Introduction to Data Anlytics
As applicable to Business Developmentâ€‹

Using data to understand the real world.

The data types that best suit the situation

Standard methodologies to reduce both risk & Errors

A glossary of terms appears below
Introduction to Data Analytics in Business
Data analytics plays a pivotal role in modern business, transforming raw data into actionable insights that drive decisionmaking. From improving operational efficiency to identifying growth opportunities, businesses use various analytical techniques to stay competitive.
The core methods can be categorized as Descriptive, Diagnostic, Predictive, and Prescriptive Analytics, each serving a unique function in understanding and optimizing business performance.
Descriptive Analytics: This technique summarizes historical data, providing a clear picture of what has happened over a specific period. It is useful for tracking performance and trends through reports, charts, and dashboards.
Diagnostic Analytics: Building on descriptive insights, diagnostic analytics helps explain why things happened. It digs deeper into the data using techniques like drilldowns, data discovery, and correlations to identify the root causes of trends or issues.
Predictive Analytics: Predictive models use historical data to forecast future outcomes. Techniques such as machine learning, data mining, and statistical modeling are employed to predict trends, customer behavior, and business risks.
Prescriptive Analytics: The most advanced form, prescriptive analytics, recommends actions based on predictive insights. Using algorithms and simulations, it suggests the best course of action to achieve desired outcomes, considering various scenarios and constraints.
In addition to these types, Data Testing ensures the validity of data and models through statistical tests, while Regression Analysis and Multiple Correlation help businesses understand relationships between variables. Regression models predict outcomes based on one or more independent variables, while multiple correlation analysis assesses how several factors collectively influence a dependent variable.
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Glossary of Statistical Techniques and Methods
Mean (Average): The sum of all values divided by the number of values. It gives a central point of the data distribution.
Median: The middle value in a dataset when the numbers are arranged in order. It helps represent the center of skewed distributions.
Mode: The value that appears most frequently in a dataset. It is useful for categorical data.
Standard Deviation: A measure of the amount of variation or dispersion in a dataset. A low standard deviation means the data points tend to be close to the mean, while a high standard deviation means they are spread out.
Variance: The square of the standard deviation, it measures how much the data points differ from the mean.
Correlation: A statistical measure that describes the degree to which two variables move in relation to each other. It ranges from 1 to +1, where +1 means perfect positive correlation and 1 means perfect negative correlation.
Regression Analysis: A statistical method to determine the relationship between a dependent variable and one or more independent variables. Common forms include linear and logistic regression.
Multiple Regression: An extension of regression analysis where two or more independent variables are used to predict the dependent variable.
ANOVA (Analysis of Variance): A method used to compare the means of three or more samples to see if at least one is significantly different from the others.
ChiSquare Test: A statistical test used to determine if a significant relationship exists between two categorical variables.
TTest: A test used to determine if there is a significant difference between the means of two groups, commonly used in comparing test scores, profits, etc.
ZTest: Similar to a ttest but used when the sample size is large, and the population variance is known.
PValue: A probability score that helps to determine the significance of your results in hypothesis testing. A low pvalue (typically ≤ 0.05) indicates strong evidence against the null hypothesis.
Hypothesis Testing: A method for testing a claim or hypothesis about a parameter in a population, using sample data.
Confidence Interval: A range of values, derived from the sample data, that is likely to contain the true value of an unknown population parameter.
Sampling: The process of selecting a subset of individuals from a population to estimate characteristics of the whole population.
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Outliers: Data points that are significantly different from other observations in the dataset. These can affect the results of an analysis.
Time Series Analysis: A method used for analyzing data points collected or recorded at specific intervals over time to forecast future trends.
Bayesian Statistics: A method of statistical inference that uses Bayes' theorem to update the probability for a hypothesis as more evidence becomes available.
Cluster Analysis: A method used to group a set of objects in such a way that objects in the same group (or cluster) are more similar to each other than to those in other groups.
Factor Analysis: A technique used to reduce the number of variables by identifying underlying factors that explain the pattern of correlations within the dataset.
Principal Component Analysis (PCA): A dimensionalityreduction technique used to reduce the complexity of datasets by transforming them into a set of uncorrelated variables called principal components.
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Monte Carlo Simulation: A computational algorithm that uses repeated random sampling to simulate and understand the behavior of complex systems or processes.
Logistic Regression: A regression model used for binary outcomes, often used in classification problems.
KaplanMeier Estimator: A nonparametric statistic used to estimate the survival function from lifetime data, often used in medical research.
MANOVA (Multivariate Analysis of Variance): An extension of ANOVA that allows for comparing more than one dependent variable across different groups.
Survival Analysis: A branch of statistics that analyzes timetoevent data, such as the time until a product fails or the time until a patient relapses.
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RSquared (R²): A statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model.
CrossValidation: A technique for assessing how a predictive model performs by partitioning the data into subsets, training the model on one subset, and validating it on another.
Bootstrap Method: A resampling technique used to estimate statistics on a population by sampling a dataset with replacement.
This glossary provides an overview of essential statistical techniques used in data analysis for business decisionmaking, forecasting, and optimization.
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