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TOP-5 Most In-Demand AI Skills in 2023

TOP-5 Most In-Demand AI Skills in 2023

AI is the future. One does not need to be an oracle like Nostradamus to predict that. AI has not only influenced the technology sector but the corporate world as well. Professionals willing to switch over to AI and its sibling sectors need to have a number of specific skills.

The following are some of them that are going to rule 2023. But before going into the exact skillset, one also needs to have some mathematical background. Let’s start with it then.

Sound mathematical background
Good programming skill
Distributed computing
Shell scripting
Signal processing
Most Popular AI Jobs

1. Sound mathematical background

An AI professional needs to have mastery of several applied mathematical streams. So it’s always a good idea to ramp up your core mathematical skills if needed. The following are some of the mathematical disciplines in which you need to have a firm grip.

Linear algebra: Linear algebra, and its big brother abstract algebra, is the basis of most of AI and ML. Linear algebra forms the mathematical background of many fields related to AI such as computer vision and machine learning. They extensively use vectors, matrices, and tensors, and Linear algebra, and abstract algebra acts as the mathematical backbone of these structures. If you want to perform well in the long run, you better start working on your linear algebra skills.
Statistics: One thing that you possibly can NOT ignore if you want to become proficient at machine learning (or any kind of pattern recognition, in general) it’s got to be statistics. Statistics is defined as the branch of mathematics concerned with the collection, analysis, and interpretation of data. Although data science is in itself a big subject and encompasses many other disciplines, you need to have at least some statistical understanding.
Probability theory: AI without probability is like peanut butter without peanut. The comparison was lame, but the point is that probability is the core of any kind of data analysis and AI. Good understanding of the basics of probability and probability distribution can give you a solid kickstart. Be it hardcore probability topics like various generative and discriminative models or Support Vector Machine, you are going to need probability sooner or later.
Graph theory: Graph theory is one of those sectors that at first glance might not look all that important, but would become important upon closer investigation. Professionals having a Computer Science background might be at some advantage here as graph theory is considered to be an integral part of CS. But professionals coming from other streams are almost always caught off-guard. A large part of the traditional AI (for example, pathfinding algorithms like A* or graph search algorithms like MCTS) involves various graph-theoretic elements. You would see applications of graph theory in very applied fields like Computer Vision.
Convex optimization (or optimization techniques as a whole, if you are picky): Data analysis and machine learning are all about optimization. Optimization techniques in itself is a huge topic, but having a decent understanding of convex optimization goes a long way in giving you a headstart in ML over others.

2. Good programming skill

As an AI professional you need to be well-versed with programming; you at least need to have a minimum amount of familiarity with some languages like Python, R, Scala, Matlab, Java, and C++. Each of the languages has its own advantages and is employed in very specific domains.

Python: Python is known to be syntactically much simpler than languages like C++ or Java and as a result, is utilized for fast prototyping. Technically, it’s a high-level, general-purpose, interpreted language. The philosophy of Python is to make the code readable while keeping the code as small as possible. Additionally, some of the best and most well-known pattern recognition and computer vision libraries are also available which is a huge bonus.
C++: C++ is primarily used for boosting the execution times. Due to being a compiled language (compared to being an interpreted language like Python) and the presence of primitive data types, it’s much faster than Python; at the order of 10-100 times in some cases. As opposed to languages like Python or R, C++ always had a bias towards system programming and embedded devices where you face a lot of resource restrictions. Despite being a pretty old language, C++ is still heavily used in desktop application development, graphics-related tasks, and web-search and SQL servers.
Java: Java has seen a tremendous amount of interest in recent years thanks to data analysis tools like Spark, Flink, Hive, Spark, and Hadoop. Officially released in 1995, Java has seen both good and bad times till now. It became nearly a dead language until it was officially included in the Android operating system. As they are natively written in Java, it automatically becomes the first choice. Moreover, it supports predictive and analysis tools like Weka, Java-ML, MLlib, and Deeplearning4j which is another big catalyst.
R: R is a dynamically-typed, scripting language. R is relatively less known compared to Python, although their expressiveness is nearly the same. R is heavily used for any kind of statistical task. Many statistical and graphical tools (e.g Dplyr, BioConductor, Knitr, Ggplot2, etc.) are available in R that is used by the analytics community. It’s widely used in analytics related to email communication, social media, and Bioscience.

R supports many advanced features. Programmers can even manipulate R objects using other languages like C, C++, or Python. One can even link and call C, C++, or Fortran code at runtime.

3. Distributed computing

Datasets are the heart of any kind of data analysis and predictive analysis. And good dataset needs a huge amount of computational resources. So much in fact that a single machine in many cases can’t handle the task. That’s where distributed computing and Big Data analysis comes into play. Many of the AI and ML tasks these days involve using the aforementioned frameworks like Hadoop, Spark, Flink, and Apache Storm. AI engineers looking to enhance their skillset are always encouraged to upskill their big data analysis skills.

4. Shell scripting

Shell scripting (bash scripting, as many likes to call it) is another integral part of any AI or pattern recognition task. A shell script is a program designed to be run by the Unix shell. Tons of other shells are out there such as KornShell (ksh), Almquist shell (ash), Powershell (msh), Z shell (zsh), Tenex C Shell (tcsh), Perl-like shell (psh), etc. But at the end of the day, bash still remains head and shoulder ahead of the rest of its competitors.

As Linux-based machines are used in any kind of professional or corporate setup, good knowledge of shell scripting is a must-have. Familiarity with commands like awk, grep, ssh, etc. goes along with securing a good position.

5. Signal processing

If you’re into machine learning or computer vision, you would be having a hard time moving forward without the proper knowledge of signal processing.

Signal processing is a field that focuses on analyzing, modifying, and synthesizing any kind of signal (e.g. audio, radio, or even image). Techniques like wavelets and contourlets are pretty common for feature extraction in machine learning. Fourier analysis and convolution are two of the most common and heavily used techniques in Computer Vision.

Most Popular AI Jobs

As you keep upskilling yourself and continue gathering new knowledge, a new window of opportunity keeps opening. The following are some of the most popular roles.

1. Data scientist: The primary aim of data scientists is to extract useful and valuable information from large-scale data using various statistical and machine-learning tools. They need to be proficient at using data analysis tools like Spark, Pig, Hive, query languages like SQL, scripting, and programming languages like Python, Scala, and Perl.

In a business environment, data scientists are responsible for extracting valuable information for predicting customer behavior and identifying revenue opportunities. But that is just one aspect of the responsibility of the data scientists. A data scientist’s responsibility often includes setting best practices for data mining, data interpretation, and usage of analysis tools.

2. Machine Learning engineer: ML engineers are responsible mostly for applying predictive models to large datasets. They need to be experienced in the software development pipeline and well-versed in using ML and statistical tools.

A typical machine learning engineer is responsible for many different tasks. Some of these responsibilities include performing various statistical analyses on the data, exploration, and visualization of data to understand the insights, analyzing and understanding the errors and bias in the used models, management of the data pipeline, and data engineering (making sure that good data flow is always maintained between the database and backend) among others.

3. Business Intelligence Developer: Business intelligence developers’ job is much more business-centric and their primary aim is to extract business and market trend information from the data. They are thus responsible for increasing the profitability and efficiency of an organization by modeling and maintaining complex data for cloud platforms.

Simply put, a business intelligence developer is both an engineer and a developer. They are often in charge of the development, deployment, and maintenance of the business intelligence interfaces. Their responsibility set includes data visualization, ad hoc reporting, setting requirements for intelligence tools, participation in warehouse designing, and many others. The professionals often use a host of query modeling tools to extract insights from the data.

AI is a huge subject that needs a fair amount of time to master. It is a conglomeration of several related fields including Machine Learning, Computer Vision, Natural Language Processing, Robotics, and many others. At the end of the day, you need to be patient. Patience and perseverance are the keys to making yourself successful.