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:

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.