Data Science is emerging as one of the most popular fields in recent times. Most companies are enthused about the vast opportunities this field can be to their companies. For this reason, companies employ Data Scientists to assist them in gaining more insights into their business, market trends and predictive analysis.
Data Scientists play a vital role in companies decision making and are mostly responsible for analyzing and handling a large amount of unstructured and structured data.
There are numerous of tools used by Data Scientists for their data science applications (analysis and prediction). Get to know about the 9 most widely used tools, their features and capabilities. Get to know how best they can fit your analytics needs.
MATLAB
A Platform to process math information. Also used to stimulate neural networks.
JUPYTER
Writing of live code, visualizations and presentations. Supports programming languages such as python, R, & Julia
NLTK
Natural Language Toolkit. It is widely used for language processing. it comes with Python.
TENSORFLOW
Standard tool for ML. Use to build models easily using intuitive high-level APIs.
EXCEL
Most commonly used data analysis tool. Used for Data processing, and complex calculations.
TABLEAU
Data visualization software. Connects to almost any database and creates data visuals.
APACHE SPARK
Most used data science tool. Extremely powerful for large scale data processing.
BIGML
Simplifies machine learning and makes it easy for beginners. Build models in 3-4 clicks
D3.JS
Create interactive data visualizations. JS library for visualizing data with HTML, CSS, and SVG.
