Data Analysts use a variety of tools and methods to provide their organizations with a better understanding of past trends and current performance and to offer recommendations for future activities. The methods a company or business chooses to work with data can affect not only the quality and type of insights that are gleaned from the dataset, but lead to different professional outcomes. This article explores the seven most commonly used methods for data analytics.

Time Series Analysis

Time plays an integral role in data analytics. Those who seek to predict financial market trends or utility consumption rates incorporate time into their models. Time can serve as an independent variable used to make forecasts about the future. Time series analysis is a statistical technique focused on analyzing trends or events that happen in a time series or particular interval.

There are several different characteristics of time series that can be modeled to make accurate predictions:

  • Autocorrelation pertains to the similarities between observations as a function of the gap of time between them.
  • Seasonality is the term for periodic fluctuations, such as how energy consumption may be higher during the day than at night, or how online sales tend to go up before the holidays.
  • Stationarity pertains to when a time series’ statistical properties remain constant over time. Stationary time series are the most desired for modeling; those that aren’t stationary often must be transformed so that they are stationary.

Monte Carlo Simulation

Monte Carlo simulations provide a way to model the probability of various outcomes in a process that involves random variables and therefore can’t be predicted easily. This technique provides an understanding of how risk and uncertainty factor into models for prediction and forecasting. This collection of techniques for computation tend to yield approximate solutions to mathematical problems like integration and optimization.

Monte Carlo simulation is a powerful tool in many fields, like science, finance, engineering, and supply chain, as it can be applied to a range of problems. It is also relevant for simulating chemical, biological, or physical systems.

Cohort Analysis

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Cohort analysis is a technique that separates data into groups that share common characteristics before analysis occurs. It enables organizations to detect, isolate, and evaluate patterns, which can lead to better user retention, as well as a deeper understanding of a cohort’s behavior.

Advanced cohort analysis typically involves:

  • Extracting raw data: SQL is used to extract raw data from a database. This data is then exported using spreadsheet software.
  • Creating cohort identifiers: User data is separated into different buckets, like date of first purchase or graduation year.
  • Computing life cycle stages: After each user has been assigned to a cohort, the time between customers’ events is calculated to yield life cycle stages.
  • Designing graphs and tables: Visual representations of user data comparisons are rendered using PivotTables and graphs.

Factor Analysis

Factor analysis involves condensing a set of variables into just a few, which makes the process of working with data more streamlined. By uncovering the deeper underlying factors behind the data, factor analysis provides a way of handling these variables instead of focusing on lower-level concerns. This form of analysis is often referred to as “dimension reduction” because the number of dimensions in data can be reduced into just one or a few super-variables.

Factor analysis is a family of statistical methods that help to unearth deeper concepts, tendencies, or traits that may not be blatantly obvious or may be difficult to measure, such as IQ. This technique plays an integral role in fields such as market research, sociology, field biology, and psychology.

Dispersion Analysis

Dispersion analysis is a statistical method that’s used to identify the effect of various separate factors on experimental results. Once these factors are detected, this information is used to plan future experimental variations. By measuring an item’s central tendency, a single value can be generated and used as a representation of the entire value. Calculating the measure of dispersion is then helpful for studying the variability of the items. This process allows researchers to compare two or more series.

Some of the most popular methods for calculating statistical dispersion are interquartile range, standard deviation, and variance. Dispersion analysis can be applied to a wide range of problems in fields, such as finance, biology, or economics.

Decision Trees

In data analytics, decision trees are used to visually represent the decision-making process as well as various decisions. This tree-like model is popular in machine learning in order to devise a strategy to accomplish a specific goal. Decision trees visually depict the possible outcomes of a set of related options. One possible action can then be weighed against the others based on variables like cost. Decision trees can also be applied to classification and regression problems.

Many professions use decision trees to effectively handle nonlinear datasets. This tool is relied on by engineers, lawyers, as well as those working in business.

Cluster Analysis

Cluster analysis is a powerful statistical method used for data mining and processing. It involves grouping items based on how closely associated they are. This tool allows organizations to pinpoint specific types of behavior, groups of customers, or sales transactions.

Cluster analysis is an unsupervised learning algorithm, which means the total number of data clusters remains unknown before running the model. The most common use of cluster analysis is classification; subjects are separated into groups so that each subject is more similar to other subjects in its group than to subjects outside the group.

This method of analysis is generally used in situations where there are no prior assumptions about the relationships existing within the data. It has applications in the financial sector; insurance providers rely on it for detecting fraudulent activity, and banks that perform credit scoring use it as well. In addition, cluster analysis plays an integral role in market research for identifying categories such as the earning bracket or age group, as well as for audience segmentation. In the healthcare sector, researchers use cluster analysis to discover correlations between geographical location and the occurrence of a particular illness.

Hands-On Data Analytics Classes

A great way to learn more about data analytics is to enroll in one of Noble Desktop’s data analytics classes. Courses are offered in New York City, as well as in the live online format in topics like Python, Excel, and SQL.

In addition, more than 130 live online data analytics courses are also available from top providers. Topics offered include FinTech, Excel for Business, and Tableau, among others.

Courses range from three hours to six months and cost from $219 to $27,500.

Those who are committed to learning in an intensive educational environment may also consider enrolling in a data analytics or data science bootcamp. These rigorous courses are taught by industry experts and provide timely, small-class instruction. Over 90 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, data visualization, data science, and Python, among others.

For those searching for a data analytics class nearby, Noble’s Data Analytics Classes Near Me tool provides an easy way to locate and browse approximately 400 data analytics classes currently offered in the in-person and live online formats. Course lengths vary from three hours to 36 weeks and cost $119-$27,500.