MBSAP DATA PREPARATION SERVICES

Data preparation is the process of collecting, blending, organizing, and structuring data to make data analysis outcomes better and fast. It is a critical component of data analysis as it weeds out all the unimportant and irrelevant elements in datasets. Good data preparation makes it possible to have data that do not have any calibration issues or discrepancies between datasets and thereby deliver insights that are on track.

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    Data Preparation Services We Offer

    As a leading data preparation service providing company, we understand each client’s unique business requirements and provide clients with customized services. As a well-known data preparation services providing company, we provide our range of services to our clients. Some of these services include –

    Data Cleaning Services

    Data generated from different sources contain a lot of unwanted elements that must be weeded out to ready it for proper analysis. Data cleaning services entails correcting common problems and other errors in data. It is an operation performed in the first stages of data preparation. The objective is to make the data less messy and more useful. We leverage our domain expertise to identify misfit, messy, corrupt, or erroneous data and rectify it.

    Data tend to have wrong values, because of reasons such as incorrect typing, duplicated data, corrupted data, and so on. We correct or prepare the data using various methods such as statistics to differentiate normal data from outliers, identify redundant rows of data and remove them, mark empty values, impute blank values using learned model or statistics, remove duplicate rows and columns.

    Feature Selection

    The objective of data analysis is to develop models that assist in making a prediction. Feature selection is a technique that involves picking a group of input features that can take the place of a variable target and assist build a prediction model. This is an important part of the preparation process as irrelevant or redundant variables can mislead the algorithms completely and lead to improper predictions.

    Our feature selection technique banks on groups that use both the target variable and those that do not. The target variable is broken into further groups that select features automatically to fit the model; select features to develop the best performing model, and give a score to each feature so that a close performing subset can be selected. We primarily bank on statistical methods for finding input features. The right method is chosen based on the input variable data types and the best possible statistical methods to be used.

    Data Transforms

    This is a preparation stage in which a change in the data variable distribution is carried out. We make use of a range of techniques to transform data and apply it to output and input variables. Data may be categorical or numeric, with variable subtypes for each. At this stage, a numeric variable is either converted to an ordinal variable or code as a categorical variable as Boolean variables or integers.

    We specialize in Discretization Transform, Ordinal Transform, or One-Hot Transform. In Discretization transform, we code a numeric variable to ordinal. In Ordinal Transform we code a categorical variable into integer and in One-Hot Transform, we code a categorical variable into binaries

    Feature Engineering

    This a type of data preparation in which new input variables from the available data are created. Our subject matter experts identify new features that can be interpreted from the data. A common approach is to create copies of numerical input variables with a simple mathematical operation, such as multiplying them with other input variables or raising them to power.

    Feature engineering is carried out to add a broader context to a single observation. Sometimes it also helps in breaking down a complex variable and providing a more straightforward and simple perspective on the input data.

    Dimensionality Reduction

    The dimensionality of Data is the number of input features for a dataset. In this type of data preparation, the inputs can be scaled up or scaled down to any number of variables to create volumes of different dimensions. Unlike feature selection, the input variables, in this case, are not directly related to the original input variables. This makes the projection a bit hard to interpret. The big advantage of this technique is that it removes linear dependencies between correlated variables.

    As the name implies, dimensionality reduction is the reduction of data from a high-volume space into a low-volume space so that the low-volume representation has some relevant and meaningful properties of the original data. We leverage the two most common approaches to dimensionality reduction i.e., Principal Component Analysis (PCA) and Singular Value Decomposition (SVD).