Part 1: Technical Skills Required for Data Analysts
First, it’s essential to understand what a data analyst does. At risk of stating the obvious — all data analysts are concerned with, well, data. They use technical tools to parse through large quantities of raw information and develop meaningful insights in the process. Data analysts are also often responsible for removing corrupted data, determining data quality, and preparing reports for their employer.
All of these tasks, as you might have already guessed, require data analysts to have a well-developed toolbox of technical skills. Here are a few to focus on.
1. Data Visualization
As the term suggests, data visualization is a person’s ability to present data findings via graphics or other illustrations. The purpose of this is simple: It facilitates a better understanding of data-driven insights, even for those who aren’t trained in data analysis. With data visualization, data analysts can help a business’s decision-makers (who may lack advanced analytical training) to identify patterns and understand complex ideas at a glance. This capability empowers you — the data analyst — to gain a better understanding of a company’s situation, convey useful insights to team leaders, and even shape company decision-making for the better.
Data visualization may even allow you to accomplish more than data analysts traditionally have. As one writer for SAS Insights notes, “Data visualization is going to change the way our analysts work with data. They’re going to be expected to respond to issues more rapidly. And they’ll need to be able to dig for more insights — look at data differently, more imaginatively. Data visualization will promote creative data exploration.”
Already, data visualization has become a necessary skill. According to a recent study conducted by LinkedIn Learning, “recent graduates are much more likely to learn hard skills when they first enter the workforce. And these hard skills revolve around analyzing data and telling stories with insights gleaned from the data.” The number one skill reported by participants? You guessed it: data visualization.
2. Data Cleaning
As any Marie Kondo aficionado will tell you, cleaning is an invaluable part of achieving success — and data cleaning is no different! It’s one of the most critical steps in assembling a functional machine learning model and often comprises a significant chunk of any data analyst’s day.
“Although we often think of data scientists as spending most of their time tinkering with ML algorithms and models, the reality is somewhat different,” tech writer Ajay Sarangam notes for Analytics Training. “Most data scientists spend around 80 percent of their time cleaning data. Why? Because of a simple truth in ML: Better data beats fancier algorithms.”
With a properly cleaned dataset, even simple algorithms can generate remarkable insights. On the flipside, uncleaned data can produce misleading patterns and lead a business towards mistaken conclusions. By necessity, data analyst qualifications require proper data cleaning skills — and there are no two ways around that.
MATLAB is a programming language and multi-paradigm numerical computing environment that supports algorithm implementation, matrix manipulations, and data plotting, among other functions. Businesses interested in big data have begun turning to MATLAB because it allows analysts to drastically cut down on the time they usually spend pre-processing data and facilitates quick data cleaning, organization, and visualization. Most notably, MATLAB can execute any machine learning models built in its environment across multiple platforms.
Understanding MATLAB isn’t a required skill for data analysts per se; however, given its wide-reaching applications and usefulness, having at least a working understanding of the environment can boost your marketability to employers.
R is one of the most pervasive and well-used languages in data analytics. One poll conducted by the Institute of Electrical and Electronics Engineers’s (IEEE) professional journal, Spectrum, found that R ranked fifth in a list of the top ten programming languages used in 2019. R’s syntax and structure were created to support analytical work; it encompasses several built-in, easy-to-use data organization commands by default. The programming language also appeals to businesses because it can handle complex or large quantities of data.
Given its popularity and functionality, learning R should be high on the priority list for any aspiring data analyst.
Learning Python, though, should be the top priority for would-be analysts. This high-level, general purpose programming language landed the number one spot in IEEE’s Spectrum 2019 survey, and for a good reason — it offers a remarkable number of specialized libraries, many of which pertain specifically to artificial intelligence (AI).
Python’s applicability to AI development is particularly important. According to data published by Statista, the AI software market is on track to grow 154 percent year-over-year and achieve a projected height of $22.6 billion by the end of 2020. Understanding Python is a skill data analysts need to keep current in an increasingly AI-concerned professional landscape. Those interested in furthering their familiarity of Python should also look into its ancillary programs such as Pandas (an open-source data analysis tool that works in symbiosis with Python’s programming language) or NumPy, a package which assists Python users with scientific computing tasks.
6. SQL and NoSQL
If you want to break into data analytics, there are several database languages that you will need to be familiar with — if not fluent in — right off the bat.
The first and foremost of these is Structured Query Language, better known by its acronym, SQL. SQL might have been created in 1970, but it remains invaluable to this day. In modern analytics, SQL persists as the standard means for querying and handling data in relational databases.
This might seem counterintuitive, given that the analytics sector is in a near-constant state of growth and development. Data scientist Josh Devlin approaches this apparent lapse in an article for DataQuest, writing: “Why should someone who wants to get a job in data spend time learning this ‘ancient’ language? Why not spend all your time mastering Python/R, or focusing on ‘sexier’ data analyst skills, like Deep Learning, Scala, and Spark? While knowing the fundamentals of a more general-purpose language like Python or R is critical, ignoring SQL will make it much harder to get a job in data.”
He has a point. The truth is, SQL has a foothold in companies everywhere. Its functionality and maintained effectiveness have kept demand high among companies, and its popularity shows no sign of abating soon. Learn SQL; if not for its function, then for your job prospects. Branded versions of SQL such as MySQL offer opportunities for gaining a greater understanding of relational database management systems.
On the flipside, you also should focus on building your aptitude with NoSQL databases. As the name suggests, NoSQL systems don’t organize their data sets along SQL’s relational lines. By this definition, NoSQL frameworks can effectively structure their information in any way, provided the method isn’t relational. As such, it’s all but impossible to point to any one structure as the “standard” NoSQL framework. However, if you want to gain experience in NoSQL structures, it may be helpful to experiment with a framework like MongoDB, which organizes its database along flexible hierarchies instead of tabular relations.
7. Machine Learning
While machine learning isn’t a skill in the way data cleaning or learning a programming language might be, understanding it can help you become competitive in the data analytics hiring field.
As mentioned earlier, Statista research indicates that artificial intelligence and predictive analytics comprise significant areas of investment right now. While not all analysts will find themselves working on machine learning projects, having a general understanding of related tools and concepts may give you an edge over competitors during your job search.
8. Linear Algebra and Calculus
When it comes to data analytics, having advanced mathematical skills is non-negotiable. Some data analysts even choose to major in mathematics or statistics during their undergraduate years just to gain a better understanding of the theory that underpins real-world analytical practice!
Two specific fields of mathematical study rise to the forefront in analytics: linear algebra and calculus. Linear algebra has applications in machine and deep learning, where it supports vector, matrix, and tensor operations. Calculus is similarly used to build the objective/cost/loss functions that teach algorithms to achieve their objectives.
However, you may find that you don’t need to build a robust theoretical background before pursuing real-world applications. Some in tech actually suggest taking the opposite track. For example, in the 2019 article “Mathematics for Data Science”, Towards Data Science writer and data analyst Ibrahim Sharaf El Den advised taking a top-down approach.
“Learn how to code, learn how to use the PyData stack (Pandas, sklearn, Keras, etc..), get your hands dirty building real-world projects, use library documentation and YouTube or Medium tutorials,” he explains. “You’ll start to see the bigger picture, notice your lack of theoretical background, to actually understand how those algorithms work […] studying math will make much more sense to you!”
That said, there is no one correct way to become a data scientist. Explore and find an educational route that works for you!
9. Microsoft Excel
Stressing the importance of Microsoft Excel skills almost seems laughable when one considers the significantly more advanced technology data analysts have at their disposal. To borrow a quote from Irish business writer Anne Walsh, “Mention Excel to techies, and it’s often dismissed with a sniff.”
And it’s true — Excel is clunky in comparison to other platforms. Yet Microsoft’s workhorse spreadsheet platform is used by an estimated 750 million people worldwide. The term “Excel skills” frequently appears under the qualifications section for jobs posted on hiring services like Indeed or Monster. For all its apparent low-fi capabilities, Excel is well-used among businesses.
Moreover, Excel, well, excels is automating certain features and commands for better data analysis. Excel has its own programming language, VBA, which it uses to create macros, or pre-recorded commands. When deployed correctly, VBA can save human analysts a lot of time on frequently-performed, repetitive projects such as accounting, payroll, or project management. Microsoft also developed its Analysis ToolPak with statistical modeling and data analysis in mind. As the company’s help center explains, “You provide the data and parameters for each analysis, and the tool uses the appropriate statistical or engineering macro functions to calculate and display the results in an output table. Some tools generate charts in addition to output tables.”
If you’re interested in learning more about the technical skills you need to further your career in data analytics, check out Columbia Engineering Data Analytics Boot Camp.
Part 2: Key Soft Skills Data Analysts Need
All of the above technical skills are required for data analysts — but technical talent alone won’t carry you to a successful career. You could be a stellar data analyst on paper and still never get hired. The reason is simple: Technical capability isn’t the be-all-end-all for aspiring data analysts. There are several softer, non-industry-specific skills data analysts require to succeed. There are too many to list in this piece easily, so we’ll focus on two essential skills: critical thinking and communication.
10. Critical Thinking
It’s not enough to simply look at data; you need to understand it and expand its implications beyond the numbers alone. As a critical thinker, you can think analytically about data, identifying patterns and extracting actionable insights and information from the information you have at hand. It requires you to go above and beyond and apply yourself to thinking, as opposed to only processing.
Becoming a critical thinker can be difficult, but you can hone such skills by challenging yourself. The next time you find yourself facing an analytical task or exercise, try to think — what is the meaning behind the pattern you see? What does the data say about what has been accomplished? What shortfalls does it indicate? Don’t overlook the importance of honing your critical thinking skills when you prepare for a career in data analytics.
At the end of the day, you need to be able to explain your findings to others. It doesn’t matter if you’re the most talented, insightful data analyst on the planet — if you can’t communicate the patterns you see to those without technical expertise, you’ve fallen short.
Being a good data analyst effectively means becoming “bilingual.” You should have the capability to address highly technical points with your trained peers, as well as provide clear, high-level explanations in a way that supports — rather than confuses — business-centered decision-makers. If you can’t do so, you may still need to build your skill set as a data analyst.
Explore Your Options!
Data analytics could be the career for you, but to succeed in the field, you need to gain the requisite skills. Explore academic opportunities near you; parse through available undergraduate degrees and master’s programs. If you’re looking for a quicker and more financially-feasible solution, consider enrolling in a data analytics boot camp! These multi-week educational options offer learners an opportunity to gain a thorough background in the tech discipline of their choice at a reasonable price point.
Explore your educational opportunities and begin developing a solid foundation of data analyst skills. A world of data analytics awaits!