Data Analytics vs Data Science: What You Need to Know to Choose the Right Career Path
Data science is a broad field with a great outlook, but it can be difficult to decide on what aspect to specialize in. This is particularly true for those trying to choose between data science and data analytics. This article will give you a better idea about the biggest differences between data analytics and data science and how to choose between both careers.
What Is Data Science?
Data science is a discipline that combines multiple fields like statistics, scientific methods, artificial intelligence (AI), and data analysis to extract actionable insights from data and help in making informed decisions. For example, data scientists are skilled professionals that develop statistical models, write algorithms, and utilize predictive analytics in their work.
What Is Data Analytics?
Data analytics is a discipline that involves analyzing raw data to uncover patterns, extract valuable information, and draw conclusions. It includes data analysis and management processes along with various analytics tools and techniques. The primary goal of data analytics is to find potential trends and solve problems by using statistical analysis.
What’s the Difference Between Data Science and Data Analytics?
The key difference between data science and data analytics is their approach to data and the multidisciplinary fields they each focus on. Data science is a combination of various disciplines, including mathematics and computer science, while data analytics focuses on statistics and statistical analysis.
Also, data science puts more emphasis on finding meaningful correlations between large datasets, whereas data analytics mostly focuses on more specific answers to data science questions. Data science is a broader field, and data analytics falls under its umbrella.
Data Science vs Data Analytics: Side-by-Side
|Studies, analyzes, and interprets data to draw meaningful conclusions that can be used to make decisions to improve business performance.
|Examines datasets to gain insights and draw conclusions to solve current problems and establish new ways of presenting data.
|Gathers, stores, analyzes, and interprets data along with identifying new trends and reporting data findings.
|Collects data, identifies relevant patterns in a dataset, organizes acquired information for analysis, and cleans and analyzes data.
|Employed by the healthcare and pharmaceutical, telecommunications, finance, automotive, energy, and information technology (IT) industries.
|Employed by the business intelligence, healthcare, finance, retail, and agriculture industries, along with the government.
|Earns an average salary of $103,930, according to the Bureau of Labor Statistics.
|Earns an average salary of $86,200, according to the Bureau of Labor Statistics.
Can a Data Analyst Become a Data Scientist?
Yes, a data analyst can become a data scientist if they acquire the necessary analytical skills and education. Data science is a more advanced level of data analytics, and the technical skills required for each discipline overlap.
With the right training or education, a data analyst can transition into data science. Data analytics is also a good place to start a career in data science and can make the career journey easier. You should start with learning predictive analysis, statistics, and data management tools.
Data Science and Analytics Salary
According to the Bureau of Labor Statistics (BLS), the median annual salary for data scientists is $103,930, and the median salary of data analysts, which is listed under operations research analyst, is $86,200. Both of these salaries are higher than average and can grow even more over time as you get more experience.
Working in Data Science
Job roles in data science include a lot of number crunching, deep research, critical thinking, and strategic decisions. Your daily tasks will include working with data, communicating with stakeholders about your findings, and keeping up with business trends. You will also have to make crucial decisions that may help to enhance the company’s operations.
Data Scientist Roles and Responsibilities
- Data Collection: The experts that specialize in this field scour databases to find data they can use in their analysis.
- Data Analysis: This is carried out to uncover hidden patterns and trends.
- Data Interpretation: Specialists interpret the data they have acquired to find solutions to problems and base their decisions on their findings.
- Decision-Making: Data scientists have to make decisions from their data interpretation to help with business or company growth.
- Communication: After using the data to make decisions, they then communicate their findings with other executives, stakeholders, or managers.
Data Scientist Skills and Tools
- SQL: These experts need to learn SQL to interact with databases. The best scenario is to practice SQL on real databases and learn by doing.
- Communication Skills: Data scientists need strong oral and verbal communication skills to interact with other team members and company executives.
- R programming: R is a programming language that data scientists need to tackle statistical problems, and it is one of the best programming languages to learn for data scientists.
- Machine learning: This skill is required to solve various data science problems involving the prediction of major organizational outcomes.
- Teamwork: Many data scientists’ daily tasks involve work with different teams on projects, which requires an excellent teamwork spirit.
Working in Data Analysis
Working in data analysis involves a lot of data manipulation as well as other responsibilities that vary depending on the industry. Data analysts typically have to gather and analyze data, remove corrupted data, make decisions, and work with engineers, programmers, and other team members to find ways to improve existing data and make better decisions.
Data Analyst Roles and Responsibilities
- Data Extraction: Experts get the necessary data from primary and secondary sources using automated tools.
- Data Cleaning: Specialists ensure that the data is not corrupt, has no duplication or error, and is of high quality.
- Data Filtering: Data analysts examine reports and performance indicators to find and fix errors in the code they’re working with.
- Spotting Patterns: They spot patterns, future trends, and insights to make relevant recommendations to their clients.
- Prepare Reports: They prepare reports to show business trends, patterns, and predictions.
Data Analyst Skills and Tools
- Python: Analysts need basic knowledge of programming languages, like Python, to perform advanced and predictive analysis on datasets.
- Spreadsheet Tools: Tools like Microsoft Excel or Google Sheets are essential for analytics.
- Data Visualization Tools: Tools like Tableau and Power BI are needed to easily identify and present findings.
- Critical Thinking: To uncover and solve problems, generate accurate predictions, and make meaningful decisions, data analysts must have the ability to think clearly.
- Project Management: These professionals should be able to handle big projects, diagnose problems, and predict outcomes.
How to Kickstart Your Career as a Data Scientist or Data Analyst
There are different educational paths to take to start a career in data science and data analytics. The purpose of this section is to introduce you to the different education options and guide you to making the right decision. Degrees, courses, and bootcamps are all included to provide more information on data science and data analytics.
Data Science and Analytics Degrees
There are different degrees that you can apply for when starting a career in data science and data analytics. You can get a Bachelor’s Degree in Computer Science or any IT-related field like computer engineering, data engineering, and information technology. You can also proceed to get a master’s degree for better opportunities and career advancement.
Data Science and Analytics Courses
You can also start a career in this specific field by taking online courses from online platforms like Coursera, edX, Udemy, and Udacity. These platforms make it easy for you to learn the relevant skills needed to start a career at your own pace. They are flexible, short-term, and affordable.
Data Science and Analytics Bootcamp Programs
The best data science bootcamps and best data analytics bootcamps are becoming more and more popular. They are well-known for their short-term, intensive, and affordable programs. They also offer different learning formats so students can access them more easily. Most bootcamps also offer career services to help you become job-ready.
|Full-time (15 weeks), flexible (20,40, or 60 weeks)
|Remote, New York
|Data Analytics Bootcamp
|Full-time (four months), part-time (six months)
|$12,250 (full-time), $8,000 (part-time)
|NYC Data Science Academy
|Data Science Bootcamp
|Full-time (12 weeks), part-time (four to six months)
|Remote, New York
|Data Science Immersive
|Full-time (12 weeks), part-time (10 weeks)
|$3,500 (part-time), $15,950 (full-time)
|Data Analytics Career Track
|Full-time (six months)
Choosing Between a Data Analytics and Data Science Career
There are different factors to consider when deciding on what career path to choose when it comes to data analytics and science. This section will cover all factors that you should consider before deciding which is best for you.
Your educational background is very important in choosing which career path to pursue. You should consider a career that is in line with your academic knowledge. Data science is a better option for people with strong technical, analytical, and mathematical skills as it is more complex than data analytics.
Your interest also matters because you need to be satisfied and feel fulfilled with whatever you decide. Whatever your choice is, it must be something that you are eager and curious to learn and practice. Even if data science isn’t your dream job, being interested in it helps.
Your choice must align with your career path. It should be something that would shape you, and help you achieve your ultimate career goal. If you want to become a musician later on, chances are that data science isn’t the best choice for you.
Your skill strengths, especially your soft skills, should align with the skill sets required by the field. It is important to have the relevant skills to grow and advance in your chosen career path. You should have statistics, programming, database management, analytics, and predictive modeling skills to excel as a data analyst.
You can also consider which field has more job opportunities for you to explore. The more jobs are available, the more opportunities there will be to advance your career in various industries and enhance your skills. With more hands-on experience and time, you will be able to work with advanced statistics and even software engineering.
Both fields have a different salary range, so you should do a bit of research and decide on how much money you would like to earn before making your choice. You can even specialize further in one of these fields if you want to earn a higher salary.
Data Science vs Data Analytics: Which Is Right for You?
Data science and data analytics can both be right for you if you are good with numbers and have a strong eye for detail. However, data science requires more advanced skills and more experience. Data analytics is more straightforward and can be used as a stepping stone for a data science career. Either way, these careers are good options to pursue.