Course Content

• Introduction:
  • What is Google Analytics?
  • How does Google Analytics work?
  • Key Definitions.

• Get Started:
  • An overview of accounts.
  • Create your account.
  • Install your tracking tag.
  • Creating views.
  • Adding filters to views.

• Core Concepts:
  • Explore the interface.
  • Understanding the reports.
  • Metrics and Dimensions.
  • A look at sources.

• Additional Interface Features:
  • Interact with Graphs.
  • Using data table.
  • Using Annotations.

• Using Reports:
  • New interface changes.
  • What is segmentation?
  • Setup basic filters.
  • Setup OR vs AND filters.

• Audience Reports:
  • What are audience reports?
  • Looking at your audience overview.
  • Audience demographics and interests.
  • Geographical data.
  • Use the behavior report to see engagement.
  • Use the technology report to see devices used onsite.

• Acquisition Reports:
  • What are Acquisition Reports?
  • Acquisition Report overview.
  • View the All Traffic report by channels.
  • View the All Traffic report by source and medium.
  • Explore referral traffic within the All Traffic Report.

• Social Reports:
  • Social Reports overview.
  • Network Referrals.
  • Tracking shared content with the Landing pages report.
  • Measure the value of the social with the Conversions Report.

• Behavior Reports:
  • Behavior Reports at a glance.
  • Use Site Content reports.
  • Review Site Speed.

• Track Events:
  • A closer look at the Event Tracking.
  • Review Event Reports.

• Conversion Reports:
  • Configure Goals.
  • Exploring Goal Reports.

• Additional Features:
  • Real Time data.
  • Add custom campaign tracking,

Course Content:

• The World of Statistics:
  • Why Statistics matter in your life.
  • Is my data set good?
  • Understanding statistics with the use of charts.

• The Center of the Data:
  • The middle of the data: Means and Medians.
  • Medians for data sets with even numbers and data points.
  • Weighted Mean.
  • The mode: Find it and understand it.

• Data Variability:
  • The range.
  • Standard Deviation: Calculate it and understand it.
  • How many standard deviations?
  • Outliers.

• Distribution and Relative Position:
  • Z-Score: Measuring by using standard deviation.
  • Empirical rule: What symmetry tells us.
  • Calculating percentiles: Where do you stand?

• Probability Explained:
  • Defining probability.
  • Examples of probability.
  • Types of probability.

• Multiple Event probability:
  • Probability of two events: Either event or Both Events.
  • Conditional probability.
  • Independence vs Dependence relationship.
  • Bayes theorem and false positives.
  • Even more of Bayes theorem.

• How Objects are arranged:
  • Permutations: The order of things.
  • Combinations: Permutations without regard for order.

• Discrete vs Continuous Probability:
  • Difference between Discrete vs Continuous probability.

• Discrete Probability Distributions:
  •  Mean and standard deviations of discrete probability distributions.
  • Expected monetary value.
  • Binomial Experiments: When there are only two possible outcomes.

• Continuous Probability Distributions:
  • Curves and random variables.
  • The famous bell-shaped curve.
  • Fuzzy central-limit theorem.
  • Using the Z-transformation to find probabilities.

• Beyond Data and Probability:
  • Understanding data and distributions.
  • Probability and random variables.

• Sampling:
  • Sample considerations.
  • Random samples.
  • Alternatives to random samples.

• Sample Size:
  • The importance of sample size.
  • The central limit theorem.
  • Standard error (for proportions).
  • Sampling distribution of the mean.
  • Standard error (for means).

• Confidence Intervals:
  • One Sample is all you need.
  • What exactly is a confidence interval?
  • 95% confidence intervals for population proportions.
  • Do you want to be more than 95% confident?
  • Explaining unexpected outcomes.
  • 95% confidence intervals for population means.

• Hypothesis Testing:
  • Is this result even possible?
  • How to test a Hypothesis in four steps.
  • One tailed vs two-tailed tests.
  • Significance test for proportions.
  • Significance test for means (acceptance sampling).
  • Type I and Type II errors.

• The Statistics Series: 
  • What lies ahead in Statistics Fundementals.

• Small Sample Sizes:
  • T-statistic vs Z-statistic.
  • T-score tables and degrees of freedom.
  • Calculating confidence intervals using T-Scores.

• Comparing Two Populations (Proportions);
  • Explanation of Two Populations.
  • Set up a comparison.
  • Hypothesis Testing.

• Comparing Two Populations (Means):
  • Basics of comparing two population means.
  • Visualizing (re-randomizing).
  • Set up a confidence interval.
  • Hypothesis Testing.

• Chi-Square:
  • Introduction to chi-square.
  • Curves and Distribution.
  • Goodness-of-fit test.

• ANOVA: Analysis of Variance:
  • What is Analysis of Variance?
  • One-Way ANOVA and the total sum of squares SST.
  • Variance within and variance between (SSW and SSB).
  • Hypothesis and f-statistics.

• Introduction to Regression:
  • What is Regression?
  • The best-fitting line.
  • The coefficient of determination.
  • The correlation coefficient.

Course Contents:

• Customer Analytics Overview:
  • The importance of customer analytics.
  • The customer life cycle.
  • Applied analytics with customer life cycle.
  • Sources of customer data.
  • The customer analytics process.
  • Use case: Online Computer Store.

• Will you become my Customer?
  • The customer acquisition process.
  • Find high propensity prospects.
  • Recommend the best channels for contact.
  • Offer chat based visitor propensity.
  • Use Case: Determine customer propensity.

• What Else Are You Interested In?
  • Upselling and Cross-Selling.
  • Find items bought together.
  • Create customer group preferences.
  • User-item affinity and recommendations.
  • Use case: Recommend Items.

• How much is your Future Business worth?
  • Generate customer loyalty.
  • Create customer value classes.
  • Discover response patterns.
  • Predict Customer Lifetime Value (CLV).
  • Use Case: Predict CLV.

• Are You Happy with Me?
  • Improve customer satisfaction.
  • Predict intent of contact.
  • Find unsatisfied customers.
  • Group problem types.
  • Use Case: Group problem types.

• Will You Leave Me?
  • Prevent customer attrition.
  • Predict customers who might leave.
  • Find incentives.
  • Discover customer attrition patterns.
  • Use Case: Customer Patterns.

• Best Practices:
  • Device Customer Analytics processes.
  • Choose the Right Data.
  • Design data processing pipelines.
  • Implement continuous improvement.

Course Contents:

• What Is Data Mining and Predictive Analytics?
  • Introduction.
  • The definition of Data Mining.
  • What's Data Mining and Predictive Analytics?
  • What are the essential elements?

• Problem Definition:
  • Introduction.
  • Determine the business objective.
  • Identify an intervention strategy.
  • Estimate the ROI.
  • Program management.

• Data Requirements:
  • Introduction.
  • Customer footprint.
  • Flat file.
  • Understand your target.
  • Select the data for modeling.
  • Understand integration.
  • Understand data construction.

• Resources You'll Need:
  • Introduction.
  • Understand data mining algorithms.
  • Assess team requirements.
  • Budget time.
  • Working with SME.

• Problems you will face:
  • Introduction.
  • Deal with missing data.
  • Resolve organizational resistance.
  • Degrade models.

• Finding the Solution:
  • Introduction.
  • Search the solution space.
  • Unexpected results.
  • Trial and Error.
  • Construct Proof.

• Putting the Solution to Work:
  • Introduction.
  • Understand propensity.
  • Understand Metamodeling.
  • Understand Reproducibility.
  • Master Documentation.
  • Time to Deploy.
• CRISP - DM and the Nine Laws:
  • Introduction.
  • Understanding CRISP-DM.
  • Laws 1 and 2.
  • Law 3.
  • Law 4 and 5.
  • Laws 6,7 and 8.
  • Law 9.

Course Content:

• The Basics:
  • What is Big Data?
  • Business Intelligence and company financials.
  • Basics of financial regression analysis.
  • Predict values with regression analysis.
  • Conventional financial forecasting.

• Forecasting in Finance:
  • Decide on a finance question.
  • Gather financial data.
  • Clean financial data.

• Performing Forecasting:
  • Financial forecasting applications.
  • Applied forecasting with data.
  • Regressions for forecasting.
  • Use Excel for regressions.

• Interpreting Forecasting Results:
  • What do the results mean?
  • Confidence interval around the result.
  • Perform stress testing.

Course Content:

• The Basics:
  • Basics of economic analysis.
  • Sources of economic data.
  • Economic forecasting methods.
  • Regression analysis in economics.
  • Predicting values with regressions.

• Economic Cycles:
  • Trend analysis in forecasting.
  • Serial correlation in data.
  • Analyzing results.

• Forecast Economic Trends:
  • Fixed effects regressions.
  • Binary regressions.
  • Advanced regression applications.
  • Difference in differences analysis.

• Use Economic Forecasts:
  • Understanding economic output.
  • Forecast accuracy.
  • Scenario analysis.

Course Content:

• Meta-Analysis: The Basic Idea:
  • Combine many empirical findings.
  • Closer look at effect sizes.
  • Need for a standard measure.

• Two Groups: Continuous Outcome Measure:
  • Raw mean difference.
  • Standard Mean difference: Independent groups.
  • Standard Mean difference: Dependent groups.

• Two Groups: Binary Outcome
  • Risk and odds ratios.
  • Logarithms in risk and odd ratios.

• Confidence Intervals:
  • Odd Ratios.
  • Single Study.
  • Meta-Analysis.