There’s never been a better time to learn data analytics and enter the workforce as a data scientist. The job landscape is promising, opportunities span multiple industries and the nature of the job often allows for remote work flexibility and even self-employment.
Plus, many data analytics experts boast a high median salary, even at entry-level positions.
With technology reaching new heights and a majority of the population having access to an internet connection, there’s no denying that Big Data and data analytics have become hot topics in recent years – and a growing need. According to IBM, the number of jobs for data professionals in the U.S will increase to 2,720,000 by 2020.
Demand for knowledgeable data analytics professionals currently outweighs the supply, meaning that companies are willing to pay a premium to fill their open job positions.
But the skillset and job opportunities within data science go beyond the tech and digital spaces. Let’s take a look at what you need to know as a analyst or data scientist – and what you’ll learn when you take our courses.
Why Choose a Career in Data Analytics?
As mentioned above, there are quite a few practical reasons a data analytics career might be appealing, including:
But there are quite a few less obvious reasons working in data analytics can be a great choice, too:
Interested? Let's take a look at the skills you'll need to learn, and then dive into some of the different roles you can get with data analytics skills.
What Skills Are Required for a Job in Data Analytics?
As you delve into the 10 jobs we have here and start applying for positions in the data analytics field, you’ll notice many of them require the same foundational skills. Make sure you’ve mastered these before you start sending your cover letter and portfolio to potential employers.
And, if you find a skill that you still need to learn, remember that you can take an affordable, self-paced data science course that will help you learn everything you need to know for a successful career in data science.
Python is currently one of the most commonly used programming languages.
Having a solid understanding of how to use Python for data analytics will probably be required for many roles. Even if it’s not a required skill, knowing and understanding Python will give you an upper hand when showing future employers the value that you can bring to their companies.
If you’re ready to advance your programming language proficiency, learn how to manipulate and analyze data, understand the concept of web scraping and data collection, and start building web applications, consider enrolling in our Python for Data Science: Fundamentals Course.
SQL (Structured Query Language)
Working with data sources is a necessary aspect of data analytics.
Early in your career, you’ll need at least a basic understanding of SQL. SQL (pronounced sequel) is often a major component of these positions. When you go to interview, listen for hiring managers’ mentions of this programming language when asking about your work with databases.
The experience you’ll get in our SQL courses will give you a good foundation. Like Python, SQL is a relatively easy language to start learning. Even if you are just getting started, a little SQL experience goes a long way.
Knowing the basics of SQL will give you the confidence to navigate large databases, and to obtain and work with the data you need for your projects. You can always seek out opportunities to continue learning once you get your first job.
Data Visualization Skills
Knowing how to visualize data and communicate results is a huge competitive edge for job seekers.
On the job market, these skillsets have high demand (and high pay)! Regardless of the career path you’re looking into, being able to visualize and communicate insights related to your company’s services and bottom line is a valuable skillset that will turn the heads of employers.
In this way, data scientists are a bit like data translators for other people in the organization that aren’t sure what conclusions to draw from their datasets.
At Dataquest, students are equipped with specific knowledge and skills for data visualization in Python and R using data science and visualization libraries.
11 Types of Jobs that Require a Knowledge of Data Analytics
Before you take the time to learn a new skill set, you’ll likely be curious about the earning potential of related positions. Knowing how your new skills will be rewarded gives you the proper motivation and context for learning.
Lots of employers are hiring for these positions, both remote and onsite, worldwide. Here are a few positions worth looking into – and their median incomes, according to popular job search websites.
1. Business Intelligence Analyst
A business intelligence analyst's most fundamental job is to find patterns — and value — in their company and industry data.
At most companies, this is a kind of data analyst role. BI Analysts will be expected to be comfortable analyzing data, working with SQL, and doing data visualization and modeling. Like most data roles, this job also requires strong communication skills so that you can communicate your results convincingly to others at the company.
BI Analysts earn an average salary of $95,838 per year, plus an average $5,000 cash bonus.
2. Data Analyst
Data Analysts do exactly what the job title implies — analyze company and industry data to find value and opportunities.
Data analysts can be found in every industry, and job titles can vary. Some roles will have industry-specific names like "healthcare data analyst." "Business analyst", "intelligence analyst", and similarly-named roles often share a lot with data analyst roles.
Unlike data scientists, they're typically not expected to be proficient in machine learning. But most data analyst jobs require programming and SQL skills, as well as statistical knowledge, comfort with the data analysis workflow, and data visualization skills.
The average salary for a data analyst is $75,253 per year, with an additional bonus of $2,500.
3. Data Scientist
Much like analysts in other roles, data scientists collect and analyze data and communicate actionable insights. Data scientists are often a technical step above of data analysts, though. They are the ones who are able to understand data from a more informed perspective to help make predictions. These positions require a strong knowledge of data analytics including software tools, programming languages like Python or R, and data visualization skills to better communicate findings.
These positions are challenging – and rewarding, with an average salary of $91,494. The demand for data analytics experts with technical backgrounds is at an all-time high.
Dataquest has multiple learning paths that are tailored to provide you with everything you need to hone your technical skills, including the Data Scientist Path that will help you become a certified data scientist.
4. Data Engineer
Data engineers often focus on larger datasets and are tasked with optimizing the infrastructure surrounding different data analytics processes.
For example, a data engineer might focus on the process of capturing data to make an acquisition pipeline more efficient. They may also need to upgrade a database infrastructure for faster queries. These high-level data analytics professionals are also well-paid, with median salaries being comparable to data scientists at $90,963.
5. Quantitative Analyst
A quantitative analyst is another highly sought-after professional, especially in financial firms. Quantitative analysts use data analytics to seek out potential financial investment opportunities or risk management problems.
The median salary for quantitative analysts is $82,879. They may also venture out on their own, creating trading models to predict the prices of stocks, commodities, exchange rates, etc. Some analysts in this industry even go on to open their own firms.
6. Data Analytics Consultant
Like many of these positions, the primary role of an analytics consultant is to deliver insights to a company to help their business. While an analytics consultant may specialize in any particular industry or area of research, the difference between a consultant and an in-house data scientist or data analyst is that a consultant may work for different companies in a shorter period of time.
They may also be working for more than one company at a time, focusing on particular projects with clear start and end dates.
These positions are best for those who like change, and those who have a narrowed interest and expertise in a field of study. Analytics consultants are also in a great position to work remotely, another enticing factor about this role to consider.
Compensation varies widely by industry, but $78,264 is a representative salary for the role.
7. Operations Analyst
Operations analysts are usually found internally at large companies, but may also work as consultants.
Operations analysts focus on the internal processes of a business. This can include internal reporting systems, product manufacturing and distribution, and the general streamlining of business operations.
It’s more important for professionals in these roles to have general business savvy, and they often have technical knowledge of the systems they’re working with. Operations analysts are found in every type of business, from large grocery chains, to postal service providers, to the military.
Operations Analysts make an average of $67,353 per year, with an average additional cash bonus of $2,500. Due to the versatile nature of this data analytics job and the many industries you may find employment in, the salary can vary widely.
8. Marketing Analyst
Digital marketing also requires a strong knowledge of data analytics. Depending on your other complementary skills and interests, you could find yourself in a specific analytics role within a company or agency, or simply applying your data science expertise as a part of a larger skill set.
Marketers often use tools like Google Analytics, custom reporting tools and other third party sites to analyze traffic from websites and social media advertisements. While these examples require a basic understanding of data analytics, a skilled data scientist has the ability to create a long-term career in marketing.
A lot of money could be wasted on campaigns that do not drive traffic, so marketing professionals will continue to need analysts to make smart decisions about how to leverage existing resources.
While digital marketing positions have a wide salary range, marketing analyst salaries average $66,571, and can rise above six figures for senior and management-level positions.
9. Project Manager
Project managers use analytics tools to keep track of a team’s progress, track their efficiency, and increase productivity by changing processes.
Project managers need at least a working understanding of data analytics, and often more.
These positions are found internally at large corporations, and frequently in management consulting. Another example of a career trajectory for project managers could be moving into product and supply chain management, which companies rely on to maintain profit margins and smooth operations.
A typical salary for a project manager is around $73,247.
10. IT Systems Analyst
Systems analysts use and design systems to solve problems in information technology.
The required level of technical expertise varies in these positions, and that creates opportunities for specialization by industry and personal interests. Some systems analysts use existing third-party tools to test software within a company, while others develop new. proprietary tools from their understanding of data analytics and the business itself.
A typical systems analyst in the US makes around $68,807.
11. Transportation Logistics Specialist
A transportation logistics specialist optimizes transportation of physical goods, and could be found in large shipping companies, like Amazon, UPS, naval transport companies, airlines and city planning offices.
A data analytics background is especially helpful in this job because transportation logistics specialists need to reliably identify the most efficient paths for products and services to be delivered. They must look at large amounts of data to help identify and eliminate bottlenecks in transit, be it on land, sea or in the air.
With seasoned professionals in this industry making around $79,000 per year, a transportation logistics specialist is an appealing career path for individuals who are detail-oriented, technical and forward thinkers.
A data analytics background also helps transportation logistics specialists, among others, to focus on the most important issues, seeing potential problems and solutions in context and communicating those effectively.
Data Analytics Opportunities Around The Globe
These are just a few of the many high-paying jobs which require knowledge of data analytics. Specific figures from this article are for the median salaries in the United States, all cities included.
Salaries in each city may vary and reflect local demand and general cost-of-living expenses. Boston, Portland, and Denver, for example, have become hotspots for data analytics positions.
While the numbers included in this article represent a typical salary in the United States, opportunities for data analytics professionals can be found all around the globe. Many of them can even be done remotely, allowing you the highly-desired opportunity to work from anywhere in the world on a competitive US salary.
Whether your goal is to get a full-time job in a new industry, advance in your existing career, or work for yourself in the data analytics field, Dataquest can prepare you for the opportunity. With the portfolio-building missions and projects in Dataquest’s Data Analyst path, a community of mentors, and a strong alumni network, you’ll have all you need to become a certified data analyst and be set up to get the job of your dreams.
Data Analyst Career Path: Pros and Cons
Data analysis roles can be great, but they're not for everyone! Let's take a look at some of the pros and cons of these roles:
On the "pros" side are many of the things discussed earlier in this article, so we don't need to repeat ourselves here. High salaries, great career prospects, and satisfying work are among some of the many perks of data analytics work.
The "cons" tend to be a bit more case-by-case.
Some of them have to do with your personal preferences. If you don't enjoy programming, or at least find solving problems with code satisfying, then a job in data analytics probably isn't for you. Similarly, a lot of data analysis work involves cleaning up messy data.
You'll almost never be handed a perfect CSV that's ready for instant analysis. If you aren't willing to clean data sometimes, this isn't a good profession to get into!
Some of the cons have to do with the specific companies you might end up at. Because data science is a relatively new field, there's a lot of inconsistency in job titles — "data analysts" at one company could be asked to do advanced machine learning, while "data analysts" at another company might be asked to do data entry. This is something you need to be careful to look out for when you're on the job hunt.
Similarly, many companies know that "big data" is important, but don't actually have a good idea of how to effectively integrate skilled data workers into their organization. This is another red flag to watch out for during the job hunt — a big source of job dissatisfaction for data analysts is getting the job but then not getting the support they need from the company to be successful.
Thankfully, most of the "cons" are avoidable if you're careful during your job hunt! The only other "cons" are a matter of personal preference. Do you enjoy working with data? Only you can answer that. Why not sign up for a free Dataquest account and find out?