data science aims to make connections and frame questions in order to provide answers in the future. Data analytics is a modest room in the house of data science, which houses all the methods and tools. Data analytics is a more focused and focused version of data science.

Data analytics is primarily concerned with putting historical data into perspective, whereas data science is more concerned with machine learning and predictive modelling. Data science is a multidisciplinary approach to solving analytically complicated business problems that include algorithm creation, data inference, and predictive modelling. Data analytics, on the other hand, encompasses a number of different fields of statistics and analysis.

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Choosing Between a Data Analytic1. Think about your personal experiences.s and Data Science Career You can begin considering which path is the best fit for you once you have a good understanding of the differences between data analytics and data science, as well as what each career entails. Three major aspects should be considered when determining which path is best aligned with your personal and professional goals.

1. Think about your personal experiences.

While data analysts and data scientists are similar in many ways, their differences stem from their professional and educational backgrounds, according to Martin Schedlbauer, professor and director of Northeastern University's Khoury College of Computer Sciences' information, data science, and data analytics programmes, which include the Master of Science in Computer Science and Master of Science in Data Science.

Data analysts, as previously stated, evaluate massive data sets to discover trends, construct charts, and create visual presentations to assist businesses in making better strategic decisions. Analysts often pursue an undergraduate degree in a science, technology, engineering, or math (STEM) major, as well as an advanced degree in analytics or a similar field, to align their education with these jobs. They're also looking for math, physics, programming, databases, modeling, and predictive analytics experience.

Data scientists, on the other hand, are more concerned with designing and building new data modeling and production processes. According to Schedlbauer, an advanced degree such as a master's in data science is required for professional progress since they use a range of approaches to comb through data, such as data mining and machine learning.

It's crucial to study these educational prerequisites while deciding which job route is suitable for you. If you've already decided to advance your career with an advanced degree, you'll likely have the necessary educational and work experience to pursue either option. If you're still contemplating whether or not going back to school is right for you, a data analytics position might be a better fit, as employers are more likely to consider people without a master's degree for these positions.

Whatever path you take, considering your current and desired level of education and experience should help you limit down your choices.

If you decide to pursue a graduate degree to jumpstart your career, make sure you select a school that will assist you in achieving your objectives. Northeastern University, for example, emphasizes experiential learning, allowing students to get the skills and hands-on experience they need to succeed in the profession.

2. Think about your passions.

Are numbers and statistics your thing, or do you have interests in computer science and business?

Numbers, statistics, and programming are all favorites of data analysts. They operate nearly completely in databases as gatekeepers for their organization's data, uncovering data pieces from complicated and often disparate sources. According to Schedlbauer, data analysts should have a thorough awareness of the industry they operate in. If this describes you, a data analytics position might be the ideal professional fit for you.

Data scientists need a mix of math, statistics, and computer science skills, as well as an interest in — and knowledge of — business. If this description more closely matches your background and experience, a position as a data scientist may be the best fit for you.

In either case, knowing which career aligns with your personal interests will give you a better notion of the type of work you'll love and excel at. Take the time to consider this aspect of the equation, since matching your work to your passions can go a long way toward keeping you happy in your job for years to come.

Data scientists need a mix of math, statistics, and computer science skills, as well as an interest in — and knowledge of — business. If this description more closely matches your background and experience, a position as a data scientist may be the best fit for you.

In either case, knowing which career aligns with your personal interests will give you a better notion of the type of work you'll love and excel at. Take the time to consider this aspect of the equation, since matching your work to your passions can go a long way toward keeping you happy in your job for years to come.

3. Think about your ideal pay and career path.

For data scientists and data analysts, different levels of experience are necessary, leading in varied levels of compensation.

Data analysts might earn anywhere from $83,750 to $142,500 per year. However, because these experts mostly work with databases, they can boost their earnings by learning additional programming languages like R and Python.

Data analysts with more than 10 years of experience, on the other hand, generally maximize their earning potential and move on to other employment, according to PayScale. According to Blake Angove, director of technology services at IT recruiting agency LaSalle Network, two popular career options after earning an advanced degree are switching into a developer role or becoming a data scientist.

According to Schedlbauer, data scientists are more senior than data analysts because they often have a doctoral degree, advanced abilities, and are often more experienced. As a result, they are frequently paid more for their labor. Data scientists make an average annual pay of $105,750 to $180,250, according to RHT.

Professionals in data science have a bright future ahead of them, with many prospects for development to senior positions such as data architect or data engineer.

Which data profession is best for you?

Given the considerable variances in function responsibilities, educational requirements, and career trajectory, data analysts and data scientists have deceptively similar job titles.

In any case, Schedlbauer explains that competent employees for data-focused occupations are in high demand in today's employment market, thanks to organizations' strong desire to make sense of—and profit from—their data.

You may pick which career is the best fit for you and get started on your route to success once you've examined aspects like your background, personal interests, and desired compensation. Hopefully, you might have found this article insightful.

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