Data Analysis
Mindset and Skill
#Skill is not just how fast you solve a problem but how many different #perspectives/ #techniques / #approach you can find to solve the problem
Domain Knowledge
If you don't understand your #domain, you won't understand your #data, you will miss the #insights and your model will not be built based on the domain needs. It is easy to do .fit, .predict but its harder to find the hidden feature variables before building the model. Understand your #data before building your ML use case. #perspective #machinelearning #datascience
General Guideline - How I evaluate data science candidates?
The field is evolving on a daily basis. We need passionate, curious learners with an experimentation mindset!!!
- Find #Insights, Turn it into Why-questions
- Seek Surprises sudden #peaks, #lows, Harness it into How-Questions
- Plot data in different dimensions #Month / #Year / #Sales / #Products / #Divisions, Find #Insights in every #perspective
Mindset and Skill
#Skill is not just how fast you solve a problem but how many different #perspectives/ #techniques / #approach you can find to solve the problem
Domain Knowledge
If you don't understand your #domain, you won't understand your #data, you will miss the #insights and your model will not be built based on the domain needs. It is easy to do .fit, .predict but its harder to find the hidden feature variables before building the model. Understand your #data before building your ML use case. #perspective #machinelearning #datascience
General Guideline - How I evaluate data science candidates?
- Different business problems solved and their ML lessons learned, Deep Dive on Implementation, Algo used, Features Evaluated
- Data pipeline set up challenges faced to deploy in production
- How do you keep track of new papers / evaluating and learning different frameworks
- How much do you code on a daily basis for work / personal learning
- Ability to bring different perspective/techniques solving problems
- How Algos works, Learning's with Overfitting, Underfitting, variable selection
#DataScience Solutioning #Lessons Learnt
Step -1 - Understand / Solving a problem from business #perspective is first move
Step -2 - Scaling the solution is next milestone
Step -3 - Hosting it / Porting it is the last milestone
All the steps are important but priority relies on completion of each task in sequence so that we do not go back to fix them again. Hire talent accordingly #machinelearning #vision #perspectives
Analyze -> Solution -> Scale -> Deploy (Talent pool varies accordingly, Get the mix to excel)
Document can be downloaded from link
Happy Learning!!!
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