Great Learning

My experience was very satisfying, as I had the opportunity to open my mind to these new technologies, to learn a bit more deeply about their functioning, and at least to understand what is behind them.

It was a very challenging course because of the amount... Read More of information to assimilate; however, the material is designed so that we can review it and gradually understand the different topics.

The teachers and the classes were excellent, and the patience, support, and follow-up from the staff are extraordinary.

I am motivated to continue exploring the material and take future courses.

MIT-PE is a fantastic professional education route! I took a summer fermentation technology course from MIT-PE in 2004. From the knowledge I gained in that one summer program, I became a senior scientist in fermentation and bioprocess engineering and... Read More was critical in the research and development of sustainable aviation fuel. It was natural for me to decide on MIT-PE ADSP when I decided I wanted to expand my skills into data science and learn Python, deep learning, machine learning, and AI.It is a super intensive 12- week program consisting of 8 courses with three main projects: a first coding and statistics project, an elective project and a capstone project. The courses are the following: Foundations:Python and Statistics, Data Analysis and Visualization, Machine Learning, Practical Data Science, Deep Learning, Recommendation Systems, Elective Project, and capstone Project. The first week of the actual program, you learn Python. The second week, you learn descriptive and inferential statistics. If you took statistics in college, imagine all that you learned in that one undergraduate course in one semester and compressed it into one week. That can give you an idea of the compression and intensity of the courses. Each course has 30 to 40 mini videos no longer than 20 minutes. The stats course even had historical background videos. The videos get straight to the point and are very engaging. Each course also has live lecturers. These are world class Massachusetts Institute of Technology (MIT) college professors performing real world research in data science, statistics, engineering and the biological and mathematical sciences. The professors give excellent live lectures focusing on concepts and thinking and really get you excited. here is a different MIT world class professor for each course. Those lectures are each 2 hours long during the week and you might have 2 to 4 per course. Each of the 8 courses also has case studies written in Jupyter Notebooks. On the weekends both Saturday and Sunday you have mandatory mentor 2-hour sessions each where mentors go cases by case through the Jupyter notebooks. During the mentor session is where the ‘Applied Data Science’ happens. You apply the concepts you learned from the professors in the case studies during the mentor sessions. The mentors are live and this is where you can ask highly detailed or code questions. They offer incredible support through Slack. You are assigned your own program manager. Your program Manager is your cheerleader, facilitator and main support. The Program Managers and mentors are part of Great Learning. Great Learning is a learning facilitator, and they are the producers of all the mini videos that get into the details of the courses. MIT professors provide high-altitude concept live lectures. Great Learning provides all the support, mentoring and tutorials, and detail-oriented training. Great Learning uses real-world industry experts. This is a global industry with global experts in data science. Great Learning appears to be an India based company, so you learn mathematics, statistics, and data science concepts from Indian data scientists, engineers, and computer scientists. And they are beyond excellent! That is the beauty of online education. You can learn from the absolute best leaders in the field. MIT-PE leverages online learning by offering the very best instruction and instructors. The live lectures and mentoring lectures are all recorded that day and uploaded by the afternoon in case you miss them. I was on the west coast, so they were 7:00 am for professor lectures and 6:00 am for mentor lectures. The timing was not ideal, but I did it and it was worth it! They also offer written transcripts of all lectures and mentoring sessions. The program offers a lot of support, cheerleading and motivational support and empathy from the program managers is really nice and sometimes required. The mentored learning sessions are very practical, detail oriented and hands on applications of coding the concepts learned by the professors in their live lectures. The lectures from the world class lecturers are conceptual and big picture. The AI mentor is very helpful on the 3 main projects. Gemini is also very helpful. Plan on everything taking twice as long as you originally expected.Plan accordingly. I would recommend to anyone who is considering taking this program to understand the commitment required. It is not self-paced. It is rigorous. It is an intense workout. So be prepared. But it will be one of the most rigorous and rewarding programs you can take. You will learn so much your brain will be exploding! They have complimentary starting courses to get you up to speed before the actual program session starts. Take advantage of the pre-course prep. Learn how to navigate around in Olympus learning platform before your ever attend program orientation. The lectures are usually 2 hours long. They do post recordings of them on the Olympus learning platform. You will probably have to go through them a few times because there is so much great information. Pre-read the class lecture slides and pre-reads before the lectures, watch the lectures without taking notes during the live presentations so you can absorb the concepts, tips and tricks the guest lecturers provide. Then go back and re-watch the lectures to take your notes. Focus on getting the concepts down to take the quizzes. Use the case studies to practice your work in Google Colab and Jupyter notebooks. Your mentoring sessions on the weekends are also 2 hours long each day and they go through case studies. Get two monitors. I wish I had had two monitors. One for the mentor describing the case study and one for the case study Jupyter Notebook open and operating in Colab. Just because you go through the case studies with the mentor does not mean you know what you are doing. Copy the case studies and practice code in the case studies. Hands-on work is essential. This is a rigorous fully immersive learning experience. When you get to the neural networks and recommenders, plan on doubling the time to complete projects. This is not because of the rigor of the coursework per se it is because you are using shared services in Google Colab. In fact, once you hit the 5th course in the program, you might want to buy Google Colab Pro for $9.99 per month to get a higher ranking as a shared user and possibly increase your quota. Extract files only once per notebook. Extracting files exceeds your google quota if you do it more than once you will start to have all kinds of problems. This is a Google problem not MIT-PE problem. When you do your projects, have a clear strategy and plan before you even start coding. Organizing code is important so as you learn the concepts and work in the case studies, pay attention to how the code is organized. It is fun! and model building is a BLAST! Once you are done with the program, you will probably want to go back to everything you couldn't do the first time and improve all your work. You need 60% in every class in the course to get the certificate. That is not 60% overall but 60% per each class and project. You will be pushing for 100%, but when you get down to time and the last few projects, you just always need to remember what you need for the certificate. Data Science is just plain fun! And MIT-PE and Great Learning ignite a passion for data science! So, get in there and just do it!The cost of my program starting in September 2024 was $3700 USD. It might be more now. But they do offer early enrollment specials and some different financing alternatives. I just slapped it on my credit card and thought of it as investment in myself. Data Scientists make anywhere from $80K to $500K USD so I figured it was a decent investment.

Enrolling in the MIT Professional Education - Applied Data Science & Machine Learning program has been a career-defining decision. The program provided a deep dive into machine learning algorithms, deep learning models, and natural language processing... Read More (NLP), offering me a comprehensive understanding of the core areas driving modern AI innovations. The hands-on projects were especially valuable, allowing me to apply my learning to real-world AI-driven applications.

The curriculum was not only rigorous but also highly practical, equipping me with the technical skills required to excel in a rapidly evolving industry. The instructors were incredibly knowledgeable and provided consistent support throughout the program, ensuring I never felt lost—even when tackling complex concepts.

This program has significantly shaped my career outlook, instilling the confidence and expertise to pursue new opportunities in AI and machine learning. If you're looking to elevate your skills and embark on a career in this dynamic field, I highly recommend this program. It offers the knowledge, tools, and support necessary to succeed.

The MIT ADSP program stands out for its thoughtful structure, engaging live sessions, and practical approach through projects and case studies. The live sessions are highlight in the overall program and highly valuable to get great understanding of the... Read More foundational concepts.

I recently completed MIT Professional Education's Applied Data Science Program, and I can confidently say it has been one of the most enriching educational experiences of my career. The program's thoughtful structure, combining foundational Python and... Read More statistics with advanced machine learning concepts, provided an excellent learning trajectory.

What truly sets this program apart is its expert faculty lineup. Learning directly from MIT professors was incredible - their ability to break down complex concepts whilst maintaining academic rigour was remarkable. The live virtual sessions were engaging and interactive, making complex topics like neural networks and recommendation systems accessible and practical.

The hands-on approach was particularly valuable. The program doesn't just teach theory - it emphasises practical application through real-world projects. The capstone project, spanning roughly three weeks, allowed me to apply everything from exploratory data analysis to advanced clustering and segmentation techniques on actual business problems. The inclusion of current topics like ChatGPT and Generative AI modules shows how up-to-date the curriculum is.

The support system was exceptional. Having industry mentors provided invaluable insights into how these concepts apply in the real world. The Program Manager was consistently supportive, ensuring we stayed on track with our learning objectives.

What I particularly appreciated was the "low code" approach, making the content accessible while not compromising on depth. The program successfully bridges the gap between theoretical understanding and practical implementation, especially with its focus on using Python for data science applications.

For anyone looking to transition into data science or enhance their existing skills, this program provides an excellent foundation with its comprehensive curriculum, expert faculty, and strong emphasis on practical application. The 16.0 CEUs and MIT Professional Education certificate are valuable additions, but the real worth lies in the knowledge and confidence gained through this well-crafted learning journey.

This course was extremely well structured from top to bottom. The live courses were very informative and having the ability to watch recordings of the sessions (for folks like myself that could not attend live due to work) was super helpful and allowed... Read More me to put in the work to learn on my own time. The mentored learning sessions on the weekends really helped cement and more thoroughly understand the concepts from during the week.

I did not have any background in data science prior to starting this course, but if you have a background in computer science and programming, then you should be able to understand and apply your knowledge to learn the techniques and libraries used for data science. The program faculty provides that foundational understanding of the approaches and techniques used, which helped me understand how data scientists think and work.

Overall this course helped me really learn about Data Science and acquire a skillset which is very important and transferrable in today’s world. The material and tools used throughout the program were truly class and easy to use, allowing learners to focus solely on the material without needing to worry about environment configurations.

I would highly recommend this course to anyone that is looking to learn both conceptual and practical applications of data science. The team at MIT and Great Learning were massively helpful and supportive.

Having completed courses in Data Mining and Data Science in the past, I enrolled in the Applied Data Science Program (ADSP) as a refresher. This program was essential for achieving my goals and reigniting my passion for data science. The well-structured... Read More curriculum and challenging nature motivated me to dedicate my weekends to producing high-quality work. I particularly valued the courses on Deep Learning and Introduction to Artificial Intelligence.

The support from the Program Manager and mentors fostered a strong sense of community. Their quick responses and helpful resources empowered me to strive for innovative, real-world solutions.

Additionally, collaborating with my teammates during mentor sessions allowed us to share our experiences and concerns openly, enhancing our learning journey.

This course was challenging yet rewarding as a pharmacist exploring data science. It’s well-structured, with expert instructors and practical content. Though intense, it deepens understanding and builds confidence. A great choice for beginners or skill... Read More enhancement!

The No Code AI & Machine Learning course by MIT and Great Learning offers a transformative dive into the world of AI and ML. It combines approachable professors, supportive mentors, and seamless materials on a user-friendly platform. This fast-paced program... Read More simplifies complex topics, making it ideal for anyone eager to explore AI tools.

The MIT Data Science and Machine Learning Course runs from September 2024 to January 2025.
It is exceptionally well-structured to ensure students gain a deep understanding of the subject. The course includes numerous lectures, but they are carefully organized... Read More to be easily digestible.
It covers a comprehensive range of topics, starting from basic statistics and fundamental machine learning models to deep learning, ChatGPT, and generative AI, offering a thorough overview of key areas in modern data science.
I truly enjoyed this course because it gave me a clear, holistic understanding of data science as a field. While grasping the fundamental theories and core principles can be challenging, I found that going through these basics is essential for achieving a deeper comprehension of modern data science practices.
Among all the topics, I found deep learning the most fascinating. There is so much more to explore in this rapidly evolving field.
For anyone looking to master data science, I highly recommend this course!