Data Science Tools
In the previous article, you had an overview of data science workflow. Although knowing the process and workflow is very crucial, learning is incomplete without implementation.
To do so, you need to learn and be aware of the data science tools that can help you achieve it.
What are the Tools?
For a beginner, learning to use the Data Sc tools can be both fun and challenging.
There are a huge amount of ways to perform data science, however, this article would focus on the tools that are quite popular and easy to start with.
5 tools that can help you start with:
Learn a programming language. Python is popular.
Pandas to perform data analysis.
Matplotlib & Seaborn to visualize your data.
Scikit-Learn to apply Machine Learning (ML).
Python Anywhere to deploy your ML app.
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 ).
3-Week timeline
