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All That You Need To Know About The Data Preparation Process In Data Analytics

Most Widely Used Tools In Hadoop Ecosystem For Crunching Big Data

It is a fact that most of a Data Scientists spend their time in gathering, cleaning and preparing the data for analysis. As datasets come in various sizes, it is extremely important for a data scientist to reshape and refine the datasets into usable datasets, which can be leveraged for analytics. Here let’s look at data preparation, its importance and how it is done.

Know the Process Involved in Data Preparation for Data Mining
  • Data Cleaning
    The primary important step of the data preparation task that deals with correcting inconsistent data is filling out lost values and smoothing out unwanted data. There could be many rows in the dataset that do not have value for attributes of interest or there could be imperfect data or duplicate records or some other random error. All these data quality issues are moved in the foremost step of data preparation. Unused or unwanted data is tackled manually or through various regression or clustering techniques.
  • Data Integration
    Data Integration step involves – schema integration, verifying data conflicts if any and handling redundancies in data.
  • Data Transformation
    This step implies removing any unwanted stuff from the data, normalization, aggregation and generalization.
  • Data Reduction
    Database might contain petabytes of data and running analysis on the complete data present and that could be a time consuming process. In this step data scientists obtain a reduced representation of the data set that is usually smaller in size but gets almost same analysis
  • Data Discretization
    Dataset usually consists of 3 types of attributes- continuous, nominal and ordinal. Some algorithms accept only categorical attributes. Data discretization aids data scientist divide continuous attributes into intervals and also helps reduce the data size – preparing it for analysis.

Data preparation is not an art and hence it is necessary for aspiring data scientists to learn with aspiration, Python and R language to be successful in this data science process. All data is not pure!

If you are an aspiring data scientist and would like to learn more about the data preparation tools like Python and R, then be a part of Kelly Technologies advanced analytics career program of Data Science Training In Hyderabad.

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