Saturday, March 23, 2019

List of Skills Required for a Career in Artificial Intelligence

by Editor (editor), , May 15, 2018

Following are the basic skills that are required for a successful career in AI:


A culmination of automation, robotics, and sophisticated software programs, Artificial Intelligence (AI) has become one of the most sought-after fields in the technology industry. However, there has also been much speculation about the impact of AI on future jobs. According to a report by Gartner, while AI is expected to replace 1.8 million jobs with automation by 2020, it is also expected to create 2.3 billion more jobs. Needless to say, the demand of professionals trained in AI has seen a significant increase in the past couple of years, and if the forecast is to be trusted, this demand is most likely to increase. Therefore, now is as good a time as any for people who are interested in making a career in this field.

A career in AI demands an analytical bent of mind with good problem-solving skills. AI professionals are expected to be adept at programming computers for knowledge, reasoning, perception, planning, and learning. More important than anything, a successful and long-lasting career in AI needs foresight about technological innovations to make sure that the applications being developed remain competitive.

While there are many artificial intelligence courses available on portals, the following are the basic skills that are required for a successful career in AI:

  • Machine Learning:

A core subject of artificial intelligence, machine learning is built on the idea that computers can learn from data, identify patterns, and then make decisions without the need of any explicit human programming. Competence in machine learning is therefore important for the automatic analysis of complex data models.

  • Python:

Python has emerged as an irreplaceable language in artificial intelligence, mostly because of its flexibility. Python can produce highly complex packages and modules which are capable of carrying out jobs like statistical analysis, exploratory data analysis (EDA), predictive analysis, etc., for machine learning and artificial intelligence.

  • R:

Similar to SAS (Statistical Analysis System) and SPSS (Statistical Package for Social Sciences), R is most widely used as a statistical language. Although it is comparatively less diverse and flexible as compared to Python, R is well-suited for its application in data visualisation and analysis. Since advanced AI applications occasionally utilise statistics, therefore R programming is very likely to be used while developing artificial intelligence.

  • Data science:

Data science uses scientific methods to draw conclusions from data through the discovery of patterns. For this reason, artificial intelligence needs data scientists to set algorithms that can monitor data to give meaningful insights. Data science thus helps shorten the gap between the collection of data and its interpretation into meaningful insights.

  • Hadoop:

Hadoop is a framework that is used for storage and processing of extensive data sets. It is used to work with areas of AI that need sophisticated algorithm designs, such as in fields related to neural networks, deep learning, and self-healing systems. Since these are the key areas in the advancement of AI, domain expertise of Hadoop proves lucrative for professionals who are looking for a career in artificial intelligence.

  • Big Data:

Big data analyses the large volumes of structured and unstructured data that is generated by businesses every day. AI, when clubbed with trends and patterns generated by Big Data analytics, presents augmented scopes of operation that cannot be ignored. With Big Data, Machine Learning algorithms can work on real-time data sets, instead of working on sample spaces. Natural language processing and image processing, one of the key developments in AI are made accurate through the large volume of real-time data sets regarding language and dialects.

  • Java:

The portability, transparency, and ease of maintenance that comes with Java make it an efficient choice for jobs related to search algorithms, genetic programming, and artificial neural networks that AI professionals have to usually deal with. Furthermore, developing artificial intelligence apps on Java is convenient due to its easy use and debugging, facilitated visualisation, better user interactions, and simplified work with large-scale projects. Java also makes the application of graphics and interfaces look appealing.

  • Data mining:

Data mining is the incorporation of all the strategies that are used to make meaningful sense of large volumes of data and is used in instances where relevant data patterns need to be identified. Data mining is important in generating new insights from pre-existing sets of data, and the tools help in predicting future trends. For this reason, strategies combined with machine learning, Big Data, deep learning, and machine learning are inevitable for the development of an efficient AI-powered application.

  • Spark:

Apache Spark is a data processing system that combines the use of its in-memory data processing techniques with development Application Program Interfaces (APIs) to smoothly execute streaming, machine learning, and Structured Query Language (SQL )workloads. It works extremely well with the Hadoop ecosystem, and therefore is able to run a variety of different workloads in a single, unified engine. Therefore, Spark forms an integral part of artificial intelligence as it allows programmers to deal with large datasets in a quick and efficient manner.

  • SAS

Statistical Analysis System, or SAS, is an integrated software system that allows the programmer to perform tasks related to statistical and mathematical analysis, application development; and data entry, retrieval, and management. As the boundaries of what we can achieve from AI are regularly pushed, SAS continues to provide simplified solutions by developing models with modern machine learning algorithms. WIth new programming languages coming into play, SAS makes it easy to code projects with a variant of languages, including Python, R, Java, and Lua.

AI professionals also need to learn how to translate sophisticated technical information in a language others can understand efficiently. Since the development of an AI project demands brainstorming, there is a need of good communication skills that allow them the ability to work with people in a single team while effectively putting across their ideas.

About the Writer

Editor is an editor for BrooWaha. For more information, visit the writer's website.
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