Why DataMites for an Artificial Intelligence Engineering Course?

There is no doubt that data science is the most sought-after career path of this century. Optimistic estimates from credible sources show that the demand for data scientists far exceeds qualified data science professionals.

  • Data science is expected to create 11.5 million new jobs by 2026, according to the US Bureau of Labor.
  • Linkedin named data science jobs as the fastest growing.
  • There is an estimated shortage of nearly 190,000 professionals with data science skills in the United States alone, according to McKinsey Global Institute.

Governments, universities and educational institutions around the world have stepped up to alleviate the shortage by facilitating, improving and creating new data science programs, aligned with industry requirements.

Who can pursue a career in data science?

Data science is a multidisciplinary field combining statistics, programming skills, machine learning, and domain expertise to extract insights for business decision-making. Although each field of data science is a broad subject in itself, we only need the applied knowledge of these fields in data science.

There is a common misconception that data science is related to software engineering and only developers or programmers can pursue it. It is simply not true. The fact is that candidates without technical expertise, without a background in computer science or information technology, are most likely to make the career transition to data science.

Thus, data science requires applied knowledge in statistics, machine learning, and essential programming. In other words, NO difficult prerequisites to pursue a career in data science. Anyone with good analytical skills, data intuition, and an interest in learning data science fields can pursue a career in data science.

How to Choose the Right Data Science Course?

Unsurprisingly, the market is flooded with thousands of data science courses from numerous universities and institutes. Also, there is a plethora of free data science courses.

These courses are offered in various formats, learning modes, durations and costs.

Top 10 Areas to Consider When Choosing Data Science Course:

  1. Learning mode
  2. Curriculum
  3. Duration
  4. Batch size
  5. Profiles of mentors/teachers
  6. Industry Alignment
  7. Certificates
  8. Projects
  9. Internship opportunities
  10. Employment assistance

Learning mode:

There are many self-study courses in data science at the basic levels. As we move towards intermediate and advanced data science concepts, self-study is not recommended. As you tend to waste a lot of time understanding concepts, there is no option to get your questions cleared up with a live mentor.

It is highly recommended to choose a course with an instructor-led live training option, either in-person or online data science training.


The curriculum should be comprehensive and cover programming skills, statistics, and machine learning from fundamentals to advanced levels. It is recommended to choose a course that is accredited by reputable certification bodies.


There are courses of different durations ranging from one month to 2 years. It is important to analyze training/coaching hours rather than course duration. For example, a 6-month course with learning hours of 20 hours/week is better than a one-year course with learning hours of 4 hours/week. A 20-hour week gives the right impetus to learn Data Science concepts optimally.

A course lasting 6 to 8 months with 20 hours/week would be ideal.

Batch size:

This is an important area that many overlook when choosing live instructor-led training. It is NOT recommended to join courses with more than 50 learners as it is not possible for mentors to provide personalized attention. In many cases, mentors only cover simple concepts and avoid advanced or confusing topics to keep queries to manageable levels.

An ideal lot size would be 25-40 to provide a good learning environment as a group.

Profile Mentors/Trainers/Coach:

It is recommended that mentors/trainers/coaches not only have academic knowledge, but also industrial experience in delivering real-world data science solutions. When choosing Data Science training, validate the profiles of the mentors.

Industry Alignment

The course should be aligned with changing industry requirements. It is recommended to use current industry practices, platforms and packages and to complete projects with industry-validated use cases.


Reputable data science certifications add significant value to your profile and open windows for data science job opportunities. It is recommended to validate the credibility of the certifications.

Data science projects

The best way to learn and master the concepts of Data Science is to work on as many projects as possible. It is recommended to have at least 4-5 end-to-end data science projects as part of the course.

It is strongly recommended to do a real-time project, a client project or a startup project, as it not only helps to appreciate the business value of a Data Science project, but also adds significant value to your CV.

Internship opportunities

Although not all courses offer group internships, it is strongly recommended that you explore courses with internships at AI companies as a group. A data science internship is an easy way to transition into a career in data science.

Employment assistance

Preparing for the job requires guidance from industry experts. It is recommended that the course offers employment support services such as CV development, mock interviews, employment support, etc.

Why DataMites® for Data Science Courses?

DataMites® is a global data science and AI institute with over 50,000 learners in the last 7 years of experience. DataMites® offers popular instructor-led courses as well as in-person training in most major cities.

DataMites® Popular Data Science Courses

  1. Certified Data Scientist
  2. AI expert
  3. Data Engineer
  4. Python Developer
  5. Machine learning expert

Among these, Certified Data Scientist is the most popular job-oriented data science course.

DataMites® Certified Data Scientist Course Highlights:

  • 7 month course, A comprehensive program validated by IABAC®, an EU accreditation body.
  • Live instructor-led training in most time zones.
  • Mentors are industry experts with decades of experience, from reputable universities with PhDs.
  • The average batch size of DataMites® is 20, ensuring healthy mentor-learner interactions.
  • DataMites® offers globally recognized certification from IABAC®, the International Association of Business Analytics Certification with NASSCOM Future Skills and JAINx – JAIN, Considered a University.
  • A 3-month internship is included as part of the course.
  • Proven work assistance service with the best track record in the industry.

Contact DataMites® for free data science advice and demo sessions.

About Stuart M. McFarland

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