5 Best Data Science Tools for Beginners in 2023

Data Science tools

Introduction –

Enterprises are investing in user data, data-handling processes, and extracting valuable insights through data science tools. Due to the increasing demand for data science, data analysts and data scientists can witness significant growth in data science tool development. Most of the enterprise’s data plays a central role in strategic planning and decision-making. Various data science tools exist in the market that helps in rendering better insights from granular data.

This article is a quick guide to the five best data science tools beginners can use to kickstart their careers.

What is Data Science?

Data science is an interdisciplinary domain that has evolved as a separate vertical to analyze, understand, and extract business insights buried in data. It uses techniques like data mining, analyzing large data amounts, and programming skills, along with various tools for analysis and visualization. All these work in tandem to uncover valuable intelligence for their organizations. Do you know, according to the MarketsAndMarkets research, the global data science market will expand at a CAGR (Compound Annual Growth Rate) of 30%?

What is Data science?

What are Data Science tools?

To extract insights from granular data, enterprises need to obtain, analyze, and properly present these meaningful data. The data science tools come in various shapes and sizes to make this happen. There are two basic categories of data science tools. These are:

· Self-service tools: These tools are explicitly for technical experts having programming proficiency and a sound understanding of statistics and computer science.

· Business tools: These tools help business analysts automate all the visualization and decision-making insights from analyzed data.

Data science tools help analysts and scientists drill down complex data by extracting, processing, and analyzing structured & unstructured data. These tools aid in reducing time and cost while increasing efficiency to generate valuable insight by coupling statistics, programming, predictive analytics, and machine learning techniques. In the past, data analysts and data scientists used to rely on powerful computations for managing large volumes of data. Even the analysis took lots of calculations and processing power. But with the advent of modern data analysis tools and processes, the overall data mining and analysis cost minimizes considerably. Modern data analysis tools also help in increasing the efforts and efficiency of making better business decisions.

Top 5 Data Science tools for Beginners –

In this competitive market, analyzing data and extracting insights through traditional approaches will be useless. Modern and sophisticated data analysis tools empower Data Analysts and Data Science professionals to work faster, easier, and more effectively. These tools also compound several hundreds of methods for enabling standardization in analysis, data cleanup, and rendering data visualization simultaneously. Without further ado, let us now jump straight into the top five data science tools for beginners.

i. TensorFlow: It is a well-known new-age technology tool that works well for data analysis, machine learning (ML), artificial intelligence (AI), etc. It helps data analysts, scientists, and data science professionals to build data analysis algorithms & models. Google Brain team is the brainchild behind this open-source library that leverages dataflow programmers to deal with numerical computation & large-scale supervised and unsupervised learning. Beginners can use TensorFlow for image recognition, hand-written character classification, RNN, word embeddings, NLP for human languages, sequence-to-sequence models, and PDE (partial differential equation) projects. TensorFlow also helps companies in sales analysis and prediction production projects. Medical science and healthcare devices using data science also leverage this tool for determining accurate solutions.

ii. Matlab: Matlab serves as a multi-paradigm numerical computation processing tool. Due to its multidisciplinary capability and robust simulation feature, it gained popularity in data science also. It allows the implementation of matrix functions, algorithmic computations, and calculating statistical models on given data. This programming platform helps beginners and expert data science professionals work on flat files, structured datasets, databases, etc. Matlab comes with explicit data types meant for data analysis and aid in reducing a lot of time in data pre-processing.

iii. Tableau: Tableau is one of the leading data visualization and Business Intelligence tools used by top firms from diverse industry backgrounds. This tool improves decision-making and analyzing data through enriched visuals. It is a package of robust graphics that helps in rendering interactive visualizations. Tableau can blend with spreadsheets, databases, OLAP (Online Analytical Processing) systems, etc. Beginners can also use this tool for visualizing geographical data and plotting longitudes and latitudes. Almost all fortune 500 companies use this tool to get better corporate insights and work according to the market demands. More than 63,298 companies use Tableau, and the count is still on.

iv. Excel: Microsoft Excel is another popular and well-accepted data analysis tool. This Microsoft spreadsheet tool helps in mathematical calculation, data processing, formula-driven operations, visualization, and complex computations. It is a recommended tool for freshers and beginners in the field of data science. This traditional data science tool is still classical yet packed with filters, formulas, slicers, and tables. It enables data science aspirants to customize analytical efforts and examine their data on a smaller scale. It is suitable for data report processing or data analysis on a smaller scale. With Excel, data science professionals cannot work with unstructured data.

v. BigML: BigML is another well-known data science tool for beginners. It caters to an interactive, cloud-based GUI platform that can process data for analysis. As the name suggests, it also renders machine learning (ML) algorithms for practical implementation. It makes it easy for beginners to make decisions through visuals. It can deliver data analysis through predictive modelling and helps in risk analytics, sales forecasting, decision-making, and product innovation. The corporation BigML developed this tool with the philosophy of “making machine learning simple.” For educational purposes, beginners can create free accounts. Around 600+ universities across the globe are leveraging this data science tool for learning and implementation purposes.

Conclusion –

The success behind most analytics strategies goes to these robust tools. Understanding the appropriate use of data science tools is crucial for beginners to develop practical benefits for any project. We hope this list of top five data science tools has given you a clear understanding of what to use to kickstart your learning.

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