Five Tools that I use Daily as Data Science — part 2
In the first part of the tools that I use daily as a Data science engineer, I introduced 6 tools that I use daily. However, the first 6 tools are basic tools and libraries and I wanted to introduce you to the more useful and more advanced tools. Here are other tools which are useful tools.
Scikit-learn
Machine learning algorithms are implemented using the Python module Scikit-learn. A tool that is frequently used for analysis and data science is simple and straightforward to implement.
It supports several machine learning features, including data preprocessing, classification, regression, clustering, dimensionality reduction, etc.
The usage of sophisticated machine learning methods is made simple by Scikit-learn. Therefore, it is an excellent platform for research needing fundamental Machine Learning and is also used in scenarios that call for rapid prototyping. It uses a number of the Python language’s fundamental libraries, including SciPy, Numpy, Matplotlib, etc.
TensorFlow
A common machine learning tool is now TensorFlow. For complex machine learning algorithms like Deep Learning, it is frequently employed. TensorFlow was named by its creators after multidimensional arrays called Tensors.
It is an open-source, dynamic toolkit renowned for its effectiveness and strong computational capabilities. TensorFlow is compatible with CPUs, GPUs, and more recently, more potent TPU platforms.
As a result, it has a processing advantage over other systems that have never been seen before.
Tensorflow offers a wide range of applications because of its tremendous processing power, including speech recognition, picture classification, drug discovery, image and language synthesis, etc. Tensorflow is a tool that all data scientists with a focus on machine learning should be familiar with.
Weka
Weka, also known as the Waikato Environment for Knowledge Analysis, is a Java-based machine learning program. It is a collection of different data mining machine learning algorithms. Several machine learning capabilities, including classification, clustering, regression, visualisation, and data preparation, are included in the Weka package.
It is an open-source GUI program that makes it simpler to apply machine learning algorithms using a user-friendly interface.
Without writing a single line of code, you can comprehend how machine learning works on the data. For Data Scientists new to machine learning, it is perfect.
Excel
Probably the most used tool for data analysis. Today, Excel is widely used for data processing, visualisation, and complex calculations. Excel was created by Microsoft primarily for spreadsheet computations.
Excel is an effective data science analysis tool. Excel is still a powerful tool for data analysis, despite being the standard.
There are many different formulas, tables, filters, slicers, etc. in Excel. Excel also allows you to design your unique formulas and functions. Even while Excel is not suitable for handling enormous amounts of data, it is still the best option for making effective spreadsheets and data visualisations.
You can use SQL to edit and analyse data by connecting it to Excel. Excel has an interactive GUI interface that makes information pre-processing simple, therefore many data scientists use it for data cleansing.
The introduction of ToolPak for Microsoft Excel has made it considerably simpler to compute sophisticated analyses. It still lacks in sophistication when compared to considerably more sophisticated Data Science tools like SAS. Overall, Excel is the perfect tool for data analysis on a small and non-enterprise level.
MATLAB
For processing mathematical data, MATLAB is a multi-paradigm numerical computing environment. Matrix functions, algorithmic implementation, and statistical data modeling are made easier by this closed-source program. The majority of scientific areas make use of MATLAB.
MATLAB is used in data science to simulate fuzzy logic and neural networks. The MATLAB graphics library allows you to build robust visualisations. Signal and image processing also use MATLAB.
This makes it a very adaptable tool for data scientists since they can take on all the challenges, from powerful Deep Learning algorithms to data cleaning and analysis.
Furthermore, MATLAB is the best Data Science tool due to its simple integration for enterprise applications and embedded systems.
Additionally, it aids in automating a variety of processes, from data extraction to the reuse of scripts for decision-making. The fact that it is closed-source proprietary software, however, is a drawback.
To see the first 6 tools click on the following link Six Tools That I Use Daily as Data Science. For the additional 5 tools please visit the following page Five Tools that I use Daily as Data Science — part 3.
