 Top 5 most in-demand AI skills in 2020. Sound mathematical background An AI professional needs to have mastery in 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 the AI and ML. Linear Algebra forms the mathematical background of many fields related to AI such as computer vision and machine learning. 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. 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 the probability distribution can give you a solid kick start. 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. Convex Optimization 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 head start in ML over others. 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 familiarities 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. 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 to 100 times in some cases. 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. 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 are available in R that is used by the analytics community. Distributed computing Datasets are the heart of any kind of data analysis and predictive analysis. And good data set 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. 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, but at the end of the day, bash still remains head and shoulder ahead of the rest of its competitors. 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. Most popular AI jobs Data scientist The primary aim of the data scientists is to extract useful and valuable information from large-scale data using various statistical and machine learning tools. Machine learning engineer ML engineers are responsible mostly for applying predictive models to large data sets. Business intelligence developer Business intelligence developer's job is much more business-centric and their primary aim is to extract business and market trend information from the data. 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.