Data Analysis vs. Data Science: 5 Things to Consider


SOURCE: MAKEUSEOF.COM
MAR 09, 2022

In the world of big data, you will often encounter two disciplines: data science and data analytics. They both require different (but overlapping in certain areas) competencies and skill sets.

Nonetheless, both fields of study are highly lucrative and offer good opportunities to those interested in going the extra mile.

If you're looking to make some important career decisions, but you're unsure of which option to choose, read on about the top points of differentiation.

1. Educational Background

The fields of data analytics and data science are enriching and specialized. This means that if you're entering any of the two professions, you need to be well-prepared to tackle the educational challenges that might come your way.

Data Analytics

A basic bachelor's degree is required to start a career as a data analyst. To embark on this career path, you need to opt for an undergraduate program that will give you a working understanding of SQL and query development for RDBMS and data structure schema operations.

You will also need knowledge of statistical programming using R or Python. Additionally, knowledge of machine learning (ML), artificial intelligence (AI), custom algorithm development, data management around information collection and storage are extra advantages.

In short, you need an undergraduate degree in IT, computer science, maths, or statistics to kick-start your career in data analytics.

Data Science

An aspiring data scientist's safest bet is to seek a bachelor's and master's degree in Computer Science, Information Technology, mathematics, or statistics. If you wish to change your career trajectory and adopt a career as a data scientist, you need a minor degree in any one of these streams.

The core knowledge required for data science roles should prepare you for provisioning, collecting, organizing, processing, and modeling business data.

Additionally, you can gain expertise in data visualization, API-based data collection, and preparation. A degree in applied mathematics and statistics will further help you with exploratory data analysis, allowing you to follow and establish patterns, devise test models for bespoke challenges, and much more.

Lastly, an additional skill set in ML and AI is handy when establishing models for AI-based predictions. Thus, an undergraduate degree in data science, computer science, or computer engineering should set you off on a data scientist's career path.

2. Job Roles and Work Responsibilities

As a data analyst, your job roles and responsibilities will vary as you start your journey in these fields. Depending on your level of expertise, you might notice certain changes which will help you tackle difficult situations in your job role.

Data Analytics

In data analytics, you will primarily be analyzing, visualizing, and mining business-specific data.

On the whole, data analytics roles will need you to handle responsibilities like:

  • Cleaning, processing, validating, and exemplifying the integrity of data
  • Perform exploratory data analysis of large data sets
  • Implement ETL pipelines and conduct data mining
  • Conduct statistical analysis using logistic regression, KNN, Random Forest, and Decision Trees
  • Build and manage machine learning (ML) libraries while writing automation codes
  • Acquire fresh insights with ML tools and algorithms
  • Identify data patterns to make well-informed data-backed predictions

Data Science

Data science includes driving insights and drawing inferences from contextual data within the business.

Some additional responsibilities might include:

  • Collecting and interpreting data
  • Identifying relevant patterns in a dataset
  • Performing SQL-based data queries and sub-queries
  • Querying data using RDBMS tools like SQL, Python, SAS, and many others
  • Gaining fluency in tools for predictive, prescriptive, descriptive, and diagnostic analytics
  • Acquiring skills in visualization tools such as Tableau, IBM Cognos Analytics, and others

3. Essential Skill Sets

Since both roles are specialized, they require specific skill sets before you can excel in either of the fields. To make the most out of either profession, you need to advance your skill sets and make the most out of what you can.

Data Analytics

Analytics requires advanced knowledge of intermediate statistics with problem-solving skills.

Additionally, it's best if you can upskill yourself in the following:

  • MS Excel and SQL databases to slice and dice data
  • Business intelligence tools to master reporting
  • Learn tools like Python, R, and SAS to manage, manipulate and work with datasets

Despite being an IT-oriented role, becoming a data analyst doesn't require you to hail from an engineering background.

Instead, it's worthwhile to learn statistics, database management, and data modeling, along with predictive analytics, to master the tricks of the trade.

Data Science

In data science, you need to be savvy in mathematics, advanced statistics, predictive modeling, Machine Learning, and programming in the following fields:

  • Big Data tools expertise in Hadoop and Spark
  • Expertise in SQL, NoSQL, and PostgreSQL databases
  • Knowledge of data visualization tools and a few languages like Scala and Python

4. Working Tools

One or more of these tools are essential to master data analytics and data science roles. To be the best at what you do, we advise you to learn as many of these as possible.

Data Analytics

  • Data Visualization: Splunk, QlikView, Power BI, and Tableau
  • ETL: Talend
  • Big Data Processing: Spark, RapidMiner
  • Data Analytics: Microsoft Excel, R, and Python

Data Science

  • Applied Data Science: SAS, KNIME, RapidMiner, PowerBI, DataRobot
  • ETL: Apache Kafka
  • Big Data Processing: Apache Hadoop, Spark
  • Data Visualization: Tableau, BigML, Trifacta, QlikView, MicroStrategy, and Google Analytics
  • Data Analytics: Microsoft Excel, Apache Flink, SAP Hana, MongoDB, MiniTab, and SPSS
  • Programming: R, Julia, and Python
  • Programming Libraries: TensorFlow for Python-based data modeling

5. Career Opportunities

Irrespective of which field you choose, the idea is to land a good, well-paying job. Depending on which role you choose, the job roles will also change accordingly.

Here are some popular career choices to look forward to in data analytics and data science.

Data Analytics

  • Business Intelligence Analyst
  • Data Analyst
  • Quantitative Analyst
  • Data Analytics Consultant
  • Operations Analyst
  • Marketing Analyst
  • Project Manager
  • IT Systems Analyst
  • Transportation Logistics Specialist

Data Science

  • Data Analyst
  • Data Engineers
  • Database Administrator
  • Machine Learning Engineer
  • Data Scientist
  • Data Architect
  • Statistician
  • Business Analyst
  • Data and Analytics Manager

Data Science vs. Data Analytics: The Final Verdict

All in all, data scientists have a more advanced skill set. As a result, the average data scientist earns more than the average data analyst. But you can always start your career as a data analyst and then lean towards data science later on.

Besides data analytics and data science, a few other domains are available if you're interested in data-centric roles. For starters, you can look at data architecture and data engineering positions. There are plenty of courses available in the market, which can help you hone your skills in these fields.

ABOUT THE AUTHOR

Gaurav Siyal (55 Articles Published)

Gaurav Siyal has two years of writing experience, writing for a series of digital marketing firms and software lifecycle documents.