Be Multi-Skilled To Increase Your Career Prospects
Gone are the days when one could do well in their career with just one skill. Companies are now looking for resources with expertise in multiple areas.
Likewise, the role of a data scientist has evolved a lot in the last few years and it will continue to evolve. This evolution of responsibilities of a data scientist often leads to a dilemma on what skill sets are required to be an effective player in an organization. To produce quantifiable results, one needs to widen the skill sets to become a full-stack data scientist.
Who is a full-stack data scientist?
In short, a full-stack data scientist is a jack-of-all-trades. One who can start from the very scratch of business understanding, data cleaning & preparation, perform analysis, churn our hidden insights, build predictive models, and then finally help deploy the model and integrate the models with business applications for consumption.
A full-stack data scientist specializes deeply in some areas of a data science project life cycle, yet has enough knowledge to play a key role in other areas of the life cycle. This ideally means as a full-stack data scientist, one should be ready and able to play a role in any or all the phases of the life cycle starting from ideation, going through development and execution till the end of integration with business applications.
A full-stack data scientist should be able to facilitate consumption of their ML or AI models into business applications through REST APIs and should be able to display their model predictions through software applications.
What roles does a full-stack data scientist play?
A full-stack data scientist needs to wear multiple hats and play a pivotal role in the following phases of a data science project life cycle:
- Identifying the business problem
- In-depth understanding of the business process & operations
- Identifying data sources
- Data engineering, ETL, ELT
- Data Cleaning & Preparation
- Data Exploration, Analysis & Interpretation
- Model deployment & productionizing output
- Model performance monitoring
- Analyzing impact
- Model drift & decay analysis
Required skillsets for a full-stack data scientist
We have listed down a high-level skill set required for executing any data science project. Any soft skills brought to the table are also extremely important to deliver an impactful outcome.
- Business acumen
- Collaboration and communication
- Identifying data sources
- Able to perform ETL or extend support to the ETL team
- Coding in programming tools like Python
- Performing data exploration & statistical analysis
- Build predictive models
- Deploy models
Given the wide range of skill sets, it is not possible to master each and every area of work. While specializing in a few areas, one will still need to have a good understanding of the remaining areas.
What kinds of courses are suitable to be a full-stack data scientist?
Well, there are probably none or a few data science courses that cover all the topics that can make you a full-stack data scientist. While it is not possible to cover all the topics in a course, it is also very much needed that these topics are covered for the learners to make them capable for the job market where they can be of real value to the organizations.
We intend to do the same but without compromising over quality. So we are coming up with a course very much designed to keep working professionals in mind, regular classes so that you do not lose touch with the topics, a cyclical revisit of various topics, more importantly, real-life industry use cases that cover applications of simple statistical techniques to complex machine learning or deep learning algorithms. And our course is not just another data science course – the course is not a bunch of pre-recorded videos with a few hours of mentoring. Its fully live online sessions with lots of hands-on, practical applications would need every learner to be a thinker and creative. And yes – it covers full-stack.
We mean it.
Who are we?
We are a team of industry experts who have joined hands to bring to you a data science course that can help you learn more with less stress, remember more with ease, apply concepts in real life with complete clarity & creativity and boast of a holistic data science knowledge.
Disclaimer: If you want only a certificate without knowledge, we are probably not the right guys.