4 Data Science Skills to Help Devs Thrive in the Age of AI

In the ever-evolving tech industry landscape, disruption and opportunity go hand in hand. Recently, there have been many questions about the implications of the rapid adoption of Machine Learning (ML) and Artificial Intelligence (AI) in terms of the future of various fields and skill sets, including data science.

While some people fear this type of change, we believe it is important to update your skills to stay relevant in the industry. In today’s tech world, data literacy has become a crucial asset for anyone working in the field, presenting many possibilities. We strongly believe that data scientists and developers can leverage data science skills and advance their careers.

Considering the changes in the tech industry, we believe that AI will also transform the field of data sciences. It will remove many barriers and make it much easier for developers to learn and enter the field of data science. In particular, AI can cause disruptions in data sciences by making tasks easier. AI will likely not do the jobs of developers and data scientists, but it will surely make things easier for them.

In this blog, we’ll discuss a directional shift in the skills that companies are hiring for and beginning to prioritize now. Data science has become a crucial skill set, and if you’re interested in learning it, we’ll be sharing a few reliable resources to ramp up your skill set.

How Are Data Science and AI Changing Hiring Practices in the Tech Industry?#

One significant change we’ve seen on the hiring side is that companies are focusing and directing their resources toward developers with profound data science skills. This indicates that data science is not going out of trend anytime soon.

Technologies that allow developers to leverage ML and AI more effectively are rapidly emerging. Similarly, the demand for developers who can incorporate these technologies into traditional roles is also rising. In short, developers with data science skill sets are getting an advantage in the marketplace.

Key Data Skills That Will Help You Stay Ahead #

It’s important to note that you don’t necessarily need to switch careers to benefit from learning data science. Even acquiring proficiency in just one or two of the skills mentioned below can grab the attention of recruiters and hiring managers. One of the most promising attributes of a candidate is their ability and passion to learn new technologies.

Why? Well, successful companies prioritize the future. Innovation isn’t solely driven for innovation’s sake — it’s also a means of survival. Companies consistently seek to attract talent that can help them move ahead of the competition. By showcasing a forward-thinking mindset, you communicate to employers that you have the potential to bring value in the future, surpassing the contributions you can make at present.

Now, let’s dive into the skills that will provide the greatest leverage for your time and efforts.

Python and Its Libraries#

The lingua franca of the data science world, Python, is one of the most in-demand programming languages. With powerful libraries and flexibility, Python is a must-have skill for data scientists and IT professionals.

One of the main reasons why Python is at the top is because it’s simple and easy to learn. And that is why it’s also popular among both senior and junior developers. Python is an incredibly versatile language that excels at efficiently handling data science tasks, making it an ideal primary option for this list. Whether you’re a beginner data scientist or a seasoned IT professional, learning Python will benefit you in the best possible ways.

Let’s learn about some of the important Python libraries:

• NumPy: NumPy is a powerful Python library for numerical computing and array manipulation.

• Pandas: Pandas are used for data manipulation and analysis, providing powerful data structures and data analysis tools.

• Matplotlib: Matplotlib is a comprehensive Python library for creating static, animated, and interactive visualizations.

• Scikit-Learn: Scikit-Learn library is used for machine learning, offering a wide range of tools and algorithms for data analysis and predictive modeling.

• TensorFlow: TensorFlow is an open-source library for machine learning and deep neural networks, known for its flexibility and scalability.

• PyTorch: PyTorch is a widely used open-source library for machine learning that emphasizes dynamic neural networks and efficient GPU acceleration.

• SciPy: SciPy is a scientific computing library that provides a wide range of mathematical algorithms and functions.

Machine Learning Principles and Algorithms#

Machine learning principles and algorithms can enhance your problem-solving skills and improve existing solutions. Moreover, it’s becoming more common for developers to have a basic understanding of machine learning concepts and algorithms.

There are many problems in development — like recommendation systems, image and video processing, and predictive modeling — which are basically machine learning problems. When you understand and have knowledge of machine learning algorithms, it will be easier for you to solve such problems and find effective solutions for them. Most importantly, if you want to work in FAANG companies like Meta, Amazon, or Google, learning the basics of Machine Learning will give you an edge. Remember how often you’re recommended a product or service based on past behaviors!

Here are some algorithms used in ML that the average developer is aware of (you may have come across them in computer science courses):

• Linear and Logistic Regression

• Decision Trees

• K-Nearest Neighbors (KNN)

If you want to dig deeper, these algorithms are more specialized in data science and machine learning:

• Random Forests

• Support Vector Machines (SVMs)

• Naive Bayes

• K-Means Clustering

• Principal Component Analysis (PCA)

• Neural Networks/Deep Learning

SQL and Database Management#

Learning SQL and relational databases remain crucial in the era of AI, especially if they are working in data-intensive fields. SQL proficiency empowers individuals to work with diverse data sources, address real-world business challenges, and bridge the gap between traditional systems and AI-powered applications.

Whether you’re working with databases like MYSQL, PostgreSQL, or MongoDB, it’s important to understand how to manipulate and manage data. SQL and database management are crucial skills for a developer because they enhance your ability to work with data.

Statistical Analysis#

Data science is about making sense of data, and statistical analysis is where this happens. While AI and automation are taking the weight off statistical analysis, humans still have so much to do. Critical thinking, judgment, and interpretation are tasks still reserved for humans.

AI can help you answer “what” and “how,” but the “why” and “so” parts still remain for skilled people. So, statistical analysis is yet another important skill in the AI age. We have mentioned some fundamental statistical analysis skills for developers looking to specialize in data science. Have a look below.

• Descriptive statistics: Learn to summarize data and describe using measures like mean, median, variance, and standard deviation.

• Inferential Statistics: Learn about probability distributions to understand uncertainty.

• Probability Theory: Gain knowledge about binomial distribution, Poisson distribution, and normal distribution.

• Time Series Analysis: Gain expertise in analyzing and identifying seasonality, trends, and forecasting values using different methods like autoregression and moving averages.

• Data Cleaning and Preprocessing: Learn how to handle missing data, outliers, and data imputation techniques to prepare data for statistical analysis.

How Will Artificial Intelligence Change the Role of a Data Scientist?#

AI is becoming a powerful tool, and it will revolutionize the role of data scientists in various ways. The actual function of a data scientist is to bridge the gap between data and human understanding. And with the advent of AI, we can see time-consuming tasks being streamlined efficiently. 

Data scientists must also keep up with their skills as AI evolves in the tech industry. While they will still require a solid foundation in statistics, mathematics, and programming, they will increasingly incorporate expertise in AI and ML techniques. Data scientists must stay updated with the latest AI algorithms, methodologies, and best practices to effectively leverage AI in their projects.

As we wrap up, we all can appreciate the importance of data science no matter what evolution is happening in the tech world. And it is becoming increasingly crucial to have data science skills, be it through mastering Python or creating visualizations. Educative offers various interactive data science courses and paths. If you’re a beginner, check out Grokking Data Science or Data Science for Non-Programmers. You can explore further and check out more data science-related courses exclusively at Educative.

Happy Learning, Folks!