LaMana Donadelle

There are 3 main reasons Business Analytics is an enticing career choice.

The first is that there is a high demand for business analysts, and the relatively low supply of skilled workers means that salaries are higher in this field.

Another reason is for the challenge of solving interesting problems. Analysts are typically people who are interested in solving complex puzzles.

Finally, those who are curious about how things work can use their skills in analyzing data to uncover previously unknown truths. This curiosity is one of the most important traits of a successful analyst, as it provides more motivation to solve the challenging problems.

A business analyst can take many roles depending on the data and type of project.

The most common roles are that of an interpreter, in which the analyst uses Descriptive Analytics to tell the story of what happened; an oracle, in which the analyst uses Predictive Analytics to predict future events; and a consul, in which the analyst uses Prescriptive Analytics to provide advice on the best course of action.

What Makes a Business Analyst Successful?

An analyst becomes successful due to a combination of “hard” and “soft” skills. The hard skills are more tangible, and refer to what the analyst can do and with what tools, while the soft skills are less flashy on a resume, but equally or even more important than the hard skills.

Examples of the important hard skills are an aptitude in math or statistics, experience coding and using analytical software, and knowledge of the subject matter.

The most desirable soft skills are determination, curiosity, interest, creativity, judgment, and skepticism, and communication.

Machine learning engineers are at the intersection of software engineering and data science. They use big data tools and programming frameworks to create production-ready and scalable data science models that can handle terabytes of real-time data.

Machine learning engineer jobs are best for anyone with a background that combines data science, applied research, and software engineering. You can thrive if you have strong mathematical skills, experience in machine learning, deep learning, neural networks, and cloud applications, and programming skills in Java, Python, and Scala. It also helps to be well-versed in an integrated development environment (IDE), like IBM Watson Studio.

Source: 10 Awesome & High-Paying AI Careers to Pursue in 2022(opens in a new tab), Springboard, by Sakshi Gupta, 2022

Data scientists use machine learning and predictive analytics to gain insights from large amounts of data. To prepare, you should build your expertise in big data platforms and tools, perhaps including Hadoop, Pig, Hive, Spark, and MapReduce. It would be helpful if you are fluent in at least two programming languages, including structured query language (SQL), Python, Scala, and Perl. You should also invest some time learning descriptive and inferential statistics.

This is a field in which most people have earned a master’s or doctoral degree. You would also benefit from non-technical workplace skills, like communication, collaboration, intellectual curiosity, and business acumen.

Source: Top 5 Jobs In AI and Key Skills Needed To Help You Land One(opens in a new tab), SimpliLearn, by Eshna Verma, 2022

These professionals make sure AI systems are used responsibly. They review models for fairness, check compliance with regulations, and create guidelines to minimize bias and protect privacy.

AI Product Managers plan and oversee AI-driven features from start to finish. They set priorities, coordinate with engineers and designers, and make sure the result delivers real value to users. It’s part strategy, part execution, and part communication.