Top 10 Data Analyst Skills You Need to Get Hired in 2022
considered a job in data analysis, now is the time to take the leap. The Bureau of Labor Statistics projects that, between now and 2028, there will be a 20% ascend in the number of available data if you’ve ever analyst jobs.
But what does it take to fill one of these coveted roles? To do their work, data analysts have to have a diverse adeptness set. This includes a vigorous substructure in fundamental mathematics, data analysis techniques, and some soft skills.
In this post, we’ll visually examine the key skills that you’ll need to land your first job as a data analyst, and how to keep progressing in your vocation.
What Is a Data Analyst?
Data analysts comb through the immensely colossal troves of data to which companies have access, and look for patterns and trends. For example, data analysts might utilize the available data to endeavor and determine which SKUs are most popular for a categorical customer segment, or what kinds of discounts work most proficiently adept at different times of the year. Becoming a data analyst requires business astuteness and the faculty to take a holistic approach. Not only does the job demand sharp technical skills. It additionally requires that you have soft skills, a construal of statistical analysis, and vigorous data visualization skills.
Part 1: Technical Skills Required for Data Analysts
1. Data Visualization
As the term suggests, data visualization is a person’s faculty to present data findings via graphics or other illustrations. The purport 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 avail a business’s decision-makers (who may lack advanced analytical training) to identify patterns and understand intricate conceptions at a glance. This capability potentiates you — the data analyst — to gain a better understanding of a company’s situation, convey serviceable insights to team bellwethers, and even shape company decision-making for the preponderant.
Data visualization may even sanction you to accomplish more than data analysts traditionally have. As one inditer for SAS Insights notes, “Data visualization is going to transmute the way our analysts work with data. They’re going to be expected to respond to issues more expeditiously. And they’ll need to be able to dig for more insights — visually examine data differently, more imaginatively. Data visualization will promote ingenious data exploration.”
Already, data visualization has become a compulsory adeptness. According to a recent study conducted by LinkedIn Learning, “recent graduates are much more liable 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 adeptness reported by participants? You conjectured it: data visualization.
2. Data Cleaning
As any Marie Kondo aficionado will tell you, cleaning is an invaluable part of achieving prosperity — 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 paramount chunk of any data analyst’s day.
“Although we often celebrate data scientists as spending most of their time tinkering with ML algorithms and models, the authenticity is remotely different,” tech inditer Ajay Sarangam notes for Analytics Training. “Most data scientists spend around 80 percent of their time cleaning data. For what purport? Because of simple veracity in ML: Better data beats fancier algorithms.”
With a congruously cleaned dataset, even simple algorithms can engender remarkable insights. On the flip side, uncleaned data can engender illuding patterns and lead a business towards misconstrue conclusions. Perforce, data analyst qualifications require felicitous data cleaning skills — and there are no two ways around that.
MATLAB is a programming language and multi-paradigm numerical computing environment that fortifies algorithm implementation, matrix manipulations, and data plotting, among other functions. Businesses intrigued with sizably voluminous data have commenced turning to MATLAB because it sanctions analysts to drastically cut down on the time they conventionally spend pre-processing data and facilitates expeditious data cleaning, organization, and visualization. Most eminently, MATLAB can execute any machine learning model built in its environment across multiple platforms.
Understanding MATLAB isn’t a required adeptness 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 (IEEE) professional journal, Spectrum, found that R ranked fifth in a list of the top ten programming languages utilized in 2019. R’s syntax and structure were engendered to fortify analytical work; it encompasses several built-in, facile-to-use data organization commands by default. The programming language additionally appeals to businesses because it can handle intricate or sizably voluminous 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 purport programming language landed the number one spot in IEEE’s Spectrum 2019 survey, and for a substantial reason — it offers a remarkable number of specialized libraries, many of which pertain concretely to artificial perspicacity (AI).
Python’s applicability to AI development is categorically paramount. 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 terminus of 2020. Understanding Python is an adeptness data analysts need to keep current in an increasingly AI-concerned professional landscape. Those intrigued with furthering their familiarity with Python should additionally look into its ancillary programs such as Pandas (an open-source data analysis implement that works in symbiosis with Python’s programming language) or NumPy, a package that avails Python users with scientific computing tasks.
6. SQL and NoSQL
If you opt to break into data analytics, there are several database languages that you will require to be acclimated with — if not fluent in — right off the bat.
The first and foremost of these is Structured Query Language, better kenned by its acronym, SQL. SQL might have been engendered in 1970, but it remains invaluable to this day. In modern analytics, SQL persists as the standard designation for querying and handling data in relational databases.
This might seem counterintuitive, given that the analytics sector is in a near-constant state of magnification and development. Data scientist Josh Devlin approaches this ostensible lapse in an article for DataQuest, inditing: “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 fixating on ‘sexier’ data analyst skills, like Deep Learning, Scala, and Spark? While kenning the fundamentals of a more general-purport 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 veracity is, that SQL has a foothold in companies everywhere. Its functionality and maintained efficacy have kept demand high among companies, and its popularity shows no designation of abating anon. Learn SQL; if not for its function, then for your job prospects. Branded versions of SQL such as MySQL offer opportunities for gaining a more preponderant understanding of relational database management systems.
On the flip side, you withal should fixate on building your aptitude with NoSQL databases. As the denomination suggests, NoSQL systems don’t organize their data sets along SQL’s relational lines. By this definition, NoSQL frameworks can efficaciously structure their information in any way, provided the method isn’t relational. As such, it’s all but infeasible to point to any one structure as the “standard” NoSQL framework. However, if you optate to gain experience in NoSQL structures, it may be auxiliary to experiment with a framework like MongoDB, which organizes its database along flexible hierarchies in lieu of tabular cognitions.
7. Machine Learning
While machine learning isn’t an adeptness in the way data cleaning or learning a programming language might be, understanding it can avail you to become competitive in the data analytics hiring field.
As mentioned earlier, Statista research designates that artificial perspicacity and predictive analytics comprise paramount areas of investment right now. While not all analysts will find themselves working on machine learning projects, having a general understanding of cognate implements 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 opt to major in mathematics or statistics during their undergraduate years just to gain a better understanding of the theory that underpins genuine-world analytical practice!
Two categorical fields of mathematical study ascend to the forefront in analytics: linear algebra and calculus. Linear algebra has applications in machine and deep learning, where it fortifies vector, matrix, and tensor operations. Calculus is similarly used to build the objective/cost/loss functions that edify algorithms to achieve their objectives.
However, you may find that you don’t need to build a robust theoretical background afore pursuing genuine-world applications. Some in tech genuinely suggest taking the antithesis track. For example, in the 2019 article “Mathematics for Data Science”, Towards Data Science inditer and data analyst, Ibrahim Sharaf El Den exhorted taking a top-down approach.
“Learn how to code, learn how to utilize the PyData stack (Pandas, sklearn, Keras, etc..), get your hand's dirty building genuine-world projects, utilize library documentation and YouTube or Medium tutorials,” he explicates. “You’ll start to visually perceive the more sizably voluminous picture, notice your lack of theoretical background, to genuinely understand how those algorithms work […] studying math will make much more sense to you!”
That verbally expressed, there is no one redress way to become a data scientist. Explore and find an inculcative route that works for you!
Part 2: Key Soft Skills Data Analysts Need
All of the above technical skills are required for data analysts — but technical aptitude alone won’t carry you to a prosperous vocation. 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-categorical skills data analysts require to prosper. There is an extravagant quantity to list in this piece facilely, so we’ll fixate on two essential skills: critical celebrating and communication.
9. Critical Cerebrating
It’s not enough to simply visually examine data; you require to understand it and expand its implicative insinuations beyond the numbers alone. As a critical ruminator, you can celebrate 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 mentally conceiving, as opposed to only processing.
Becoming a critical cogitator can be arduous, but you can hone such skills by challenging yourself. The next time you find yourself facing an analytical task or exercise, endeavor to celebrate — what is the denotement about the pattern you visually perceive? What does the data verbalize about what has been accomplished? What shortfalls does it designate? Don’t overlook the paramountcy of honing your critical cerebrating skills when you prepare for a vocation in data analytics.
At the cessation of the day, you require to be able to expound your findings to others. It doesn’t matter if you’re the most aptitudinal, insightful data analyst on the planet — if you can’t communicate the patterns you optically discern to those without technical expertise, you’ve fallen short.
Being a good data analyst efficaciously designates becoming “bilingual.” You should have the capability to address highly technical points with your trained peers, as well as provide clear, high-level explications in a way that fortifies — rather than discombobulates — business-centered decision-makers. If you can’t do so, you may still need to build your adeptness set as a data analyst.