What is a Data Analyst?
These days, data seems to be a part of everything we do. Phones and internet browsers use and collect data in order to customize customer experience and to inform companies about their audience. Data is a part of phone games, popular apps, and social media. Although it is a fairly recent invention, it’s growing fast. At the beginning of 2020, there was an estimated 44 zettabytes of data existing in the world. That’s 44 followed by 21 zeroes!
With so much data in the world, and with data as such a constant in all of our lives, there is a need for professionals who can analyze that data and suss out the human element.
Data Analyst Job Description: Is it more of Data or Analysis ?
As stated above, the job of Data Analysts is to analyze data and determine what it tells us about the human beings using that data. According to Northeastern University, a Data Analyst “serves as a gatekeeper for an organization’s data so stakeholders can understand data and use it to make strategic business decisions.”
All the data collected from those aforementioned apps, games, social media sites and more tells Data Analysts something about the preferences of the users who generate that data. The goal of a Data Analyst is to search for patterns and trends and find the significance in those trends. For instance, what is the main age demographic of users on Twitter? What do they engage with the most?
From there, Data Analysts communicate these patterns and trends to the proper channels in order to allow them to make more informed decisions about their target audience. Other Data Analyst responsibilities include:
- Collecting data from multiple sources — at least a primary and secondary source.
- Maintaining databases, including fixing coding errors.
- Making sure that the data is clear and easy to read, by humans or machines.
- Create charts and visual presentations showing trends in the collected data.
This is the perfect bridge between coding and studying human behavior, so it’s perfect for the statistics nerd who’s also interested in things like sociology and anthropology. As a Data Analyst, you are a problem solver, a sort of intermediary between the customers and the executive leadership of the business. By using data to get a sense of what the target audience wants, you can solve complex problems and make both customers and business executives happier.
Data Analyst vs. Data Scientist
Data Analysts are often confused with many other data jobs, but particularly Data Scientists. These jobs are similar and often work in tandem, but they are not, in fact, identical. In fact, it’s the areas that set Data Analysts apart that allows them to work well with Data Scientists.
A Data Analyst:
- Utilizes the data collected to draw insights based on patterns and trends.
- Works with defined sets of data to answer the questions and needs of the business.
- Works through data mining, statistical analysis, and SQL to retrieve data.
- Creates databases and data systems in order to better interpret the data.
- Prepares reports based on their findings of current trends in data.
A Data Scientist, on the other hand:
- Creates algorithms and statistical models in order to get a sense of what’s unknown.
- Organizes undefined data sets through the use of multiple data collection tools.
- Creates predictive algorithms in order to collect the necessary information from the data.
- Works with object-oriented programming and machine learning.
- Works with undefined sets of data to find the solution to complex problems.
It ultimately comes down to question and answer. Data scientists create algorithms that pose the questions a business might have about their target audience or why their profits dropped over the last quarter. Data Analysts collect data and search the patterns and trends for the answer to those questions and then creates a report to communicate that to the business.
Data Analyst vs. Business Analyst
Another career often confused with Data Analyst is that of business analyst. Both are data-oriented, analytical jobs, but the focus is different. A business analyst has a more internal focus, analyzing data to find ways to make the business run more efficiently or save coss. Business analysts deal with the internal performance of the business and report to executives as well as shareholders.
Data Analysts, on the other hand, take a more external approach, looking at data trends for meaningful insights into their target audience and industry trends. Data Analysts look for ways to promote engagement or determine why certain elements of the business are rising and falling based on the way people interact with the business.
Types of Data Analytics & Careers
But “Data Analyst” is more of an umbrella career covering multiple types of data analytics. Some are meant to look at past patterns while others focus their energy on predicting outcomes based on data. These various data analytics often overlap, but each has a different focus.
Descriptive Data Analytics
These are the data analytics that cover things like quarterly earnings, monthly website traffic or social media engagement, or whether the budget was exceeded or not in the past year. However, descriptive data analytics without further inspection do not tell analysts much, which is why they’re often used in conjunction with…
Diagnostic Data Analytics
Think of descriptive data analytics as examining the “what” and diagnostic data analytics as examining the “why.” Diagnostic analytics looks over descriptive data conclusions of the past searching for patterns, determining why earnings went down in the past quarter...or why they went up.
Predictive Data Analytics
Predictive data analytics takes what has been learned from diagnostic analytics and applies that to the future. Let’s say that the introduction of a new website layout correlated to a significant drop in engagement. Predictive data analytics might lead to the business fixing the layout...and sending out a mass email announcing that they worked out the kinks.
Prescriptive Data Analytics
Prescriptive analytics not only looks to the future but also looks to industry trends and the successful habits of the business’s competition in order to determine what steps the business should take next.
Data Analysts Qualifications & College Degrees
There is no one set degree for a Data Analyst to pursue, though it must be something relevant to data analysis. Some degrees that Data Analysts can pursue at college include Business, Computer Science, Mathematics or Statistics. If you'd rather get a fast training, you can join an intensive bootcamp in Data Science to get all the required skills to master data analytics and succeed in this position.
Data Analyst Skills: you’ll need to learn how to program & conduct analysis
Skills that you will need in order to have a successful career as a Data Analyst include:
- Proficiency in a programming languages related to data science such as Python
- Proficiency in data clearing languages, especially SQL and variations such as PL/SQL and T-SQL
- Experience with data warehousing and maintaining databases and data systems
- An eye and creative mind for clear data visualization
- Proficiency with Microsoft Excel
- Experience with machine learning, while not required, is certainly a leg up in the industry
- Data extraction and loading (ETL)
- Experience with various operating software
There are also non-technical skills and traits that come in handy when working as a Data Analyst, such as:
- Clear communications skills
- Creative approach to problem solving and analysis
- Attention to detail
- A love of research
- The ability to work as a team
- Strong business intelligence
Data Analyst Interview Questions & knowledge about Data Science
So you’ve set your heart on a career as a Data Analyst, gotten the proper education, and you have all the right skills. Now it’s time to start your career as a Data Analyst. One of the most essential parts of any job hunt is the interview. While every interview will be different depending on the company, here are some questions you can expect:
Why Do You Want To Be a Data Analyst?
This will tell the interviewer how much thought you’ve put into this chosen career path and why it’s important to you.
What Do Data Analysts Do?
The interviewer will want to see that you know your stuff. They may ask you to explain what a job as a Data Analyst entails before describing it themselves.
Have You Ever Not Been Able to Meet a Deadline?
Be honest in this answer, but frame it in a positive light. Explain what you learned from the situation and how you’ll apply that going forward.
What Data Analyst Software Are You Proficient In?
Different companies will use different software, so it’s important for a Data Analyst candidate to be well-versed in various forms of software, especially software that may have been referenced in the original ad for the job.
What Has Been Your Most Difficult Data Analysis Project Thus Far?
Again, this is a question that gauges your experience and allows you to share what you learned from the experience. If the project went well, you can also explain how you overcame the difficulties.
What Were Your Daily Duties In Your Past Data Analyst Position?
This job might vary from your past Data Analyst job. This allows the interviewer to get a sense of what your process is from start to finish of a data analysis project.
Many Data Analyst job candidates on Glassdoor reported being asked to answer logic puzzles, theoretical scenarios that might require data analysis, to test their skills.
How Much Does a Data Analyst Make?
The biggest reason to become a Data Analyst is that jobs in data are constantly on the rise. From insurance companies to social media platforms to small businesses with an online presence, there is a plethora of businesses in need of Data Analysts. Because of this, you could find yourself working in almost any industry — or in various industries. This is the farthest thing from a “dead end job.”
Because of the high demand, and because there’s relatively low competition, Data Analysts often make competitive pay compared to other careers. Salary.com states that the average income for a Data Analyst is between $66,713 and $85,613 in the US. And that’s often just to start. With yearly bonuses and the chance for growth, Data Analysts can even make up to 6 figures in their career. This number is also based on the city in which you live and cost of living, of course.
How To Become a Data Analyst
There’s never been a better time to get started as a Data Analyst! If you’re passionate about problem solving and looking for a career with diverse challenges and even industries, a career where you can combine both your people skills and your computer skills, work as a Data Analyst could be exciting and fulfilling. Demand for Data Analysts is high, but applicants need to be prepared before they go on the job hunt. The question is how to get started?
Le Wagon has more than 40 campuses worldwide throughout Europe, Asia-Pacific, the Americas, and the Middle East, helping people develop the skills they need to achieve their dream careers in data analysis, Data Science, or Web Development. We offer a Data Science course, including valuable information for potential Data Analysts like:
- Programming languages
- 2 weeks on data analysis
- Data visualization
- Machine learning
- Relational database and SQL
- Prep for upcoming interviews
Here, you’ll learn from experts, pair up with partners to tackle real data challenges, and attend talks and workshops to motivate you in pursuing this career.
Best of all, attending the course once will offer you access to the vast community — including online platforms, job fairs, and networking events — long after you graduate. Le Wagon gives you the tools and the community to not only get your dream job as a Data Analyst but to thrive in your field.
Ready To Get Started?
Download our syllabus below to discover our Data Science bootcamp and learn more about our alumni and community! And for answers to frequently asked questions, head here.
Ready to launch your career in Data Science?
Join our Data Science bootcamp! You’ll learn all the necessary skills of a Junior Data Scientist and build your own data products from scratch.Learn Data Science