Data is big, and getting bigger all the time! Huge amounts of data are flowing into organizations of all sizes and from a variety of verticals, and this data could potentially be a great source of valuable insights to guide organizations in taking strategic decisions regarding their future course of action.
This is what lies at the root of the demand for the data science industry. It works to convert data into knowledge, to turn information into actionable insights, and to aid organizations in making data-driven decisions. Companies are constantly expanding their collection and usage of data, and they require people who can parse through it to derive insights through applying artificial intelligence (AI), machine learning (ML), and other technologies.
It is no surprise that a career as a data science professional is one of the top choices for those looking for a path to take up. What they stand to gain is great knowledge about the newest tools and techniques, high salaries, and rapid growth. Plus, there is undeniably the prestige of the data scientist’ tag on the job profile.
Here are the top five data science frameworks used in this field:
Apache Kafka
Originally developed by LinkedIn and conceived as a messaging queue, Kafka was later donated to the Apache Software Foundation. This now serves as an open-source stream-processing software platform written in Java and Scala and seeks to enable a high-throughput, low-latency, and unified platform that can handle data feeds in real-time. What makes it popular in the data science industry is its ability to access and provide huge volumes of data from a variety of internal platforms. Brands known to be using Apache Kafka include Airbnb, LinkedIn, and Netflix.
Jupyter Notebooks
This came out of the IPython Project in 2014, supporting interactive data science and scientific computing across all programming languages. It is an open-source web application with the ability to create and share documents containing live code, equations, visualizations, and narrative text. Jupyter Notebook is a very powerful tool that allows a data science professional to develop and present a data science project interactively. The intuitive nature of the workflow makes it suitable for a variety of purposes, including data cleaning and transformation, data visualization, numerical simulation, statistical modeling, and more. The project has partnerships with many companies (Continuum Analytics, Github, Google, Microsoft, Rackspace) and universities (George Washington University, NYU, UC Berkeley).
Pandas
Pandas is an open-source software library written for the Python programming language, sometimes referred to as the Microsoft Excel of Python. It can be used for analysis, manipulation, and visualization of data. This data science framework offers tools to merge, shape, reshape, and slice data sets, and it is a great choice when the operation must be performed on data that is incomplete, messy, and unlabeled. Its functionalities include data structures and operations to manipulate numerical tables and time series. Pandas is an excellent choice to conduct data analysis in engineering, finance, social sciences, and statistics. Being skilled in working with Pandas is helpful when applying for jobs for analysts and Python specialists.
Scikit-learn
One of the popular open-source ML libraries for Python, Scikit-learn is a go-to choice in the data science industry. It is well-documented and it aims to provide a set of common algorithms to Python users through a consistent interface. It is typically used for smaller data sets, though it does include a capable algorithm set for conducting out-of-core classification, regression, clustering, and decomposition. Its popularity among the large community of developers and ML experts means it is constantly being worked on, and larger data handling capabilities, better efficiencies in memory and speed, and newer models keep coming up.
TensorFlow
Developed at Google, TensorFlow is an open-source ML library suited to numerical computation using data flow graphs. The nodes in the graph represent mathematical operations, while the edges of the graph represent the multidimensional data arrays (tensors) communicated between them. It works well for the data science professional who wants to create and experiment with deep learning architectures, with a convenient formulation for data integration such as inputting graphs, SQL tables, and images together. It is portable and can run on the CPU, GPU, desktops, mobile devices, and servers. Given that it was developed at Google, it is constantly being updated with new features. Well-known customers include Airbus, IBM, and Twitter.