Entries by Ivy Professional School | Admin Sept 2021

How to Stay Up-to-Date with the Latest Happenings and Trends in Data Science

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In the world of data science, things change and evolve very quickly. If you do not remain updated with the latest happenings and trends in data science, you will definitely fall behind. With new developments emerging constantly, it can be challenging to keep up with the latest advancements. There are several strategies that data scientists can use to stay informed about the data science trends in 2023.

Books are always relevant to keep up with the trends in Data Science

Books have always been the traditional source of gaining knowledge, and they remain relevant even now. Books are a great way to learn a new subject, even if they may not always be the easiest or fastest to read. Some of the most experienced data scientists and researchers have written books on data science that will remain relevant and important forever. They are a great way to remain updated about the latest trends in data science.

Authors of textbooks are usually experienced in their fields and provide the information needed in a chronological fashion. Though information is easily available these days, it is important to consume information chronologically to make more sense of it.

Textbooks generally provide a more detailed and comprehensive exploration of a subject than blogs or articles, or a whitepaper. Using textbooks to gain knowledge on various topics in data science becomes particularly important when you are trying to build a strong foundation for a specific subject.

Some top books you can go for inlcude:

Take online courses and certifications to keep up with Data Science trends in 2023

Online courses and certifications provide a convenient way to learn new skills and technologies in data science. Such courses cover a wide range of topics, including machine learning, data visualization, and deep learning, and are taught by industry experts and academics. Numerous online courses and certifications such as Udacity, Coursera, Udemy, edX cover a wide range of topics, from the basics of data science to advanced machine learning techniques which are building the future of data science.

If you want to upgrade your data science skills, Ivy Professional School’s data science courses are the best option. Through these courses, students receive top-notch training from experienced faculty from esteemed institutions such as IITs, IIMs, and US universities and have the opportunity to work on real-world analytics problems through capstone projects and internships.

The courses offer the flexibility of attending in-person or online live classes. Ivy provides lifetime placement assistance to its students, ensuring they have access to the right resources to achieve their career goals.

Some of the most popular data science courses from Ivy Professional School include:

Follow thought leaders to know about the latest trends in data science 

Once you are working in the data science domain, following thought leaders and experts in the data science community on social media platforms such as LinkedIn, Twitter, and Medium is a great way to stay informed about the latest trends in data science and AI. Experts often share valuable insights, knowledge, updates and data science news. Social media gives the option of interacting with them by commenting on their posts as well.

Some of the leaders you can follow include Geoffrey HintonYann LeCun, Andrew NgFei-Fei Li, Ian GoodFello, Andriy Burkov, Demis Hassabis, Cassie Kozyrkov, Andrej Karpathy, Alex Smola, among others.

YouTube : Another way to stay updated with the latest trends in data science

If you prefer audio-visual learning, YouTube is a great option to stay updated with the latest technologies and get data science news. For data science courses, statistical and mathematical concepts, and detailed tutorials on programming, YouTube has free lectures from leading institutes such as MIT, Stanford, Harvard, Oxford, and Princeton, which is a great way to gain knowledge in a subject in a cost-efficient and time-saving manner.

If you are a beginner or even an experienced professional who wants to learn new concepts, Ivy Professional School’s YouTube channel can greatly help you. It covers detailed videos on various topics in Python, SQL, PowerBi, Tableau, Advanced Excel, and new technologies.

Some of our popular playlists include:

Check out Ivy’s videos here.

Attend conferences and events

Attending industry events is a great way to stay informed latest data science trends. By attending sessions and workshops, you can learn about the latest tools, techniques, and frameworks being used in the industry and interact with other professionals in the field. You can also network with others, leading to new job opportunities.

Participate in online communities and forums 

Joining online communities such as Kaggle, Data Science Central can help you connect with other data scientists, participate in discussions, and learn from others’ experiences. In these forums, you can ask questions, share your knowledge, and collaborate on projects.

Experiment with latest technologies in data science

Experimenting with new tools, technologies, and frameworks can help you stay ahead of the curve, learn through practical experience, and remain updated with the latest trends in data science. By experimenting with latest technologies in data science, you can also discover new approaches and projects to solve problems and gain insights that can be applied in your work.

Staying up-to-date with the latest happenings and data science trends in 2023 is essential for professionals to remain competitive and relevant. By remaining informed, you can make better decisions, provide more effective solutions, and make a great future in data science. 

Why a Career in Data Science is Lucrative, Meaningful, and in High Demand

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Data science has emerged as one of the most promising career paths in recent years. And why not. A data science career allows you to work with cutting-edge technologies, get lucrative remuneration and directly impact a business through data driven decisions.

In fact a report states that the global data science platform market was valued at USD 95.31 billion in 2021 and is expected to grow at a CAGR of 27.6% during the forecast period. As the demand for data driven business decisioning will increase, the companies will require more and more people with capabilities to derive meaningful insights from data. This will increase the availability of jobs in data science.

Data Science market size
Source: Polaris Market Research

Here is why you should consider a Data Science career:

  • High demand for data scientists and plethora of jobs in data science

There is an exponential growth of data generated by businesses, organizations, and individuals, creating a huge demand for data scientists and analysts. Data scientists come with the capabilities to analyze and interpret complex data sets, figure out patterns and insights, and use this information to make informed business decisions.

They are crucial in the organization, and their output directly impacts business decisions. The demand for jobs in data science is expected to continue to grow in the coming years. A data science career path is quite sought after for people with strong mathematics, statistics, and computer science background.

  • A data science career is lucrative

Data science career  is considered one of the most lucrative career paths in the current job market. The demand for skilled data scientists is at an all-time high, and the salaries and benefits offered to data scientists clearly showcase the demand. The data scientist career path depends on several factors, such as years of experience, location, and industry.

Data Scientist Salary
Image Source: Glassdoor

As data scientists can work in a variety of industries, it gives them a wide range of job opportunities and the ability to explore different industries and career paths. As the demand for data-driven decision-making continues to grow, we can expect to see the demand for skilled data scientists soar.

  • Meaningful work 

A data scientist’s work has a significant impact on businesses, organizations, and society. A data scientist works on complex data sets and  identify patterns and insights that help businesses make informed decisions. A data scientist working in healthcare analyzes patient data to figure out patterns that can eventually lead to more effective treatments and better outcomes.

 A data scientist working in finance handles and analyzes customer data that can help to prevent frauds or improve the customer experience. A data science acreer  offers opportunities to work on meaningful projects that have a real impact on the world. 

  • A career in data science allows you to work with cutting edge tech

Data scientists work on cutting-edge technologies as they need to analyze and interpret complex data sets. Data scientists are often at the forefront of technological advancements, developing and implementing innovative solutions to solve complex business problems.

Data scientists use many tools and technologies, including programming languages like Python, R, and SQL and machine learning frameworks like TensorFlow and PyTorch. They also use various data visualization tools like Tableau and Power BI to present their findings in an easy-to-understand format. As the sector advances, data scientists will play a crucial role in developing and implementing innovative solutions to solve complex problems.

Kick start your Data Science Career

If all of these sound exciting, a great way to start a data science career will be by joining a course that understands your needs.

Ivy Professional School is a great choice that provides students with the necessary skills and guidance to launch a successful career in analytics. Ivy starts from the basics and gradually moves to advanced concepts in analytics so that even beginners can easily grasp concepts. 

Students are trained by faculty from elite institutions like IITs, IIMs, and US universities and exposed to real-world analytics problems through capstone projects, case studies, and internships. At Ivy Professional School, students receive support from teaching assistants to address their questions and concerns. Ivy offers sessions on building effective resumes and conducting mock interviews to prepare students for job opportunities. These resources help ensure that Ivy students are fully prepared and job-ready for future opportunities in data science.

Watch this video to get more tips on how to build a Career in Data Science 

How to Build a Data Science Portfolio to Land you a Dream Job.

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Data science is an exciting field that provides the opportunity to have a lucrative career, use cutting-edge technology, and help businesses make informed decisions through data. As the demand for data-driven insights grows across industries, the scope of data science careers is expected to expand even further.

Data science Portfolio: what is it ?

A good data science portfolio for beginners  is a collection of projects, code samples, and analysis that will demonstrate the skillset of a data scientist. It is a way for data scientists to showcase their abilities to potential employers or clients.

Building a portfolio is crucial for a data science career. As a beginner in the field, a data science portfolio allows candidates to showcase their skills, stand out from the competition, provide context around their work, and show their commitment to ongoing learning and development. 

Here are a few ways how you can build a portfolio to kick-start your career in data science: 

Choose your focus area

A data science portfolio for beginners should be narrowed down to a focus area within data science where you have built your expertise. It could be anything-natural language processing, computer vision, data visualization, or machine learning. It will help showcase your skills and expertise in a particular field. You have to make sure you choose an area you are passionate about and have a good knowledge base. 

Lets look a few araes you can work on:

– Exploratory Data Analysis:

You can do exploratory data analysis on a publicly available dataset and showcase your findings clearly and concisely.

– Predictive Modeling:

Using machine learning algorithms, you can build a predictive model to predict a certain outcome, such as customer churn or credit risk.

– Time Series Analysis:

You can perform a time series analysis on a dataset to forecast future trends or identify patterns over time.

– Deep Learning:

If you possess advanced skills, you can build a deep learning model to perform tasks like image classification or text generation.

You must remember that the projects you choose must be aligned with your interests and showcase your data science skills to potential employers.

Choose your Projects

After carefully choosing your focus area, select a few projects that will showcase your skills in that area. The projects in a data science portfolio for a beginner should be able to display a wide range of skills, such as data cleaning, data visualization, statistical analysis, and machine learning. For every project, you must ensure that you provide context around the problem you want to solve. You must clearly show the methods you used to solve this problem and the reasoning behind your methodologies to solve a particular business problem.

Showcase your code on GitHub

A data science portfolio for beginners must include the code you used to complete the projects. This can be in the form of Jupyter Notebooks or GitHub repositories. This will help to showcase your ability to write clean, organized, and well-documented code to employers.

Let’s take an example to figure out the impact showcasing your project in GitHub can create. Vaishnav Bose, a student at Ivy has shown different projects he has undertaken various projects which he has highlighted on GitHub.

GIt projects

Highlight your results

You must showcase your results by including charts, graphs, and visualizations that demonstrate the impact of your work. Highlighting your results is important to help potential employers understand how your work can impact their business.

Write a blog discussing your projects

An online presence discussing about your skills and the projects that showcase those skills is a must have in a data science portfolio for beginners these days. You can maintain a blog or create a website for yourself where you can discuss each project you took up and how you solved a particular problem. This can be a great addition to your resume and impress potential employers.

Medium article

Aritra Adhikari, an ex-Ivy student in this medium post has highlighted how he predicted customer lifetime value for an auto insurance company.

LinkedIn is also a great place where you can talk about the projects you are working on and get feedback as well as noticed by recruiters.

Make it concise

Most potential employers will skim through your portfolio. No one has the time to read through elaborate project details. Your job should be to make the portfolio concise and easy to navigate. The potential employers should be able to figure out all the details of a particular project easily without spending considerable time on it.

Seek feedback

Getting feedback from peers or mentors is crucial in creating an effective data science portfolio for beginners. It can help you identify your weak spots, areas where you need improvement and provide expert suggestions to make your portfolio stand out. When seeking feedback, it is important to be open to constructive criticism and willing to make changes to your portfolio based on the feedback you receive.

A Data Science Portfolio is a must have 

The field of data science is highly competitive, and you must have a strong portfolio that will make you stand out from the rest to get the job of your choice. A data science portfolio for a beginner must include a variety of projects presented in a clear and organized manner. This will help you to build credibility with your potential employers and ultimately get your dream job.

How a B.Com Graduate Became a Data Analyst- Roshni’s Inspiring Journey

Analytics is an exciting career due to the high demand for professionals who can analyze data and provide insights,, the chance to work with cutting-edge tools and technologies, and the satisfaction of making a significant impact on businesses and organizations. Starting a career in

Top 5 Online Data Engineering Courses with Certification

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A data engineer’s role involves designing, developing, and maintaining the systems and infrastructure necessary for processing, storing, and analyzing massive datasets. They oversee the creation and management of data pipelines, maintaining databases, ensuring the quality of data, and integrating diverse data sources. Data engineers are the backbone of data-driven organizations working on efficiently and effectively using data in decision-making processes.

With the ever-increasing amount of data being generated and collected by businesses, we are witnessing a growing demand for skilled data engineers. To tap into this industry and make a rewarding career in data engineering, here are some top courses that can kickstart your career as a data engineer.

Top 5 Online Data Engineering Courses

1. Cloud Data Engineering Certification Course – Ivy Professional Course

Ivy equips students with all the skills to launch a successful data engineering career. Ivy Professional School’s Cloud Data Engineering certification course provides hands-on experience with real-life projects and case studies in Big Data Analytics and Data Engineering. The students are assisted by teaching assistants to clear their questions and doubts. Students have the option to attend live face-to-face or online classes. Ivy also provides lifetime placement support to its students. Ideally, the course takes 6 months to complete.

The course covers important topics such as:

  • SQL and Database Understanding
  • Big Data Terminologies
  • Data Warehousing
  • Apache Hive, Apache Pig, Apache Sqoop, Hbase (NoSQL Database), Apache Flume, and Apache Airflow
  • Scala
  • Spark
  • Kafka
  • Cloud Essentials and Fundamentals of Azure

The course will allow students to join the fast-growing and rewarding data industry. A student can learn from anywhere, as the option of joining live classes online is also available. The course also focuses on holistic development, providing students with essential job-oriented skills such as CV building, LinkedIn profile building, networking skills, and interview skills, among others.

2. Data Engineering Course – EdX

This course provides information on various aspects of data engineering, including the principles, methodologies, techniques, and technologies involved. Students will learn how to design and build databases, manage their security, and work with various databases such as MySQL, PostgreSQL, and IBM Db2. Students will be exposed to NoSQL and big data concepts including practice with MongoDB, Cassandra, IBM Cloudant, Apache Hadoop, Apache Spark, SparkSQL, SparkML, Spark Streaming.

3. Udacity Data Engineering with AWS

In this course, students will get all the necessary skills to build their data engineering career. Students will acquire knowledge related to various concepts such as creating data models, constructing data warehouses and data lakes, streamlining data pipelines through automation, and handling extensive datasets.

They will learn how to design user-friendly relational and NoSQL data models that can handle large volumes of data. Students will also acquire the skills to construct efficient and scalable data warehouses that can store and process data effectively. They will be taught to work with massive datasets and interact with cloud-based data lakes. Students will learn how to automate and monitor data pipelines, which involves using tools such as Spark, Airflow, and AWS. 

4. Online Data Engineering Courses – Coursera

Coursera offers a variety of courses in data engineering. The courses discuss all the skills needed to excel in a data engineer role. They discuss the various stages and concepts in the data engineering lifecycle. The courses teach various engineering technologies such as Relational Databases, NoSQL Data Stores, and Big Data Engines.

5. Data Engineering Courses – Udemy

Just like Coursera, Udemy offers a plethora of courses in data engineering. Some popular ones include Data Engineering using AWS Data Analytics, Data Engineering using Databricks on AWS and Azure, Data Warehouse Fundamentals for Beginners, Taming Big Data with Apache Spark and Python – Hands On, among others.

Online data engineering courses provide an excellent opportunity to acquire new skills and knowledge Whether you are a beginner or an experienced professional, these courses can help you gain a deeper understanding of data engineering concepts, stay up-to-date with the latest trends and technologies, and accelerate your career.

Data Engineer vs Data Scientist: Understanding the Distinctions and Synergies

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If you do not know much about the analytics industry, you may be under the impression that a data scientist and a data engineer do the same thing-they analyze data and deduce insights from them to come up with business solutions. Though they both work with data, a data engineer and a data scientist have a different set of responsibilities. 

If you are keen to build a career in data analytics or data engineering, it becomes important to understand what their exact responsibilities are and how to develop the required skills if you want to embark on either of the roles.

What does a Data Engineer do?

A data engineer designs, develops, and maintains the systems and infrastructure that make it possible to process, store and analyze large datasets. They oversee the creation and management of data pipelines, maintain databases, verify data quality and merge diverse data sources.

Data engineers collaborate with data scientists, business analysts, and other stakeholders to understand the organization’s data requirements and create data solutions that fulfil their business requirements.

Skills needed to be a Data Engineer

To be a successful data engineer, one needs a diverse range of skills, such as:

  • Strong programming skills: To become a successful data engineer, you must be proficient in at least one programming language, such as Python, Java, or Scala.
  • Database knowledge: You must clearly understand databases, data warehousing, and data modeling. You should be familiar with various data processing and storage technologies like Hadoop, Spark, and NoSQL databases.
  • ETL and data pipeline creation: ETL (Extract, Transfer, Load) tools help get data from different sources and store it in the database for analysts to work on. 
  • Cloud computing skills : A data engineer must have knowledge of cloud computing platforms like AWS, Google Cloud, or Azure.
  • Communication : Data engineers work closely with data analysts, data scientists, and business stakeholders. Communication and teamwork skills are crucial to excel in this field.

    Skills needed to be a Data Engineer


Ivy Professional School’s Cloud Data Engineering certification course provides hands-on experience with real-life projects and case studies in Big Data Analytics and Data Engineering. The course teaches important topics such as SQL and Database Understanding,  Python, big data terminologies, data warehousing,  Hadoop, Apache Hive, Scala, Spark, Kafka, Big Data in Azure.

Scope of a Data Engineer

Data engineering is a rapidly growing field with many career opportunities for skilled professionals. As we become increasingly aware of the importance of data, data engineers can expect to enjoy strong demand for their skills and expertise for many years to come.

What does a Data Scientist do? 

The job of a data scientist involving analyzing data and bringing out meaningful information from it that can solve business problems. At a more advanced level, they develop and implement AI-based algorithms that can simplify business challenges. They also create data visualizations and dashboards that allows stakeholders to identify trends and patterns in complex data and make decisions based on that. Data scientists work in various industries, including finance, healthcare, e-commerce, and marketing.

Skills needed to be a Data Scientist

You need to possess the following skills to become a successful data scientist: 

  • Strong statistical and mathematical skills: It is absolutely mandatory to have a solid foundation in statistics and mathematics to develop models and algorithms for data analysis.
  • Strong Programming skills: You should be proficient in at least one programming language, such as Python, R, or SQL, to manipulate, clean, and analyze data.
  • Data wrangling and cleaning: You need to be able to extract, clean, and transform data from various sources to prepare it for analysis.
  • Business understanding: As a data scientist, you solve problems for a business or client. You have to understand the business context in which you are working and the ability to translate data insights into actionable business recommendations.
  • Communication and storytelling: Data scientists must communicate their findings effectively to technical and non-technical stakeholders.

    Skills needed to be a Data Scientist


Ivy Professional School’s Data Science Certification course equips students with all the skills to launch a successful analytics career. It is a comprehensive program that covers a range of topics, including data analytics, machine learning, visualization, deep learning, and soft skills.

 Ivy students are trained by faculty from IITs, IIMs, and US universities and exposed to real-world analytics problems through capstone projects, case studies, and internships. The students are assisted by teaching assistants to clear their questions and doubts. Ivy also provides CV-building sessions as well as mock interview sessions to make the students completely job-ready. 

 Scope of a Data Scientist 

The scope of a data scientist’s career is also quite broad and varied, with many opportunities for growth and advancement. Data science as a field is rapidly growing as companies are heavily relying on data-driven decisions across industries.

Both the roles provide exciting opportunities to grow and succeed in your career. It is up to you to determine your interest and choose a clear path to build a long-term career in them.

GPT-4 Has Arrived: What Makes It So Important?

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The never-ending rumors of OpenAI bringing out GPT-4 finally ended last week when the Microsoft-backed company released the much-awaited model. GPT-4 is being hailed as the company’s most advanced system yet and it promises to provide safer and more useful responses to its users. For now, GPT-4 is available on ChatGPT Plus and as an API for developers.


The newly launched GPT-4 can generate text and accept both image and text inputs. As per OpenAI, GPT-4 has been designed to perform at a level that can be compared to humans across several professional and academic benchmarks. The new ChatGPT-powered Bing runs on GPT-4. GPT-4 has been integrated with Duolingo, Khan Academy, Morgan Stanley, and Stripe, OpenAI added.


This announcement follows the success of ChatGPT, which became the fastest-growing consumer application in history just four months ago. During the developer live stream, Greg Brockman, President and Co-Founder of OpenAI Developer Livestream that OpenAI has been building GPT-4 since they opened the company.


OpenAI also mentioned that a lot of work still has to be done. The company is looking forward to improving the model “through the collective efforts of the community building on top of, exploring, and contributing to the model.”

What’s new in GPT-4?

So, what makes GPT-4 stand out from its predecessors? Let us find out: 

Features of GPT-4

  • Multimodal 

One of the biggest upgrades for GPT-4 has been its multimodal abilities. This means that the model can process both text and image inputs seamlessly.


As per OpenAI, GPT-4 can interpret and comprehend images just like text prompts. Any specific type or image size does not bind this feature. The model can understand and process all kinds of images- from a hand-drawn sketch, a document containing text and images, or a screenshot.

  • Performance

OpenAI assessed the performance of GPT-4 on traditional benchmarks created for machine learning models. The findings have shown that GPT-4 surpasses existing large language models and even outperforms most state-of-the-art models.

As many ML benchmarks are written in English, OpenAI sought to evaluate GPT -4’s performance in other languages too. OpenAI informs that it used Azure Translate to translate the MMLU benchmark. 



Image: OpenAI




OpenAI mentions that in 24 out of 26 languages tested, GPT-4 surpassed the English-language performance of GPT-3.5 and other large language models like Chinchilla and PaLM, including for low-resource languages like Latvian, Welsh, and Swahili.


  • Enhanced capabilities

To differentiate between the capabilities of GPT-4 and GPT-3.5, OpenAI conducted multiple benchmark tests, including simulating exams originally meant for human test-takers. The company utilized publicly available tests like Olympiads and AP free response questions and also obtained the 2022-2023 editions of practice exams. We did not provide any specific training for these tests.

Here are the results: 


exam results of gp4

Image Source: OpenAI

  • Safety

OpenAI dedicated six months to enhancing GPT-4’s safety and alignment with the company’s policies. Here is what it came up with: 

1. According to OpenAI, GPT-4 is 82% less likely to generate inappropriate or disallowed content in response to requests.

2. It is 29% more likely to respond to sensitive requests in a way that aligns with the company’s policies.

3. It is 40% more likely to provide factual responses compared to GPT-3.5.

OpenAI also mentioned that GPT-4 is not “infallible” and can “hallucinate.” It becomes incredibly important to not blindly rely on it.


GPT-4 is a gamechanger

OpenAI has been at the forefront of natural language processing advancements, starting with their GPT-1 language model in 2018. GPT-2 came in 2019. It was considered state-of-the-art at the time.

In 2020, OpenAI released its latest model, GPT-3 which was trained on a larger text dataset. It led to improved performance. Finally, ChatGPT came out a few months back.

Generative Pre-trained Transformers (GPT) are learning models that can produce text with a human-like capability. These models have a wide range of applications, including answering queries, creating summaries, translating text to various languages (even low-resource ones), generating code, and producing various types of content like blog posts, articles, and social media posts.

Top Data Science Podcasts You Cannot Afford To Miss in 2023

Staying up-to-date with the latest happenings in data science is crucial due to the field’s rapid growth and constant innovation. Beyond conventional ways to stay updated and get information, podcasts can be a fun and convenient way to access expert insights and fresh perspectives. They can also provide crucial information to help you break into a data science career or advance it successfully.

Here is a list of some popular podcasts any data enthusiast cannot afford to miss this year. 

Michael Helbling, Tim Wilson, and Moe Kiss are co-hosts who discuss various data-related topics. Lighthearted in nature, the podcast covers a wide range of topics, such as statistical analysis, data visualization, and data management.

 Professor Margot Gerritsen from Stanford University and Cindy Orozco from Cerebras Systems are the hosts, and it features interviews with leading women in data science. The podcast explores the work, advice, and lessons learned by them to understand how data science is being applied in various fields.

 Launched in 2014 by Kyle Polich, a data scientist, this podcast explores various topics within the field of data science. The podcast covers machine learning, statistics, and artificial intelligence, offering insights and discussions.

Not So Standard Deviations is a podcast hosted by Hillary Parker and Roger Peng. The podcast primarily talks about the latest advancements in data science and analytics. Staying informed about recent developments is essential to survive in this industry, and the podcast aims to provide insights that will help listeners to do that easily. By remaining up-to-date with the latest trends and innovations, listeners can be in a better position to be successful in this field. 

Hosted by Xiao-Li Meng and Liberty Vittert, the podcast discusses news, policy, and business “through the lens of data science,” Each episode is a case study of how data is used to lead, mislead, and manipulate, adds the podcast.

Data Stories hosted by Enrico Bertini and Moritz Stefaner is a popular podcast exploring data visualization, data analysis, and data science. The podcast features a range of guests who are experts in their respective fields and discuss a wide variety of data-related topics, including the latest trends in data visualization and data storytelling techniques.

Felipe Flores, a data science professional with around 20 years of experience hosts this podcast. It features interviews with some of the top data practitioners globally. 

Dr.Francesco Gadaleta hosts this podcast. It provides the latest and most relevant findings in machine learning and artificial intelligence, interviewing researchers and influential scientists in the field. Dr. Gadaleta hosts the show on solo episodes and interviews some of the most influential figures in the field.

Making Data Simple is a podcast that is hosted by AL Martin, the IBM VP of Data and AI development. The podcast talks about the latest developments in AI, Big data, and data science and their impact on companies worldwide.

Hosted by Emily Robinson and Jacqueline Nolis, the podcast provides all the knowledge needed to succeed as a data scientist. As per the website, the Build a Career in Data Science podcast teaches professionals diverse topics, from how to find their first job in data science to the lifecycle of a data science project and how to become a manager, among others.

Discretion: The list is in no particular order.

What is TinyML? : An Introduction to the Revolutionary Framework

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A mere century ago, no one could have imagined we would be so reliant on technology. But, here we are, constantly being introduced to some of the smartest, trendiest, and mind-boggling automation procedures. The modern world will come to a screeching halt without the intervention of up-to-the-minute software, framework, and tools. TinyML is the new addition to the category of up-to-date technologies and telecommunications. 

There are very few authentic resources available that put light on TinyML. TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers, authored by Daniel Situnayake and Pete Warden, is a prestigious and reliable source that answers the question: ‘what is TinyML?’. TinyML is an advancing field that combines Machine Learning and Embedded Systems to carry out quick instructions on limited memory and low-power microcomputers. 

Another important feature of TinyML – the only machine learning framework it supports is TensorFlow Lite. Not sure what TensorFlow is? Check the detailed guide on TensorFlow published by Ivy Professional School. 

Waiting long for machine learning magic is not a pleasant experience in every situation. When regular Machine Learning comes across commands like ‘Okay Google’, ‘Hey Siri’, or ‘Alexa’, the response can be time-intensive. But, the goal is quick responses from small directions like these. The desired fast reaction is only possible when the TinyML application is in effect. 

It’s time to dive deep into the discussion of TinyML:

What is TinyML?

TinyML is a specialized study of Machine Learning that sits in the middle of embedded systems and machine learning (ML). It enables expansion and disposition of complex ML models on low-power processors that have limited computational abilities and memory. 

TinyML allows electronic accessories to overcome their shortcomings by gathering information about the surroundings and functioning as per the data collected by ML algorithms. It also enables users to enjoy the benefits of AI in embedded tools. The simplest answer to ‘what is TinyML?’: TinyML is a framework to safely transfer knowledge and intelligence in electronic devices using minimal power. 

The rapid growth in the software and hardware ecosystems enables TinyML application in low-powered systems (sensors). It warrants a real-time response, which is highly in demand in recent times.

The reason behind the growing popularity of TinyML in the real world is its ability to function perfectly fine without necessitating a strong internet connection, and massive monetary and time investment. It is rightly labeled as a breakthrough in the ML and AI industry.

TinyML has successfully addressed the shortcomings of standard Machine Learning (ML) models. The usual ML system cannot perform its best without entailing massive processing power. The newest version of ML is ready with its superpower to take over the industry of edge devices. It does not disappoint by demanding manual intervention such as connecting the tool to a charging point just to process simple commands or perform small tasks. 

The application enables the prompt performance of minute but integral functions while eliminating massive power usage. A father figure in the TinyML industry, Pete Warden says, TinyML applications should not necessitate more than 1 mW to function. 

If you are not well-versed in the basic concept of machine learning, our blog might help you understand it better. 

Some examples of TinyML applications in the real world

New-age data processing tools and practices (Data AnalyticsData EngineeringData VisualizationData Modeling) have become mainstream due to their ability to offer instant solutions and feedback.

TinyML is solely based on data computing ability; it’s just faster than others. Here are a few uses of TinyML that we all are familiar with but probably, were not aware of the technology behind these: 

  • The ability of cars to detect animals on the streets
  • Audio-based insect detection
  • Keyword identification
  • Machine monitoring
  • Gesture recognition
  • Object classification

Benefits of TinyML

Quick Action

Usually, a user anticipates an instant answer or reaction from a system/device when a command is stated. But a thorough process involving the transmission of instructions to the server and device capacitates the outcome. As one can easily fathom that this long process is time-consuming and thus the response gets delayed sometimes. 

TinyML application makes the entire function simple and fast. Users are only concerned with the response; what goes inside does not pique the interest of many. Modern electronic gadgets that come with an integrated data processor are a boon of TinyML. It encourages the fast reaction that customers are fond of. 

Keeps Information Secure

The exhaustive system of data management, transmission, and concocting can be intense. It also accelerates the risk of data theft or leak. TinyML safeguards user information to a great extent. How? The framework allows data processing in the device. The growing popularity of Data Engineering has also skyrocketed the need for safe data processing. From an entirely cloud-based data processing system to localized data processing, data leak is not a common problem for users anymore. TinyML erases the need to secure the complete network. You can now get away with just a secured IoT device. 

Consumes Less Energy

A comprehensive server infrastructure is an ultimate foundation to ensure safe data transfer. As TinyML reduces the need for data transmission, the tools also consume less energy compared to the models manufactured before the popularity of the field. The common instances where TinyML is in use are microcontrollers. The low-power hardware uses minimal electricity to perform its duties. Users can go away for hours or days without changing batteries, even when they are in use for an extended period. 

Minimal Internet Bandwidth

Regular operations using ML demand a strong internet connection. But, not anymore when TinyML is in action. The sensitive sensors seize information even without an internet connection. Thus, no need to worry about data delivery to the server without your knowledge.

Shortcomings of the TinyML Application

Though it’s almost perfect, but not free from flaws. When the world is fascinated by the potential of TinyML and constantly seeking answers to ‘what is TinyML?’; it’s important to keep everyone informed of the challenges the framework throws at users. Combing through the internet and expert views, a few limitations of TinyML have been listed here:

Unpredictable Power Use

Regular ML models use a certain amount of power that industry experts can predict. But TinyML does not leverage this advantage as each model/device uses different amounts of electricity. Thus, forecasting an accurate number is not possible. Another challenge users often face is an inability to determine how fast they can expect the outcome of commands on their device. 

Limited Memory    

The small size of the framework also limits the memory storage space. Standard ML models weed out such complications. 

Sectors where TinyML is revolutionizing the market:


The current retail chains manually monitor the stocks. The precision and accuracy of state-of-the-art technologies (such as TinyML) deliver better results compared to human expertise. Tracking inventories becomes straightforward when tinyML is in action. The introduction of footfall analytics and TinyML has transformed the retail business. 


TinyML can be a game-changer for the farming industry. Whether it’s a survey of the health of farm animals or sustainable crop production, the possibilities are endless when the latest technologies are combined and adopted. 
Sector wise application of Tiny ML

Manufacturing/Production Industry

The smart framework expedites factory production by notifying workers about necessary preventative maintenance. It streamlines manufacturing projects by implementing real-time decisions. It makes this possible by thoroughly studying the condition of the equipment. Quick and effective business decisions become effortless for this sector. 

Road Congestion/Traffic

TinyML application simplifies real-time information collection, routing, and rerouting of traffic. It also enables fast movement of emergency vehicles. Ensure pedestrian safety and reduce vehicular emissions by combining TinyML with standard traffic control systems.

Wrap Up

Experts believe we have a long way to go before we can claim TinyML as a revolutionary innovation. However, the application has already proved its ability and efficiency in the machine learning and data science industry. With an answer to the question ‘what is TinyML?’, we can expect the field to advance and the community to grow. The day is not far away when we will witness the application’s diverse implementation that none has envisaged. TinyML is ready to go mainstream with the expansion of supportive programming tools.

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