Discover the importance of structured learning in the pursuit of success in the data science field.
Prudhvi hailed from a modest background, nestled in the heart of a middle-class family where his father served as a government employee. Growing up, Prudhvi witnessed the value of hard work and dedication firsthand, instilled in him by his father's unwavering commitment to his job. With dreams of a brighter future, Prudhvi pursued his passion for numbers and logic, graduating with an MSc in Statistics.
Why did Prudhvi want to pursue data science?
During his college years, Prudhvi's fascination with the world of data science began to blossom, when he saw how different data patterns and trends could be identified using statistics.
As he delved deeper into the realm of statistics, he found himself captivated by the endless possibilities that data analysis and interpretation offered. However, fate had its own plans as Prudhvi graduated amidst the chaos of the COVID-19 pandemic, throwing a wrench into his job prospects.
Why Prudhvi struggled for a job after M.Sc?
Though he did his masters in statistics, he was not good with a few subjects, especially coding. This proved to be a stumbling block, hindering his chances of securing a desirable position in the competitive industry.
Determined to bridge the gap in his knowledge, Prudhvi turned to online resources in a bid to sharpen his knowledge. However, he soon got overwhelmed and often got diverted due to the different kinds and the number of resources available. There was no structure to his learning, and he felt lost!
Why did Prudhvi choose OdinSchool?
That's when he thought, he should probably join a course that could offer him structured learning. But he got into a whirlpool of thoughts on how to choose one.
Initially, he thought of taking up a course at Ameerpet (a place in Hyd known for quick-fix courses) but then he felt he would loose on placements (no.1 criteria for him at this point) and experience of learning under industry experts.
Next, Prudhvi felt that he should stick with online resources as there are some that provide structured content. Again, he would loose on placements.
After much research on various data science courses, he shortlisted OdinSchool. He felt very positive after reading their google reviews and success stories. When spoke to their counselor, he not only explained their data science course curriculum but also as to why OdinSchool does not believe in 'pay-after placement' model.
Upon enrolling in their data science course, Prudhvi was greeted with a comprehensive curriculum tailored to his needs, emphasizing both theoretical knowledge and practical application. With access to experienced instructors and a supportive community of peers, Prudhvi found himself thriving in his newfound learning environment.
Through rigorous practice sessions and hands-on projects, Prudhvi honed his skills in data analysis and programming, gradually overcoming his initial struggles with coding. The school's emphasis on doubt clarification and personalized mentorship proved invaluable, providing Prudhvi with the guidance and support he needed to excel.
Beyond technical skills, OdinSchool also equipped Prudhvi with essential soft skills such as communication, professionalism, and leadership through personality development classes.
Prudhvi's strong advice for every data science aspirant
Don't manage, actually do your project. This means don't do the projects just for the sake of it, there are many resources online where you can either copy the code or buy the project. If you do this you will fail miserably in the job interview because if you don't own the project, you will definitely stumble somewhere.
Prudhvi's Inspiring Interview
Discover the power of dedication and learning from this inspiring interview. Prudhvi has also answered some basic but important questions.
Now-a-days, the term 'data scientist' keeps getting thrown around a lot losing its basic identity. Why does this happen and please explain us the exact difference between a data analyst, data scientist and data engineer? And which is the easy role when compared to others?
Prudhvi - If we consider a hypothetical situation of cooking happening here, then a data analyst is somebody who cleans and cuts the vegetables. He basically takes care of everything required for cooking to take place smoothly. Data Scientist is somebody who cooks as per the recipe. In between data analyst or data scientist can also take the responsibility of making the dish visually appealing and explaining the recipe to the customer. Then data engineer is somebody who takes care of the entire kitchen.
Now, at some companies, a data analyst who prepares everything for the cooking will also do the cooking or vice versa. Hence, the term 'data scientist' is being used loosely for many roles.
Which is the easier role when compared to other data science roles for an entry-level position?
Prudhvi - Data analyst is easier when compared to others. This role teaches all the basic stuff required for cooking to happen without any hiccups. Example - Naresh who successfully transitioned from a non-IT background into data analysis at Capgemini.
