Web Analytics With R Course

Learn Web analytics with R In-depth

Beginner

Join 283 Students

Approx. 1 week
Assumes 1 hr/day (work at your own pace)

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  1. 12m 43s
    1. What is Web Analytics
      4m 28s
    2. What you should know
      1m 20s
    3. Who should go for this course
      1m 13s
    4. Intro to Tool that is to be used
      1m 38s
    5. How this course will help
      4m 02s
  2. 34m 01s
    1. Why Web Analytics
      4m 16s
    2. What is Presite, Postsite and Onsite Visitors
      3m 42s
    3. Questions to consider to proceed
      5m 02s
    4. Some insights that we can get from Web Analytics
      3m 15s
    5. Why is it useful for businesses
      3m 42s
    6. Who uses Web Analytics
      3m 8s
    7. Metrics for Web Analytics Report
      3m 42s
    8. Why invest in Web Analytics
      1m 47s
    9. Setup a web analytics tracking system
      1m 40s
    10. Discussion on E-commerce and Non-Ecommerce data
      3m 47s
  3. 50m 58s
    1. How data collection works
      4m 34s
    2. How you can start with your account
      4m 17s
    3. Understanding data reporting
      2m 43s
    4. Understanding Web analytics framework and KPIs
      14m 21s
    5. Choosing a right web analytics tool
      5m 54s
    6. Web Analytics and Business Organisations
      1m 6s
    7. Stakeholders and Expectations
      2m 34s
    8. Reporting to stakeholder's need
      5m 17s
    9. Understanding Business Strategy
      4m 41s
    10. Measuring Marketing Preformance
      5m 31s
  4. 20m 17s
    1. Understanding origin of R and Rstudio
      3m 50s
    2. Advantages of using R in Web Analytics
      6m 11s
    3. Introduction to R Interface
      4m 47s
    4. Introduction to Rstudio Interface
      3m 08s
    5. Installation Process of R and Rstudio tool
      2m 21s
  5. 24m 43s
    1. Understanding Vectors and variables
      9m 5s
    2. Understanding Dataframe
      03m 17s
    3. Understanding Factors, Subsetting and Summarizing methods
      06m 20s
    4. R help Feature
      03m 51s
    5. Importing data into R
      02m 10s
  6. 18m 52s
    1. Introduction to RGoogleAnalytics Package
      2m 39s
    2. Understanding Devtools and GitHub
      1m 39s
    3. Authentication to Google API
      5m 50s
    4. Understanding combinations of Dimensions and metrics
      2m 10s
    5. Extracting Data
      2m 15s
    6. Google trends Data Extraction
      1m 49s
    7. Interpreting the results for Decision making
      2m 30s
  7. 48m 32s
    1. Understanding and Differentiating new users and returning users
      5m 39s
    2. Hierarchy of visits, visitors and page views
      3m 29s
    3. Sorting data by Browser capabilities
      5m 11s
    4. Analyzing data from mobile browsers
      5m 27s
    5. Optimizing Traffic by time of day
      4m 02s
    6. Tracking your Marketing
      7m 20s
    7. Identifying slow performing pages
      2m 57s
    8. Understanding Internal Site search behavior
      4m 13s
    9. Analyzing type of Audience visiting your Website
      8m 20s
    10. Measuring Return on Investment (ROI)
      1m 54s
  8. 22m 7s
    1. What is Visualization
      1m 34s
    2. Benefits of Visualizing Data on Graphs
      2m 18s
    3. Useful Graphs to Visualize Information
      2m 22s
    4. Understanding Map plotting in R
      1m 10s
    5. Plotting Geographic Information of Visitors on map
      6m 43s
    6. Analyzing Revenue on Histogram
      2m 58s
    7. Analyzing Campaign cost on Run Chart and Boxplot
      5m 02s
  9. 20m 56s
    1. What is Time Series
      1m 12s
    2. How do we use Time Series Data
      2m 36s
    3. Components of Time Series
      2m 40s
    4. Flavors of Time Series
      3m 46s
    5. Steps of Time Series Forecasting in R
      7m 20s
    6. Forecasting using Exponential Smoothing
      2m 39s
    7. Validation of Forecast
      43s
  10. 3m 33s
    1. Further Resources
      1m 41s
    2. Helpful Books and Links
      1m 52s

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