Why becoming a good Data Analyst is paramount on your path to becoming a Data Scientist I see a lot of recent graduates and people who are thinking of switching career want to be #DataScientists because of latest uptick in media mentions as well as 6 figure #salary potential They think becoming a data scientist involves grasping good command over 10 #MachineLearning algorithms using #Titanic or #Iris dataset and they are ready to tackle real world challenges. IT DOESN'T WORK LIKE THAT! I have been in #data industry for over 2 years now and I still feel there is so much more to do, you know why? Because, there are times when the business problem presented in front of me involves everything data without the scientists part. I am responsible for Data Gathering, Data Cleaning and EDA. Once the data is ready, that's when you are supposed to do the scientists part. Data Gathering, EDA, Data Cleaning, Data Visualization are all part of being a good data analyst. Your ability to take the business problem, understanding the underlying data and making "descriptive" recommendations is what separates you from an "analyst" and a "good analyst" I spend 50-70% of my time identifying my data sources. So focus on becoming a good analyst first before deciding if you are ready to be scientists.
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