Is Data Science a Career Worth Pursuing?
If you’re unsure whether a data science career is the right choice, you should know that data science and data analytics have become in-demand professions in the last decade. The US Bureau of Labor Statistics cites data science as among the top 20 fastest-growing occupations.
In this article, we’ll dive into:
- What is data science
- The specializations within data science
- How data science works
- The difference between data scientists and analysts
- Data science careers and top industries in New Jersey
- How you can become a data professional
What is Data Science?
Thanks to the proliferation of internet connectivity, increased use of smart devices and business leaders recognizing the importance of data, data is being created and collected at warp speed. You may be wondering, "How are we making sense of all these data?"
That’s where data science comes in.
Data science is a field of study that generates insight from the massive amounts of data we produce daily. Some of the responsibilities of a data professional include sifting through tremendous amounts of data (also known as big data), processing or transforming data and extracting meaningful information. Professionals today specifically aim to help make business decisions or predictions about future trends.
Data science is a collection of tools and skills, but it is also a mindset of thinking through business problems with data.
Within the data science field, several branches of specializations and roles allow organizations to use data as part of their growth strategy.
Specializations Within Data Science
While data science originated as a field for mathematicians and computer scientists, it has evolved to meet the demands of today’s internet-connected world. Business leaders today desire professionals with a focus and aptitude for tech. Still, anyone with the passion and interest for the following specializations can find a way to break into the field, regardless of their current experience level.
Machine learning and artificial intelligence are recent technologies that have become prominent in the data science field. Devices, business products and services revolving around these technologies have exploded over the years, making data creation even more unprecedented than ever before.
The evolution of these technologies, however, has also brought several benefits to businesses. Artificial intelligence and machine learning make it easier for companies to improve their processes, make decisions quicker with more data and automate customer communications.
Data analysts use a process called data mining and tap into historical data to identify trends and meaningful information from a large set of data. Professionals within this specialization obtain accurate data to help the company or client cut costs, solve problems, understand their customers and prepare for future trends.
The appeal for data lies in the promise of “seeing the future” and making strategic decisions that will benefit the business. There is meaningful information in the data companies capture, but only with the right processes and professionals can that data be put to good use.
Data analytics can be classified into four broad categories.
- 1. Descriptive analysis asks, “what happened?” When reviewing your monthly bank statements for the past year, you’re using descriptive analysis to create a digestible snapshot of your income and expenses.
- 2. Predictive analysis asks, “what will likely happen in the future?” You would use historical data, patterns and trends to estimate a likely outcome. It builds not only on what happened but why—giving you a clear picture of what’s to come in the future.
- 3. Prescriptive analysis asks, “what are the best next steps?” It allows you to see possible opportunities or catastrophes within potential outcomes. Prescriptive analyses rely on algorithms, machine learning and advanced modeling to weigh all possible combinations and how they may affect the future.
- 4. Diagnostic analysis asks, “why did it happen?” This type of analysis goes deeper than surface-level findings from your descriptive analysis to understand root causes and discover anomalies.
For business leaders and stakeholders to make strategic decisions, they must review historical data and reports to know what outcomes their decisions may yield. Business intelligence allows strategic decision-making and while it’s not entirely a new concept to make data-driven decisions, the methods are evolving.
Data visualization, automation and machine learning are helping organizations improve and accelerate their data processing to make smarter decisions and innovate quickly.
As a data professional specializing in business intelligence, you will need to get familiar with tools such as Power BI to analyze and visualize your data.
Would you prefer to read a 5-page report or look at one graph that synthesizes all of that information?
Efficiently and accurately communicated information allows stakeholders to make decisions much faster and with more confidence. Data visualization helps you resonate with your audience and bring complex or large amounts of data to life. Datapine.com offers a great selection of examples showing data visualization in action.
Successful data visualization relies on the type of chart you use, the integrity of your data and the colors and labels you choose. One essential element, however, is your ability to create a narrative around your data. Storytelling is an effective way to influence your audience because it helps them interpret your findings with context.
Data architecture is a result of the explosion of data in recent decades. As data creation sped up, data architecture was necessary to critically analyze how data was collected, processed and stored.
Data Architects play an important role in setting up data engineers with a blueprint for how the database will function, the same way an architect would design the blueprint of a building. As a result, they create pathways for data to be moved seamlessly from place to place.
As new technologies became more widely available for business operations, such as cloud storage and automation, data architecture has evolved to work within these structures.
Database administration and management are critical roles that require extensive data architecture knowledge.
While the data architect designs the database framework, the data engineer relies on their coding skills to build and maintain the framework used to warehouse and manage data. Both professionals work hand in hand to ensure that the process is seamless and that data is easily accessible to data analysts, scientists and executives.
A data warehouse is a type of database containing enormous amounts of organized data for the sole purpose of analysis and reporting. Data engineers ensure the data loaded into the warehouse is clean, formatted and usable for those who need to access it.
These specializations are part of the data science world. As you learn more about the field, you will discover a wide range of responsibilities, roles and opportunities. With this article, you will discover how anyone can become a data professional, even with little or no previous experience.
How Does Data Science Work?
Ensuring Quality Data
Before we dive into how data science works, it’s important to understand that not all data is usable or meaningful and that data alone has no real significance. Data is only as valuable as its ability to answer a question or solve a problem based on the organization’s needs.
As a data professional, you are the one identifying which data sets will uncover the answers, then manipulating and organizing them to derive tangible findings that will further decision-making.
With that in mind, let’s look at two types of data a professional can encounter.
Structured Data vs. Unstructured Data
Data can exist in two forms. Unstructured data lack an identifiable structure or format. Its lack of structure makes it hard for computer programs to identify how to read it as data. Unstructured data can include voice memos, social media accounts or video footage.
Structured data conform to a data model and can be grouped into categories for easy processing. Think of an Excel table and how the rows and columns visibly organize data. We usually source structured data from online forms, metadata and servers.
Both types of data can be used at any given time, each type providing its own nuance to the bigger story.
The Data Life Cycle
The data life cycle is not a one-size-fits-all formula, but we can break it down into specific steps to guide data professionals throughout their projects.
First, you will need to identify the project’s goal, which begins by asking questions to understand your organization’s or client’s needs. As you narrow down these needs, you will create your metrics for success, which are known as business requirements.
Next, you will determine the best technique to reach accurate findings. There are several ways to analyze data and it is up to the data analyst to decide the best approach.
With that established, it’s time to clean and transform your data—this can be the most time-consuming stage, but it is the most necessary. In this stage, you will identify and eliminate anomalies that would interfere with the integrity of your final data set. Duplicate information, invalid characters and data not formatted correctly are just a few things to look out for during the cleaning phase.
With clean data, it is now possible to integrate software that will make it easy to pull information and provide insights. Now is when you start working with dashboards and other reporting tools. Whether you choose to visualize your data may depend on your business requirements. But, as previously mentioned, data visualization is a great way to bring insights to life and help tell a deeper story.
It may be appropriate to use algorithms, machine learning and AI to understand your data sets further and gain deeper insights.
Now that you have clean data and integrated software, you can build a narrative that influences stakeholders to take actions based on your findings.
- Were the right questions answered?
- Did stakeholders and business executives walk away with clear insights for their decision-making?
If so, you met the business requirements and you’re ready to move on to your next project.
That’s right. A data professional’s work never stops!
Data Scientist vs. Data Analyst: What’s the Difference?
Data analysts and data scientists are valuable for a variety of reasons, including their ability to address an organization’s needs, work on collecting and formatting data, reduce the cost of doing business, support all team members and more. That being said, data scientists and data analysts perform different functions that ultimately complement each other.
What Do Data Scientists Do?
Data scientists take big data and use specific methods to help optimize business operations—whether it’s logistics, customer service, consumer behavior or employee performance. With most of our lives happening online, resulting in unique data every nanosecond, the demand for qualified data scientists and analysts will only continue to increase.
- Top skills for data scientists include math and statistics, coding, machine learning and predictive analysis.
- The responsibilities of a data scientist include collecting data, managing big data and deploying machine learning.
What Do Data Analysts Do?
Data analysts answer a set of well-defined questions asked by the business. They work primarily with data from a single source and organize and sort through data to identify solutions. A data analyst moves into the realm of data scientists when they move beyond just analyzing data and begin making predictions with their data.
- Top skills for data analysts include business intelligence, data visualization and strong written and verbal communication.
- The responsibilities of a data analyst include organizing and modeling data, identifying meaningful patterns and reporting on informed conclusions to help drive strategic business decisions.
Why Are Data Scientists and Analysts Important?
Each professional is necessary to reach the client’s ultimate goal.
That’s why training in both data science and analytics is the best way to position yourself as a valuable professional throughout your career. While both have separate functions, their foundations are similar and each can harness these learnings to become a versatile, well-rounded data professional.
Data: The Ultimate Optimization Tool
A business can use data to understand their customer’s behavior to learn when they will be more responsive to their messaging, which products customers prefer and when they might need an upgrade or product refill. Data is how companies improve on all fronts, whether it’s identifying productivity slumps or monitoring operational risks.
Here are just a few ways data can impact businesses:
- Strategic decision-making and problem-solving
- Predictive analysis
- Pattern discovery
- Reduce operational risks
- Understand employee performance
- Improve customer experience
Most In-Demand Data Science & Analytics Jobs
New Jersey is one of the top paying states for data scientists and a metropolitan area with the highest employment level for professionals in this field (source: bls.gov). If you’re thinking about pursuing a tech career as a data scientist, here are some career paths to explore.
Working as a Data Scientist can require you to call on several skills and tools. Not only are you developing predictive insights and meaningful conclusions for stakeholders across any given organization, but you’re also using machine learning and AI, managing databases, automating data collection and providing clear communication to your team members.
Business Analysts are unique in this industry because they act as a bridge between data engineers and executive decision-makers. While they don’t need to have a deeper understanding of math and coding, they do have to tap into their breadth of knowledge in data, business, finances and technology to be effective.
Data Architects decide precisely how a data management system should be built, organized and maintained. They design the ‘blueprint’ of a data management system, the same way an architect would design the blueprint of a building and create pathways for data to be moved seamlessly from place to place.
A Database Administrator or Manager focuses on everything related to an organization’s database of information, from design to maintenance to security. They also focus on continuously improving the database system.
Data Engineers prepare data for data scientists. They rely heavily on coding skills to design and build the digital infrastructure to store, also known as “warehousing” and manage data.
Business Intelligence Architect
The Business Intelligence Architect (BI Architect) more commonly builds dashboards, creates visualizations and works closely with executives and decision-makers to deliver reports and recommendations.
What Are Other Data Science Jobs?
The types of data science career paths you can explore range from analytical to engineering. Learning the foundations of data science expands your knowledge and helps you decide on a specialty if you do choose to focus on one specific career path.
- 1. Machine Learning Engineer
- 2. Applications Architect
- 3. Business Intelligence Developer
- 4. Statistician
- 5. Data Analyst
- 6. Data Architect
- 7. Marketing Analyst
- 8. Big Data Engineer
- 9. Database Administrator
- 10. Data and Analytics Manager
Entry-Level Data Science Jobs
Entry-level doesn’t mean low contribution. Even as an entry-level data professional, you would be expected to keep up with the demands of data collection, analysis and visualization. More often than not, these can be remote positions with attractive salaries.
Here are just a few entry-level data science jobs you can expect to find:
- Junior Data Analyst: In this role, you will apply machine learning methods to specific business problems, ensure data quality and design and develop models that track impact over time.
Average salary: $15 per hour.+
- Customer Data Analyst: This position requires substantial research and analysis skills to observe and report market trends and opportunities for improvement.
Average salary: $68k a year.+
- Associate Data Scientist: This role expects you to be proficient in business intelligence analysis and data mining. You will conduct hands-on research, build predictive models and create dashboards.
Average salary: $97k a year.+
- Data Labeling Quality Specialist: This role is responsible for maintaining data entry and coding integrity by preparing source data for database entry and ensuring coding will increase database searches. Research and attention to detail are highly valued.
Average salary: $69k a year.++
+Data from salary.com
++Data from glassdoor.com
Best Industries for Data Scientists and Analysts
- 1. Retail and E-Commerce
- 2. Medicine
- 3. Banking and Finance
- 4. Construction
- 5. Transportation
- 6. Communications
- 7. Media and Entertainment
- 8. Education
- 9. Manufacturing
- 10. Agriculture
- 11. Energy
- 12. Cybersecurity
- 13. Digital Marketing
- 14 Government
What Are the Necessary Skills to Become a Data Professional?
You may not need a Ph.D. to be a data professional, but obtaining certain skills will help you become successful in this field. You may already have transferable soft skills; however, if you’re not confident in the following technical areas, you may want to consider enrolling in a data science and analytics training program to develop and hone new competencies.
10 Technical Skills for Data Professionals
- 1. IT infrastructure
- 2. Fundamentals of analytics
- 3. Business intelligence
- 4. Database knowledge (SQL)
- 5. Working with unstructured data
- 6. Competent in statistics and probability
- 7. Proficiency with a programming language (Python)
- 8. Machine learning
- 9. Model deployment
- 10. Data visualization
10 Soft Skills for Data Professionals
- 1. Communication
- 2. Collaboration
- 3. Proactive problem solving
- 4. Attention to detail
- 5. Public speaking
- 6. Project management
- 7. Curiosity
- 8. Time management
- 9. Creativity
- 10. Business acumen
Best Data Science Tools to Learn
Tableau is a data visualization tool that allows you to understand data from multiple sources, spot trends quickly and share your dashboards with stakeholders.
Python is a programming language used to develop software and, in data science, is used for data processing. It’s the most popular programming language today due to its low learning curve and is recommended for all data professionals.
Excel is a powerful tool for data scientists. You can run advanced analytics, dashboards, execute queries and more! While there are countless database and dashboard software available, Excel remains widely used across organizations.
SQL, also known as Structured Query Language, is used to perform actions and commands within a database. Like Python, SQL is widely used and is easy to pick up as a beginner.
How to Start Becoming a Data Scientist Without a Degree
Thanks to the internet and our connected world, we have nearly unrestricted access to anything we could ever want to learn. This democratization of information allows anyone to learn new skills, level up their current knowledge and gain a deeper understanding of the topic of their choice.
If you’re looking for the best path to become a data scientist without a degree, here are some options.
It’s getting easier by the day to find a free online course that suits your needs. You may not get real-time input from an expert or mentor, but you can begin to understand the basics and get a feel for what it might be like for you in the data science field.
Bootcamp Training Courses
If you want to take a more serious route without investing years into a degree program, a bootcamp, like the NJIT Data Science & Analytics Bootcamp, can be a great alternative to a four-year degree.
Our part-time classes take place over the course of ten months during the evenings and weekends, so you can keep your day job while you prepare for your new career. If you’re on the fence about the bootcamp route, try our 30-hour Introductory Course, which covers foundational concepts and gives you a feel for the learning environment and curriculum.
Data Science and Data Analyst Certifications
Certifications are a way to stand out and show that you are knowledgeable in your field. There are many data science certifications to choose from, like IBM Data Analyst Professional Certificate, IBM Data Science Professional Certificate and Microsoft Certified: Data Analyst Associate.
Many of the certification exams are self-paced and the credentials don’t expire, so you won’t have to worry about retaking them every few years. Some certifications require experience and training to apply.
When you enroll in the NJIT Data Science & Analytics Bootcamp, you’ll receive training applicable to the three certifications mentioned above. While our bootcamp is a career-prep program and not a certification-prep program, you can still walk away with the knowledge and skills to help you prepare for industry-recognized certifications.
Becoming a Data Scientist at 35, 40 or 50
It’s never too late to change careers and explore a new field. If you want to learn the foundations or become a better data scientist, career-prep programs can help you learn current techniques and get familiar with new technologies.
It’s not uncommon for career changers to dabble in a few topics with free online resources and that’s always a great starting point! However, a career-prep program like the NJIT Data Science & Analytics Bootcamp provides comprehensive and structured lessons with accelerated, hands-on training.
You can read about our Data Science & Analytics Bootcamp here.
Start Your Data Science and Analytics Training Online
Data accumulation is not slowing down. With the tech sector in New Jersey showing an increasing demand, there has never been a better time to break into a tech career. The NJIT Data Science and Analytics Bootcamp can help you launch your data science career in just 10 months. Our evening and weekend schedule means you can keep your day job while training with real data professionals.
As a learner, you will also receive unrivaled support, including professional development and networking opportunities as well as hands-on simulation training where you face real-world challenges and gain practical experience.
Get started today by scheduling a call with a dedicated admissions advisor. Ask about test-driving our program with our stand-alone 30-hour Introductory Course.