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.
1. R Programming Language
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.
2. Apache Spark
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.
3. Python (SciKit Learn)
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.
4. Distributed architecture
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.
5. Apache Hadoop
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.
6. NoSQL
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!
7. K-means Clustering Algorithm (Python)
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. That means it will try to minimize the sum of distances (within groups) that points in each group are from their respective centroid.
8. Principal Component Analysis (Python)
This algorithm is used for reducing the dimensionality of data by identifying and removing redundant features, which can drastically help simplify different types of problems when working with large datasets. It’s also great at making unlabeled data more manageable since PCA facilitates feature extraction, which allows you to work with fewer dimensions while still maintaining most of the information contained within them. This lets you avoid needing to deal with tons of individual cases where every single one would need its own unique representation, instead just a few representative components are enough for most purposes. As is the case with most machine learning algorithms, there are tons of libraries that you can use to implement PCA into your codebase.
9. Dimensionality Reduction (Python)
Dimensionality reduction is a form of data processing that allows you to transform the original features of an object into a smaller number of new variables called principal components. This works best when handling large datasets with redundant information – it helps reduce the noise and preserve the signal so you’ll have higher-quality results overall. Principal components are derived from linear combinations of the original attributes so it’s good for both statistical modeling as well as visualization purposes. Dimensionality reduction is often used in conjunction with other algorithms like K-Means clustering or PCA to clean up datasets before they’re used for training purposes.
10. Bootstrapping and Resampling Methods (Python)
Bootstrapping is a type of resampling where data is sampled to test the accuracy or efficacy of an algorithm. This helps determine how well the model will perform when trained on unseen data in addition to evaluating how much variance there is between different samples. There are several types of bootstrap methods you can use for these purposes such as sampling with replacement, random sampling without replacement, and sample augmentation which can be done by adding noise into datasets that don’t have any. Another form of resampling that’s often used in conjunction with bootstrapping algorithms (like bagging and boosting is called cross-validation. This helps reduce the bias that can result from using just one dataset for training purposes.
Conclusion:
Not only are these 10 algorithms essential for your data science toolbox, but they’re also highly sought-after by recruiters looking to hire new talent. If you want to remain competitive in the job market it’s imperative that you get familiar with them ASAP! Many of them are implemented through open source libraries so be sure to check out what’s available for whichever language you use including R and Python. Once you get started practicing programming challenges will become a lot easier so don’t give up on mastering these important skills!
