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35 Best Data Science Tools for beginners to master

Last updated on March 1, 2021, 12:02 p.m. 6357 Views

Kirandeep Kaur

Kirandeep Kaur |

3+ Technical Content Writer. and who is passionate about his career

35 Best Data Science Tools for beginners to master

Last updated on March 1, 2021, 12:02 p.m. 6357 Views

Kirandeep Kaur


35 Best Data Sciecne Tools for beginners to master

Programming is an essential part of data science. It is mostly acknowledged that if a person is well-versed in programming that is, who understands the fundamentals of programming like loops, logic, functions, etc. has higher chances of becoming a data scientist. But today's era is not limited to the programmers only. If a person who has strong logical reasoning and analytical skills but has not studied programming in his school can also become a data scientist. The good news is that regardless of programming skills, there are a few GUI-driven data science tools that can be effectively used by non-programmers. 

So, all you need is the minimal knowledge of algorithms that you want to implement in your data science-related tasks. In this blog, we’ll share Data Science tools beneficial if you don’t have any prior programming knowledge and the tools beneficial for those who have a fair knowledge of programming. With everything on a data scientist’s plate (skills and everything!), I have rounded up this blog for data science aspirants who want to begin their careers in data science. This blog will make you aware of 35 data science tools that can be used by a programmer data scientist or a non-programmer data scientist. Yes, this is a one-stop solution for all who want to begin with data science – a really rewarding career of the 21st century!

Let us begin!

Data Science Tools : 

  1. Rapid Miner

It is Data Science software that is an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analysis. It is a well-go platform for non-programmers as well as programmers. Rapid miner is currently known as the premium tool used for data mining. The reason behind the popularity of this data science framework is because of the coverage of requirements of a data scientist from data preparation to validation and deployment. Rapid Miner takes care of everything!

Benefits of Rapid Miner:

  • RapidMiner Studio is for data preparation, visualization, and statistical modeling
  • RapidMiner Server provides central repositories
  • RapidMiner Radoop is for implementing big-data analytics functionalities
  • RapidMiner Cloud is a cloud-based repository
  1. Data Robot

Data Robot is known as an advanced automated machine learning platform. It lets you accelerate the success of AI with machine learning. Data Robot incorporates the knowledge, experience, and best practices of the top data scientists by delivering top levels of automation, accuracy, transparency, and collaboration to create high-level AI-driven models. It makes it easier to implement a wide range of machine learning algorithms that includes clustering, classification, and regression models.

Benefits of Data Robot:

  • It helps the non-programmers model optimization by detecting the best pre-processing and features engineering by employing text mining, variable type detection, encoding, imputation, scaling, transformation, etc.
  • Parallel processing by using distributed algorithms to scale large datasets
  • Building & deployment data science models with just a few clicks without writing any code
  1. Trifacta

Trifacta is a data wrangling and data analysis tool of data science. With a very intuitive UI, it is used by non-programmers and programmers for data cleaning and preparation. “Every click, drag, or select within Trifacta leads to a prediction where the system intelligently assesses the data at hand to recommend a ranked list of suggested transformations for users to evaluate or edit. For more advanced users, the automated guidance and parsing of data accelerates the efficiency of their work.” By Scheuermann co-founder of Trifacta.

Benefits of Trifacta:

  • Trifacta Wrangler will help you in exploring, transforming, cleaning, and joining the desktop files together.
  • Trifacta Wrangler Pro is an advanced self-service platform for data preparation.
  • Trifacta Wrangler Enterprise is for empowering the analyst team.
  1. Apache Kafka

Apache Kafka is a distributed platform that effectively forms processes of data progressively in real-time. Data Scientists utilize this tool to fabricate constant data pipelines and spilling applications since it engages you to distribute and buy into floods of records, store surges of records in an open-minded way, and procedure surges of records as they happen.

Benefits of Kafka:

  • Runs as a cluster on one or more servers
  • Cluster stores streams of records in categories called topics
  • Each record includes a key, value, and timestamp
  • Has of four core APIs: Producer API, Consumer API, Streams API, and Connector API
  1. KNIME

KNIME is an awesome tool for training machine learning models. Its GUI is awesome to get started with. It produces results on par with most tools and is free of cost as well.

Benefits of KNIME:

  • Deploy quickly and scale easily
  • More than 1,000 modules
  • Hundreds of ready-to-run examples
  • A comprehensive range of integrated tools
  • The widest choice of advanced algorithms available
  1. Data Wrapper

Data Wrapper is a considered option for Data science Visualization. It is an ideal solution for any business intelligence task because it provides rapid visualizations. It is not something as big as other data visualization software like Tableau, but for data visualization in the form of bar charts, pie charts, line charts, column charts, etc., a Data wrapper is an amazingly quick tool. In a nutshell, it is a non-programmer’s helping hand for beautiful data visualization.

Benefits of Data Wrapper:

  • Copy and paste data from Google Sheets and Excel, upload CSV files, or link to URLs to create live updating charts
  • Highly responsive charts
  • Interactive maps and charts
  • Hover over bars, lines, or maps to uncover the underlying values and comprehend a better view of the entire chart
  • Export chart as png or pdf
  • Customized layout
  1. Orange

Orange is another tool built with GUI for non-programmers. It offers data visualization and visual programming. The orange tool focuses more on data mining. It has an extensive collection of libraries that supports classification, regression, and clustering-like tasks.

Benefits of Orange:

  • Interactive data visualization
  • Explore statistical distributions, box plots, and scatter plots, or dive deeper with decision trees, hierarchical clustering, heatmaps, MDS, and linear projections.
  • Visual programming
  • The best tool to teach data mining
  • Add-ons functionality
  1. BigML

BigML as its name suggests is more on the machine learning side rather than data analytics. It is for non-programmers because of its user-friendly GUI. It is a visually engaging interface that allows even beginners and amateurs to program models and executes actions. BigML has built-in algorithms for solving regression, clustering, classification, association discovery problem, etc.

Benefits of BigML:

  • BigML enables real-time predictions with its dedicated machine image that you can implement on a private cloud.
  • It can be easily integrated with React API to connect service-oriented architectures
  • It supports the automated processes with one-line codes only
  1. D3.js

D3.js is based on JavaScript features and is used as a client-side scripting language tool. D3.js is a JavaScript library that allows you to create interactive visualizations and animations on your web browser. With various APIs of D3, dynamic visualization and analysis of data can be implemented in your web browser. It makes documents dynamic by allowing updates on the client-side and actively using the change in data to reflect visualizations on the browser. Moreover, it is an exceptionally useful tool for data scientists who are working on IoT-based devices.

Benefits of D3.js:

  • Works with basic JavaScript features
  • Useful for making interactive visualizations
  • Can be combined with CSS
  • Can create animation transitions
  • Useful for client-side interactions in IoT
  1. MATLAB

MATLAB is a multi-paradigm numerical computing environment that is useful in handling scientific data. It is a closed source programming that encourages framework capacities, algorithmic execution, and statistical modeling of data. It is most broadly utilized in scientific disciplines. In the ecosystem of Data Science, MATLAB is useful in simulating neural networks and fuzzy logic. With the use of the MATLAB graphics library, powerful visualizations can be created. It is also very useful in image processing and signal processing. Thus, MATLAB is a versatile tool for data scientists and more advanced for Deep learning algorithms.

Benefits of MATLAB:

  • A numerical computing environment
  • Capable to process complex mathematical operations
  • Powerful graphics library
  • Exceptional in Deep learning-related tasks
  • Easy integration with embedded systems
  1. MS Excel/Spreadsheet

Microsoft Excel is one of the widely used software throughout the world. MS Excel is particularly not for the use of data science projects but it supports resources for a non-programmer to dream about working in data science. Excel is helpful in summarizing data, visualizing data, and data wrangling, etc. which is enough to process all the collected data.

Benefits of MS Excel:

  • Effective data analysis
  • All in one data management tool to import, explore, clean, analyze, and visualize your data.
  • The perfect tool for beginners
  1. ggplot2

GG in ggplot means Grammar of Graphics. The ggplot2 package allows you to create data visualizations in R. Even though ggplot2 is part of the tidyverse collection, it predates the collection and is important enough to mention is it's own. The reason behind the popularity of ggplot2 is because it allows you to create professional-looking visualizations fast using easy-to-understand syntax. R includes plotting functionality built-in, but the ggplot package is generally considered superior and easier to use and is the number one R package for data visualization.

Benefits of ggplot2:

  • It is straightforward with your plotting requirements
  • Plotting here is exploratory
  • You can even add complexity to your visualizations
  • Save plots as objects
  • No repetition of code is required
  1. Tidyverse

Technically, it is a collection of R packages that can be used for data science. Key packages in the collection include dplr for data manipulation, ready for importing data, ggplot2 for data visualization, and many more.

Benefits of Tidyverse:

  • Consistent function
  • Workflow coverage
  • A path to data science education
  • A parsimonious approach to the development of data science tools and the possibility of greater productivity
  1. Shiny

Shiny is a package of R that allows you to create interactive web apps using R. With Shiny, you can construct functionality that allows people to interact with your data, analysis, and visualizations as a web page. Shiny is particularly powerful because it eradicates the need for web development skills and knowledge when creating apps and allows you to focus on your data.

Benefits of Shiny:

  • You can communicate results via interactive charts, visualizations, text, or tables.
  • If you already know R, you can rapidly develop a cool Shiny app
  • Built-in capabilities let you share your work easily with colleagues and friends
  • Interactive design
  1. Pandas

The Pandas library is worked for cleaning, controlling, changing, and imagining information in Python. In spite of the fact that it's a solitary bundle, its nearest simple in R is the tidyverse assortment. Notwithstanding offering a ton of accommodation, Pandas is additionally regularly quicker than unadulterated Python for working with information. Like R, Pandas exploits vectorization, which accelerates code execution.

Benefits of Pandas:

  • Data representation - easy to read and well suited for data analysis
  • Easy handling of missing data
  • Easy to convert data structures to DataFrame objects
  • Easy syntax and fast operations
  • Tools for reading and writing data between in-memory data structures and different file formats
  • Data subsetting and filtering
  • Time-efficient - because it is easy to use and powerful enough to handle a lot of qualitative data
  • Flexible - enables to reshape and pivot data sets easily
  • Handles large datasets
  • Native to Python
  1. Matplotlib

The Matplotlib library is an amazing plotting library for Python. Data Scientists frequently utilize the Pyplot module from the library, which gives a standard interface to plotting data. The plotting usefulness that is remembered for pandas call Matplotlib in the engine, so understanding matplotlib assists with tweaking plots you make in pandas.

Benefits of Matplotlib:

  • Fast and efficient
  • Uses high-quality graphics and plots to print and view a range of graphs such as histograms, bar charts, pie charts, scatter plots, and heat maps.
  1. NumPy

NumPy is a central Python library that gives usefulness to logical figuring. NumPy gives a portion of the center rationale that Pandas is based upon. Typically, most Data Scientists will work with pandas, however knowing NumPy is significant as it permits you to get to a portion of the center's usefulness when you have to.

Benefits of NumPy:

  • Powerful package for scientific computing and data manipulation in Python
  • Allows to work with multi-dimensional arrays and matrices
  1. Scikit-Learn

Scikit-learn is the most well-known machine learning library for Python. The library gives a lot of devices based on NumPy and Matplotlib that take into account the arrangement and preparation of machine learning models. Available model types include classification, regression, clustering, and dimensionality reduction.

Benefits of Scikit-Learn:

  • Simple and efficient tools for data mining and data analysis
  • It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means, etc.
  • Accessible to everybody and reusable in various contexts.
  • Built on the top of NumPy, SciPy, and matplotlib.
  1. TensorFlow

The framework is extremely powerful, that is, you can do a lot of things with it, but can be useful for simple things may be a huge overkill. One thing TensorFlow is great at is deep learning. One major example is RankBrain, an advanced keyword processing tool used by Google in its search engine. It is MLCC (Machine Learning Crash Course with TensorFlow APIs) – an extremely useful introduction to ML developed by Google.

Benefits of TensorFlow:

  • Flexible architecture for deploying computation to one or more CPUs or GPUs in a desktop, server, or mobile device with one API
  • Nodes in the graph represent mathematical operations, while graph edges represent the multidimensional data arrays communicated between them.
  • A great option for conducting machine learning and deep neural networks but applies to a wide variety of other domains
  1. Tableau

Tableau can be used by individuals as well as teams and organizations. It can work with any database. It is easy to use because of its drag-and-drop functionality. The function speed of Tableau and reporting functions is commendable. The important feature of Tableau is its capability to interface with databases, spreadsheets, OLAP, etc.

Benefits of Tableau:

  • Mobile device management
  • JavaScript API enhancements
  • REST API enhancements
  • Revision history
  • Document API
  • ETL refresh
  1. QlikView

QlikView is the only software that can go head-to-head with Tableau in terms of power. It is one of the most popular data science tools used by businesses around the world. However, their new-generation tool, QlikSense is almost perfect in all areas. With Associative Engine & governed multi-cloud architecture, it manages to be super powerful at enterprise scale.

Benefits of QlikView:

  • It can manipulate huge data sets instantly with accuracy
  • Automated data integration
  • It can convert the data into graphical analytics
  • Easier to use and reporting
  • One-stop solution for dashboards and graphical analysis
  • Low-cost investment
  1. Google FusionTables

It is a cloud-based platform for data management that focuses on collaboration, ease of use, and visualizations. Google FusionTables is basically a data visualization web application tool for data scientists that allows them to collect, visualize, and share data tables.

Benefits of FusionTables:

  • Visualize bigger table data online
  • Combine with other data on the web
  • Make a map in minutes
  • Search thousands of public Fusion Tables or millions of public tables from the web that you can import to Fusion Tables
  • Import your own data and visualize it instantly
  • Publish your visualization on other web properties
  1. MS Power BI

MS Power BI is an investigation administration that conveys experiences to empower informed, quick and exact decisions. The tool transforms data into staggering visuals and offers them with others on any device. It visually investigates and examines data on the device and in the cloud. Power BI works together on and shares customized dashboards and intuitive reports and scales through the organization with built-in administration and security. 

Benefits of MS Power BI:

  • Minimal upfront costs
  • View MS Power BI reports across multiple platforms and devices
  • Consolidate multiple data sources through MS Power BI
  • Instantly share dashboards
  • Drag-and-drop functionality
  • Drill-down functionality
  • Scheduled Data Refresh
  1. SAS

SAS is one of those Data Science tools specifically designed for statistical operations. It is highly reliable and used by large organizations to model and organize their data.

Benefits of SAS:

  • Strong data analysis capabilities
  • Flexible 4th generation programming language
  • Supports various types of  data format
  • Support data encryption algorithms
  • Support report output format
  1. Apache Hadoop

     It is an open-source platform that facilitates a network of computers to solve problems that require a wide range of datasets and computer power. It has the power to store tons of data. It is utilized for high-level computations and data processing.

Benefits of Apache Hadoop:

  • Highly scalable platform
  • Cost-effective solution to manage large datasets
  • Flexible and fast
  • Resilient to failure
  • Utilizes Hadoop Distributed File System (HDFS) for massive data storage
  1. MS HD Insight

MS HDInsight is a cloud stage given by Microsoft to the reason for data stockpiling, handling, and investigation. Organizations, for example, Adobe, Jet, and Milliman utilize Azure HDInsight to process and oversee large amounts of data.

Benefits of MS HDInsight:

  • Spin up Hive, Spark, LLAP, Kafka, HBase, Storm, or R Server clusters within minutes, deploy and run your applications and allow HDInsight to do the rest.
  • Run the most critical and time-sensitive workloads across thousands of crores and TBs of memory in a reliable manner
  • A productive platform for analytics
  1. Google BigQuery

Google BigQuery is an exceptionally adaptable and serverless data warehouse tool that is intended for gainful analysis of data with unrivaled value execution. Since there is no foundation to deal with, the client can concentrate on revealing important experiences utilizing SQL without access to a DBA (database administrator). Data is broken down by making a consistent data warehouse over columnar storage and furthermore the data from object storage and spreadsheets. The tool is helpful in creating quick dashboards and reports with the in-memory BI engine. BigQuery permits the client to safely share experiences within the organization and beyond as datasets, queries, spreadsheets, and reports.

Benefits of Google BigQuery:

  • Flexible architecture speeds up queries
  • Offers a scale-friendly pricing structure
  • Access the data you need on demand
  • Deploys artificial intelligence to optimize your datasets
  1. Snowflake

Snowflake is a completely relational-based ANSI SQL data warehouse tool with the goal that the client can use the tools and skills of their association as of now. Updates, deletes, analytical functions, transactions, stored procedures, appeared views, and complex joins give the client the full abilities that he needs to take advantage of their data.

Benefits of Snowflake:

  • Scalable, efficient, and cost-effective tool
  • Empower users to improve business decisions and optimize efficiency
  • Helpful in identifying new business opportunities and industry trends
  • Deliver accurate data-driven feedback on business initiatives
  • Pay only for what you use
  1. H2O.ai

H2O.ai is the company behind the products related to machine learning like H2O with the objective to make machine learning easier to learn for everyone. It was created to use the most popular programming languages of data science like Python and R.

Benefits of H2O.ai:

  • Implement the majority of machine learning algorithms like generalized linear models, classification algorithms, boosting machine learning, etc.
  • Supports Deep Learning
  • Supports integration with Apache Hadoop to process and analyze a large amount of data
  1. ParseHub

ParseHub is one of those scraping tools that can deal with a broad rundown of various sorts of substance, including gatherings, settled remarks, schedules, and maps. It can likewise manage pages that contain validation, Javascript, Ajax, and the sky's the limit from there. ParseHub can be utilized as a web application or a work area application fit for running on Windows, macOS X, and Linux.

Benefits of ParseHub:

  • Easy quick select feature. Just point & click on a webpage to extract the information you want
  • Flexible and powerful
  • Built for interactive & complicated websites
  • Split-second feedback loop
  • Seamless navigation between pages
  • Automatic IP rotation
  • Cloud hosting & scheduling
  1. NLTK

Natural Language Processing is the emerging technology of data science. It incorporates the development of statistical models that help computers to understand human language. Such statistical models are a part of Machine Learning and with the help of its algorithms users are able to assist computers in understanding natural languages. Python language comes with a collection of libraries called Natural Language Toolkit (NLTK) developed for this particular purpose only.

Benefits of NLTK:

  • Useful in text analysis
  • Useful in natural language processing tools
  • Contains a number of text corpora
  • A rich Python library
  1. RStudio Desktop

RStudio Desktop is the most mainstream environment for working with R. It incorporates a code editor, an R console, notebooks, tools for plotting, troubleshooting, etc. Moreover, Rstudio (the organization that made Rstudio Desktop) is at the center of present-day R development, utilizing the engineers of the tidyverse, shiny, and other significant R packages.

Benefits of RStudio Desktop:

  • It is specifically designed to make it easy to write scripts
  • Convenient to view and interact with the objects stored in your environment
  • Easy to set your working directory and access files on your system
  • Accessible graphics for a normal user
  1. Jupyter

It is an interactable environment through which Data Scientists can play out the entirety of their obligations. It is additionally an integral asset for storytelling with different presentation features available in it. Utilizing Jupyter Notebooks, one can perform data cleaning, statistical computation, visualization and make predictive machine learning models. It is 100% open-source and, hence, liberated from cost. There is an online Jupyter environment called Collaboratory which runs on the cloud and stores the data in Google Drive.

Benefits of Jupyter:

  • A single platform that combines code, rich text, images, videos, animations, mathematical equations, plots, maps, interactive figures and widgets, and graphical user interfaces, into a single document.
  • Notebooks are saved in JSON format which makes them easily shareable
  • nbconvert tool converts notebook to accessible formats like HTML and PDF
  • nbviewer tool allows rendering a publicly available notebook straight in the browser
  • Language independent tool
  • Easy customization
  • Extensions with some magical commands
  • Interactive code and data exploration
  1. Anaconda

Anaconda is a distribution of Python structured explicitly to assist you with getting the scientific Python tools installed. Prior to Anaconda, the main alternative was to introduce Python itself and afterward install packages like NumPy, Pandas, Matplotlib one by one. That wasn't generally a direct procedure, and it was frequently hard for new learners. It incorporates the entirety of the principal bundles required for data science in one simple introduction, which spares time and permits you to begin rapidly. It additionally has Jupyter Notebooks properties and makes beginning another data science venture effectively available from a launcher window. It is the prescribed method to begin utilizing Python for data science.

Benefits of Anaconda:

  • Supports more than 1500 Python and/or R data science packages
  • Simplifies package management and deployment
  • Support tools to easily collect data from sources using machine learning and AI
  • It creates an environment that is easily manageable for deploying any project
  • Anaconda is the industry standard for developing, testing, and training on a single machine
  1. Apache Spark

Apache Spark or simply Spark is an all-in-all powerful analytical engine and is the most used Data Science tool. Apache Spark is designed to handle batch processing and stream processing. It has the API functionality to create repeated access to data for machine learning or storage in SQL and many more. The API functionality helps data scientists to make powerful predictions with the given data. Spark offers various APIs that are programmable in Python, Java, and R. But the most powerful conjunction of Spark is with Scala programming language which is based on Java Virtual Machine and is cross-platform in nature.

Benefits of Apache Spark:

  • Fault tolerance
  • Exclusive speed
  • Dynamic in nature
  • Lazy evaluation
  • Real-time stream processing
  • Reusability
  • Advanced analytics
  • In-memory computing

Data Science Tools for Non-Programmers

Data Science Tools for Programmers

DataRobot

MS Excel/Spreadsheet

Tableau

RapidMiner

Trifacta

QlikView

IBM Watson Studio

Amazon Lex

BigML

Data Wrapper

Orange

Data Robot

D3.js

MATLAB

R Packages/Libraries

ggplot2

Tidyverse

Shiny

Pandas

Matplotlib

NumPy

Scikit-Learn

TensorFlow

Tableau

QlikView

Google FusionTables

MS Power BI

SAS

Apache Hadoop

MS HD Insights

Google BigQuery

Snowflake

H2O.ai

NLTK

RStudio Desktop

Jupyter

Anaconda

Apache Spark

Endnotes

There are numerous Data Science tools in the market. Deciding which one to use for your project is considered based upon the application features that you want to implement. If the choice seems daunting – well, that’s because it is! If you have any questions, don’t hesitate to get in touch. We’re always happy to share our knowledge or offer our expertise.

Data science is the skill and technology that every industry is craving. Having a data science skillset in the current era means having a great demanding career option in your pocket. If you are also dreaming of becoming a data scientist then check Data Science training at Codegnan. We have trained hundreds of data scientists until now. 

The salary of a data scientist in India ranges from INR 365k per annum to 500k per annum.

Our data science training will help you master data science analytics skills through real-world projects in multiple domains like Big Data, Data Science, and Machine Learning.  The trending word of data science is waiting for you to be skilled.

Data science is the skill and technology that every industry is craving. Having a data science skillset in the current era means having a great demanding career option in your pocket. If you are also dreaming of becoming a data scientist then check Data Science training at Codegnan. We have trained hundreds of data scientists until now. 

The salary of a data scientist in India ranges from INR 365k per annum to 500k per annum.

Our data science training will help you master data science analytics skills through real-world projects in multiple domains like Big Data, Data Science, and Machine Learning.  The trending word of data science is waiting for you to be skilled.

 

 

 

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