Today, we are going to list down the top Data Science tools that can make the career path of a Data Scientist smooth and much easier. Let’s have a look at the topics discussed in this blog.
Introduction to Data Science
Data Science is the process of drawing useful insights from raw data using a combination of different components such as domain expertise, knowledge of math and statistics, programming skills, and Machine Learning models. The data insights are later translated by business users and key decision-makers of an organization into tangible business value.
Data Science has turned out to be one of the most popular tech fields in the 21st century. As almost every industry, including healthcare, travel, automobile, defense, and manufacturing, has applications associated with Data Science. These wide ranges of applications and increased demand for maximizing business value have led to the development of various Data Science tools.
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Top Data Science Tools
Data Science tools are used for diving into raw and complicated data (unstructured or structured data) by processing, extracting, and analyzing it to dig out valuable insights by applying different data processing techniques such as statistics, computer science, predictive modeling and analysis, and Deep Learning.
To deal with zettabytes and yottabytes of this structured or unstructured data every day and finding valuable insights from it, Data Scientists use a wide range of tools at different phases of the Data Science life cycle. The significant feature of these tools is that we don’t have to use many sophisticated programming languages to implement the Data Science procedure as these tools contain some predefined algorithms, functions, and very user-friendly graphical user interfaces (GUIs).
There are ample Data Science tools in the industry. Hence, picking one for your journey and career can be a tricky decision. To make a decision on which tools to pick and learn for your career, check out the below listed most demanding Data Science tools.
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Python is one of the most dominant languages in the field of Data Science today because of its flexibility, ease in terms of syntax, open-source nature, and also, its ability to handle, clean, manipulate, visualize, and analyze data. Python was essentially developed as a programming language. However, it offers a wide range of libraries, such as TensorFlow, Seaborn, etc., attractive for both programmers and Data Scientists alike. Moreover, there are various other tools connected to and built with the help of Python, such as Dask, SciPy, Cython, Matplotlib, and HPAT.
Key features and usage:
- Used for data cleaning, data manipulation, data visualization, and data analysis
- Provides a wide range of libraries such as Pandas, NumPy, Matplotlib, and many more
- Helps establish a connection with various other tools such as Cython and Dask
- Preferred by almost all Data Scientists, beginners as well as experienced professionals
R is a powerful and respected programming language in the world of Data Science used extensively for statistical computing and graphics. It provides numerous packages and libraries that support different phases of the Data Science life cycle. Apart from all its functionality, R has an incredibly large and supportive community as well, where you can find answers to any question or query you encounter while working with R.
To use this magical language and play around with it, you need RStudio. It is an open-source software that helps you handle, clean, and manipulate data, and then analyze the same. RStudio provides a user-friendly interface for using the R programming language effectively.
Key features and usage of the R programming language:
- Provides a large and coherent collection of tools for data analysis
- Offers effective data handling and storage facilities
- Perfect for statistical computing, design, and analyses
- Provides graphical functionalities for data analysis, displaying the output either on the computer screen or on papers
Apache Hadoop is an open-source framework that helps in the distributed processing and computing of large datasets over a cluster of 1000s of computers, i.e., it can store and manage piles of data. It is an ideal tool when you need to deal with large data processing and high-level computations.
Check out some of the important features and usage of Hadoop:
- Efficiently scales massive amounts of data on 1000s of Hadoop clusters
- Uses Hadoop Distributed File System (HDFS) for data storage and to achieve parallel computing
- Provides fault tolerance and high availability even in unfavorable conditions
- Provides the integrated functionality with other data processing modules, such as Hadoop YARN, Hadoop MapReduce, and so many others
BigML is a scalable Machine Learning platform that allows users to leverage and automate techniques such as classification, regression, cluster analysis, time series, anomaly detection, forecasting, and other prominent ML methods in a single framework. BigML provides a fully interchangeable, cloud-based GUI environment that aims to reduce platform dependencies for processing Machine Learning algorithms. It also offers customized software for using Cloud Computing for organizational needs and requirements.
Below given are the major features of BigML and its usages:
- Helps process Machine Learning algorithms
- Builds and visualizes Machine learning models with ease
- Deploys methods such as regression (linear regression, trees, etc.), classification, and time-series forecasting for supervised learning
- For unsupervised learning, uses cluster analysis, association discovery, anomaly detection, etc.
Statistical Analysis System (SAS)
SAS is a statistical and complex analytics tool developed by SAS Institute. It is one of the oldest Data Analysis tools mainly built to deal with statistical operations. SAS is commonly used by professionals and organizations that heavily rely on advanced analytics and complex statistical operations. This reliable commercial software provides various statistical libraries and tools that a Data Scientist professional can use for modeling and organizing the given data.
What are the key features and usage of this Data Science tool? Check out the below list:
- Easy to learn as it comes with ample tutorials and dedicated technical support
- Offers a simple GUI yet powerful reports
- Carries out the analysis of textual content, even with typo identification
- Provides a well-managed suite of tools, dealing with areas such as data mining, clinical trial analysis, statistical analysis, Business Intelligence applications, econometrics, and time series analysis
DataRobot is an AI-driven development and automation platform that helps in building accurate and automotive predictive models. DataRobot assists in the easy implementation of a wide range of Machine Learning algorithms, including regression, classification, and clustering models.
Key features and usage of this tool are as follows:
- Provides parallel programming by directing 1000s of servers to perform multitasking on data analysis, data validation, modeling, and so on
- Offers lightning-fast speed when it comes to building, training, and testing Machine Learning models
- Helps in scaling up the whole Machine Learning process
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Here are some of the other important features this tool offers:
- Emphasizes the usage of web standards to utilize the full potential of modern browsers
- Merges powerful visualization modules and a data-driven process into Document Object Model (DOM) manipulation
- Helps apply data-driven transformations to documents after binding data to DOM
Excel refers to the powerful analytical tool used extensively in the field of Data Science as it helps build powerful data visualizations and spreadsheets that are ideal for robust data analysis. Excel comes with numerous formulae, tables, filters, slicers, etc., and apart from all that functionalities, it also allows users to create their own custom formulae and functions. It can also be connected with SQL and further used for data analysis and data manipulation. Data Scientists use Excel for data cleaning purposes as well due to its interactable GUI environment that helps them pre-process the data with ease.
The other key features of Excel are as given below:
- Good for cleaning and analyzing 2D (rows and column) data and easy for beginners
- Helps sort and filter data with a single click to quickly and easily explore your dataset
- Offers pivot tables to summarize data and operate functions such as sum, count, and other metrics in a tabular format
- Visualizations extracted help present a variety of creative solutions
Azure HDInsight is a full-fledged cloud platform created by Microsoft to aid processes such as data processing, storage, and analytics. Big enterprises, including Jet, Adobe, Milliman, etc., use this tool to store, process, manage, and extract valuable insights from tons of data.
Check out the other features and usages of this tool below:
- Provides support for integrating with different tools such as Apache Spark and Apache Hadoop for data processing
- Uses Windows Azure Blob as the default storage system to effectively manage sensitive data across thousands of nodes
- Provides Microsoft R Server as a function that supports R for performing statistical analysis and helps in creating robust Machine Learning models
Jupyter is the open-source Data Science tool that is predominantly used for coding Python programs but also supports other languages such as Julia, R, and Fortran. Jupyter works as a computational notebook that consists of different components, including code, visualizations, equations, and text.
One of the most prominent features of Jupyter is that you can easily share your code files or your work with your peers in the form of an executable notebook and have interactive output in the form of mind-blowing plots, images, etc. You can easily integrate this tool with other tools used massively in the process of data analysis like Apache Spark.
Important characteristics of this tool include the following:
- Supports more than 40 programming languages
- Offers a user-friendly interface for executing code files
- Provides interactive features with the help of computational kernels
- Establishes connections with other data-driven solutions such as Apache Spark
TensorFlow is a powerful library that revolves around Artificial Intelligence, Deep Learning, and Machine Learning algorithms and helps in creating and training models and deploying them on various platforms such as smartphones, computers, and servers to achieve the functionalities assigned to the respective models.
It is considered to be one of the most flexible, fast, scalable, and open-source Machine Learning libraries used extensively in the field of production and research. Data Scientists prefer TensorFlow as it uses data flow graphs for numerical computations.
Important points to note about TensorFlow are as follows:
- Provides the architecture for deploying computation on diverse types of platforms, which include servers, CPUs, and GPUs
- Offers powerful tools to operate with data by filtering and manipulating it for performing data-driven numerical computations
- Flexible in conducting Machine Learning and deep neural network processes
Matplotlib is a visualization and plotting library developed for Python. It is one of the most powerful tools for generating interactive graphs with the analyzed data. Matplotlib is mainly used for plotting much-needed and complex graphs using simple Python code. By working with this tool, you can create different types of graphs, such as histograms, bar plots, scatter plots, etc., using Pyplot, which is considered an essential module of Matplotlib.
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Major features and usage of this tool are listed below:
- Builds diverse plots, histograms, power spectra, bar charts, scatterplots, error charts, and more with simple lines of code
- Helps create compelling visualizations
- Provides certain formatting function line styles, axes properties, font properties, etc., which can be used to increase the readability of plots
- Offers several export options to extract the plot or visualization and put it on the platform of your choice
Tableau is referred to as a data visualization software that comes with powerful graphics to create interactive visualizations. It is massively used by industries working in the field of Business Intelligence and analytics.
The most significant features of Tableau is its ability to interact with different spreadsheets, databases, OLAP (Online Analytical Processing) cubes, etc. Apart from these features, Tableau can also visualize geographical data by plotting longitudes and latitudes on maps.
The following are the other main features of Tableau:
- Allows to connect with and extract data from multiple data sources and has the ability to visualize massive datasets to find patterns and correlations
- The desktop feature helps create customized reports and dashboards to get real-time insights and updates
- The cross-database join functionality allows you to make calculated fields and join tables for solving complex data problems
MATLAB (matrix laboratory) is a multi-paradigm programming language that helps in providing a numerical computing environment for processing mathematical expressions. The most significant feature of this software is that it helps users with algorithmic implementation, matrix functions, and statistical modeling of data, and it is massively used in different scientific disciplines.
MATLAB is used in the field of Data Science for simulating fuzzy logic and neural networks and for creating powerful visualizations.
What are the other usages of this software? See below:
- Helps develop algorithms and models
- Merges the desktop environment with a programming language for iterative analysis and design processes
- Provides an interface consisting of interactive apps to test how different algorithms work when applied to the data at hand
- Helps automate and reproduce your work by automatically generating a MATLAB program
- Scales up the process of analysis to run on clusters, the cloud, or GPUs
RapidMiner is a software that provides an integrated Data Science platform used for data pre-processing and preparation, Machine Learning, Deep Learning, and predictive modeling deployment.
In Data Science, RapidMiner provides tools that allow you to design and modify your model from its initial phase until its deployment.
Characteristics of RapidMiner:
- Uses the computational power of free studio and enterprise server resources for efficient model development
- Allows integration with Hadoop with its built-in RapidMiner Radoop
- Automated modeling helps generate predictive models
- Provides the feature of remote execution for analysis processes
QlikView is a leading Business Intelligence and analytics tool used for conversational analytics, data integration, and converting raw data into informative insights. It facilitates in-memory data processing and helps store the processed data in self-created reports.
QlikView is also considered as one of the most powerful Data Science tools for visually analyzing data to derive useful business insights, and it is used by more than 24,000 organizations globally.
Listed below are the features and usages of this BI and Data Science tool:
- Provides tools to create powerful dashboards and detailed reports
- Offers in-memory data processing for the efficient and fast creation of reports for end-users
- Generates and automates the associations and relations in data using the data association feature
- Helps maximize the performance of every kind of enterprises (big or small) with the help of features such as collaboration and sharing, data security provisions, integrated framework, and guided analytics for efficient working of an organization
Data Science Tools : Conclusion
In this data-driven world, data is playing a determining role for any organization surviving in this competitive era, and by utilizing this data, Data Scientists require to provide the key decision-makers of any firm with impactful insights, which is almost impossible to imagine without the use of the above-listed powerful Data Science tools.
Data Science tools provide a way for analyzing data, creating interactive visualizations with aesthetics, and developing powerful and automated predictive models using Machine Learning algorithms, which ease the process of extracting and delivering more valuable insight from the raw and useless data.
After going through the entire blog, you might have understood that one of the most prominent features of all these Data Science tools is that they provide a user-friendly interface with built-in functions for conducting computing on data, increasing the efficiency, and reducing the amount of code needed to extract value from the given data resources for fulfilling the needs of the end-users. Therefore, selecting one tool among them should depend on the specific requirements of different use cases.
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