Data Science Tools

Python

There are a lot of programming languages that can help you start with, however, Python can achieve the same with less effort and that is why so many companies are using Python on a daily basis.

• User-friendly coding syntax

• Easy-to-understand code

• Strong community support

• Easy Portability on multiple platforms

• Data products support

These have made Python a first choice from small enterprises to large enterprises.

Pandas

Pandas is an open-source data analysis tool built on top of Python. It is quite fast and provides a lot of functionalities to analyze data and make conclusions.

• Load data in different formats from various sources into Pandas data frames

• Dataframes are easy-to-handle data structures similar to Excel

• Slicing, indexing, and subsetting data frames for investigation

• Data preparation functionalities

• Describing data and data types

• Handling missing values and ways to impute them

• Basic examination of data quality (why is it important). 

• Detection of outliers.

(image source: pandas )

Matplotlib & Seaborn

Data Visualization is the most important part of Exploratory data analysis ( EDA ) in the data understanding & preparation phase. For this, there are majorly two widely used libraries - Matplotlib and Seaborn.

Matplotlib is a popular tool for creating static, animated, and interactive visualizations. Seaborn is based on Matplotlib and provides feature-rich functionalities for statistical analysis.

• Matplotlib allows users to visualize patterns in data

• Pandas data frames can be fed into Matplotlib and Seaborn easily

• Customize the plots and export them into standard image formats

• Visual distribution of missing data

• Control figure size and image quality

• Analyze correlation and more data dependencies

• Visualize the output of hypothesis testing and other statistical inferences

• Detect outliers and clean data

• Visualize feature importance 

• A lot of Integrated third-party useful libraries

Scikit-Learn

Data science is incomplete without predictive analytics and scikit-learn empowers this process by providing a set of reusable and efficient tools for all kinds of predictions. 

• Preprocessing techniques to decrease biasedness in data

• Support for both supervised and unsupervised problems

• Wide range of Classification, Regression, and Clustering algorithms

• Advanced ensemble algorithms to reduce overfitting issues and reduce biasedness

• Cross-validation technique to measure the true performance of models

• Dimensionality reduction support

• Scope of fine-tuning algorithms by changing parameters

• Evaluation metrics to analyze the performance of models

• Built on pandas and matplotlib - easy to integrate

Python Anywhere

After model evaluation, the best-performing model should be deployed in the cloud to give access to this from anywhere. ( more on cloud computing)

Python Anywhere is a platform ( PAAS or platform as a service) that provides free deployment of ML models with a website link to access it from anywhere.

• Support for a full Python environment

• Easy deployment and access. 

• 3 months of no-cost deployment ( as of 2023 ).