Data Analytics vs Data Science: What Skills, Tools, and Knowledge Are Needed for Each Field?
The field of data science includes many different ideas and concepts. For example, big data is an umbrella term that describes the collection and analysis of large datasets to reveal trends and patterns to gain actionable insights. Both data analysts and data scientists work with big data, which can sometimes cause confusion.
This guide will walk you through the main differences between data analytics and data science to help you determine which is the right choice for you. You can build your career path according to the best data science jobs and annual salary information. Read below to learn the difference between data analytics and data science.
What Is Data Analytics?
Data analytics refers to the examination of defined data sets using a variety of different tools. Data analysts work with the specific goal to understand trends, gain meaningful insights, create reports, and translate results to visual formats that allow businesses to make informed decisions. They can conduct descriptive, diagnostic, predictive, or prescriptive analysis.
Data Analyst Job Description
Data analysts are responsible for finding actionable data to be processed for better decision-making and enhanced performance. With statistical analysis, they visualize, interpret, design, and present data as simply as possible. They must reveal past errors and ensure that sound business decisions are made in the future.
What Is Data Science?
Data science is a multidisciplinary field that involves data inference, predictive modeling, and algorithms. It is a critical process consisting of building, cleaning, and organizing data with advanced statistics to solve complex problems. In simpler terms, data science doesn’t just analyze data, it also predicts likely outcomes and designs systems to cater to future challenges.
Data Scientist Job Description
Data scientists collect and clean the data first before it is analyzed. Then, they question the data, predict likely outcomes, and build automated systems that would help respond to future problems. They employ advanced statistical tools in modeling frameworks for businesses. To become a data scientist, you can attend a bootcamp or get a degree.
What Is the Biggest Difference Between Data Science and Data Analytics?
The major difference between the two is that while data analytics focuses on analyzing specific data from the past, data science is concerned with learning the patterns from large datasets. Data science is broader and takes into account future errors based on the analysis from big data pools with machine learning.
Other Key Differences Between Data Science and Data Analytics
- Scope. Data science comprises other disciplines such as data analytics, predictive analytics, data mining, machine learning, and software engineering. Data analytics is a more streamlined discipline that responds to more specific needs.
- Knowledge Required. An advanced knowledge of statistics and programming is needed in data science, which is a much more technical and mathematical field, while data analytics requires an intermediate understanding of statistics.
- Tools and Skills. Data science employs tools like Hadoop, Cassandra, SQL, and visualization tools like Tableau. On the other hand, data analytics uses software like Excel to sort and arrange data and Power BI to report insights.
- Area of Implementation. Data science is incorporated in AI, cloud computing, and corporate analytics. On the other hand, data analytics is integrated into business systems, healthcare, product manufacturing, and other industries.
Data Science and Analytics Salary Overview
According to the Bureau of Labour Statistics (BLS), the median salary for data scientists, which are listed under computer and information research scientists, is $126,830 per year. The median salary for data analysts, listed under operations and research analysts, is $86,200. However, note that salaries vary according to location, industry, and level of expertise.
Top Career Paths in Data Science
|Data Science Roles||Average Salary|
|Machine Learning Scientist||$134,000|
|Machine Learning Engineer||$130,530|
Top Career Paths in Data Analytics
|Data Analytics Roles||Average Salary|
|Business Intelligence Analyst||$85,274|
Best Data Science and Analytics Courses for Beginners
Being clear on the differences between data analytics and data science can help you determine which type of training you will need to break into either field. To help you do this, we’ve listed some of the best courses to learn the basics of data science and data analytics. These are all online courses to strengthen your knowledge of either one of these fields.
- Duration: 8 weeks
- Cost: Free
- Skills Covered: Data analysis using statistical models, basic data structures, data visualization and reports
This project-based course is designed to teach you the basics of analyzing and programming data with R. In addition, you will learn of the applications of R in the development of data structures and data cleaning. With a bit of statistical knowledge, you can learn this data analyst skill in no time.
- Duration: 12 hours
- Cost: $399
- Skills Covered: Excel analytic functions and conditional formatting, Solver Tool, hypothesis testing, Power BI
This course teaches you the foundational concepts of data analysis using Excel, Solver Tool, and Power BI. In addition, you will discover how to present data effectively to influence and enhance the decision-making process in your organization. These analytics tools are vital if you want to master business intelligence software.
- Duration: 3 hours
- Cost: Free
- Skills Covered: Data transformation, data exploration with visualization, simple dashboards
Pluralsight’s course will teach you the basics of data wrangling and data transformation for easy analysis and visualization with Power BI. With the skills learned in this course, you can keep your data updated and organized at all times. According to PayScale, people with Microsoft Power BI skills earn an average salary of $70,000.
- Duration: 4 months
- Cost: Free
- Skills Covered: Machine learning, logic and planning, statistics, Bayesian networks
This course exposes you to the basics of AI and its modern applications. The skills learned are relevant in areas like medicine, biomonitoring, manufacturing and production, and cyber security. You will also start to gain more knowledge of machine learning techniques and exploratory data analysis.
- Duration: 12 hours
- Cost: $39.99
- Skills Covered: Word2vec, GloVe, Gensim, recurrent neural networks (RNN)
In this course, you will gain insight on Word embedding using Word2vec and GloVe. In addition, through exposure to RNN, you will learn the fundamentals of data science like language modeling and text generation, prediction problems, speech recognition, and machine translation.
- Duration: 3 weeks
- Cost: Free
- Skills Covered: Python, Pandas, writing and defining data
This course covers the fundamentals of Python and its application in data science. With the skills you learn, you will know how to handle data with a programming language and apply it in defining variables. In addition, you will discover how to read, write, and manipulate data using the Pandas library.
Data Analytics vs Data Science: Which Field Should I Go Into?
The decision on whether you choose data analytics or data science will depend on your professional interests and educational background. If you are interested in studying large data sets to find trends, develop visual aids, and help businesses make more strategic decisions, data analytics can be a great career choice.
However, if you are more interested in the design and construction of innovative processes for data modeling and production, and have a strong background in computer programming as well as a STEM degree, data science might be a better choice. Either way, you can earn high salaries and the best data science careers with these skills.