5 Ways Learning Data Science, AI can help You Succeed in Your Career

Learn about data science and AI to become successful in professional careers in 2022

An array of new-age technologies is supporting three vital business needs: automation of processes, gaining insights through data analysis, and engaging with customers effectively. Operating and sustaining AI tools and software requires a specific set of skills, knowledge, and attributes that are lacking among graduates. The gap between demand and supply in the fields of data and AI is an opportunity for freshers and professionals to upskill and make a successful career.

Here are 5 ways learning Data Science, AI can help you succeed in your career

Growing demand for AI and data science talent across industries

AI and data science technologies are being adopted by almost every sector for better outcomes. Despite their size, all businesses are looking at leveraging data to drive efficiencies. Therefore, they are continually looking to hire people who can collect, read, and analyze data to enhance business outcomes. These job opportunities will continue to surge beyond 2021.

Supply does not match demand

Businesses across sectors are seeking to acquire AI and data science talent, even during the ongoing economic crunch. Since the talent pool is too small for the soaring demand, skilled professionals have been highly sought after. To bridge the skill gap, larger companies are also offering upskilling in AI and data science, and free educational resources to their employees.

Push your comfort zone

Looking at others' codes may feel like you are cheating. It's okay to look at others' codes on Kaggle. You won't understand all the code at first and that's completely okay and normal too. If you are indeed comfortable with all the code in a notebook, you are not really learning anything new from that notebook. Push your comfort zone.

Focus on the basics

Start learning the basic machine learning algorithms. Soon you will figure out its beautiful, elegant, and exciting applications. Oftentimes, you won't necessarily need to be able to write down the Maths or the formulae of how a specific algorithm or model works. But knowing how the model works, and the reasoning behind it will be enough for now, unless you want to opt into ML research specifically of course.

Take your skills to the next level

You are proficient in Python, R, and SQL. You have developed intuition to analyze almost any data. And you know how to apply ML models. Now is the time to take your skills to the next level. The last most crucial thing you will need to learn is the art of putting together the data pipeline, integrating with cloud services like AWS, Azure, IBM Cloud, Hadoop, Spark, to name a few, and pushing it into production. Again, there are loads of resources online. You just have to look them up.