Priyanka parihar

Okay, so, hello, my name is Priyanka, and I have seven years of work experience into IT, where I have worked as a software QA, I have worked for various clients in the banking domain, I have worked as a QA for the e-commerce applications, and I have worked as a QA for MNCs like Google, and yeah, so I had recently, two years ago, I have made a transit into the career as a data analyst, where I had been working for an e-commerce brand here back in India, where I had been analysing their sales for the various products, so yes, my profile is diverse, at the same time, I had been a co-founder for one of the startups in UK, so this is what my brief looks like.

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I haven't worked in data pipeline as such because we had a separate data engineering team so yeah but this is something which I'm currently learning

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To leverage SQL window functions to simplify complex reporting and improve dashboard performance in Tableau. So basically, we will be using the SQL to simplify the query, the end result. And if the data is pretty clean, if the data is pretty sorted, I think that gives better data. That gives us the leverage and that gives us the ability to make, to give us clear insights. So once we have the clear data, we can actually have thorough insights to the graphs and the charts through SQL queries, where it involves finding a particular solution, finding the particular log or finding or simplifying the data or cleaning the data. This is how we can leverage SQL windows function, solving the queries, complex queries involving subqueries. So yes.

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Now, I have to have the calculative field of formula is given, then low when some revenue, okay, so when some revenue is smaller equals to 1000, then low when some revenue, okay, so they're talking about, what is the formula presented in the code segment, smaller or equal to 1000, then low when some revenue is greater. I think the code here looks pretty okay, but because here when the revenue is less than or equal to 1000, then we're classifying it as, we're counting it as low, and when some is greater than 1000, and some revenue is smaller or equal to 5000, then medium. Else, undefined, I think here it needs a fix.

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It depends upon the situation, it depends on the kind of data, basically SQL is used to clean the data. I think SQL is a pretty good option. Because Tableau at one point of time, we are not, I understand that we do pivot and we do clean the data, but I think SQL. And then I will just look at the logs and I will go through the entire code snippet of how does it look like.

Basically, Python libraries for, we choose for advanced data analysis is NumPy and Pandas, and it depends, like, if that's NumPy is basically for numerical solutioning, and I had been using Pandas mostly for, you know, like, data cleaning, where we use data frames. At the same time, the advanced data libraries are Scikit-learn and TensorFlow, which then advances towards machine learning and Matplotlib, where you draw all the vectors and the graphs. So, yes.

Python's role in automating data and tasks. I have not much worked on data towards, you know, into snowflakes or basically building the pipelines or data. So, yes.