Big Data & Business Intelligence

Explore data collection and cleaning as the first step in business intelligence, assess databases, gather and build internal data, merge datasets, and clean data to ensure decision accuracy.

Evaluate commercial databases carefully to avoid black box data and governance issues, weighing manual versus automatic data entry, and consider Excel or your own data sets with appropriate analysis tools.

Explore data collection options—buy, build, or gather free data—using macroeconomic sources, firm data, and surveys. Learn to design a model first, then select relevant data and address biases.

Merge disparate data sets into a single, usable dataset for analysis by reconciling varying frequencies and selecting appropriate merge keys. Explore time dependent versus static data and exact match validation.

Identify and correct data errors in large data sets by dropping illogical values, flagging beyond three standard deviations, and applying mean, median, and standard deviation checks, Bedford's and Benford's laws.

Explore data collection and cleaning challenges in big data, including missing data, skewness, unobservable variables, and subsample effects, with practical strategies for merging, unit of analysis, and data quality checks.

Explore data structure basics, assess data accuracy, and apply ratios, key metrics, categorical variables, and data imputation to drive business intelligence analyses.

Explore how business intelligence uses gathering, cleaning, and structuring data to answer quantitative questions, merge datasets, and support future analyses with methods like regression and decision trees.

Assess data quality by checking outliers, accuracy, and variable construction, using debt-to-assets ratios to gauge risk, applying Bedford's law for authenticity, and evaluating means, medians, and percentiles.

Select meaningful variables and build ratios, rates of change, and log transformations to structure data for reliable prediction, risk assessment, and valuation in big data and business intelligence.

Learn to structure data with categorical and binary variables, using danger, gray, and safe zones, and apply decile and quintile rankings to analyze time-series data and predict outcomes.

Explore missing data handling using imputing techniques—last value, linear interpolation, and regression prediction. Assess the impact of dropping data and apply smoothing with moving averages for clearer trends.

Explore business intelligence fundamentals of data analysis, including an overview, univariate analysis for common business questions, regression analysis for forecasting, and advanced regression techniques, with a preview of next course.

Learn to gather and structure data for a big data project, apply regression analysis, and use nominal, ordinal, and interval measurements with mean, median, and mode.

Explore variability in univariate data using range, interquartile range, and standard deviation, then apply z-scores and t-scores for inferential statistics and hypothesis testing with Excel, Stata, and SAS.

Master multiple regression to explain variation in a dependent variable using multiple independent variables, incorporate intercept and coefficients, convert qualitative data to numbers, and predict outcomes with Excel data analysis.

Explore advanced regression analysis and fixed effects to predict sales while controlling for omitted variables, assess spending on sales incentives, and understand data limitations beyond Excel.

Analyze how regression and logistic regression estimate the impact of sales culture and incentives, compare SAS and Stata, and apply difference-in-differences AB testing with control and treatment groups.

Review data analysis by interpreting coefficients, standard errors, significances, and r-squared values to see how investing in sales culture affects future sales and model accuracy.

Explore interpreting results from big data analytics and apply insights to your company, covering univariate analysis, regressions, economic significance, and project limitations.

Interpret univariate statistics like means and medians and multivariate statistics that reveal how sales interact with location features, enabling proper interpretation for informed business decisions and future forecasting.

Explore univariate analysis by examining means, medians, and percentiles over time; analyze pre- and post-change sales to understand significant impacts and driving factors.

Use multivariate regression to identify drivers of sales, evaluating variables like time with customers, sales commission, and marketing, via Excel's data analysis tool.

Explore the difference between statistical significance and economic significance in regression analysis, using marketing spend and sales to illustrate meaningful units, profitability, and decision thresholds.

Analyze the 5th-to-95th percentile range, data availability, and a high r-squared to reveal limitations in small data sets for big data projects and forecasting.