Data Science  Tools You Should Be Proficient In

10

Fundamental and essential for data analysis, MS Excel provides a user-friendly interface, built-in formulas, & various data visualization elements.

1

MS Excel

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2

A robust analytics engine offering real-time and batch processing, Apache Spark is faster than traditional tools like Hadoop, supporting ML projects & multiple programming languages.

Apache Spark

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3

Python

Widely used for data analysis, Python is a high-level programming language with extensive libraries for tasks such as data cleaning, visualization, and analysis.

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4

R Programming

A powerful language for statistical computing and data analysis, R boasts a strong community, extensive packages, and scalability for various data science tasks.

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5

A popular computational notebook, Jupyter supports multiple programming languages (e.g., Python, Julia, R) & is known for its interactive data visualization & code execution.

Jupyter Notebook

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6

An open-source NoSQL database management system, MongoDB is valuable for managing semi-structured & unstructured data, handling large volumes, & executing advanced analytics.

MongoDB

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7

A JavaScript library for creating interactive data visualizations on web browsers, D3.js supports dynamic visualizations and integrates with various data formats.

D3.js

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8

A widely accepted tool for machine learning and deep learning, TensorFlow enables the development of ML/DL models, supports visualization, & handles complex numerical computations.

TensorFlow

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9

A top data visualization and Business Intelligence tool, Tableau helps in solving advanced data analysis and visualization problems with interactive dashboards.

Tableau

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Scikit-learn

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10

A free machine learning library built on Python, Scikit-learn offers a wide range of supervised & unsupervised learning algorithms, making it ideal for rapid prototyping & model execution.

This comprehensive guide will help you prepare for all aspects of the interview process.  Don't miss out on your next opportunity!

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