As far as data science is concerned, the capability to analyze, organize, and contextualize the data is highly important. Statistical techniques are data-driven that lets us perform operations on the data to obtain meaningful insights.
Here are some of the top statistical techniques that every data scientist should learn. After all, they are the fundamental ingredient in the training of a modern data scientist.
- Linear Regression
Linear Regression is a method to predict an outcome by fitting the best linear relationship the features (or independent variables) and the dependent variables. The best fit is done by making sure that the difference between the actual value and predicted outcome is as small as possible.
We have two types of linear regression – Simple Linear Regression and Multiple Linear Regression. Simple Linear Regression uses a single independent variable to predict the outcome while Multiple Linear Regression uses more than one feature.
Data analysis can help the data scientists build GST Software to help the government GST evasion. Linear regression will be used for this software.
- Classification
In machine learning as well as statistics, classification is the technique where we classify data into a given number of classes. The main objective is to identify the category or class to which a new data will fall under.
Some of the common classification techniques are Logistic Regression, Naïve Bayes Classification, Stochastic Gradient Descent, K-Nearest Neighbours, Decision Tree, Random Forest, and Support Vector Machine.
- Dimensionality reduction
The term dimensionality reduction is self-explanatory. When we have a dataset with multiple features, it becomes tough to work on it. That is why we switch to dimensionality reduction resulting in a big computational saving.
We can do dimensionality reduction through feature pruning. With feature pruning, we remove the features that are insignificant in the analysis. For instance, in our dataset, we may have 15 features, out of which 10 have a high correlation with the output. We can remove those 5 low correlation features that aren’t worth computation.
Another common technique for dimensionality reduction is Principal Component Analysis (PCA) that creates a vector representation of features showing their correlation with the output.
- Resampling methods
Resampling methods involve drawing samples from training set repeatedly and refitting a model of our choice on each sample so get additional information about that model. The main aim behind a data science project is to create a model using training data, and then make predictions on an unseen new data.
Resampling methods help us understand how our model would perform on the test data. Two most common resampling methods are:
- Cross-validation
- Bootstrap
Cross-validation is used to estimate the test error in our model to determine its performance (model assessment) or select the appropriate level of flexibility (model selection).
And then Bootstrap gives a measure of the accuracy of a parameter estimate of a given model.
- Shrinkage
Shrinkage fits a model involving all p predictors, but the estimated coefficients are shrunken towards zero relative to the least square estimates. Regularization (or shrinkage) helps in reducing the variance.
This method also helps in performing variable selection. Two common techniques for shrinking are ridge regression and lasso.
Ridge regression looks for coefficient estimates that reduce RSS, but they have a shrinkage penalty. Ridge regression puts the data into n directional space and then shrinks the coefficients of the low-variance components more than the high-variance.
Lasso is the short for Least Absolute Shrinkage and Selection Operator. Lasso regression not only reduces the over-fitting but also helps in feature selection. In Lasso regression, the default value of the regularization parameter is 1.
