Top 10 tools a data scientist should use in 2021

The work of a data scientist centers around the process of extraction of meaningful data from unstructured information and analyzing that data for necessary interpretation. This requires a lot of useful tools. The following are the top 10 most necessary tools that a data scientist needs to know about in 2021.

Python

Python is the most widely used programming language for data science and machine learning and one of the most popular languages overall. The Python open-source project’s website describes it as “an interpreted, object-oriented, high-level programming language with dynamic semantics,” as well as built-in data structures and dynamic typing and binding capabilities. The site also touts Python’s simple syntax, saying it’s easy to learn and its emphasis on readability reduces the cost of program maintenance. The multipurpose language can be used for a wide range of tasks, including data analysis, data visualization, AI, natural language processing, and robotic process automation. Developers can create web, mobile, and desktop applications in Python, too. In addition to object-oriented programming, it supports procedural, functional, and other types, plus extensions written in C or C++.

Jupyter Notebook

Jupyter Notebook is an open-source web application that enables interactive collaboration among data scientists, data engineers, mathematicians, researchers, and other users. It’s a computational notebook tool that can be used to create, edit and share code, as well as explanatory text, images, and other information. Jupyter users can add software code, computations, comments, data visualizations, and rich media representations of computation results to a single document, known as a notebook, which can then be shared with and revised by colleagues. As a result, notebooks “can serve as a complete computational record” of interactive sessions among the members of data science teams, according to Jupyter Notebook’s documentation. The notebook documents are JSON files that have version control capabilities. In addition, a Notebook Viewer service enables them to be rendered as static web pages for viewing by users who don’t have Jupyter installed on their systems.

Apache Spark

Apache Spark is an open-source data processing and analytics engine that can handle large amounts of data, upward of several petabytes, according to proponents. Spark’s ability to rapidly process data has fueled significant growth in the use of the platform since it was created in 2009, helping to make the Spark project one of the largest open-source communities among big data technologies. Due to its speed, Spark is well suited for continuous intelligence applications powered by near-real-time processing of streaming data. However, as a general-purpose distributed processing engine, Spark is equally suited for extract, transform and load uses and other SQL batch jobs. Spark initially was touted as a faster alternative to the MapReduce engine for batch processing in Hadoop clusters.

D3.js

Another open-source tool, D3.js is a JavaScript library for creating custom data visualizations in a web browser. Commonly known as D3, which stands for Data-Driven Documents, it uses web standards, such as HTML, Scalable Vector Graphics, and CSS, instead of its graphical vocabulary. D3’s developers describe it as a dynamic and flexible tool that requires a minimum amount of effort to generate visual representations of data. D3.js lets visualization designers bind data to documents via the Document Object Model and then use DOM manipulation methods to make data-driven transformations to the documents. First released in 2011, it can be used to design various types of data visualizations and supports features such as interaction, animation, annotation, and quantitative analysis. D3 includes more than 30 modules and 1,000 visualization methods, making it complicated to learn. In addition, many data scientists don’t have JavaScript skills. As a result, they may be more comfortable with a commercial visualization tool, like Tableau, leaving D3 to be used more by data visualization developers and specialists who are also members of data science teams.

TensorFlow

TensorFlow is an open-source machine learning platform developed by Google that’s particularly popular for implementing deep learning neural networks. The platform takes inputs in the form of tensors that are akin to NumPy multidimensional arrays and then uses a graph structure to flow the data through a list of computational operations specified by developers. It also offers an eager execution programming environment that runs operations individually without graphs, which provides more flexibility for research and debugging machine learning models. Google made TensorFlow open source in 2015, and Release 1.0.0 became available in 2017. TensorFlow uses Python as its core programming language and now incorporates the Keras high-level API for building and training models. Alternatively, a TensorFlow.js library enables model development in JavaScript, and custom operations can be built in C++.

Keras

Keras is a programming interface that enables data scientists to more easily access and use the TensorFlow machine learning platform. It’s an open-source deep-learning API and framework written in Python that runs on top of TensorFlow and is now integrated into that platform. Keras previously supported multiple back ends but was tied exclusively to TensorFlow starting with its 2.4.0 release in June 2020. As a high-level API, Keras was designed to drive easy and fast experimentation that requires less coding than other deep learning options. The goal is to accelerate the implementation of machine learning models, in particular, deep learning neural networks through a development process with “high iteration velocity,” as the Keras documentation puts it. The Keras framework includes a sequential interface for creating relatively simple linear stacks of layers with inputs and outputs, as well as a functional API for building more complex graphs of layers or writing deep learning models from scratch.

Xplenty

Xplenty, is data integration, ETL, and an ELT platform that can bring all the data sources together. It is a complete toolkit for building data pipelines. This elastic and scalable cloud platform can integrate, process, and prepare data for analytics on the cloud. It provides solutions for marketing, sales, customer support, and developers. Sales solution has the features to understand your customers, for data enrichment, centralizing metrics & sales tools, and for keeping your CRM organized. Its customer support solution will provide comprehensive insights, help you with better business decisions, customized support solutions, and features of automatic Upsell & Cross-Sell. Xplenty’s marketing solution will help you to build effective, comprehensive campaigns and strategies. Xplenty contains the features of data transparency, easy migrations, and connections to legacy systems.

IBM SPSS

IBM SPSS is a family of software for managing and analyzing complex statistical data. It includes two primary products: SPSS Statistics, a statistical analysis, data visualization, and reporting tool, and SPSS Modeler, a data science and predictive analytics platform with a drag-and-drop UI and machine learning capabilities. SPSS Statistics covers every step of the analytics process, from planning to model deployment, and enables users to clarify relationships between variables, create clusters of data points, identify trends and make predictions, among other capabilities. It can access common structured data types and offers a combination of a menu-driven UI, its command syntax, and the ability to integrate R and Python extensions, plus features for automating procedures and import-export ties to SPSS Modeler. Created by SPSS Inc. in 1968, initially with the name Statistical Package for the Social Sciences, the statistical analysis software was acquired by IBM in 2009, along with the predictive modeling platform, which SPSS had previously bought. While the product family is officially called IBM SPSS, the software is still usually known simply as SPSS.

PyTorch

An open-source framework used to build and train deep learning models based on neural networks, PyTorch, is touted by its proponents for supporting fast and flexible experimentation and a seamless transition to production deployment. The Python library was designed to be easier to use than Torch, a precursor machine learning framework that’s based on the Lua programming language. PyTorch also provides more flexibility and speed than Torch, according to its creators. First released publicly in 2017, PyTorch uses arraylike tensors to encode model inputs, outputs, and parameters. Its tensors are similar to the multidimensional arrays supported by NumPy, another Python library for scientific computing, but PyTorch adds built-in support for running models on GPUs. NumPy arrays can be converted into tensors for processing in PyTorch, and vice versa.

Knime

KNIME, for data scientists, will help them in blending tools and data types. It is an open-source platform. It will allow them to use the tools of their choice and expand them with additional capabilities. It is very useful for the repetitive and time-consuming aspects. Experiments and expands to Apache Spark and Big data. It can work with many data sources and different types of platforms.

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Analytics Insight provides a glimpse at the Top 10 AI Trends you must know in 2021.

It is very difficult to find out one industry, that has not adopted smart machines and models, integrated with Artificial Intelligence across the world. The world has embraced the amazing functionalities of Artificial Intelligence and machine learning algorithms to boost productivity as well as ensure higher customer engagement. People are using these smart machines, even in their homes to adjust to this fast-paced life in the tech-driven era. Indeed, there is an immense scope of Artificial Intelligence in the upcoming years to enhance the standard of living of society.  The Artificial Intelligence market size is expected to reach US$266.92 billion by 2027, with a CAGR of 33.2%. Artificial Intelligence trends are instigating organisations as well as common people to wait for further new AI innovations. Hence, let’s take a glimpse at the top 10 Artificial Intelligence trends in 2021 to know what is waiting for us in the nearby future.

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Top 10 Artificial Intelligence Trends you must know in 2021

Ethical AI

Some reputed companies such as Google, Microsoft, Apple, Facebook and other tech giants are building ethical AI to follow an ethical framework with four essential principles for effective data governance— fairness, accountability, transparency well to explainability. This is currently the most popular Artificial Intelligence trend in 2021 for providing the inside look into its own system to stakeholders. These companies are initiating multiple programmes and research to encourage other companies to adopt ethical AI with personalised strategies as per the requirements of a business.

Explainable AI

Explainable AI is a part of ethical AI that provides a complete explanation of how the Artificial Intelligence models and machine learning algorithms are working inside to generate the appropriate meaningful business insights and predict the future. Companies leveraging disruptive technologies are required to maintain transparency to stakeholders with a full explanation. But it is creating controversy because companies do not want to disclose all their steps and processes to the public for patent purposes in a cut-throat competitive market.

Predictive Analytics

Predictive analytics enables all kinds of businesses to identify the trends of consumers for a better understanding of consumer behaviour in the current scenario. It predicts all potential responses from the target audience by employing personalised data that are collected for a long time. The advancement in Artificial Intelligence and machine learning algorithms are providing more accurate predictions and insights to maintain better customer engagement and gain higher ROI from the global market.

Emotional AI

Emotional AI, is one of the most popular Artificial Intelligence trends in 2021because this technology can sense, learn and interact with multiple human emotions. It is also known as affective computing that enhances human-robot communication to a whole new level. Emotional AI can understand consumer behaviour through verbal as well as non-verbal signals. Hi-tech cameras and Chatbots can easily detect various types of human emotions by studying the reactions to certain contents, products and services. This advancement in Artificial Intelligence has an immense scope in the retail industry in the nearby future.

AI with AR and VR

Augmented Reality and Virtual reality are already providing immersive experiences to consumers as well as industries all around the world in these recent years. The combination of these three disruptive technologies- Artificial Intelligence, Augmented Reality and Virtual Reality has the potential to revolutionise the world with its amazing functionalities. The trio has already started to transform the relationship between customers and companies by providing extra personalisation and customisation of products and services to meet the needs and wants of each customer.

AI in Robotics

Robotics is taking over industries with its useful functionalities in every possible way around the world. There is a common presence of Artificial Intelligence in Robotics solutions that makes robots smarter and intelligent like never before. It is, indeed, a powerful combination to enhance customer service cost-effectively. Robots can perform successful surgeries, dance, protect employees from harmful environments and many more activities by leveraging Artificial Intelligence into RPA.

AI in Cybersecurity

The data-driven world has created a data explosion in these recent years that is difficult for organisations to protect the sets from malicious hackers. The integration of Artificial Intelligence in cybersecurity has created more advanced and powerful defence against harmful cyberattacks like phishing, ransomware, virus and so on. AI can instantly detect any unusual activity in the existing systems and alert the employees as soon as possible. It is making it more difficult for hackers and frauds to enter any system. Artificial Intelligence enhances cybersecurity through intelligent code analysis and configuration analysis with activity monitoring.

AI in Computer Vision

The integration of Artificial Intelligence, in Computer Vision, has transformed existing computer systems into smart computers with the following functionalities— analysing human posture and movements, tracking humans and vehicles for collecting data as well as for law enforcement officers, analysing videos with the help of hi-tech CCTVs, facial recognition of the needed person, detecting different levels of diseases as well as identifying objects for autonomous vehicles.

AI in IT

The IT sector has embraced the functionalities of Artificial Intelligence amidst the ongoing coronavirus pandemic. It is continuously revolutionising the IT sector and helping in boosting productivity efficiently. Artificial Intelligence is providing the utmost security to protect the confidential data from potential threats and data breaches, helping programmers in writing better code by overcoming software bugs, taking over the boring, tedious and repetitive back-end duties, identifying and predicting complex problems, assuring the quality of products and services and much more assistance without any human intervention.

AI in IoT

Artificial Intelligence, has a tremendous scope in IoT (Internet of Things) with the help of 5G network. The implementation of Artificial Intelligence into IoT can help smart devices such as wearable devices, virtual assistance, refrigerators, etc. to analyse data and make smart decisions efficiently based on the collected data without any human intervention. It is used to optimise a system and enhance performance to meet the needs and wants of the target audience.

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Analytics Insight Presents the Top 10 Big Data Statistics for You to Know in 2021.

The future is bright for companies that use Big Data and analytics in this cut-throat competitive market. People are generating more than 2.5 Qn bytes of real-time data due to globalization and digital transformation in the tech-driven era. IoT is also providing data through multiple smart devices, social media accounts, and search engines. The scope of Big Data is increasing at an increasing rate that leads to more job opportunities in the field of Data Science and other disruptive technology fields. Ample Big Data software tools are available to beginners as well as professionals for effective data management to generate interactive reports for meaningful in-depth business insights. Thus, reputed companies and start-ups have started adopting Big Data by investing millions of dollars. Let’s look at the top 10 Big Data statistics to predict the nearby future of this data-driven world.

Top 10 Big Data Statistics You Must Know in 2021

According to Statista, the market survey report showed that the total amount of data being consumed globally was forecasted to increase rapidly to 64.2 zettabytes in 2020 and 79 zettabytes in 2021 while it is projected to grow to over 180 zettabytes up to 2025. It also reported that the installed base of storage capacity will increase at a compound annual growth rate of 19.2% from 2020 to 2025.

The Big Data and business analytics revenue report from Statista showed the forecast of the Big Data market that it will grow to US$274.3 billion by 2022 with a five-year CAGR of 13.2%. The global cloud data center IP traffic will reach almost 19.5 zettabytes in 2021.

BARC, reported that organizations are reaping the benefits of Big Data— 69% chance of better strategic decisions, 54% chance of enhanced operational process control, 52% for a better understanding of consumers as well as 47% for effective cost reduction. The organizations that are reaping the benefits of Big Data reported an average 8% increase in revenues while there is a 10% reduction in costs.

Statista, forecasted that the Big Data market segment will grow up to US$103 billion by 2027 with a share of 45% from the software segment. The market is expected to receive annual revenue of US$274 billion in the next year, 2022.

Forbes, predicted that more than 150 zettabytes or 150 trillion gigabytes of real-time data will need analysis by 2025. Multiple companies dealing with structured data need different things than the companies using unstructured data. Forbes found that over 95% of companies require some help to manage the multiple sets of unstructured data while 40% of companies claimed that they need to deal with Big Data more frequently.

StrategyMRC, predicted that the Hadoop and Big Data Market will experience substantial growth from US$17.1 billion in 2017 to US$99.31 billion in 2022 with a 28.5% CAGR. The Big Data market is expected to jump US$30 billion in value in 2021 and 2022.

It is predicted by Statista, that the global Big Data revenue will experience a major shift in using Big Data in services, hardware, and software. In 2021, there is 24% in services, 16% in hardware, and 24% in software while there will be 33% in services, 24% in hardware as well as a whopping 46% in software use in 2027.

According to Wikibon, the Big Data and analytics, and application database solutions are expected to grow from US$6.4 billion in 2017 to US$12 billion by 2027 with a 6% CAGR, within a span of ten years. The demand for open-source platforms in the Big Data ecosystem such as Hadoop, Kafka, Spark, and TensorFlow can decline due to its direct address to Artificial Intelligence, machine learning, deep learning, or Data Science. But the hybrid deployment of data analytics platforms such as Hadoop, NoSQL, in-memory, streaming, and many more databases will experience a growth in market share for data lake and data fabric solutions.

Sigma, had a market survey to show how many business leaders are keen to adopt Big Data and analytics in their business. The result showed that 39% were not sure about the data-driven culture in organizations, 46% admitted that the lack of domain expertise creates a challenge for delivering relevant data models.

According to ReedSmith, the outbreak of the coronavirus pandemic has increased the rate of Big Data breaches and cyberattacks like scams, phishing, and ransomware to above 400%. The pandemic has forced people to use smart devices more than ever for online transactions and other purposes at home. Thus, there is a data explosion in the digital world that has created ample opportunities for malicious hackers.

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