Many businesses have adopted data science, and data scientists are now in high demand for organizations that are focused on using data. Predictive analytics, conversational AI systems, and perhaps other functions of data science leverage techniques like ML and the power of big data to attain additional insight and attributes.
In today’s article, we will understand and know about the popular data science techniques that every aspirant or data science professional must learn and leverage. Let’s get started without any further ado.
Top Data Science Techniques You Must Know About
Below mentioned are the top data science techniques that every aspirant, as well as a successful data scientist, must know and leverage.
1. Classification
What category does this data belong to is the main question data scientists try to answer when they encounter classification problems. Data classification is done for a variety of reasons. If the data contains an image of handwriting, you might be curious to know what letter or number it corresponds to. If the information relates to loan applications, you might be curious about whether it belongs in the “approved” or “declined” category. Other classifications might be concerned with figuring out how to treat patients or whether a message is spam.
Among other things, the following are some of the algorithms and techniques that data scientists employ to categorize data:
- Decision trees – These are a particular kind of branching logic system that combines trees of machine-generated specifications and values to group data into predetermined categories.
- Naïve Bayes classifiers – Bayes classifiers can aid in classifying data into straightforward categories by harnessing the power of probability.
- Support vector machines – To categorize data into different groups, SVMs attempt to draw a line or plane with a large margin.
- K-nearest neighbor – According to the categories of a data point’s closest neighbors in the data set, this method uses a straightforward “lazy decision” method to determine what category it should fall under.
- Logistic regression – Contrary to its name, it is a classification technique that uses the concept of fitting the data to a line to make a distinction between multiple categories on either side. Instead of permitting more fluid correlations, the line’s shape causes data to be shifted to one category or another.
- Neural networks – In particular, deep learning neural networks with many hidden layers are used in this method. With very large training data sets, neural networks have demonstrated impressive classification capabilities.
2. Regression
What if you were interested in understanding the relationship between various data points rather than trying to determine which category the data belongs to? Regression’s main goal is to provide an answer to the query, “For the given data, what can be the predicted value?” It is a straightforward regression between one independent variable and one dependent variable, sometimes it’s a multidimensional regression that strives to describe the association between several parameters. This clear and simple notion derives from the statistical concept of “regression to the mean.”
Regression analysis can be performed using several classification techniques, including neural networks, decision trees, and SVMs. In addition, data scientists have access to the following regression techniques:
- Linear Regression – This method, which is one of the most well-liked data science techniques, seeks the line that, provided the correlation between variables, better fits the data getting reviewed.
- Lasso Regression – The term “lasso,” designates a procedure that needs a subset of data in a final structure to obtain the predictive power of linear regression analysis models.
- Multivariate Regression – This involves utilizing various techniques to identify lines or planes that match a variety of dimensions of data that could potentially contain a large number of variables.
3. Clustering and association analysis
To answer the question of “How does data form into groups, and which groups do multiple data points pertain to,” another set of data science techniques is used. Analytics applications can benefit from the information that data scientists can find in clusters of related data points that have different traits in common. The following are some of the available clustering methods:
- K-means cluster – A data set’s “centroids,” which show where various clusters are located, are found by a k-means algorithm, which then assigns data points to the closest cluster after identifying a predetermined number of clusters in the data set.
- Mean-shift clustering – This method of clustering data using centroids can be used alone or in combination with k-means clustering to enhance the results.
- DBSCAN – DBSCAN, which stands for “Density-Based Spatial Clustering of Applications with Noise,” is a different method of finding clusters that employ a more sophisticated method of identifying cluster densities.
- Gaussian mixture models – Instead of treating the data as a collection of singular points, GMMs group the data into clusters according to a Gaussian distribution.
- Hierarchical clustering – In a manner akin to a decision tree, this method locates clusters using a hierarchical, branching approach.
A similar yet distinct method is association analysis. The main goal is to identify correspondence rules that define the similarities between various data points. We are trying to identify groups that the data belongs to, which is similar to clustering. Instead of simply identifying clusters of data points, what we’re attempting to do in this situation is predict when they will occur together. While the objective of association analysis is to measure the strength of association between data points, the objective of clustering is to divide a large data set into distinguishable groups.
Final Words
With this, we reach the concluding part for today’s topic. Data science techniques are highly crucial to understand because sooner or later, you’ll require them to become successful at the tasks you are obligated to. To summarize what we went through, we discussed the different and popular data science techniques which one should know, including classification, regression, and clustering and association analysis techniques.
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