Data scientists are using a lot of techniques in their daily life to solve data problems easily.

In today's world where data is the new treasure, organisations across different industries are highly adopting data science technology. Data science is a combination of statistics, machine learning and mathematics under a single roof that solves the once complex problems. The impact of a data science project relies on the variable amount and kind of data collected from a source. As data grows, it becomes imperative to understand what a few baselines techniques need to be selected. Data science is a field that spreads over several disciplines. It incorporates scientific methods, processes, algorithms, and systems to gather knowledge and work on the same. With technologies like machine learning and deep learning gaining significant traction, data scientists continue to ride the crest of an incredible wave of innovation and technological progress. Data scientists are using a lot of techniques in their daily life to solve the problems easily. Henceforth, Analytics Insight brings you a list of data science techniques that data scientists adopt for better outcomes.

Top 10 Data Science Techniques that Professionals should Master

Regression analysis

Regression analysis is a machine learning algorithm that can be used to measure how closely related independent variables relate to a dependant variable. This technique helps you see how the particular value of a dependent variable changes when any one of the independent variables varies with all others fixed. Through this approach, users get to estimate the conditional expectation or the average value of the dependant variables. Extensive use of regression analysis is building models on datasets that accurately predict the value of the dependant variables.

Classification analysis

Classification analysis is a data analysis task within data mining that identifies and assigns categories to a collection of data to allow for more accurate analysis. Classification algorithms are built having the target variable in the form of classes. The classification method makes use of mathematical techniques such as decision tree, linear programming, neural network and statistics.

Linear regression

Linear regression is a linear model that assumes a linear relationship between input variables (independent variables 'x') and output variables (dependant variable 'y') such that 'y' can be calculated from a linear combination of input variables (x). For example, if a number of students and their study hours along with grades are taken into account, this will be used as training data. A data scientist's goal here is to design a model that can predict the marks if the number of hours studied is provided.

Jackknife regression

Jackknife regression is a method used to estimate the variance and bias of a large population. This was the earliest resampling method, introduced by Quenouille in 1949 and was named by Tukey in 1958. Jackknife regression can be used as a black box, which is very robust and parameter-free, usable and easy-to-interrupt by non-statisticians.

Anomaly detection

Anomaly detection, also referred to as outlier detection is a step in data mining that identifies data points, events and observations that deviate from a dataset's normal behaviour. It has many applications in business from intrusion detection (identifying strange patterns in network traffic that could signal a hack) to system health monitoring (spotting a malignant tumour in an MRI scan), fraud detection in credit card transactions and fault detection in operating environments.

Personalisation

Personalisation is designing a system that gives suggestions to people based on their previous choices that are available in the datasets. Effective data science work enables websites, marketing offers and more to be tailored to the specific needs and preferences of individuals, using technologies such as recommendation engines and hyper-personalisation systems that are driven by matching the data in detailed profiles of people.

Lift analysis

Hypothesis testing was introduced by Ronald Fisher, Jerzy Neyman, Karl Pearson and Pearson's son, Egon Pearson. Hypothesis testing is used in making statistical decisions using experimental data. It is an act in statistics whereby an analyst tests an assumption regarding a population parameter.

Decision tree

A decision tree is an algorithm used for supervised learning problems such as classification and regression. It is a map of the possible outcomes of a series of related choices. The technique allows an individuals or organisation to weigh possible actions against one another based on their costs, probabilities, and benefits.

Game theory

Game theory is used by data scientists to analyze competitive situations in a structured way. Game theory data science is an additional concept data scientists can master to predict how rational people will make decisions that help them make effective data-driven decisions under strategic circumstances.

Segmentation

Segmented data can be extremely effective when used in marketing efforts helping you to better understand your customers and make sense of advertising campaign results. Segmentation in data science helps businesses deliver the most suitable message to different portions of the targeted audience, with each segment corresponding to specific customer needs.