How to Get Into Data Analytics If You Don’t Have the Right Degree
And why it’s a good fit for you
Data analytics is a really cool, up-and-coming industry. It’s been on the rise for a long time, which explains why so many folks are wondering how to get into data analytics. Since the internet and the widespread adoption of smartphones, insane amounts of data are created all the time. The amount of data in the world at the start of 2020 was estimated to be 44 zettabytes at the start of 2020, or 44 * 10^21 bytes. All of this data contains vast amounts of quite useful information, like purchasing patterns of consumers or indicators of diseases.
Data analysts get their hands on datasets and are tasked with making sense of them. What are the numbers saying, and what should the company do about it? A simple example is the banner of products that appear after you’ve put an item in your shopping cart containing products that customers often purchase together with it. A data analyst could be responsible for determining which products should be associated with each other in order to increase conversion rates.
Data analytics is the process of analyzing raw data to find trends and answer questions. It involves a lot of solo work in front of the screen, but if you like math and programming, it’s a great opportunity for you. You get to procure and gather data as well as clean, organize, visualize, and analyze it.
Quick note of distinction: a data scientist is responsible for designing and constructing new models for data. They create prototypes, algorithms, predictive models. A data analyst does what the name implies - she looks at data, tries to predict trends, makes visualizations, and communicates the results. In other words, data analysts analyze data. Data scientists earn $30K-$40K more per year than data analysts in the US, so it’s an important distinction.
This is how this article will walk you through everything you need to know to land your first data analytics job.
What is Data Analytics?
Before you start thinking of how to get into data analytics, you should be sure you understand the field. Data analytics is the art of making sense out of vast amounts of data. According to DOMO, since 2020, 1.7MB of data are created every second for every person on earth. It’s the task of a data analyst to find the data that is relevant to their business application, make sense of it, and find ways to apply that knowledge to improve the business.
There are many subfields within data analytics. These include descriptive, diagnostic, predictive, and prescriptive analytics. You can think of these different types of analytics in the following ways.
- Descriptive analytics is figuring out what happened.
- Diagnostic analytics answers the question: why did it happen?
- Predictive analytics tries to take existing data to predict what will happen in the future.
- Prescriptive analytics aims to figure out what should be done about all of this.
A data analyst could very well cover all of these subfields in their day-to-day work. Often, data analysts will step through all of these forms of analysis to get the most out of a dataset and optimize their business impact. If you want to get into data analytics, it’s important that you understand these different forms of analysis and know how to apply them.
Data analysts are tasked with helping businesses make data-driven decisions. Since it is straightforward to collect data, data analysts can test out their hypotheses and correct the prescriptive models they make to improve their performance and tweak the action items created from their data insights. Making hypotheses based on data, implementing your predictions, and analyzing the results is how to get into data analytics.
What Technical Skills Do You Need for Data Analytics?
Given that data analytics is the cross between math and programming, it’s an extremely technical field. You have to use a lot of different tools and technical skills together in order to get the job done. One software engineer, Margarita Hamacher, put together a comprehensive list of 7 technical skills for data analysts. Data analytics is more than just hard skills, but for anyone wondering how to get into data analytics, those technical skills would be a good place to start.
These skills include math, data visualization, machine learning, coding, and more. The math requirement can be further broken down into linear algebra, statistics, and probability, which are all really important theoretical building blocks for data analysts. It’s worth highlighting the importance of how to separate data for training and testing and enumerates the basic machine learning algorithms you should be comfortable using if not implementing.
If you want to know how to get into data analytics, it’s important that you master all of these skills, since you will need each of them to properly understand the data and accurately analyze it. Additionally, a lot of these skills are absolutely fair game for interview questions.
I’d definitely create some personal projects that use these skills and link to them in your resume. If you’re hit with a question like the one below:
“Why use feature selection? If two predictors are highly correlated, what is the effect on the coefficients in the logistic regression?”
Link to the question: https://platform.stratascratch.com/technical/2017-highly-correlated-predictors
Your answer will be much more convincing and informed if you’ve stepped through this same problem with actual data in a project. You can discuss the effect these correlated features had on your analysis of the dataset used in your project.
How to Get Into Data Analytics: Why is Data Analytics Worth Getting Into?
Data analytics is a truly fascinating field. For example, most of the classical economic theories are based on the assumption that human individuals make rational decisions. This assumption is false, and therefore makes a lot of classical economic theories entirely obsolete. For example, one old economic theory is that consumers relish choice, and while that holds true in some scenarios, making a decision can be physically exhausting, and Mark Lepper and Sheen Iyengar discovered the paradox of choice. They found that customers were more likely to purchase a jam if they were presented with 6 options instead of 24. Economic theories based on data, however, are far more accurate. Data analytics will still require a few small assumptions every now and then, but since it is based entirely on the data that is gathered, if your data is comprehensive and representative, data analytics offers an elegant and accurate way to understand the world and the decisions or habits made in it.
Data analytics is a hopping field. The U.S. Bureau of Labor Statistics predicts a 28% increase in the data science field through 2026. If you’re looking for money, the average salary for a data analyst in the US is $70K, and that is only likely to go up as the demand for data analysts increases. It’s a great time to get into data analytics, and there are easy steps you can take to do it.
Who Makes a Good Data Analyst?
Data analytics is a very technical field, so anyone who wants to know how to get into data analytics will need a strong understanding of a multitude of advanced mathematical concepts and you should be a competent programmer. If you’ve got a passion for numbers and what they can reveal to you, data analytics is the job for you, as soon as you’ve ensured you can master the technical skills outlined above to fit job requirements.
An important factor in the work of a data analyst that many do not consider is the contextual business knowledge you will need. If you are a data analyst working with tree growth data and certain values are missing from your dataset, you need to know enough about trees and the ways they grow to determine whether that data can be thrown out or what the best way to supplement it would be. You also need to be able to understand what the features of a dataset mean. If you have two features that are very similar in terms of what they mean, you may want to throw one out. You can save yourself the trouble of doing an in-depth analysis of feature dependence by using your context knowledge to evaluate the dependency between features and which ones are most relevant to the problem at hand.
Think about what your passions or your areas of existing knowledge are and how you can apply data analytics to those fields. Many people who get into data analytics do not have a formal background or degree in data analytics, so you could look to become a data analyst dealing with data of a field you have studied.
How to Prepare for an Interview in Data Analytics
A big part of how to get into data analytics is crushing your interviews for data analyst positions. In addition to being proficient in Python and able to explain the Central Limit Theorem, you could also be expected to step through how you would compare the performance of different backend engines for automated generation of recommendations. Check out the example interview question below:
Link to the question: https://platform.stratascratch.com/technical/2006-comparing-performance-of-engines
The best way to prepare for technical interviews is to practice. Answering technical questions is a skill like any other. Keep practicing both coding and non-coding questions. You can use websites like StrataScratch, which provides you with a plethora of both coding and non-coding interview questions for data analysts.
In addition to answering coding questions, like finding the popularity percentage for each user on Facebook, and technical, theoretical, non-coding questions, like explaining the different techniques for time-series forecasting, you'll need to have data-analyst relevant content for behavioral interview questions. Although the majority of your interviews will be technical interviews, with either coding or non-coding questions, it’s important that you have examples for a time when you experienced failure or an accomplishment you are particularly proud of that have to do with data analytics.
That’s why it’s so important to have personal projects that have to do with data analytics. Maybe you have a passion for rescuing animals. You could create a model that predicts what strategies are most effective for getting animals adopted. It’s even better if you get the chance to apply your model, like if you got an animal shelter to follow your recommended strategies and see if that makes a difference in adoption rates. Data analytics can be applied to any area where there is data. There’s sure to be a dataset that relates to a topic you are passionate about. Practice your skills as a data analyst on it so you can highlight them for your interviewers.
Career Options in Data Analytics
Lots of programmers and nonprogrammers who might be worried about how to get into data analytics should know that a formal educational background specifically in data analytics is not required. Not that many universities have complete degree programs for data science or data analytics. Although more universities are adding data analytics programs, the demand for data analysts is still too high for employers to require a formal data analytics or data science background. It can be quite helpful if you have a background in math or computer science, but it’s not required.
To get an entry-level data analyst job, your best bet is to be proficient in Python, and be very confident with SQL, as well as SAS, R, Tableau or other database interface tools and languages. If you do not have a background in CS and/or math, develop these skills on the side and apply them to personal projects that showcase your abilities.
Since contextual knowledge is so important, data analytics is a great field to enter from another industry. Depending on the industry whose data you're analyzing, whether it’s medical images or purchasing patterns of small and medium businesses in the online retail industry, it can be helpful and at times necessary to have significant knowledge of the sector whose data you are analyzing.
The average salary for a data analyst in the US is $70K, but it can range up to $106K for those later in their career. Our post How Much Do Data Scientists Make can help you find out about salaries in Data Analytics and how they are influenced by various factors.
Common job responsibilities of a data analyst include gathering and organizing data, ensuring compliance with data policies, performing quality control functions to ensure integrity of data, profit optimization recommendations, or establishing price and portfolio discount planning. The exact responsibilities can vary a lot from one company to another, so check the exact job descriptions to find ones that match the tasks you enjoy the most.
Final Thoughts on How to Get Into Data Analytics
Data analytics is a wonderful field with expanding horizons. There is a quickly growing demand for data analysts, meaning you would enjoy a relatively high level of job security. Since the industry is young, there is a lot of potential for significant career growth. The skills needed are many and varied, so it’s definitely not for those who are done with learning. Since the industry is expanding so rapidly, there is sure to be a lot of changes in the coming years in terms of tools that are used and new applications.
Data analytics is a great option for you if you’re looking to enter the world of technology and programming, but don’t want to go back to school or become a software engineer. Data analysts still get to code, but you get to deal with less of the headaches of being on-call or handling dev-ops. The applications of data analytics are usually fascinating, and you can have a lot of impact on the success of a business by guiding them to make data-driven decisions.