Data Science Tools and Techniques

Data science has gained immense importance in the past couple of years. It is integrated into every industry, organization, and business. Data science enables the prediction of certain parameters based on existing data. It provides insights into the emerging trends and patterns in particular datasets. Similarly, a relatively new concept of big data has been introduced in data science. Big-data refers to the enormous amount of data that is being generated for research and analysis. Data processing is a fairly complex task. First, the raw data is acquired and stored, and then the nonessential information is filtered out. Subsequently, the data is classified and validated using various analysis techniques. Finally, the data is visualized to conclude useful insights.

Data Science Techniques

Some various rules and procedures are required to complete a task. A technique is a collection of these rules. Some essential techniques used in data science are listed below.

Probability and Statistics

Probability is the measurement of the likelihood of an event. It is useful for predicting outcomes from a given dataset. Likewise, statistics deals with the collection, analysis, understanding, and visualization of data. Statistics are used to process large and complex data so that data scientists and analysts can figure out useful data trends.

Regression

Regression is an analysis method used to model and evaluate the interactions between variables and how they contribute and are related together to generate a specific outcome. Linear regression is a regression model made up entirely of linear variables. And it can be defined by a straight line.

Hypothesis testing

Testing hypotheses is an act in statistics by which an analyst checks an assumption about a population parameter. The approach employed by the analyst depends on the type and purpose of the application. Hypothesis testing is an important measure to determine the plausibility of a hypothesis by using sample data.

Data Science Tool

A tool is suitable for data science if it allows the implementation of the above techniques to perform a task. Various computer applications assist in performing data science tasks effectively. Some necessary tools are listed below.

SAS

SAS is proprietary software that is used by businesses for the analysis of data. SAS uses the primary language of SAS programming to perform statistical modeling. It supports multiple data formats, and it has data encryption algorithms. SAS is a highly reliable software, but it is very costly.

Apache Spark

Spark is an all-powerful engine for data analytics, and it is the most used tool for data science. It is an upgrade over Hadoop and can run 100 times faster than MapReduce. It can process data in real-time. Spark has several Machine Learning APIs that can assist data scientists in making powerful predictions.

BigML

BigML is a data science platform that is cloud-based and specializes in predictive modeling. It uses a broad range of algorithms for machine learning, such as clustering, classification, forecasting time-series, etc. It enables data to be visualized interactively and offers you the ability to export visual charts on your smartphone or IoT computer.

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