The fields of data science and artificial intelligence see constant growth. As more companies and industries find value in automation, analytics, and insight discovery, there comes a need for the development of new tools, frameworks, and libraries to meet increased demand. There are some tools that seem to be popular year after year, but some newer tools emerge and quickly become a necessity for any practicing data scientist. As such, here are ten trending data science tools that you should have in your repertoire in 2021.
PyTorch can be used for a variety of functions from building neural networks to decision trees due to the variety of extensible libraries including Scikit-Learn, making it easy to get on board. Importantly, the platform has gained substantial popularity and established community support that can be integral in solving usage problems. A key feature of Pytorch is its use of dynamic computational graphs, which state the order of computations defined by the model structure in a neural network for example.
Scikit-learn has been around for quite a while and is widely used by in-house data science teams. Thus it’s not surprising that it’s a platform for not only training and testing NLP models but also NLP and NLU workflows. In addition to working well with many of the libraries already mentioned such as NLTK, and other data science tools, it has its own extensive library of models. Many NLP and NLU projects involve classic workflows of feature extraction, training, testing, model fit, and evaluation, meaning scikit-learn’s pipeline module fits this purpose well.
Gradient boosting is a powerful machine-learning technique that achieves state-of-the-art results in a variety of practical tasks. For a number of years, it has remained the primary method for learning problems with heterogeneous features, noisy data, and complex dependencies: web search, recommendation systems, weather forecasting, and many others. CatBoost is a popular open-source gradient boosting library with a whole set of advantages, such as being able to incorporate categorical features in your data (like music genre or city) with no additional preprocessing.
AutoML automatically finds well-performing machine learning pipelines which allow data scientists to focus their efforts on other tasks, reducing the barrier to broadly apply machine learning and makes it available for everyone. Auto-Sklearn frees a machine learning user from algorithm selection and hyperparameter tuning, allowing them to use other data science tools. It leverages recent advantages in Bayesian optimization, meta-learning, and ensemble construction.
As data becomes increasingly interconnected and systems increasingly sophisticated, it’s essential to make use of the rich and evolving relationships within our data. Graphs are uniquely suited to this task because they are, very simply, a mathematical representation of a network. Neo4j is a native graph database platform, built from the ground up to leverage not only data but also data relationships.
This Google-developed framework excels where many other libraries don’t, such as with its scalable nature designed for production deployment. Tensorflow is often used for solving deep learning problems and for training and evaluating processes up to the model deployment. Apart from machine learning purposes, TensorFlow can be also used for building simulations, based on partial derivative equations. That’s why it is considered to be an all-purpose and one of the more popular data science tools for machine learning engineers.
Apache Airflow is a data science tool created by the Apache community to programmatically author, schedule, and monitor workflows. The biggest advantage of Airflow is the fact that it does not limit the scope of pipelines. Airflow can be used for building machine learning models, transferring data, or managing the infrastructure. The most important thing about Airflow is the fact that it is an “orchestrator.” Airflow does not process data on its own, Airflow only tells others what has to be done and when.
Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications. It groups containers that make up an application into logical units for easy management and discovery. Originally developed by Google, Kubernetes progressively rolls out changes to your application or its configuration, while monitoring application health to ensure it doesn’t kill all your instances at the same time.
Pandas is a popular data analysis library built on top of the Python programming language, and getting started with Pandas is an easy task. It assists with common manipulations for data cleaning, joining, sorting, filtering, deduping, and more. First released in 2009, pandas now sits as the epicenter of Python’s vast data science ecosystem and is an essential tool in the modern data analyst’s toolbox.
Generative Pre-trained Transformer 3 (GPT-3) is a language model that uses deep learning to produce human-like text. GPT-3 is the most recent language model coming from the OpenAI research lab team. They announced GPT-3 in a May 2020 research paper, “Language Models are Few-Shot Learners.” While a tool like this may not be something you use daily as an NLP professional, it’s still an interesting skill to have. Being able to spit out human-like text, answer questions, and even create code, it’s a fun factoid to have.
Learn about these data science tools and skills with Ai+ Training
There are a number of ways that you can learn these skills with ODSC. For starters, here are some recent Ai+ Training sessions that you can access when you subscribe to the platform:
Upcoming Live Sessions:
- Exploring the Interconnected World: Network/Graph Analysis in Python: Noemi Derzsy, PhD | Senior Inventive Scientist | AT&T Chief Data Office
- Bayesian Inference with PyMC: August 17th | Allen Downey | Computer Science Professor at Olin College
- Network Analysis Made Simple: Eric Ma | Sr. Expert in Data Science & Statistical Learning | Novartis
Highlighted On-Demand Sessions:
- PyTorch 101: Building A Model Step-by-Step
- Deep Learning (with TensorFlow 2 & PyTorch)
- Active Learning with PyTorch
- Data, I/O, and TensorFlow: Building a Reliable Machine Learning Data Pipeline
- Hands-on Parallel Computing with Dask and Pandas
- Advanced Practical Pandas – From Multi-indexing to Styling
- Getting Started with Pandas for Data Analysis