In the world of big data, you will often come across two disciplines: data science and data analytics. They both require different (but overlapping in some areas) skills and skill sets.
Nevertheless, both fields of study are very lucrative and offer good opportunities for those willing to put in the extra effort.
If you’re looking to make important career decisions but aren’t sure which option to choose, read on for the key points of differentiation.
1. School context
The fields of data analytics and data science are rewarding and specialized. This means that if you enter one of the two professions, you should be well prepared for the educational challenges that may come your way.
A basic bachelor’s degree is required to start a career as a data analyst. To embark on this career path, you should 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 added advantages.
In short, you need an undergraduate degree in Computer Science, Computer Science, Mathematics, or Statistics to kick-start your data analytics career.
The safest bet for an aspiring data scientist is to seek a bachelor’s and master’s degree in computer science, information technology, mathematics, or statistics. If you want to change your career path and take up a career as a data scientist, you need a minor degree in one of these streams.
The foundational knowledge required for data science roles should prepare you for sourcing, collecting, organizing, processing, and modeling enterprise data.
Additionally, you can gain expertise in data visualization, API-based data collection, and preparation. A degree in Applied Mathematics and Statistics will further aid you in exploratory data analysis, enabling you to track and establish patterns, design test models for bespoke challenges, and much more.
Finally, an additional skill set in ML and AI comes in handy when building models for AI-based predictions. So, an undergraduate degree in data science, computer science, or computer engineering should launch you into the career of a data scientist.
2. Professional roles and responsibilities
As a data analyst, your roles and responsibilities will vary as you begin your journey in these areas. Depending on your level of expertise, you may notice certain changes that will help you deal with difficult situations in your professional role.
In Data Analytics, you will primarily analyze, visualize, and explore business-specific data.
Overall, data analytics roles will require you to take on responsibilities such as:
- Clean, process, validate and illustrate data integrity
- Perform exploratory data analysis of large datasets
- Implement ETL pipelines and conduct data mining
- Perform statistical analysis using logistic regression, KNN, random forest, and decision trees
- Build and manage machine learning (ML) libraries while writing automation code
- Gain new insights with ML tools and algorithms
- Identify data patterns to make predictions based on well-informed data
Data science is about generating insights and drawing conclusions from contextual data within the business.
Some additional responsibilities might include:
- Data collection and interpretation
- Identify relevant patterns in a dataset
- Executing SQL-Based Data Queries and Subqueries
- Query data using RDBMS tools such as SQL, Python, SAS and many more
- Master predictive, prescriptive, descriptive and diagnostic analysis tools
- Acquire skills in visualization tools such as Tableau, IBM Cognos Analytics and others
3. Core Skill Sets
Since both roles are specialized, they require specific skills before they can excel in either area. To get the most out of either profession, you need to advance your skills and make the most of what you can.
Analysis requires advanced knowledge of intermediate statistics with problem solving skills.
In addition, it is preferable that you can improve your skills in the following areas:
- MS Excel and SQL databases to slice and dice data
- Decision-making tools to control reporting
- Learn tools like Python, R, and SAS to manage, manipulate, and work with datasets
Although it’s an IT-focused role, becoming a data analyst doesn’t require you to have an engineering background.
Instead, it’s worth learning statistics, database management, and data modeling, as well as predictive analytics, to master the tricks of the trade.
In data science, you should master math, advanced statistics, predictive modeling, machine learning, and programming in the following areas:
- Expertise in Big Data tools in Hadoop and Spark
- Expertise in SQL, NoSQL and PostgreSQL databases
- Knowledge of data visualization tools and a few languages like Scala and Python
One or more of these tools are essential to mastering data analysis and data science roles. To be the best at what you do, we advise you to learn as much as possible.
- Data visualization: Splunk, QlikView, Power BI and Tableau
- ETL: Talend
- Big data processing: Spark, RapidMiner
- Data analysis: Microsoft Excel, R and Python
- 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 analysis: 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
Whichever field you choose, the idea is to land a good, well-paying job. Depending on the role you choose, job roles will also change accordingly.
Here are some popular career choices to look forward to in data analytics and data science.
- Business intelligence analyst
- Data Analyst
- Quantitative Analyst
- Data analysis consultant
- Operations Analyst
- Marketing Analyst
- Project Manager
- IT systems analyst
- Transport logistics specialist
- Data Analyst
- Data Engineers
- Database administrator
- machine learning engineer
- Data Scientist
- Data Architect
- business analyst
- Data and Analytics Manager
Data Science vs. Data Analytics: The Final Verdict
Overall, 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 move into data science later.
Besides data analytics and data science, there are a few other areas available if you are interested in data-centric roles. To get started, you can check out the data architecture and data engineering positions. There are many courses available in the market which can help you hone your skills in these areas.
Stay on top of your game with these data-driven certificates.
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