Trending February 2024 # Top Certifications For Sas, R, Python, Machine Learning Or Big Data # Suggested March 2024 # Top 7 Popular

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We released our rankings for various long duration analytics programmes in India for 2014 – 15 last week. They were greeted with unparalleled enthusiasm and response from our audience. We continue our journey to help our audience decide the best analytics trainings and resources.

This week, we will focus on ranking short duration courses or certification courses.

Latest Rankings: Top Certification Rankings of  2024 – 2024

Scope of these rankings:

In this round, we are ranking various short duration certification courses accessible in India. So the consideration set will include courses either run in India or are available online. We will exclude courses / certifications / boot camps running in other countries.

Why rank certification courses and not institutes?

One of the conscious calls, we took while coming up with the rankings was to rank courses and not the institutes. Why? Because that is how we think and that is what we need to make our learning decisions. We usually need to find the best courses specific to learn a language or a tool. You would want to do the course which is best to learn R or SAS or Python. Institute ranking would not be the right way to make these decisions.

Hence, we decided to rank the courses individually and not institutes. So, here are the ranks for various courses:

Certification courses for SAS:

Foundation Course in Analytics by Jigsaw Academy: Foundation course from Jigsaw Academy is an ideal first course for your data science career, if you want to learn SAS. The content is lucid and leaves you with enough knowledge on the subject to start your data science career. The coverage of the course is holistic as well – it covers everything from collecting and cleaning data to how to build various predictive models. What I really like about this course is that it makes the journey of becoming a data scientist easier. With its simple step-by-step approach, it is an ideal course for those who come from a non-statistics or a non-programming background.

SAS Institute – Predictive modeler: Predictive Modeler certification from the SAS institute is probably the best short term certification available on SAS. Typically run over 5 days, this course assumes that you know Base SAS and have been using it for about 6 months (SAS also offers a Base SAS certification separately). The reason why this course has been ranked second is because of the cost. SAS charges INR 75k+ for this certification. There can be travel costs over and above and if you want to learn Base SAS as well, you would double up the cost – pretty hefty for a 10 day course. For the motivated folks, SAS institute has started offering 2 courses online for free. You can do them to start, then practice for a few days and take up this course. This will save the cost to some extent, but you still need to shell out a fortune for this course.

Certified Business Analytics Professional by Edvancer Eduventures: A lower cost option compared to the first 2 courses. This course from Edvancer covers SAS and predictive modeling comprehensively. Edvancer provides a good proposition to get 60 hours of instructor led trainings at a relatively low cost. Definitely check them out if cost is a significant constraint for you.

There are other courses offered in the industry from the likes of AnalytixLabs, EduPristine, Analytics Training Institute which are not comprehensive from the stand point of becoming a predictive modeler. You should only consider them if cost is the only factor for you to decide.

Must Read: A step by step guide to learn SAS from the scratch

Certification courses for R:

Data Science specialization on Coursera: Probably the most definitive set of courses available for free. These are easy to follow, 2 – 3 hours per week per course. You can pick the courses you need and avoid the ones which you don’t need immediately or do them simultaneously, if you have more bandwidth. The only downside to this certification is lack of guidance from a mentor. You need to rely on forums for that role.

The Analytics Edge on edX: One of the most intensive course to pursue, this course requires you to spend 15 – 16 hours every week. And if you do put them, it covers everything you need to learn with R in less than 4 months. By end of this course, you will be competing on a competition on Kaggle!

Data Science Certification from Jigsaw Academy: Again a comprehensive offering from Jigsaw with good quality content and instructors. This course provides you with all you need to know to become a data scientist. The only complain I have about the course is the cost – INR 26K for self paced and INR 42k for Instructor led might seem high, given that there are a lot of free resources available on R. On the flip side, this course will expose you to business case studies and real world problems better than any other course I have come across. If you are confident about your ability to pick up complex knowledge, stick to the free courses. But, if you are intimidated by statistics or programming and feel you need some hand holding, Jigsaw is the ideal place to learn.

Business Analytics with R from Edureka: A cost effective instructor led offering from Edureka, which covers the concepts well. You can also consider the data science offering from them for slightly higher fees and get functional knowledge about Hadoop and Machine Learning with R as well.

Certified R Programmer from Edvancer: This offering from Edvancer tries to serve people in the middle – those who are motivated enough to learn by self paced tutorials, but still need on demand support to help them out at times. Given that there is no dearth of self paced videos / tutorials on R, you should consider this course for its on demand support.

Data Analysis with R from Udacity also covers basic exploratory data analysis in R, but does not provide enough learning to build predictive models.

Must Read: A step by step guide to learn data science in R Programming

Certification courses for Python:

Mastering Python by Edureka: I personally like Python as a tool for data science. The ecosystem for Python is still evolving. Hence, it is difficult to find courses as comprehensive in Python as this one from Edureka. The course starts from basics of Python and goes on to make sure that you can apply machine learning using Python. One of the best offering to learn Python for data science.

Intro to Data Science by Udacity: Udacity has a whole bunch of courses which assume that you know Python. This particular course is a good introduction to Pandas and data wrangling using Pandas. While the course is a good introduction, it falls short on comprehensiveness and does not cover all your needs as a data scientist through this course.

Must Read: A comprehensive guide which teaches Python from the scratch

Certification courses for Machine Learning:

Machine Learning by Andrew Ng on Coursera: I would probably not be wrong, if I call this course as the most popular course on Machine Learning. Prof. Andrew Ng explains even the most complicated topics in easy to understand manner. A must do course if you want to learn Machine Learning from scratch

Learning from Data on edX: One of the most intensive course run by Prof. Abu-Mustafa. The course contains some really intensive exercises and assignments. The course is not for the people with light heart, but for those who can endure – this is the best course on the subject.

Machine Learning courses from Udacity: The machine learning offerings from Udacity fall some where in between the two courses mentioned above. They don’t simplify the subject matter to the extent Prof. Andrew Ng did and the problem sets are also not as intensive as the course on edX.

Must Read: A beginners guide to conquer Machine Learning

Certification courses for Big Data:

Big Data and Hadoop from Edureka: Although the course does not come with Wiley certification, it is a very cost effective option. The course covers Big Data and Hadoop ecosystem in good details and is clearly the most popular course from Edureka offerings.

A few other courses worth mentioning here are MongoDB fundamentals on Udacity and Mining massive datasets on Coursera. As the name suggests, the course on MongoDB provides you all the basics of working with MongoDB. Mining massive datasets on the other hand is a blend of machine learning and Big Data.

Short note about the methodology:

You can read more details about our methodology here. We ranked the courses on 4 parameters:

Breadth of the coverage

Quality of the content

Industry recognition

Value for Money

End Notes: If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.

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Sas Vs R: What Is Difference Between R And Sas?

What is SAS?

SAS stands for Statistical Analysis Software which is used for Data Analytics. It helps you to use qualitative techniques and processes which allows you to enhance employee productivity and business profits. SAS is pronounced as SaaS.

What is mean by R?

R is a programming language is widely used by data scientists and major corporations like Google, Airbnb, Facebook etc. for data analysis.

R language offers a wide range of functions for every data manipulation, statistical model, or chart which is needed by the data analyst. R offers inbuilt mechanisms for organizing data, running calculations on the given information and creating graphical representations of that data sets.

Google Trend SAS vs R

KEY DIFFERENCE

SAS is commercial software, so it needs a financial investment, whereas R is open source software, So, anyone can use it.

SAS is the easiest tool to learn. So, people with limited knowledge of SQL can learn it easily; on the other hand, R programmers need to write tedious and lengthy codes.

SAS is relatively less frequently updated, whereas R is an open-source tool, continuously updated.

SAS has good graphical support, whereas the Graphical support of the R tool is poor.

SAS provides dedicated customer support, whereas R has the biggest online communities but no customer service support.

Why use SAS?

Access raw data files and data in an external database

Analyze data using statics, descriptive, multivariate techniques, forecasting, modeling, and linear programming

Advanced analytics feature allows you to make changes and improvements in business practices.

Helps businesses to know about their historical data

Why use R?

R offers a useful programming constructs for data analytics like conditionals, loops, input and output facilities, user-defined recursive functions, etc.

R has a rich and expanding ecosystem and plenty of documentation available over the internet.

You can run this tool on a variety of platforms including Windows, Unix, and MacOS.

Good graphics capabilities Supported by an extensive user network.

R Vs Sas Stackoverflow Questions

History of SAS

SAS was developed by Jim Goodnight and John Shall in 1970 at N.C. University

Initially, it was developed for Agricultural Research.

Later, it expanded to a gamut of tools to include Predictive Analytics, Data Management, BI, among others.

Today 98 of the world’s top companies in fortune 400 uses SAS data analytical tool for Data analysis.

History of R

1993- R is a programming language developed by Ross Ihaka and Robert Gentleman

1995: R first distributed as an open-source tool under GPL2 license

1997: R core group and CRAN founded

1999: The R website, chúng tôi launched

2000: R 1.0.0 released

2004: R 2.0.0 released

2009: First edition of the R Journal

2013: R 3.0.0 released

2024: New R logo adopted

SAS Vs. R: Key Differences

Differences between SAS and R

Parameters SAS R

Availability / Cost SAS is commercial software, so it needs a financial investment. R is open source software, So, anyone can use it.

Ease of Learning SAS is the easiest tool to learn. So, people with limited knowledge of SQL can learn it easily. R programmers need to write tedious and lengthy codes.

Statistical Abilities SAS offers a powerful package which offers all types of statistical analysis and techniques. R is an open source tool which allows users to submit their own packages/libraries. The latest technologies are often released in R first.

File Sharing You can’t share SAS generated files with another user who does not use SAS. Since anyone uses r, it is much easier to share files with another user.

Updates SAS relatively less frequently updated. R is an open source tool, so it is continuously updated.

Market Share Currently, SAS is facing stiff competition from R, and other Data analytical tool as a result market share of SAS is gradually declining. R has seen exponential growth in the last past five years with its increasing popularity. That is why its market share is increasing rapidly.

Graphical Capabilities SAS has good graphical support. However, it does not offer any customization. Graphical support of R tool is poor.

Customer Support SAS provides dedicated customer support. R has the biggest online communities but no customer service support.

Support for Deep learning Deep Learning in SAS is still in its early stages, and there’s a lot to work for before it matures.

Job Scenario SAS analytic tool is still the market leader as far as corporate jobs are concerned. Many big companies still work on SAS. Jobs on R have been reported to increase over the last few years.

Salary Range The average salary for any SAS programmer is $81,560 per year in the U.S.A. The average salary for an “R” programmer” ranges from approximately $127,937 per year for Data scientists to $147,189 per year.

Best features

Variables

Mixins

Nested rules

Maintainable

Functions

Data analysis

Graphics and data Flexible statistical analysis

Highly interactive

Famous companies using Airbnb, StacShare, Asana, Hubspot Instacart, Adroll, Opbandit, Custora

TIOBE Rating 22 16

Feature of R

R helps you to connect to many databases and data types

A large number of algorithms and packages for statistics flexible

Offers effective data handling and storage facility

Collect and analyze social media data

Train machines to make predictions

Scrape data from websites

A comprehensive and integrated collection of intermediate tools for data analysis

Interface with other languages and scripting capabilities

Flexible, extensible, and comprehensive for productivity

Ideal platform for data visualization

Features of SAS

Operations Research and Project Management

Report formation with standard graphics

Data updating and modification

Powerful Data handling language

Read and write almost any data format

Best data cleansing functions

Allows you to Interact with multiple host systems

The Final Verdict: R vs SAS

After comparing some main differences between both these tools, we can say that both have their own set of users. There are many companies, who prefer SAS because of data security issues, which show despite a drop in a recent year, there is still a huge demand for SAS certified professionals.

On the other hand, R is an ideal tool for those professionals who want to do deep cost-effective Data analytics jobs. The numbers of startup companies are increasing all over the world. Therefore, the demand for R-certified developers is also increasing. Currently, both have equal potential for growth in the market, and both are equally popular tools.

Choose Best Python Compiler For Your Machine Learning Project – Detailed Overview

This article was published as a part of the Data Science Blogathon.

Introduction

programming language and has different execution environments. It has a wide range of compilers to execute the python programs eg. PyCharm, PyDev, Jupyter Notebook, Visual Studio Code, and many more. The compiler is a special program that is written in a specific programming language to convert the human-readable language i.e. high-level language to machine-readable language i.e. low-level language.

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So in this blog, I am going to cover my personal favorite top 6 python compilers that are useful for Python developers and data scientists. So let’s get started!

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List of Python Compilers

Here is a wide range of compilers to execute the python programs.

PyCharm

Spyder

Visual Studio Code

PyDev

Jupyter Notebook

Sublime Text

PyCharm

It is created by Jet Brains and it is one of the best and broadly utilized Integrated Development Environment (IDE). Developers utilize this IDE for creating gainful Python and creates perfect and viable code. The PyCharm IDE assists engineers with making greater profitability and gives savvy help to the developers. It helps developers to write good quality code correctly. It saves the developers time by performing the fast compilation.

Price: Free

Language Supported: English

Supported Platform: Microsoft Windows, Mac, Linux

Developed by: Jet Brains

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Features of PyCharm

It supports more than 1100 plugins

Provides an option to write own plugin

It has a code navigator, code editor, and fast & safe refactoring

It provides developers with an option to detect errors, fast fix errors and to complete auto code, etc.

It can be easily integrated with an IPython notebook.

It provides functionality to integrate debugging, deployments, testing, etc

Pros

It is very easy to use

Installation is very easy

Very helpful and supportive community

Cons

In the case of large data, it becomes slow

Not beginners friendly

Check the official page here: PyCharm

Spyder

Price: Free

Language Supported: English

Supported Platform: Microsoft Windows, Mac, Linux

Developed by: Pierre Raybaut

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Features

Provides auto-code completion and syntax highlighting feature

It supports multiple IPython consoles

With the help of GUI, it can edit and explore the variables

It provides a debugger to check the step by step execution

User can see the command history in the console

Pros

It is open-source and free

To improve the functionalities, it supports additional plugins

Provide support for strong debugger

Cons

The very old style interface

Difficult to find the terminal in this compiler

Check the official page here: Spyder

Visual Studio Code

This IDE is developed by Microsoft in 2024. It is free and open-source. It is lightweight and very powerful. It provides features such as unit testing, debugging, fast code completion, and more. It has a large number of extensions for different uses, for example, if you want to use C++, then install C++ extension, similarly install the different extension for different programming languages.

Price: Free

Language Supported: English

Supported Platform: Microsoft Windows, Mac, Linux

Developed by: Microsoft

Features

It has an inbuilt Command Line Interface

It has an integrated Git that allows users to commit, add, pull and push changes to a remote Git repository utilizing a straightforward GUI.

It has an API for debugging

Visual Studio Code Live Share is an element that empowers you to share your VS Code case, and permit somebody distant to control and run different things like debuggers.

Pros

It supports multiple programming languages eg. Python, C/C++, Java etc

Provides auto-code feature

It has built-in plugins

Cons

Sometimes, it crashes and shutdowns

The interface isn’t all that great and it required some time to begin

Check the official page here: Visual Studio Code

PyDev

PyDev is free and open-source, people can introduce it from the web and begin utilizing it. It is perhaps the most usable IDE and liked by a large portion of developers.

Price: Free

Language Supported: English

Supported Platform: Microsoft Windows, Mac, Linux

Developed by: Appcelerator

Features

It provides functionalities such as debugging, code analysis, refactoring, etc

Provides error parsing, folding of code, and syntax for highlighting code.

It supports black formatted, virtual environment, PyLint, etc

Pros

It supports Jython, Django Framework, etc

It offers supports for different programming languages like Python, Java, C/C++, etc

Provides auto-code completion and syntax highlighting feature

Cons

When multiple plugins are installed, the performance of PyDev diminishes

Check the official page here: PyDev

Jupyter Notebook

It is one of the most widely used python IDE for data science and machine learning environments. It is an open-source and web-based interactive environment. It permits us to create and share documents that have mathematical equations, plots, visuals, live code, and readable text. It supports many languages such as Python, R, Julia, etc but it is mostly used for Python.

Price: Free

Language Supported: English

Supported Platform: Microsoft Windows, Mac, Linux

Developed by: Brian Granger, Fernando Perez

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Features

Easy collaboration

Provides the option to download jupyter notebook in many formats like PDF, HTML file etc

It provides presentation mode

Provides easy editing

Provides cell level and selection code execution that is helpful for data science

Pros

It is beginners friendly and perfect for data science newbies.

It supports multiple languages like Python, R, Julia, and many more

With the help of data visualization libraries such as matpotlib and seaborn, we can visualize graphs within the IDE

It has a browser-based interface

Cons

It doesn’t provide a good security

It doesn’t provide code correction

Not effective in real-world projects – use only for dummy projects

Check the official page here: Jupyter Notebook

Sublime Text

Sublime Text is an IDE that comes in two renditions for example free and paid. The paid variant contains additional highlights features. It has different plugins and is kept up under free software licenses. It upholds numerous other programming languages, for instance, Java, C/C++, and so on not just Python.

Sublime Text is very quick when contrasted with other text compilers. One can likewise introduce different bundles like debugger, code linting, and code completion.

Price: Free

Language Supported: English

Supported Platform: Microsoft Windows, Mac, Linux

Developed by: Jon Skinner

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Features

Provides option for customization

Instant switch between different projects

It provides split editing

It has a Goto Anything option, that allows user to jump the cursor wherever they want

It supports multiple languages such as Python, java, C/C++

It provides Command Palette

It has a distraction-free mode too

Pros

Very interactive interface – very handy for beginners

Provide plugin which is very helpful in debugging and tet highlighting.

Provide time to time suggestion for accurate syntax

It provides a free version

Working on different projects are possible at the same time

Cons

Not wors well in case of large documents

One of the most annoying things is, it doesn’t save documents automatically.

At some time, plugins are difficult to handle.

Check the official page here: Sublime Text

Frequently Asked Questions

Q1. Which compiler is best for Python?

A. Python is an interpreted language, and it does not require compilation like traditional compiled languages. However, popular Python interpreters include CPython (the reference implementation), PyPy (a just-in-time compiler), and Anaconda (a distribution that includes the conda package manager and various scientific computing libraries).

Q2. What is a Python compiler?

A. In the context of Python, a compiler is a software tool that translates Python code written in high-level human-readable form into low-level machine code or bytecode that can be executed directly by a computer. The compiled code is typically more efficient and faster to execute than the original Python source code. Python compilers can optimize the code, perform static type checking, and generate standalone executable files or bytecode files that can be run on a specific platform or within a virtual machine. Examples of Python compilers include Cython, Nuitka, and Shed Skin.

Conclusion

So in this article, we have covered the top 6 Python Compilers For Data Scientists in 2023. I hope you learn something from this blog and it will turn out best for your project. Thanks for reading and your patience. Good luck!

You can check my articles here: Articles

Connect me on LinkedIn: LinkedIn

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion. 

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Can Java Be Used For Machine Learning And Data Science?

The world is drooling over

Top Expertise to Develop For Machine Learning & Data Science

If you want to excel in any field, you first need to develop the skills. Here’s a list of all the skills required if you’re going to learn ML & data science. Math: It is all about permutations and combination complemented with your calculation ability to be able to link yourself with machines. Data Architecture: To be able to reach the core of any technology, you must have a broad idea of the data formats. Software Structures: There is no ML without software, and a data engineer should be clear with concepts related to software and their working. Programming & Languages: If you do not know anything about this, there is no ML for you. Programming languages are the essential requirement for one to be able to build a career in ML. Differencing and Data Mining: If you have no clue about data, you are a zero. To be able to learn ML, data mining, and the ability to infer the information is crucial.  

Java: Machine Learning & Data Science’s Future

Java is a technology that proves beneficial in varied arrays of development and ML. One of the critical things in ML & Data Science is algorithms. With Java’s available resources, one can efficiently work in various algorithms and even develop them. It is a scalable language with many frameworks and libraries. In the current scenario, Java is amongst the most prominent languages in AI and ML. Some of the reasons why Java is an excellent alternative for a future in Data Science, Machine Learning, and finally, Artificial Intelligence are:  

Pace of Execution

If you are arguing about the speed of coding and execution, Java takes the lead in it, which means faster ML & DS technologies. Its features of statically typing and compilation are what makes it super in execution. With a lesser run time than any other language, knowing Java means you are good to go in the ML industry.  

Coding

Indentation in Java is not a must which makes it easier than Python or R. Also, coding in Java may require more lines, but it is easier than in other languages. If you are well-versed with coding, Java will be beneficial in ML and DS.  

Learning Curve

Java has a lot of areas where one must work hard. The learning curve for Java and allied language is quicker and more comfortable than other languages in totality. Suppose you know a language better and efficiently. In that case, it means that you can enter the domain at a more accelerated pace than through any other language whose learning curve is typical of Java.  

Salary Packages

Java has been in use for 30+ years. The future salaries of people who know Java are perceived to be higher than through any other language. We are not saying that you might not have a handsome amount in your hand if one knows Python. Instead, we are just focusing that with Java’s legacy in place, the salaries you get in your growth years are expected to be more for people who know Java.  

Community

Java will complete three decades of existence and is still one of the most prevalent and popularized languages. It means that numerous people in the enterprise know the language and will provide you with support in requirements. Several people in DS and ML are working through Java. It is an additional benefit that you can avail of if you learn ML and DS with Java.  

Varied Libraries

With Java, you have access to various libraries in Java for learning ML. To name a few, there are ADAMS, Mahaut, JavaML, WEKA, Deeplearning4j, etc.

The world is drooling over Artificial Intelligence . From research institutions to corporate houses, every organization aims to create AI-driven systems to build their enterprise. Machine Learning, or more commonly known as ML, is a sub-array of AI. With ML, you can teach the machines to behave like humans, i.e. develop brains in a machine. The result is automated machines that know-how and what is to be done. One commonly used place for AI & ML is Maps. Have you noticed that it shows you the route with the least traffic and the best route? That happens through ML along with other technologies. Another hot thing in the technological sphere is Big Data and its management. Big data is a terminology utilized for data of all types. It incorporates structured, semi-structured, and unstructured data. Be it any type of organization, you will always have a lot of data related to operations, finance, marketing, manufacturing, sales, etc. How you utilize and manage this data is the work of data scientists. Machines absorb the information that is further utilized and adopted in AI is all related to Big Data. Hence, to dive into AI, you will have to be accustomed to ML and Big data . Data science, ML, big data, and AI are all interlinked and synchronized. If you are talking about turning a machine like a human, it requires you to feed it in the language that it understands. Yes, we are talking, i.e. programming languages. Some of the commonly practiced languages for ML and Decision science are Python, Java, etc. But Java is a language that one must never forget. If you know Java Outsourcing Company , you can hop on the bandwagon of ML with great ease. How will it happen? Read along to learn chúng tôi you want to excel in any field, you first need to develop the skills. Here’s a list of all the skills required if you’re going to learn ML & data chúng tôi is all about permutations and combination complemented with your calculation ability to be able to link yourself with chúng tôi be able to reach the core of any technology, you must have a broad idea of the data formats.There is no ML without software, and a data engineer should be clear with concepts related to software and their chúng tôi you do not know anything about this, there is no ML for you. Programming languages are the essential requirement for one to be able to build a career in chúng tôi you have no clue about data, you are a zero. To be able to learn ML, data mining, and the ability to infer the information is chúng tôi is a technology that proves beneficial in varied arrays of development and ML. One of the critical things in ML & Data Science is algorithms. With Java’s available resources, one can efficiently work in various algorithms and even develop them. It is a scalable language with many frameworks and libraries. In the current scenario, Java is amongst the most prominent languages in AI and ML. Some of the reasons why Java is an excellent alternative for a future in Data Science, Machine Learning, and finally, Artificial Intelligence are:If you are arguing about the speed of coding and execution, Java takes the lead in it, which means faster ML & DS technologies. Its features of statically typing and compilation are what makes it super in execution. With a lesser run time than any other language, knowing Java means you are good to go in the ML industry.Indentation in Java is not a must which makes it easier than Python or R. Also, coding in Java may require more lines, but it is easier than in other languages. If you are well-versed with coding, Java will be beneficial in ML and chúng tôi has a lot of areas where one must work hard. The learning curve for Java and allied language is quicker and more comfortable than other languages in totality. Suppose you know a language better and efficiently. In that case, it means that you can enter the domain at a more accelerated pace than through any other language whose learning curve is typical of chúng tôi has been in use for 30+ years. The future salaries of people who know Java are perceived to be higher than through any other language. We are not saying that you might not have a handsome amount in your hand if one knows Python. Instead, we are just focusing that with Java’s legacy in place, the salaries you get in your growth years are expected to be more for people who know chúng tôi will complete three decades of existence and is still one of the most prevalent and popularized languages. It means that numerous people in the enterprise know the language and will provide you with support in requirements. Several people in DS and ML are working through Java. It is an additional benefit that you can avail of if you learn ML and DS with chúng tôi Java, you have access to various libraries in Java for learning ML. To name a few, there are ADAMS, Mahaut, JavaML, WEKA, Deeplearning4j, etc. We hope that now you know why one must learn Machine Learning and Data Science in Java. With its scalability, versatility, and balanced demand, you will always have to work with Java.

Data Interoperability & Machine Learning In 2023 & Beyond

Modern business systems are integrated, and if an AI-powered solution is added to a business’s digital mix, it needs to have the ability to work together with all the other software and tools. AI and machine learning interoperability gives that level of integration to AI-powered digital solutions. However, to achieve this interoperability, AI/ML models need to have the ability to exchange data and interact with each other. That is where data interoperability comes into play.

If you wish to achieve data interoperability in your business to leverage interoperable machine learning, this article is for you. In this article, we explore data interoperability, why it’s important for interoperable AI solutions, and what are its different types to help business leaders achieve interoperability across their digital network.

What is data interoperability?

Data interoperability is the ability of two or more systems, applications, data stores, or microservices to exchange data and interact with each other. Interoperability enables data transfer between different types of data sources, ensuring that data is accessible across a variety of formats and platforms. This allows organizations to leverage data from disparate sources for things such as analytics and data visualization, data integration, and data sharing.

What are the types of data interoperability?

There are two main types of data interoperability:

Data-level interoperability: Data-level or syntactic interoperability enables data to be shared across applications and platforms.

Semantic-level interoperability: This type of interoperability allows the data to be interpreted correctly by different machine-learning systems.

Why is data interoperability important?

Data interoperability is important for organizations because it enables data to be accessible across different formats and platforms. This helps organizations make data-driven decisions, reducing costs, increasing operational efficiency, and improving customer experience.

Organizations nowadays work with tens of thousands of data points, and it can be beneficial for them to use them in a synergistic way. Data interoperability enables AI/ML systems to communicate with other systems to produce more accurate and extensive results. 

For instance, an interoperable automated invoicing system will have the option to share the invoices with other systems (procurement, inventory management, ERP, PIM systems, etc., in different formats that are compatible with those systems.

Which industries can benefit from data interoperability? 1. Healthcare

Data interoperability in the healthcare industry allows data from multiple sources (e.g., patient records, medical devices, imaging data, etc.) to be collected and shared for better diagnosis and treatment of patients.

Data interoperability in healthcare:

2. Manufacturing

Data interoperability in the manufacturing industry enables data from machines, robots, and other equipment to be collected and shared for better production processes and quality control.

3. Financial Services

Data interoperability in financial services helps organizations manage risk by collecting data from a variety of sources, such as customer data, market data, transaction data, etc. This enables institutions to make smarter decisions about investments and pricing strategies.

4. Education

Data interoperability in the education sector enables data from multiple sources (e.g., student data, teacher data, course data, etc.) to be collected and shared for better decision-making about curriculum planning and personalized learning.

Here is a video of how data interoperability works in an educational setting:

3 Steps to achieve data interoperability Step 1

Data-level interoperability can be achieved through data integration platforms such as data warehouses and data lakes. These platforms allow data to be collected from different sources, stored in a single unified repository, organized, and accessed in various formats. This is necessary because it enables systems to adopt common data formats and structural protocols. The data can then be accessed and applied to various analytics tools and used for machine learning tasks.

Step 2

This step helps the different systems understand each other’s data. Semantic-level interoperability can be achieved by adding information about the data (metadata) and connecting each data element to a standardized, common vocabulary. This process also involves annotating and labeling the data. 

Additionally, data standards can also be used to ensure that data from multiple sources can be accessed and interpreted correctly by different AI systems. This helps organizations create uniform datasets that are consistent across various data sources.

Step 3

Now, the data vocabulary needs to be established and linked to an ontology. In order to link a vocabulary to an ontology, two approaches can be used: 

Data mapping: Which involves connecting data elements from different formats into one unified data structure. 

Data federation: Which is a technique that allows data stored in multiple data sources to be shared and accessed as if it were stored in a single data source.

Through these standards, businesses can share relevant data without relying on another information system.

If you wish to learn more about how to collect data for interoperable systems, feel free to download our comprehensive data collection whitepaper:

Further reading

If you need help finding a vendor or have any questions, feel free to contact us:

Shehmir Javaid

Shehmir Javaid is an industry analyst at AIMultiple. He has a background in logistics and supply chain management research and loves learning about innovative technology and sustainability. He completed his MSc in logistics and operations management from Cardiff University UK and Bachelor’s in international business administration From Cardiff Metropolitan University UK.

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A Quick Guide To Data Science And Machine Learning

This article was published as a part of the Data Science Blogathon.

Introduction

Do you know that data is the ultimate goal for every organization, and hence actually I believe that it is the ruler? Without data, nothing can be achieved. From a business perspective to solving problems for end-to-end applications we require data.

This data needs to be in order to derive some purpose from it. Because forms of data can be texts, images, videos, infographics, gifs, etc. Some data are structured while most of them are unstructured. Collection, analysis, and prediction are the necessary steps that are to take into consideration with this data.

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Now, what exactly are Data Science and Machine learning?

I’ll just define it for you in a simple way. All the context related to this can be similar if you search somewhere else. So data science is the science of deriving insights from data for the purpose of gaining the most important and relevant source of information. And with a reliable source of information making predictions by the use of machine learning. So I guess you might have very well understood this definition. Now my point here is that with data science you can bring meaningful insights.

Why there is a need for data science and machine learning?

Data has been there for a very long time. During earlier times analysis of data was done by statisticians and analysts. Analysis of data was done primarily to get the summary and what were the causes. Mathematics was also the core subject of interest when used for this work.

How data science and machine learning solutions?

Data science uses statistical methods, maths, and programming techniques to solve these problems. The programming techniques are extensively used for analysis, visualizing, and making predictions. So you see it does all the work of a statistician, programmer, and maths. The study of all these major areas makes the best way of dealing with such big data. Machine learning is integrated by making models from various algorithms. 

This is done for model building in data science which helps for future predictions. These predictions depend upon the new data which is given to the model without explicitly telling it what to do. The model understands it and then gives us the output or solution. For example, banks use machine learning algorithms to detect if there is a fraud transaction or not. Or if this customer will default in paying his credit card dues.

Cancer detection in the health care industry uses data science and machine learning to detect if patients are prone to cancer or not. So there are a lot of examples around us where companies are widely using this. Online food delivery companies like zomato or swiggy use for recommending us food to order based on what have we ordered in the past. This type of machine learning algorithm is a recommendation system. They are also used by YouTube, Spotify, Amazon, etc.

The Data science life cycle.

There are various steps involved in solving business problems with data science.

1. Data acquisition – this process involves the collection of data. Depends on are objectives or what is the problem that needs to be solved. By this means, we tend to gather the required data.

2. Data pre-processing – this stage involves processing data in a structured format for ease of use. Unstructured data cannot be used for any analysis because it will give wrong business solutions and can have a bad impact on consumers.

3.Exploratory data analysis (EDA) – it is one of the most important stages where all the summarizations of data by statistics and math’s. Identifying the target(output) variable and predictor(independent) variables. Visualization of data and then sorting all the necessary data that will be used for predictions. Programming plays a vital role in this. A data scientist spends almost 75% of their time on this to understand their data very well. Further in this stage data is divided into training and test data.

4. Model building – After EDA we select the most appropriate methods to build our model. This is done with the use of machine learning algorithms. Selection of algorithms like regression, classification, or clustering. As machine learning algorithms are of 3 types. Supervised learning, unsupervised learning, and reinforcement learning. There are different sets of algorithms for all these types. Selecting them depends mainly on what is a problem are we trying to solve.

5. Evaluation of model – model evaluation is done to see how efficient our model is doing on the test data. Minimizing errors and also tuning of the model.

6. Deployment of model – model deployment is done as now it is fit to cater to all the future data for making predictions.

Note: There are re-evaluation techniques involved even after deployment to keep our model up-to-date.

How all this is done?

Data science tools and frameworks are specifically used for this process. Some popular tools like jupyter, tableau, tensor flow. Programming languages such as Python and R are important to do these tasks. To know and learn any one language is sufficient. Python and R are widely used for data science because there are additional libraries that make it easy for any data science project. I prefer Python as it is open-source, easy to learn, and has huge community support across the world. Statistics, math, and linear algebra are some core subjects you need to understand before getting involved in any data science or machine learning project.

In the future, these sources of data will keep on expanding and there will be a need to harvest all of these. An important part or information to get from this data will only derive the need for data scientists and machine learning engineers.

Mohammed Nabeel Qureshi

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