Blog | Data Science and Technology

The data you inherit for analysis will come from multiple sources and would have been pulled adhoc. So this data will not be immediately ready for you to run any kind of model on. One of the most common issues you will have to deal with is missing values in the dataset. There are many reasons why values might be missing - intentional, user did not fill up, online forms broken, accidentally deleted, legacy issues etc.  Either way you will need to fix this problem. There are 3 ways to do this - either you will ignore the missing values, delete the missing value rows or fill the missing values with an approximation. Its easiest to just drop the missing observations but you need to very careful before you do that, because the absence of a value might actually be conveying some information about the data pattern. If you decide to drop missing values : df_no_missing = df.dropna() will drop any rows with any value missing. Even if some values are available in a row it will still get dropp...

Generally we think of data scientists building algorithms,exploring data and doing predictive analysis. That's actually not what they spend most of their time doing however , we can see in the in the graph most of the time Data scientist are involved in data cleaning part , as in real world scenario we are mostly getting the data which is messey, we can feed the data after cleaning , ML model will not work if the data is messey, Data cleaning is very very important so mostly data analyst and data scientists are involved in this task. 60 percent: Cleaning and organising Data According to a study, which surveyed 16,000 data professionals across the world, the challenge of dirty data is the biggest roadblock for a data scientist. Often data scientists spend a considerable time formatting, cleaning, and sometimes sampling the data, which will consume a majority of their time.Hence, a data scientist, the need for you to ensure that you have access to clean and structured data can save y...

Q1. Python or R – Which one would you prefer for text analytics? We will prefer Python because of the following reasons: Python  would be the best option because it has Pandas library that provides easy to use data structures and high-performance data analysis tools. R  is more suitable for machine learning than just text analysis. Python performs faster for all types of text analytics. Q2. How does data cleaning plays a vital role in the analysis? Data cleaning can help in analysis because: Cleaning data from multiple sources helps to transform it into a format that data analysts or data scientists can work with. Data Cleaning helps to increase the accuracy of the model in machine learning. It is a cumbersome process because as the number of data sources increases, the time taken to clean the data increases exponentially due to the number of sources and the volume of data generated by these sources. It might take up to 80% of the time for just c...