Learn How to Make AI-Driven Decisions

The eligibility criteria for this program are as follows: 

• Working professionals like early-career professionals or senior managers (IT Managers, Business Intelligence Analysts, Data Science Managers, Management Consultants, and Business Managers) who want to apply Data Science and Machine Learning techniques in their firms. 
• Working professionals like Data Scientists, Data Analysts, or Business Analysts who wish to turn vast volumes of data into valuable insights 
• Entrepreneurs interested in innovation with the assistance of Data Science and Machine Learning techniques 
• Those with academic or professional training in Applied Statistics or Mathematics will find the program easier to learn. However, participants lacking this background will need to put in extra effort, and Great Learning will offer the required assistance.

The applications follow a rolling process, which is closed when the requisite number of seats in the cohort is filled. To ensure your chances of securing a seat, we encourage you to apply as early as possible.

Complex algorithms and sophisticated tools make up a large part of a Data Scientist's day. In addition to data analysis tools, keeping up with the latest tools in data acquisition, data cleansing, data warehousing, and data visualization is becoming increasingly important as the historically separate roles of Data Scientist and analyst become merged for increased efficiency. 

Python is the lingua franca of Data Science, but knowledge of R, SAS, SQL, and sometimes Java, Scala, and Julia, among others, must also be acquired at the foundational level itself. Technical soundness is a must for moving forward toward solutions while avoiding roadblocks.

To enroll in this program, the applicants must meet the eligibility criteria mentioned earlier. The standard registration process for the eligible students is as follows: 

Step 1: Applicants will need to complete their online application form. 

Step 2: On receiving the application, the Great Learning program team will review it to determine your fit with the program. 

Step 3: If selected, you will receive an offer for the upcoming cohort. 

Step 4: Secure your seat by paying the fee.

The total program fee is USD 2500.

No. All the requisite learning material is provided online to learners through the Learning Management System (LMS). Considering these fields are vast and constantly evolving, there is always more you can learn, and there will be a list of suggested books and other resources for your in-depth reading enjoyment.

Candidates can pay the program fee through Bank Transfer and Credit/Debit Cards. They can also pay in easy installments using PayPal credit options and get interest-free payments for up to 6 months (Note that these services are subject to credit approval by PayPal). [For further details, please get in touch with us at dsml.mit@mygreatlearning.com]

Please note that submitting the admission fee constitutes enrollment in the program, and the cancellation penalties outlined below will be applied. If you are unable to attend your program, please review our dropout and refund policies below: 

Dropout requests received within 7 days of enrollment and more than 42 days prior to the commencement of the program will incur no fee. Any payment received will be refunded in full. 

Dropout requests received more than 42 days prior to the program but more than 7 days after the acceptance are subject to a cancellation fee.

Dropout requests received 22-41 days prior to the commencement of the program are subject to a cancellation fee equal to 50% of the program fee. 

Any dropout requests received fewer than 22 days prior to the commencement of the program are subject to a cancellation fee equal to 100% of the program fee. 

No refund will be made to those who do not engage in the program or leave before completing a program for which they have registered.

We accept corporate sponsorships and can assist you with the process. [For more information, please write to us at dsml.mit@mygreatlearning.com]

The salaries in the fields of Data Science and Machine Learning consistently exceed USD 100,000 annually. According to ZipRecruiter, the average salary in the U.S. for 

• Machine Learning Engineer is USD 128,000 
• Data Scientists is USD 122,000

The demand for AI, Machine Learning, and Data Science jobs spans a wide range of industries, including Healthcare, Cybersecurity, Finance, Oil & Gas, Transportation, Education, Talent Acquisition, Inventory Management, and E-commerce, particularly in areas like Recommendation Systems and Price Optimization. 

As organizations increasingly adopt AI and data-driven decision-making, professionals skilled in Data Science, Machine Learning, and Generative AI are in high demand. These roles offer strong opportunities for career growth, leadership roles, and long-term industry relevance.

To become a Data Scientist, you need to have a blend of technical expertise, analytical thinking, and real-world problem-solving skills. While a strong academic background is a plus, you should have the ability to master the essential tools and techniques of the field. 

Here’s how you can get started: 

• Build a strong foundation in mathematics, statistics, and programming, especially in languages like Python and R. 
• Gain hands-on experience with tools and frameworks used in the industry, such as TensorFlow, Scikit-learn, and SQL. 
• Develop applied knowledge through projects that simulate real-world data challenges in domains like healthcare, finance, and retail. 
• Strengthen your understanding of AI and machine learning techniques, including supervised and unsupervised learning, deep learning, and generative AI. 

One of the effective ways to build these skills is to enroll in a professional certificate course from MIT IDSS, which can significantly boost your career prospects in Data Science and Artificial Intelligence.

Data Science roles have been among the most in-demand job roles in recent years. According to LinkedIn, hiring for Data Scientists saw a 46% increase in the last year. Employment of data scientists is projected to grow 36 percent from 2023 to 2033, significantly faster than the average growth rate for all occupations. 

We are well within the era of Big Data, where decision-making drives innovation across industries, fostering business growth. Whether it’s personalizing your Netflix recommendations or shaping public policies, Data Science and Machine Learning are at the core of it all. However, raw data alone isn’t enough; skilled professionals are needed to transform data into actionable insights. That’s why companies around the world are actively hiring Data Scientists and Machine Learning experts, and nurturing a strong culture of analytics within their teams. 

If you’re looking to build a future-proof career, now is the time to upskill in Data Science, AI, and Machine Learning.

The future of Data Science is promising. It’s already reshaping how businesses operate. Organizations that leverage Data Science effectively are seeing measurable benefits across multiple areas, such as: 

• Reducing operational costs 
• Identifying new market opportunities 
• Targeting the right customer segments 
• Measuring marketing effectiveness 
• Launching better products and services 

Data has become one of the most valuable assets for modern businesses. In fact, Gartner predicted that 90% of corporate strategies would highlight data and analytics as essential business capabilities. 

As industries increasingly rely on data to drive innovation and growth, the need for skilled Data Science professionals will only continue to rise. Gaining expertise in Data Science, Artificial Intelligence, and Machine Learning is a smart investment for you to future-proof your career.

Machine Learning refers to a group of techniques used by data scientists that allow computers to learn from data. It is the underlying process that allows machines to learn from data, resulting in the recommendations and predictions you receive from Alexa. From leisure to work, our lives are made easier with Machine Learning. The responsibilities of a Machine Learning specialist encompass a spectrum that extends from creating Machine Learning models to retraining systems. 

Specialization in Machine Learning involves acquiring the necessary tools and techniques for the most crucial subset of AI. A holistic skill set, therefore, consists of exceptional technical skills as well as an inherent learning attitude.

The advancement of technology in various fields contributes significantly to the growth of industries. Hence, many businesses are using advanced Machine Learning and Data Science applications to draw the best outcomes. Let's explore some of the benefits of these rapidly evolving technology domains. 

Building Better Business Strategies 

By employing Data Science and Machine learning, organizations can develop the best business plan. Data Science and Machine learning provide solutions to develop the best business plan that supports companies' exponential growth. Today, most top-notch companies are applying Data Science and Machine learning in projects and operational management to achieve better outcomes. 

Better Research and Inventions 

Organizations must be conscious of the latest trends in their market. A data-driven business team would shape its business in the best way that suits the requirements of end customers. It would stay current with technological trends and develop a business strategy that delivers the best services. Businesses with a clear vision and expertise in data can develop groundbreaking solutions. Data-backed approaches enable businesses to add value to their products by adapting to the latest market trends and incorporating the latest technology. 

Cost Reduction 

Cost reduction is one of the major benefits that Data Science and Machine Learning contribute to any business. Small and medium-sized companies strive for endurance due to limited budgets and resources. Data Science, AI, and ML help in formulating cost-effective business solutions.