Data Science vs Machine Learning: A Comprehensive Guide

Data science and machine learning are two closely related fields that have gained significant attention and popularity in recent years. Both disciplines involve the use of data and algorithms to extract insights and make predictions, but they differ in the types of tasks they are used for and the tools and techniques they utilize.So, what exactly is data science? Data science is a multidisciplinary field that combines elements of computer science, statistics, and domain expertise to extract insights and knowledge from structured and unstructured data. Data scientists use a variety of techniques, including statistical analysis, machine learning, and visualization, to analyze and interpret large and complex datasets.Machine learning, on the other hand, is a subfield of artificial intelligence that focuses on the development of algorithms that can learn from data without being explicitly programmed. Machine learning algorithms are designed to improve their performance over time by identifying patterns in data and using them to make predictions or decisions.At first glance, it may seem like data science and machine learning are interchangeable terms, but they are distinct fields with their own unique sets of skills and expertise.So, what sets them apart? And which one is right for you? These are just a few of the questions we will explore in this comprehensive comparison of data science and machine learning.History and DevelopmentData science and machine learning have a long and intertwined history, with roots dating back to the early 20th century. One of the earliest milestones in data science was the development of the concept of statistical modeling in the 1920s, which laid the foundation for modern statistical analysis and data mining.In the 1950s and 60s, the field of artificial intelligence (AI) emerged, which included the development of machine learning algorithms that could learn from data and make decisions based on that learning. This laid the foundation for the modern field of machine learning, which has evolved significantly since then.In the 1990s and 2000s, the explosion of digital data and the proliferation of computing power made it possible to analyze and process large amounts of data in a previously unimaginable way. This led to the rise of data science as a distinct field, which combines elements of computer science, statistics, and domain expertise to extract insights and value from data.Over the past decade, the fields of data science and machine learning have continued to evolve and influence one another. Machine learning techniques, such as deep learning, have become increasingly popular and powerful, and are being applied to a wide range of data-driven tasks. At the same time, data science has become more focused on the use of machine learning techniques to analyze and interpret data, leading to the convergence of the two fields in many ways.Today, data science and machine learning are important and in-demand fields that are driving innovation and transformation in a wide range of industries and sectors. They are used to solve a wide range of problems, from predicting customer behavior and improving supply chain efficiency to detecting fraudulent activity and analyzing medical data.Key DifferencesAlthough data science and machine learning are related fields that often overlap and influence one another, there are some key differences between the two.One of the main differences is the types of tasks that data science and machine learning are used for. Data science is a broader field that encompasses a wide range of activities, including collecting and cleaning data, analyzing data, visualizing data, and communicating insights and findings. Machine learning, on the other hand, is more focused on developing algorithms that can learn from data and make predictions or decisions based on that learning.Another difference is the tools and techniques used in each field. Data scientists often use a variety of tools and techniques, including programming languages like Python and R, statistical analysis tools, and visualization tools like Tableau. Machine learning practitioners often use specialized machine learning libraries and frameworks, such as scikit-learn and TensorFlow, as well as techniques like feature engineering and hyperparameter optimization.The types of problems that data science and machine learning are designed to solve also differ. Data science is often used to answer complex questions and extract insights from data, such as predicting customer behavior or identifying trends in large datasets. Machine learning, on the other hand, is typically used to solve more specific problems, such as classifying images or predicting the likelihood of an event occurring based on past data.Some real-world examples of the differences between data science and machine learning include:● A data scientist might be tasked with analyzing customer purchase data to identify trends and patterns that can be used to improve sales and marketing strategies.● A machine learning engineer might be tasked with developing a machine learning model that can predict whether a customer is likely to churn based on their past interactions with the company.While data science and machine learning have many similarities and often overlap, they are distinct fields that are used to solve different types of problems and utilize different tools and techniques.Skills and ExpertiseBoth data science and machine learning require a combination of technical skills and domain expertise. Some of the skills and expertise that are commonly required for these fields include:● Programming languages: Data scientists and machine learning engineers often use programming languages such as Python and R to manipulate and analyze data, as well as to build and deploy machine learning models.● Statistical analysis: Both data science and machine learning involve the use of statistical analysis to understand and interpret data. Data scientists and machine learning engineers should have a strong understanding of statistical concepts and techniques, such as hypothesis testing, regression analysis, and experimental design.● Machine learning algorithms: Machine learning practitioners should have a deep understanding of a wide range of machine learning algorithms, including supervised learning algorithms like linear regression and logistic regression, as well as unsupervised learning algorithms like clustering and dimensionality reduction.● Domain expertise: Data scientists and machine learning engineers should have a strong understanding of the domain they are working in, whether it’s finance, healthcare, e-commerce, or another industry. This domain knowledge is critical for understanding the data and context in which the machine learning models will be used.In terms of the types of professionals who typically work in data science and machine learning, there is some overlap, as many professionals have skills and expertise in both fields.Data scientists are often responsible for collecting and cleaning data, as well as analyzing and visualizing data to extract insights. Machine learning engineers, on the other hand, are typically responsible for building and deploying machine learning models, as well as optimizing and maintaining those models over time.There are a variety of career paths available in data science and machine learning, ranging from entry-level positions to highly specialized roles. Some common career paths in these fields include data scientist, machine learning engineer, data engineer, and data analyst. Professionals in these roles may work in a variety of industries, including technology, finance, healthcare, and retail.Industry DemandThe demand for Data Science and Machine Learning professionals in India has grown exponentially in 2022. Companies such as Reliance Jio and Flipkart are increasingly looking for Data Science and Machine Learning experts to fill key positions in their organizations. Furthermore, various government initiatives such as Digital India are also emphasizing the importance of such professionals and creating more job opportunities. Also, considering the average pay, as per AmbitionBox:● The average salary of a Data Scientist is INR 10 LPA● The average salary of a Machine Learning Engineer is INR 6.9 LPATherefore, it is safe to say that the demand for Data Science and Machine Learning professionals in India is expected to surge in 2023 and have better results, both in terms of the number of postings and the average earnings.ConclusionIn conclusion, data science and machine learning are fields that are driving innovation and transformation in a wide range of industries and sectors. While there are some similarities between the two fields, they are distinct and are used to solve different types of problems. Data science is a broader field that encompasses a wide range of activities, including collecting and cleaning data, analyzing data, visualizing data, and communicating insights and findings. Machine learning, on the other hand, is more focused on developing algorithms that can learn from data and make predictions or decisions based on that learning.Both data science and machine learning require a combination of technical skills and domain expertise, including programming languages, statistical analysis, and machine learning algorithms. The demand for professionals with these skills is strong, and those considering a career in these fields can expect strong job prospects and competitive salaries.

How to become a certified NFT professional?

Almost every individual browsing tech news on a daily basis would come across non-fungible tokens from time to time. The fame of NFTs started building momentum in the early months of 2021 and has developed into a phenomenal trend. With a promising array of opportunities for jobs and business ventures, the demand for a Certified NFT Professional has been developing gradually.

However, many aspiring beginners in the field of NFTs don’t have any ideas regarding to learn NFT certifications and how to qualify for one. Interestingly, the right choice of NFT certification can help you build a promising career in the domain of non-fungible tokens. Let us find out some of the best practices for becoming a certified professional in NFTs.

How Can a NFT Certification Help Me?
The global NFT market has been experiencing an upward trend, and the expensive multi-million dollar sales have fuelled the demand for NFTs. As a completely new class of digital assets, non-fungible tokens are obviously subject to the concerns of uncertainty. However, the most expensive NFT sale was registered at $91.8 million in December 2021.

A certified NFT expert can capitalize on the opportunities from the massive skill gap among NFT professionals. Certifications are an obvious choice for proving your skills and knowledge in using NFTs. Therefore, any new NFT project would consider a certified expert on its team as a vital asset.

While the promises of an NFT certification course appear lucrative, the domain of non-fungible tokens is comparatively new. As a result, beginners are more likely to encounter difficulties in the journey of qualifying for NFT certifications.

Steps for Becoming a Certified NFT Professional
If you want to become NFT expert with a reliable certification, you would need the guidance of experts. The best course of action in becoming an NFT professional would focus on following a specified path rather than looking up NFT jobs directly. Here are some of the best practices you should follow in your journey of becoming a certified expert in NFTs.

Start with the Right Certification
The biggest difference in your journey to becoming a Certified NFT Professional rests in the choice of certification. With the right certification at your disposal, you can not only prove your NFT skills but also develop a solid understanding of the practical implications of NFTs. For example, the new NFT certification course by 101 Blockchains can offer you a comprehensive improvement for your career.

The course not only reflects on the fundamentals of NFTs but also their practical use cases and the methods for creating non-fungible tokens. The CNFTP certification course by 101 Blockchains offers an industry-leading certification you can use for exploring opportunities for professional development.

Make the Most of Learning Materials
The next significant task to become certified NFT professional focuses on preparing and learning for the certification of your choice. You should note that 101 Blockchains offers a dedicated training course for the CNFTP certification. Candidates preparing for the certification can use the NFT certification course to cover all the exam objectives before the final exam.

The detailed attention to different aspects of NFTs followed by the best practices for using them can support the skill development of candidates. Most important of all, you must also look for other learning resources to complement your preparations. For example, NFT news articles and insights from discussion forums and virtual communities can help you dive deeper into the world of non-fungible tokens.

Maintain Accountability
The final and most important step in the process of preparing for an NFT certification focuses on accountability. Candidates have to take control of their preparation plan and commit their efforts to ensure the best results. In addition, you must also focus on maintaining continuity in your study plan for efficient preparation.

Bottom Line
The most important thing you need for becoming a Certified NFT Professional is a reliable certification. With 101 Blockchains and their new CNFTP certification, you can find the best instrument to start your career in NFTs. The intriguing and lucrative world of non-fungible tokens has been harboring many profitable ventures in recent times. Candidates with NFT certifications can fill the missing link in empowering large-scale NFT projects.

While certifications are crucial for career growth, candidates must focus on the best practices for becoming a certified professional. Starting from the selection of the right certification like the CNFTP certification by 101 Blockchains to the preparation steps, candidates have to invest a considerable amount of effort. Begin your career in NFTs right now with an industry-leading credential.

VMware Workspace ONE 5V0-61.22 dumps questions

Prepare with VMware Workspace ONE 5V0-61.22 dumps questionsAs a member of the people working in the IT industry, do you have a headache for passing some IT certification exams? Generally, IT certification exams are used to test the examinee’s related IT professional knowledge and experience and it is not easy pass these exams. For the examinees who are the first time to participate IT certification exam, choosing a good pertinent VMware Workspace ONE 5V0-61.22 dumps questions are very necessary. Certqueen can offer a specific training program for many examinees participating in IT certification exams.

Certqueen 5V0-61.22 Sample Questions ShareWhich step accurately outlines the SQM provisioning flow from Okta to Workspace ONE UEM?

A.AirWatch Provisioning App uses SCW API to update users in Workspace ONE UEM

B.Okta provisions users to Workspace ONE Access using SCIM API

C.Workspace ONE Access utilizes SCIM API to provision users to Workspace ONE UEM

D.Okta uses Workspace ONE UEM REST API to add users to the console

Answer: C
Which step is required to set up Hub Digital Badge Service in VMware Workspace ONE Hub Services?

A.Customize the Digital Badge layout m the Hub services console

B.Configure Hub Embedded iFrame for Digital Badge in the Hub Services console

C.Configure HID Global Origo setting and C* CURE setting m the Hub Services console

D.Customize Hub templates for Digital Badge n the Hub Services console

Answer: C
Which type of certificate is uploaded to VMware Workspace ONE Access for Android Single Sign-On setup?

A.Trusted SSL

B.Root

C.Self-signed

D.Intermediate

Answer: C
Which two options are valid when managing directories m VMware Workspace ONE Access through the Manage> Directories page? (Choose two.)

A.Enable Password Recovery Assistant

B.Schedule the sync frequency

C.Configure Kerberos Auth Service

D.Change the mapped attributes list

E.Manage built-in identity provider for User Auth

Answer: B, D
Which directory type is supported when integrating LDAP-based authentication in Workspace ONE Access?

A.SecurID

B.RADIUS

C.Kerberos

D.Oracle LDAP

Answer: D
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