# What Is Data Science And What Techniques Do The Data Scientists Use?

**What Is Data Science?**

The terminology came into the picture when the amount of data had started expanding in the starting years of the 21st century. As the data increased, there was a newly emerged need to select only the data that is required for a specific task. The primary function of data science is to extract knowledge and insights from all kinds of data. While data mining is a task that involves finding patterns and relations in large data sets, data science is a broader concept of finding, analyzing, and providing insights as an outcome.

In short, data science is the parent category of computational studies, dealing with machine learning, and big data.

Data science is closely related to Statistics. But as opposed to statistics, it goes way beyond the concepts of mathematics. Statistics is the collection, interpretation of quantitative data where there is accountability for assumptions ( like any other pure science field). Data science is an applied branch of statistics dealing with huge databases which require a background in computer science. And, because they are dealing with such an incomprehensible amount of data, there is no need to consider assumptions. In-depth knowledge of mathematics, programming languages, ML, graphic designing, and the domain of the business is essential to become a successful data scientist.

**How Does It Work?**

Several practical applications provide personalized solutions for business problems. The goals and working of data science depend on the requirements of a business. The companies expect prediction from the extracted data; to predict or estimate a value based on the inputs. Via prediction graphs and forecasting, companies can retrieve actionable insights. There’s also a need for classifying the data, especially to recognize whether or not the given data is spam. Classification helps in work reduction in further cases. A similar algorithm is to detect patterns and group them so that the searching process becomes more convenient.

**Commonly Used Techniques In The Market**

Data Science is a vast field; it is very difficult to name uses of all the types and algorithms used by data scientists today. Those techniques are generally categorized according to their functions as follows:

**Classification –** The act of putting data into classes on both structured and unstructured data (unstructured data is not easy to process, at times distorted, and requires more storage).

Further in this category, there are 7 commonly followed algorithms arranged in ascending order of efficiency. Each one has its pros and cons, so you have to use it according to your need.

*Logistic Regression *is based on binary probability, most suitable for a larger sample. The bigger the size of the data, the better it functions. Even though it is a type of regression, it is used as a classifier.

The *Naïve Bayes *algorithm works best on a small amount of data and relatively easy work such as document classification and spam filtering. Many don’t use it for bigger data because the algorithm turns out to be a bad estimator.

*Stochastic Gradient Descent *is the algorithm that keeps updating itself after every change or addition for minimal error, in simple words. But a huge problem is that the gradient changes drastically even with a small input.

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*K-Nearest Neighbours *is typically common to deal with large data and acts as the first step before further acting on the unstructured data. It does not generate a separate model for classification, just shows the data nearest to the *K*. The main work here lies in determining the K so that you get the best graph of the data.

*The Decision Tree *provides simple visualized data but can be very unstable as the whole tree can change with a small variation. After giving attributes and classes, it provides a sequence of rules for classifying the data.

*Random forest *is the most used technique for classification. It is a step ahead of the decision tree, by applying the concept of the latter to various subsets within the data. Owing to its complicated algorithm, the real-time analysis gets slower and is difficult to implement.

*Support Vector Machine(SVM) *is the representation of training data in space, separated with as much space as possible. It’s very effective in high dimensional spaces, and very memory efficient. But for the direct probability estimations, companies have to use an expensive five-fold cross-validation.

**Feature Selection** – **Finding the best set of features to build a model**

*Filtering *defines the properties of a feature via univariate statistics, which proves to be cheaper in high-dimensional data. Chi-square test, fisher score, and correlation coefficient are some of the algorithms of this technique.

*Wrapper methods *search all the space for all possible subsets of features against the criterion you introduce. It is more effective than filtering but costs a lot more

*Embedding *maintains a cost-effective computation by using a mix of filtering and wrapping. It identifies the features that contribute the most to a dataset.

*The hybrid method *uses any of the above alternatively in an algorithm. This assures minimum cost and the least number of errors possible.

**Regression** – **A form of predictive modeling technique, to identify and establish the relationship between the variables in a data sample.**

*Linear regression* shows the distributed data along the best-fitted mean line. The further two types are simple and multiple; multiple has more than 1 independent variable.

*Polynomial regression *helps when the power of the variable is more than one. You will see a curve as the best-fitted line in the graph. Look out for overfitting models, and try to adjust in the best way possible.

*Stepwise regression *adds or removes variables at each step in the algorithm, without the need for human intervention.

*Ridge regression *is the same as linear regression, except that the variables, in this case, are highly correlated to each other. The shrinkage (reduction of the effect of sampling variation, it is complimentary to overfitting) almost reaches zero, as the parameters are modified.

*Lasso regression *is similar to the Ridge, but the shrinkage value of the parameter reaches the absolute zero.

*ElasticNet regression *uses both Lasso and Ridge accordingly so that there is an optimal definition for the correlated variables.

**Clustering** – **Grouping or dividing the data points so that there is more similarity within the group than the rest.**

The clustering is either hard or soft; hard clustering is the absolute grouping of data, while soft is just estimating the probability of a data point belonging to a group. There are 4 types of algorithms for clustering.

*Connectivity models *determine how far the data points are from each other. You can either start creating separate clusters and aggregate them or cluster singularly and partition them as the distance decreases.

*Centroid models *establish a similarity based on the distance of a data point from the centroid of the cluster. But you have to know the data set beforehand because you have to mention the required number of clusters at the start.

*Distribution models, *such as Gaussian or Normal, calculate the probability of the data points in a cluster belonging to the distribution. The model often suffers from overfitting.

*Density models *isolate the density regions in a given data set and create the clusters. DBSCAN and OPTICS are well-known examples of density models.

**Anomaly Detection/Outlier Analysis** – **To identify and observe the data points that lie outside the normal behavior**

*Global outliers* are the data points existing far outside the entire data set.

*Contextual outliers *are those whose values differ significantly from the others in the same context/class. They fulfill the global values but stand out within the group.

*Collective outliers *are the subset of the context whose values collectively fall out in the group.

**Time Series Forecasting** –** Working on time-based data for outputs, forecasts, and insights**

*The Naive Approach* uses the stable model, where the last observed point acts as a base for the next predicted point. It is not suitable for a dataset with high variability

*Simple Average, *as the name suggests, predicts the next point to be somewhere around the average, as the data moves around a single average line throughout. This technique cannot predict exact results but helps wherever the data shows constancy.

*Moving Average *helps when the average keeps changing periodically. Here, you can introduce a value p as the number of past data points and predict the next point based on the average of p.

*Simple Exponential Smoothing *does exactly what the name says; it smoothens the graph into a curve and then predicts based on the behavior of a few previous points.

*Holt’s Linear Trend *depicts the data from a larger period/season of time, covering a whole lot of data without any assumptions. The technique is complementary to the ones above at times and better than them at others.

*Autoregressive Integrated Moving average (ARIMA) * is the most popular technique, that includes the concepts of auto-regression and moving average. It stationarizes the series and takes seasonality into account.

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These techniques and many others such as neural networks, segmentation are used for purposes like building a good recommendation or search engine, target marketing, recognition of images/text, etc. We will be discussing the applications of data science for enterprises in the upcoming article.