Data science certification is the field that is growing the fastest and has become a buzzword that everyone knows and uses. This diverse range of fields, which have nothing in common, all work with data in various ways. It helps businesses make sense of their data and get useful insights from it. According to the GlobeNewswire report, the data science market is expected to grow at a compound annual growth rate (CAGR) of 25% by 2030. Data science mostly works with data by putting it into different groups, collecting it, organising it, cleaning it, and getting it ready for analysis and display.
Some research shows that the market demand for data science is growing every day because companies are finding new ways to use data to open new doors. With the growing need for data science, there are also a lot more job opportunities. You can get started in this field by taking an online course in data science. All of the different ways that data science analyses and draws conclusions from data require the right tools. To do well as a data science professional—data analyst, data engineer, or data scientist—you need to be very skilled with these tools. In this article, you will learn about all the latest data science tools and frameworks that data science professionals will need throughout their careers.
Top 25 Tools for Data Science in 2023
Data science tools are software applications or frameworks that help people who work in data science do things like analyse, clean, visualise, mine, report, and filter data. Each of these tools has a specific way to use it. If you get a certification in data science with Python, you will learn how to use all the current tools in data science. Let’s find out what these tools are and how they help professionals and data scientists.
Tools for many uses
- MS Excel:
It is the most important tool that everyone should know how to use. This tool makes it easy for new people to analyse and understand data. MS Excel is part of the MS Office suite of programmes. Before getting into high-level analytics, both new professionals and those who have been in the field for a while can get a basic idea of what the data is trying to say. It can help you understand the data quickly, has formulas built in, and gives you charts and graphs and other ways to look at the data. With MS Excel, people who work in data science can easily show the data in rows and columns. Even a user who doesn’t know much about technology can understand this.
Tools in the cloud
- BigML:
BigML is an online, cloud-based tool for data science and machine learning that is driven by events. The drag-and-drop features of this GUI-based tool make it easy for people with little or no experience to make models. BigML is a tool that can help professionals and businesses combine data science and machine learning projects for different business operations and processes. A lot of companies use BigML to figure out risks, analyse threats, predict the weather, and do other things. It uses REST APIs to make easy-to-use web interfaces. Users can also use it to make interactive data visualisations. It also has a lot of automation features that make it possible for users to get rid of manual data workflows.
- Google Analytics:
Google Analytics (GA) is a professional tool and framework for data science that gives an in-depth look at the performance of an enterprise website or app so that data-driven insights can be gained. Professionals in data science work in many different fields. Digital marketing is one of them. This data science tool helps with digital marketing, and Google Analytics makes it easy for the web administrator to get to, see, and analyse the website’s traffic, data, etc. It can assist businesses in determining how their customers, or end users, interact with their website. This tool can work closely with other Google products like Search Console, Google Ads, and Data Studio. Because of this, it is a popular choice for anyone who uses multiple Google products. Data scientists and people in charge of marketing can make better marketing decisions with the help of Google Analytics. With its high-end features and easy-to-use interface, even a data science professional who isn’t very technical can use it to do data analytics.
Multipurpose data science tools
- The Apache Spark:
Apache Spark is a well-known tool, framework, and library for data science. It has a powerful analytics engine that can handle both batch processing and processing in real time. It can do real-time data analysis and cluster management. It works much faster than tools like Hadoop that are used for analytic work. Besides helping with data analysis, it can also help with projects that involve machine learning. It works with a number of built-in machine learning APIs that let machine learning engineers and data scientists make models that can predict the future. In addition to all of these, Apache Spark has different APIs that programmers who use Python, Java, R, or Scala can use in their programmes.
- Matlab:
Matlab is a closed-source, high-performance, numerical, computational, simulation-making, multi-paradigm data science tool for handling mathematical and data-driven tasks. Researchers and data scientists can use this tool to do matrix operations, analyse the performance of algorithms, and model data statistically. This tool combines visualisation, mathematical computation, statistical analysis, and programming into an easy-to-use ecosystem. Data scientists use Matlab for a lot of different things, like processing signals and images, simulating neural networks, and testing different data science models.
- SAS:
SAS is a popular data science tool made by the SAS Institute for advanced analysis, multivariate analysis, business intelligence (BI), data management operations, and predictive analytics to learn about the future. This closed-source software is used by many MNCs and Fortune 500 companies for statistical modelling and data analysis. Its graphical interface, SAS programming language, and Base SAS support a wide range of data science functions. This tool makes it easy to get data from database files, online databases, SAS tables, and Microsoft Excel tables. Using its statistical libraries and tools, it can also be used to change existing data sets to get insights based on the data.
- KNIME:
KNIME is an open-source and free data science tool that is used by a lot of people. It helps with data reporting, data analysis, and data mining. With this tool, people who work in data science can quickly extract and change data. Using its modular data pipeline concept, it makes it possible to combine different data analysis and data-related components for machine learning (ML) and data mining. It has a great graphical interface that makes it easier for people who work in data science to define the workflow between the different predefined nodes in its repository. Because of this, people who work in data science need very little programming knowledge to do data-driven analysis and operations. It has visual data pipelines that help make interactive visuals for a given dataset.
- Apache Flink:
Flink is another piece of data science software from Apache that helps analyse data in real time. It is one of the most popular open-source batch-processing data science tools and frameworks. It uses its distributed stream processing engine to do different data science operations. A lot of the time, data scientists and other professionals need to analyse and compute data in real time. This includes data from users’ web activities, measuring data from Internet of Things (IoT) devices, location-tracking feeds, financial transactions from apps or services, and so on. Flink can do both parallel and pipelined execution of data flow at a lower latency in this case. It uses batch processing to deal with this huge flow of unbounded (no fixed start or end point) data streams as well as stored datasets that are bound. Apache Flink is known for its ability to process and analyse data quickly while making real-time data processing less complicated.
Programming language-driven tools
- Python:
Python is by far the most used programming language in data science. Python is also thought of as a data science tool. It helps data scientists analyse data from large datasets and different types of data (structured, semi-structured, and unstructured). This high-level, general-purpose, dynamic, interpreted programming language has a built-in data structure and a huge collection of libraries that can help with data analysis, data cleaning, data visualisation, etc. It is easy to learn and has simple grammar. It also makes it cheaper to keep data science programmes running. Since this programming language can be used to make mobile, desktop, and web apps and can also be used for data science, many people choose to learn it so they can use it for both data science and software development. It has a very helpful community, and people keep making modules and libraries that make data science and programming easier.
- R Programming:
When it comes to data science, R is a strong programming language that can compete with Python. It is often used by professionals and businesses to do statistical computing and data analysis. It has a great user interface, and its interface is automatically updated to make programming and data analysis better. It has great support from the team and the community, which makes it a valuable tool for data science. It is scalable because it has a huge number of data science packages and libraries, such as tidyr, dplyr, readr, SparkR, data.table, ggplot2, etc. R does more than just statistics and data science. It also uses powerful machine learning algorithms in an easy and quick way. This open-source programming language has object-oriented features and 7,800 packages. R Studio is used to run the whole language.
- Jupyter Notebook:
This computational notebook is a popular data science and web application that makes it easier to manage and interact with data. Aside from people who work in data science, this tool is also used by researchers, mathematicians, and even people who are just starting out with Python. It is mostly known for its easy-to-use data visualisation and computing features. Analysts and people who work in data science can run a single line of code or many lines of code. It grew out of the I Python project and works with programming languages like Julia, Python, and R.
- MongoDB:
MongoDB is a cross-platform, open-source, document-oriented NoSQL database management software that lets data science professionals manage semi-structured and unstructured data. It can be used instead of a traditional database management system, in which all the data has to be set up in a certain way. MongoDB is a tool for data scientists that helps them manage document-oriented data, store information, and get it when they need it. It is easy to use with large amounts of data and can do everything that SQL can do and more. It also endorses executing dynamic queries. MongoDB stores data as documents in a format similar to JSON, and it can also do high-level data replication. With the help of MongoDB, it’s easier to deal with big data because it makes more data available. MongoDB can do more than just basic database queries. It can also do advanced analytics. It also lets you scale your data, which makes it one of the most popular data science tools (2023).
- D3.js:
Data-Driven Document is a popular JavaScript library for data science operations. Its full name is “Data-Driven Document. It aids in the creation and presentation of interactive visualisations of data science results in web browsers. For data processing and visualisation, this great data science tool needs client-based interactions. D3.js also has APIs that let its users add different features for both analysing datasets and making dynamic visualisations that can run on any web browser. This tool also works with CSS to help you make beautiful data visualisations and animated data transitions. It also lets you make dynamic documents by letting you make changes on the client side and keeping an eye on how the data changes to make rich visualisations. It can use data that doesn’t change or get data from other servers. For making different types of charts and graphs, it can work with different formats like objects, JSON, arrays, CSV, XML, etc.
- Julia:
Julia is a general-purpose, high-level programming language that makes writing code for data science faster and easier. It can do scientific calculations quickly and optimise how experiments and strategies are put into action in datasets. Many experts in data science see it as the next step after Python. Its just-in-time (JIT) compiling power can match the speed of efficient programming languages like C++, Java, etc., especially when doing data science operations. The complex statistical calculations that are part of data science can be done quickly and with less processing power. It also lets you manually collect garbage, and people who work in data science don’t have to worry about memory management. It’s the third most popular programming language for data science after Python and R, and it’s getting more and more popular among data scientists.
Visualization Tools
- Tableau:
Tableau was started in 2003 and has since become one of the best data visualisation and business intelligence (BI) tools. It is used by top MNCs and enterprises in many different industries. Tableau helps data scientists understand and solve advanced and complicated problems with data analysis and visualisation quickly. It allows for a lot of different ways to show data, which makes it easier to understand. This tool is used by more than 60,000 companies to visualise data and make interactive dashboards.
- Matplotlib:
It is a popular library for plotting and charting data in two dimensions. It works with NumPy, SciPy, and Pandas and requires that you know how to code in Python. In 2002, John Hunter made this library for visualising data that can be used on different platforms. One of Matplotlib’s best features is that it can make complex graphs and plots with just a few lines of code. Data analysts and data scientists can use Matplotlib to make graphs like bar plots, pie charts, histograms, scatterplots, and more. This library for data science has an object-oriented API that makes it easy to add plots to other programmes that use general-purpose GUI modules like Tkinter, wxPython, etc. This tool is great for people who are just starting out with Python and want to learn how to make fast-loading data visualisations.
- Minitab:
Minitab is a well-known piece of statistical software that can be used to solve problems, find trends, and find insights from small amounts of data. It gives the most complete and best results in its class. Professionals in the field of data science use this tool to analyse and change data. This tool also makes it easier to find patterns in the way unstructured data flows. Minitab also lets people who work in data science automate different tasks and make graphs. Minitab can also use different data points to make descriptive statistics, such as the standard deviation, mean, median, etc. This tool helps with more than just handling statistical and graphical data. It also helps with regression analysis.
Tools for data science that use NLP and machine learning
- How to Learn:
Scikit-learn is a free library for machine learning built on Python code. It has a wide range of supervised and unsupervised machine learning algorithms that were made with the help of other data science tools and libraries like Matplotlib, Pandas, NumPy, and SciPy. This library is a set of different functions, such as regression analysis, data classification, data clustering, model selection, data pre-processing, dimensionality reduction, etc. Scikit-first’s job is to make it easier to run complicated ML algorithms. It has become a well-known tool that is perfect for putting machine learning into applications that need quick prototypes. Scikit Learn has a lot of community support, and anyone can help improve and develop it.
- Tensor Flow:
It is the most widely used tool in data science, and its machine learning (ML) and deep learning (DL) libraries are what make it so popular. It lets data scientists and machine learning engineers build algorithms or models for data analysis and machine learning. It also has features for showing things. Using TensorFlow, data scientists can group data science and machine learning models and train them using large datasets. This helps build systems that can come up with sensible results on their own. Data scientists and machine learning (ML) engineers use TensorFlow with Python to analyse data and learn something useful from it. This open-source library was made by the Google Brain team to handle large-scale supervised, semi-supervised, and unsupervised learning as well as complex numerical computations. TensorFlow is used by businesses for hand-written character classification, image recognition, word embeddings, natural language processing (NLP) to teach machines human languages, recurrent neural networks, sequence-to-sequence models for machine translation, and PDE (partial differential equation) simulations. Differential programming can be done by professionals in data science with this easy-to-use tool. With this tool, data scientists can easily put data science models on different apps and devices. TensorFlow uses a type of data called a “tensor,” which is an array with N dimensions. This is how it got its name.
- Rapidminer:
Rapidminer is an all-in-one tool for data science that lets you build workflows visually and automate everything. It can make any data science and machine learning model from scratch with no problems. Data scientists can also use this tool to track data in real time and do high-end analytics. Developers, non-developers, newcomers to the field of data science, and even non-technical aspirants can use this tool to practise rapid data mining, build custom workflows, and use data science functions. This graphical user interface (GUI) tool can do many different data science tasks, such as real-time data analysis, predictive analysis, text mining, complete data reporting, model validation, and so on. It also has high scalability and security, which makes it a great tool. With this tool, businesses can build their own algorithms and apps for data science from scratch.
- Data Robot:
Data Robot is a well-known data science tool where data scientists and machine learning (ML) engineers can combine data science tasks with ML and AI to make the team more productive as a whole. It lets you use datasets by dragging and dropping them on the interface. It has a simple and easy-to-use graphical user interface (GUI) that makes it faster and easier for both new and experienced data science professionals to do different data analytics tasks. Professionals can also use Data Robot to build and use hundreds of data science models at once. Enterprises use this tool to automate user data in a high-level way. This tool is also great for doing predictive analysis, and it can help people make smart, data-driven decisions that make sense.
- NLTK:
Natural Language Toolkit (NLTK) is another toolkit built on Python that helps understand natural languages and make algorithms that work with them. With the progress in understanding natural language processing (NLP) to understand spoken or written language data or to use NLP in apps, data science firms have started using data science tools like NLTK. Because it is easy to use, NLTK has become a common and popular tool among people who work in data science. Also, it works with more than 50 collections of data, such as lexical resources and WordNet, to build language-based ML models. Lots of programmes need NLTK to tokenize, show how words look, and make a parse tree so they can understand the language better. It has an easy-to-use text processing feature and helps companies make different language-based apps like machine translation, speech recognition, parts of speech tagging, text-to-speech, and word segmentation.
Big Data Tools
- Apache Hadoop:
Hadoop, which is made by Apache and written in Java, is used in data science on a large scale. The parallel data processing of this open-source software is well known. It can store and process big data, which is necessary for data analysis. Any big file is broken up into smaller pieces that are then sent to different nodes. In other words, Hadoop works by spreading large data sets and data analytics processes across many smaller nodes in a computing cluster. These nodes help divide data into smaller tasks that can be done at the same time. Hadoop can speed up the processing of both structured and unstructured data and help data scientists and other professionals manage different types of data, even as the amount of data grows.
Tools for Business Intelligence and Data Science
- Power BI from Microsoft:
Microsoft Power BI is a powerful business intelligence suite and one of the most recommended data science tools of 2023. It helps both individuals and teams make beautiful data reports and visualisation services. It can be used with other Microsoft data science tools, such as MS Excel, Azure Synapse Analytics, Azure Data Lake, etc., to improve the performance of your team and your own productivity. This tool is used by many data analytics and business intelligence companies to build their data analytics dashboards. Enterprises also use Power BI to turn data sets that don’t make sense into sets that do. This tool also helps us make a logically consistent and invariant dataset from other original data, which can help us come up with a lot of useful insights. Its clear and detailed data visualisation makes it easy for even people who aren’t tech-savvy to understand data insights.
- Q lik View:
It is one of the best business tools for data science, and it has features that other BI tools don’t have. Data scientists can use Q lik View to determine and analyse the relationships between semi-structured and unstructured data. Also, compared to other data science tools on the market, it processes data much faster. Users of Q lik View can also use colours to see how different pieces of data relate to each other. With Q lik View, people who work in data science can gather and compress data more quickly. Q lik View figures out automatically how data entities are linked, so professionals don’t have to spend much time doing this.
Learning How to Use the Tools of the Trade
We hope that this article has given you a clear picture of all the popular data science tools that professionals use to speed up their work. Data scientists and experts in data science have to work with a wide range of tools, such as programming tools, big data tools, data science libraries, machine learning (ML) tools, data visualisation tools, and data analysis tools. All of these tools and frameworks for data science help them look at small pieces of data and figure out what they mean. With the right education, you can figure out how to use these tools. Emerging India Analytics certification in data science with Python is one of the best online courses you can take to do well in a career in data science.
