F.A.Q.
Data Engineer – 80% time in any machine learning project is spent on data cleaning and data manipulation related activities. It is also important to know how and where to store data for it’s effective retrieval (flat files, relational database, no sql databases). Data engineer is responsible for all these activities.
Skills required: SQL (MySQL, PostGres), NOSQL (Mongodb), Hadoop, Apache Spark, Amazon Web Services Machine Learning, Microsoft Azure, Machine Learning, R, Python, SAS.
Machine Learning Engineer – Machine Learning engineer is responsible for fitting various models and help select best model for production. He is also responsible for monitoring model performance and perform necessary tweaks if needed.
Skills required: Statistics, Supervised learning algorithms, Unsupervised learning algorithms, Deep Learning, TensorFlow, Amazon Web Services Machine Learning, Microsoft Azure, Machine Learning, R, Python, SAS.
Data Visualization Expert – Data visualization expert is responsible for creating interesting visualizations from the data.
Skills required: Tableau, R & Python visualization libraries.
Business Analyst – Business analyst is responsible for converting business requirements into machine learning problems. He acts as a bridge between client and machine learning team.
Skills required: Good knowledge of all above areas.
Project managers / Delivery managers – To manage stakeholders, communication, risks
Skills required: Sound knowledge of all above skills + Project Management expertise
Data scientist – Data scientist should be better at statistics than a computer engineer and better at computer engineering than a statistician. Having said this, the person should be able to handle end to end and communicate effectively to the stake holders .
Skills required: Excellent knowledge of all above skills +communication+ domain (is a plus)
You will learn following skills:
Programming languages: R or Python
Data engineering: SQL, NOSQL databases, Amazon Web Services, Microsoft Azure
Data visualization: Tableau, Visualization packages in R/Python
Machine Learning: Essential statistics, different models right from linear regression to neural networks
Deep learning: Convolutional neural networks, Recurrent neural networks, TensorFlow
Course brochure contains more details about the skills.
First, we are best faculties in town. We ourselves are trainers, after spending long fruitful years in the industry. Also, visiting faculty are industry experts.
Secondly , we keep updating our courses as per the requirement in the industry.
And third, we offer in depth knowledge in very reasonable fees compared to the coverage of the contents.
We believe that trainers make all the difference. Hence we carefully select our trainers.
We have 2 master trainers, Preeti and Javed. Preeti has around 20 years’ experience in the analytics industry. Javed has more than 15 years industry experience. You can check their LinkedIn profiles.
Apart from that, we invite other trainers from various industries like banking, telecom etc. to help candidates gain industry specific insights.
Yes definitely.
Some of our past students have been manage well, exceed or have save semesters or time due to the prior exposure to the analytics, data science and machine learning.
No, your experience will not be taken away from you. You will always bring additional professional value with the set of skills you would have developed over the years. However, at the same time you should be able to utilize the professional maturity that you bring on the table.
For an example, one of our student moved from sales to analytics into the same company, by utilizing newly acquired analytical skills into his current profession.
Minimum of 6-8 hours of dedicated efforts in a week excluding classes, would prepare you towards the dream career. One needs to spend at least 9-12 months to enter into analytics.
Quite a lot. But to make things simpler for a non-programmer, we offer machine learning course using R language, which is easier to learn and use. We also offer the same course using Python language, but Python is little bit difficult to use than R.
We do not guarantee internship.
But if the course is completed successfully and if we do see spark in the candidate, we gladly pass on the resume of the candidate to our analytics partners in the industry who are constantly looking for good candidates.
However, the candidate needs to clear the interviews. We help with the interview preparation by inviting industry experts to conduct interviews of our candidates and provide feedback for improvement.
No, we will not give any such false commitment to you.
We have placed few candidates in our partner organizations in the past. But we do not provide any job guarantee.
Our goal is to impart right analytical skills to the candidate to make them ready for available jobs in the market. It depends on the candidates too, how much they learn, how much they study on their own, how they finish their assignments and projects.
We are very strict about the absenteeism. We will provide the necessary material to cover the missed classes. The candidate can also come to office during weekdays to meet the faculty if they have any specific doubts about topics missed.
In few cases, we will pass on the recordings of the missed class.
But we do not want out you to miss classes and have this alternative.
We understand, being professional or student, it is difficult to keep up all the weekends after strenuous weekdays. Hence, we give two days break after every 6-8 classes.
We will publish the entire schedule in advance, so you may plan your leaves accordingly.
Participants are expected to work on the business problem of their choice, by doing research, stages involving data acquisition, cleansing, analysis, visualization and reporting.
Capstone project is usually done in a group of 4-5,under the guidance of the industry mentor. The project should ideally start in the mid of the course and the status of research is updated at a regular interval.
At the end of the project, the project team would present the findings in the seminar.
Most of the assignments and the project to be done, as part of the course, will be based on real data.
No, but we will allow you to attend the next batch.
Fresher’s with the right skills are offered anywhere between 2.5 L to 11 L per annum. It also depends on the additional skill sets that they bring on table.
e.g. Candidate having thorough understanding of analytics/ML/DL concepts along with R and Python both, would have better chance and salary than knowing just one open source language.
Salaries offered in the analytics/ data science field are higher when compared to other fields.
However, it also depends on, what is your current drawn salary. If it is already high in your field, you may have to wait to prove your proficiency in the analytics field. But if you are really good, definitely higher salary would be waiting for you!
Yes, you can pay fees in two instalments.
First instalment is to be paid when you confirm your nomination and second is within 20 days once the program starts.
Only in certain cases, as per management discretion, three instalments would be allowed.
Yes, please speak to the counsellor in case you are joining along with your friends.
Yes, we usually would have webinar or face to face meeting arrange with the trainer before the actual class starts, to answer all your further doubts.