Advanced Data Analytics Training in Calgary, AB

Attendees may include:

  • Businesses exporting or marketing internationally.
  • Website Owners/Webmasters.
  • People who want to explore their analytics skills to the very depth.
  • Small Business Owners / Website Content Managers
  • Brand/Marketing Managers, Marketing Professionals
  • Business to Business and Business to Consumer Sales

How will I benefit?

The Data Analytics Training is tailored to participants who wish to actively apply their analytics skills to their organization. The content is geared to ensure your company has a proper foundation for their data analytics strategy and will also include advanced strategies that attendees can apply and use straight away.

Overview:

100 Hours - Weekdays and Weekends
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We understand your business is not like any other. You’re addressing specific marketing challenges and opportunities with a particular skill set, and that’s where our bespoke, in-company digital and inbound training comes in. If you’re looking to train five or more people, our bespoke option offers you the best value for money and the support of a dedicated digital marketing consultant.

Data Analytics course contents:

Course Contents:
  • Getting Started with Data Analysis:
    • Defining Data Analysis and Data Analyst.
    • Discovering if you are an analyst.
    • Understanding roles in data analysis.
    • Discovering the skills of the data analyst.

 

  • Fundamentals of Data Understanding:
    • Learning to identify data.
    • Learning about data fields and types.
    • Dealing with data we don't have.
    • Learning syntax.

 

  • Key Elements to understand when starting Data Analysis:
    • Learning to interpret existing data.
    • Finding existing data.
    • Understanding joins.
    • Understanding data and workflow.
    • Cleaning data.

 

  • Getting started with Data Project:
    • Getting started with data best practices.
    • Learning about data governance.
    • Understanding truths.
    • Discovering common mistakes of beginners.

 

  • Re-purposing Data versus Re-manufacturing Data:
    • Repurposing data.
    • Understanding source data.
    • Creating reusable data.
    • Building data sets to filter data.

 

  • Working with Business Data:
    • Understanding business rules.
    • Creating a data dictionary.
    • Creating read me information.
    • Documenting data procedures.

 

  • Chart Data Anytime and Anywhere:
    • Building basic charts visual.
    • Linking versus embedding charts and data.
    • Setting default charts and chart shortcuts.

 

  • Pivot Data Anytime and Anywhere:
    • Building Basic Pivots.
    • Modifying Pivots to make them more meaningful to read.
    • Building basic pivot charts with slicers.

 

  • Excel Tips and Tricks for Data Analysts:
    • Selecting data and naming data.
    • Learning to split text with delimiters.
    • Removing duplicates.
    • Transposing data.
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.