Hindsight is always 20/20, but someone needs to be looking into the future before it gets here. That’s the role of a data scientist, and in order to do that, they need a ton of skills at their disposal. Here are 10 in-demand skills in the world of data science that will help you get a good head start.
This programming language is used for statistical computing and graphics which makes it perfect for the field of data analysis. R boasts great support from its community via forums and even mailing lists so if you ever have any questions about this programming language there’s always someone willing to answer them.
Apache Spark has been created with expressiveness in mind, letting programmers less code while having the ability to solve more complex problems. It boasts the best performance in memory-intensive applications while allowing parallelism on clusters with ease.
Python is the programming language used for SciKit Learn; it offers an extensive range of algorithms to choose from when mining datasets making it one of the most sought-after skills in this field. With open-source libraries like pandas, matplotlib, and NLTK, engineers can get their work done quickly and efficiently without needing to reinvent the wheel time and time again. Python also has great support via forums and mailing lists which is always a bonus if you’re new to coding or struggling with something more complex than running ‘git clone.
Distributed architecture is the process of splitting work across multiple servers, each with its own processors. Data scientists need to be able to take advantage of these types of architectures whenever possible. It’s important for engineers to have some knowledge of how distributed systems run in order to get ahead of the curve when it comes to this field.
Apache Hadoop allows data scientists and engineers alike to look at large datasets that wouldn’t otherwise fit into memory on a single machine, and then process them in parallel over a network using simple programming models. Web giants Google, Facebook, Yahoo! are already utilizing the power of Apache Hadoop so it will only continue growing even more in popularity as time goes by.
The need for scalable, distributed databases that allow engineers to quickly store and retrieve data at a relatively low cost has led to the creation of No SQL technologies. Companies like Google have been utilizing this technology for years which is why it’s becoming more popular as time goes by. It’s an essential skill to learn in order to keep up with other data scientists or other engineers within your organization so don’t be left behind!
K-means clustering analysis is used for segmenting a dataset into groups based on the features you assign it. This type of algorithm assigns each point in a set into its respective group through iterative refinement.
