In the world of data science, there are three core problems: acquiring data, doing math, and taking action. The first two are easy but the third one is what drives people crazy. Algorithms, machine learning, cognitive tools, and deep learning are often not far away. That's the easy bit.
Data science is easy if you have the right people for it. Data scientists are smart people. If you provide them the information, they can create a system that delivers immense value where there is need. There is absolutely nothing more annoying to a data scientist than being able to do the math but having neither the data to run it against nor the aptness for it to be used.
Let’s picture a scenario of a crane operator loading a super container ship at a port. That's a very hard dare because of the ship’s dimension, with a huge amount of mathematics that creates a chain of probable most-optimal loadings. Crane operators, nevertheless, need to know none of that is going on in the backend and would understand none of the mathematics. They simply need to know “this container goes there, that container goes here.” Thus, complex math has to be distilled down to action. So data science is easy. Making it actionable is the hard part.
In essence, developing a keen sense of how to solve different business problems will set you apart from the field of potential hires during the interview process. So, how can we develop your data intuition? That is where project-based Learning comes into the picture.
Project-Based Learning is developing your skills through practical work and application. In the context of Data Science, this means performing data analyses on many different kinds of data using a wide variety of skills you have either mastered or are in the process of mastering.
Datatrained Education strongly believes in bridging the gap between academics and what companies actually want project-based learning helps develop your problem-solving skills when different bugs arise (aka become a better Googler) and Increase your confidence in your abilities.
At data trained all students are asked to complete 40 projects in a total of which 20 is for practice and 20 for assessment. This time-bound exercise is to get them a heads up for the coming challenges they will face as a data science professional. Students are also asked to write 2 articles on any data science-related topics to help them make aware of the ongoing in the world of data science. However, these projects are only in the test environment. Once the student completes these prerequisites he/she moves on to the internship. The internship is totally different ballgame compared to projects and that’s what differentiates us from any other institute. Projects are like batting in nets and internship is more like playing a match. The pressure is what transforms students from an aspirant to a professional.
We at Datatrained has partnered with multiple organizations that provide unpaid online internship to our students. At the very start of the internship, students need to sign an agreement with any of our partner companies, which will detail the number of hours the students need to invest on weekdays and the weekends. Generally, it’s 4 hours on average on weekdays and full days on weekends. There will also be an agreement wherein the students are not allowed to share that particular company data with any third party.
Most companies are looking for evidence that you will be able to do perform the role you are applying for. If you are working as an intern (paid or unpaid), don’t underestimate the potential that this work offers to build skills which employers value. Workplace experience, whether paid or voluntary, is one of the most successful ways for current students to gain that fierce edge in the Data Science employment market; it imparts the proof that you have the skills, capabilities and mindset the employer wants. You will earn more from the experience if you are aware of your objectives before you begin and if you reflect on what you have gained afterwards.
During the internship, the students will work on various projects or products related to data science and AI. Students will be given assignments which they need to complete within the due dates, which will be further monitored by their Project Managers and Team Leaders. You need to understand this internship will only help you in a live interview and will ultimately fetch you a job.
At the end of the 5th month of internship, that particular company will forward a candidate’s performance to a DataTrained Career Coach Team member. A candidate will need to achieve at least a minimum Ability Score of “8” out of “10” as decided by the DataTrained Placement team to sit for the final placements.
Data Science is a highly competitive domain and competition for jobs means that you need a way to stand out. In addition to you learn essential skills, DataTrained 6 month internship program can set you apart from other applicants when you start applying for jobs. Everyone's experience is different, so highlighting what is unique about your experience can help you excel. In the case that you are a finalist for a job position and haven’t had an internship experience on your resume but the other finalist has, you may lose out on a job opportunity. Act like a sponge and soak up all the knowledge you can during your internship – it will help you in the long run.