As per the Harvard Business Review, Data science jobs are amongst the most sought after and lucrative careers of the 21st century. Apparently, it has become a major and significant part of many businesses like marketing, risk control, agriculture, fraud discovery, retailing analytics, and common policy.
Data scientists use various scientific methods, processes, algorithms, and systems to extract knowledge from structured and unstructured data. Their concept is similar to data mining and big data, where they use the most powerful programming systems and algorithms to solve problems. The tasks performed by data scientists demand them to identify relevant questions, collect data from various different sources, organization of data, the transformation of data and communicating these findings for a better business outcome/ solution.
A data scientist is responsible for manipulating, extracting, pre-processing and generating predictions out of data and he requires a plethora of statistical essential data science tools and programming languages to achieve that goal.
Let’s have a look at a few of the top essential tools for working with data:
Excel
This one is probably one of the most essential tools for working with data and Data Analysis but not one of the ideal tools for non-enterprise levels. Excel provides you with many formulae, tables, filters, slicers, etc. and it gives you the liberty to make your own custom functions and formulae as well.
Excel is a powerful analytical tool for data science mostly used for spreadsheet calculations and it is widely used for data processing, visualization, and complex calculations.
Excel packs a punch with their complete overall package with calculations of the huge Data and considered as an apt choice for powerful data visualizations and spreadsheets by providing an interactable GUI environment to pre-process information.
Open Refine
This is often considered as one of the most popular and essential data science tools users need to analyze big data. Earlier it was known as the Google Refine. Open refine provide its users with many compelling characteristics that any data scientist may require during the course of their usage.
Open refine has numerous compelling characteristics that any data scientist may demand as it provides clustering, editing blocks with added values and prolonging web services. It also permits data scientists with many essential data science tools where they get to connect among several datasets. Open Refine can handle outlines in a particular domain, and that space is included in a file index with sub-directories.
Natural Language Kit
NLTK is widely used for numerous language processing techniques like Tokenization, tagging, stemming, parsing and machine learning.
Python language provides its users with a useful collection of libraries called NLTK (Natural Language Toolkit) which comprises more than 100 corpora which are a collection of data for building a machine learning model
It comes with many useful applications such as Word segmentation, Machine translation, Parts of speech tagging and text to speech recognition. As it is evident that Natural Language Processing is the most used field in Data science and clearly one of the most essential data science tools.
Matlab
MATLAB’s only limitation is a closed-source software but its easy integration for enterprise application and embedded systems make it a very essential data science tool. This data science tool is mostly used in scientific disciplines which allows matrix functions, algorithmic implementation and statistical modeling of data. MATLAB graphics library can create powerful visualizations, image and signal processing making it a very dynamic and essential data science tool.
MATLAB’s multi-paradigm numerical computing environment is considered apt for processing mathematical information and simulating neural networks with fuzzy logic as they get many solutions, from data cleaning, analysis to more advanced algorithms.
Tensorflow
Tensor flow is an open-source, ever-evolving toolkit known for its performance and high computational abilities. It is named after multidimensional arrays and is mostly used for very advanced machine learning algorithms.
TensorFlow can also run on both CPUs and GPUs and has recently emerged as one of the most essential data science tools.
SAS
SAS is widely used by data scientists and considered as one of the essential tools for working with data. It is a closed source proprietary software and uses base SAS programming language to analyze data and performing statistical modelling. It is often used by professionals working on reliable commercially advanced software. Furthermore, it provides various statistical libraries and tools that Data Scientists can use for organizing their data.
Although SAS is a reliable data science tool but the only drawback being that it is an expensive tool, it needs expensive up-gradation to the base pack and can be used for larger industries only and it falls short in comparison to many new modern open-source tools available.
Apache spark
Spark or Apache spark has many Machine Learning APIs which makes it an essential data science tool that can help Data Scientists to make powerful predictions with the given data.
It is a powerful analytics engine specifically designed to handle batch processing and steam processing and it gives many APIs which are programmable in Java, Python, and R for repeated access to data.
It is considered better than Hadoop and other big data platforms and can perform faster than MapReduce and its most powerful feature is its conjunction of spark with Scala programming language based on JVM (Java Virtual Machine) which is cross-platform in nature.
Apache Hadoop
Apache Hadoop is an open-source data science tool that allows users to store and manage monstrous data-sets on clusters of stock hardware.
It is authorized under the Apache License 2.0 and gives its users the capability to manipulate implicitly infinite coexisting assignment
Jupyter
This is an open-source powerful tool based on IPython completely free of cost helping developers to create open-source software. It runs on the cloud and provides an online environment called COLLABORATORY for storing the data in Google drive Jupyter is an interactable environment which gives dynamic tools for storytelling through interactive computing used for writing live code, visualizations, and presentations.
Jupyter is a widely popular tool that is designed to address the requirements of Data Science using Notebooks where data cleaning is done, statistical computation, visualization, and predictive machine learning models
BigML
BigML is also considered amongst the most essential data science tools for providing a user-friendly web-interface where one can create a premium account as per our data needs.
BigML uses a machine-learning algorithm like clustering, classification, time-series forecasting by providing a cloud-based GUI environment that is easy to interact with.
BigML focuses on predictive modeling and offers a single software across for sales forecasting, risk analytics and product innovation making it a very competitive tool for companies. Also, it gives the ability to export visual charts on your mobile and IOT devices by allowing interactive visualizations of data
Keras
Keras is an open-source library, capable of working on top of Tensor Flow, Theano, etc. providing a very quick application experience. It is a deep learning library formulated in python formulated to create deep learning models in assisting users to manage their data logically in an effective method.
A great thing about this data science tool is that it gives you the freedom of experimentation with deep neural networks as it is user-friendly, flexible and gives a smooth operation on CPU and GPU
Seahorse
This is one of the powerful and essential data science tools for a data scientist by providing a simple interface where users can build in composite data flows and machine learning without writing the code to crack down on big data queries through its optical programming strategy.
Although we cannot write code here, still one can customize a set of operations using Python or R which are other essential tools for working with data.
LIST OF TOOLS USED FOR DATA SCIENCE:
- R
- SQL
- GIT/ GIT HUB
- KUBERNETES
- ggplot2
Conclusion
Data science requires a vast array of tools for analyzing data, creating aesthetic and interactive visualizations for predictive models using machine algorithms.
Data science tools can deliver complex operations in one place implementing functionalities of data science without having to write the code. Also, there are several other tools available that cater to the many applications of data science.
All these are just a few compilations of data science tools catering to different data science processes, there are many available tools as per different stages in data science process- like Data storage, Data modeling, Data visualization, and exploratory data analysis.
Each day, new, advanced and user-friendly data science tools are coming up and developed by the tech giants to make the functionality simpler and easier but as we know Data science is a vast field and it is not possible to use one tool for the entire workflow.
