Top 7 Data Science Tools MAANG Companies Expect You To Know Introduction Data science tools are among the most popular tools for managing data effectively and obtaining insightful information that will improve customer engagement. It is well known that many data and technology companies, such as MAANG (Meta, Apple, Amazon, Netflix, and Google), allow data scientists to use various data science tools. Tech companies and MAANG must thoroughly understand customer behavior in this data-driven market.
Data Science Tools 1. Apache Hadoop: Data science tools are among the most popular tools for managing data effectively and obtaining insightful information that will improve customer engagement. It is well known that many data and technology companies, such as MAANG (Meta, Apple, Amazon, Netflix, and Google), permit data scientists to use various data science tools. Tech companies and MAANG must thoroughly understand customer behavior in this data-driven market.
2. Tableau Data scientists working for tech and MAANG companies can use Tableau to delve deeply into pertinent data, uncover insightful patterns, and visually tell a compelling narrative about the findings. Effective data management across a variety of data sources is facilitated by it. Tableau Data Management, Tableau Desktop, Tableau Pre Builder, Tableau Cloud, and many other products for data scientists are used by MAANG in conjunction with this data science tool for data visualization which can be mastered with the best data science certification course in Mumbai.
3. Jupyter Notebook Jupyter Classic Notebook and JupyterLab are the two types of data science tools that MAANG employs. Jupyter Notebook, an open-source web application for interactive computational environments, is a well-liked data science tool for tech companies. Documents that combine inputs and outputs into a single file have been produced by it. It is advantageous to combine code and output using a number of steps.
4. Tensorflow TensorFlow's best data science tool offers different levels of abstraction for creating and honing machine learning models using the sophisticated Keras API. It offers a variety of tools, such as Colab, TensorBoard, ML Perf, What-If Tool, and many others, to enhance workflow. Eager execution allows for quick iteration and intuitive debugging, which is ideal for data scientists who need more flexibility. Tech companies can use the distribution strategy API to distribute training across different hardware setups.