Wipro best Full Time Job Analyst | Salary | Job Role | 2024 | Gurugram
| Company Name | Wipro |
| Job Role | Analyst |
| Job Type | Full Time |
| Location | Gurugram, India |
| Qualification | Graduate In Relevant Field |
| Experience | Freshers And Experienced |
| Batch | 2017 – 2024 |
| Salary | Up To 3.6 LPA (Expected) |
Examples of Data Analyst
They have a warehouse where they store their products. And when you order from the product to your So that warehouse has a fixed space.
They can store only this much. So how will decide how much should So this work is done data scientists and data analysts. Along with that, if you want to predict the that how much sales are there on then how much sales will be
So predictive analysis will be in that. So all these work is done by data analysts.
Now if I tell the whole step by step, then first thing you have is to understand the problem statement or business problem. On the basis of you will make an action plan. You will the action plan which data you want to which channels you want to use, from the data source will come, what analysis you want to which tool you want to use, etc.
Step-by-Step Process of Data Analyst
that you can this work in this time, you can include if you few days. Third step is data collection. You have corrected the you can multiple processes like ETL, data cleaning, data missing values, you can fill it, replace it, delete it.
which is data collection and it feature engineering. If I talk full data analysis project, then this step is very important that how you feature engineering. In feature engineering, you select, like
if I tell you very normal way, then which relevant data points you select. After that, you do data cleaning, you data normalization, you manipulate that data according to your system, you change it and it takes a lot of time. For example,
or you multiple database. In some database, like in the gender column, male and female are written in classification. In some other database, it women and men. In some other database, it is written as one zero.
Or in some other it boy and girl. So, this data is not consistent. So, this data consistent by different platforms or different data. So, there are many such problems.
When you see the in each column, each set, you will it and then you clean it. It takes a lot of time. Once your data featuring is complete, you have finalized it completely. Then you go to data analysis or data modeling. It depends on the business problem.
And then the last step is data visualization. And then you Tableau or Power BI or whatever dashboard you use, Python. Based you data visualization and share it
Workload and Flexibility In Analyst
and it takes a lot of time depending on the business problem. Now the next point is how much workload you whether you get free time or Actually, there can’t a single word answer to that yes, workload is very less or workload is very high.
This is only in data science, it is the same in that it depends on what project you are working and the graph in runs sinusoidal sometimes up, sometimes down, sometimes you may have work 2 -3 hours,
It’s very rare that you have 8 -9 hours. The workload is chill if you are interested in If you are not interested, then it will cause some problems. Now let’s our next question. Do you work on data all time? As I told you about the action plan, you will that you need data in Until you get the
there no work on the So you don’t have the data at If you have then it is sample
Working with Data Analyst
You just check the data columns, channels and sources. You check the high level data if you get the you have to it. Another point to add is suppose you have a 1 -8 hours work time. It’s flexible time. If you a work to do in 2 hours,
you can enjoy the rest of the 6 hours. It’s not fixed time. You can log in at and do in the evening.
5 or 4 pm whenever you want to Most of the companies have flexible workers. So there is fixed time to work in such a short time. You have to work. There is an overall roadmap. You have to work project, its deadline, and you can say all the points or small goals that we have
and if you go to then you take lunch break, tea break and some people take extra breaks so it depends on their needs so those things happen in office too if you go to office then it will take and if
you are in office in then snacks and tea break so all this is chill and then people half an hour if you have pool, table tennis, carrom etc then they play and chat
It’s like you come in the laptop and the office table and go home in It’s like that at all. It’s chill overall. Actually, you don’t with data scientists all the You work lot of other teams in this. For example, you go data engineers for Then you data visualization experts who work Tableau or Power BI. If there is a website data, then you the marketing team.
If there a campaign run or something is running, then there is a of mixed funnels
Overall, it’s fun to on it. It’s like you work on data all the time. It’s overall experience. It depends on what project you are working on. Next question is how much you use SQL? You must have that SQL is everything SQL is all about database. It’s not like you Excel sheet or a file.
Importance of SQL In Analyst
it also important to optimize it and work on it fast. And if you don’t want to high -level models or predictions, then all that work on SQL. So that’s SQL should be at a high level and it will help you in the data analysis process. Now the next point is how much do you use and what do use in So again, it depends on your business problem.
Using Python in Data Analyst
So, the data I have used in data visualization is personal, I mean no one has yet. For that we use Tableau or Power BI. But if I analysis, then you a model. So we do data modeling on Python. Apart from this, you use Python in data analysis, which is very common use, that you use it to automate any
So you don’t want to use the same data the same excel or on the same data on the SQL and then send it. So for that we use Python. So now let’s about our last question, how much data visualization is So when all your work is done,
suppose you are modeling data, predicting data, doing data analysis, then everything is done, your end result is here. Now we use numbers, those values in charts or in any representation form, in the graphical representation form, in the dashboard.
