Special skills needed for data analyst will be listed and also discussed in this article for you to addictively learn from, while building up your career as a skilled, and not an ordinary data analyst.
Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.
With this rapid expansion comes a significant opportunity to develop your skills in data analytics, for example by enrolling in a data analytics boot camp geared towards those seeking to get into the field.
Digital transformation has become the buzzword of modern business, and talented data analysts are needed now more than ever before. Career openings beckon from nearly every industry, from telecommunications to manufacturing, retail, banking, healthcare, and even fitness.
Skills Needed for Data Analyst
According to a recent report from the International Data Corporation (IDC), the global Big Data and business analytics market has been expanding at a fast clip over the last several years, leaping from $122 billion in global revenue in 2015 to $189 billion in 2019 and driving towards a projected $274 billion for 2022.
Hence, you have a need to brew yourself with skills needed for data analyst. Here are the skills:
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.
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’ (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.
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.
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.
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.
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.
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.
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.
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.
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.
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