Data analytics is one of the most trending careers in present times. Earlier, data was considered the new oil in the industry because of its requirement in almost every sector. These days its significance is more than oil, as none of the business sectors can survive without data and insights.
Hence, there is an increasing need for professionals in this sector. And, you can find data analyst training in various educational institutes. But, the question is which one to choose amongst these courses. To help you solve this conundrum, the following is a list of features that a course for data analysis must contain.
5 must-have features of a data science and analytics course
As we have already mentioned, multiple data analytics courses are offered across various platforms. Some are made available online and others in colleges or universities. Some of them are free, some are paid, and some are based on the freemium model (a portion of the course is free and the rest is paid). But, which ones should you choose? This can be done with the help of a checklist which consists of some basic features. Without these, a data analyst training course is deemed incomplete. What features are these? Read on...
It should consist of placement assistance along with the training programme
Completing a data science and analytics course and securing a job right afterwards would help you secure a handsome salary package. Therefore, your data science course should have a placement programme and the partner companies should be some of the reputable names in the industry. Otherwise, you will not get the exposure you need to build a successful career in this domain.
The curriculum should be job oriented
If your course lacks the modules that are helpful for the job, then you are in for a rude shock, as none of the placement agencies like candidates who do not have skills that make them job-ready. By this, we mean that you should have knowledge of data science and analytics modules like Python, SQL, Power BI and Tableau. Without these courses in the curriculum, you will not even be considered for an interview. So, be on a sharp lookout while selecting the programme.
There should be live learning modules
The training methodology needs to be hands-on. The faculty should teach you all the basics of data science and analytics, as well as the prevalent practices in the industry. If you are able to grasp the whole mechanism, then you will be able to take on any role in your job in data analytics. However, if your programme does not have a live learning module, then you very well know what to do.
The programme should contain some real-world projects
Without these, you are just another data analyst in the room. Live projects enable you to test your theoretical knowledge and check your progress as a professional. Moreover, you get a chance to experience the real-world application of what you are studying and how clients and companies work, along with the type of issues you are likely to face when working as a data analyst.
The programme should offer dedicated career services
To excel in a data analyst interview, you need to give your best. And to put your best foot forward, you need to participate in interview workshops, resume building sessions, profile building exercises and one-on-one mentorship. Your data analytics training should include proper career services so that you can easily land your dream job.
A data science and analytics course enables access to one of the most prized job opportunities, which is highly rewarding if you grab it with both hands. So, it is extremely important that you find a course that fulfils your requirements and then gives you the professional advantage you need to advance in your career. In this regard, you can use the advice mentioned above to your advantage while researching for your PG in data analytics course.
And, if you are looking for a readymade solution, consider the postgraduate program in data analytics from Imarticus Learning. This course will get you placed in the top 500 companies, and you will get a chance to learn from a well-reputed faculty with decades of industry experience in data science and analytics.
