108 Data Analysis
- 108 Data Analysis
- 108.10 Data Analysis - Phases of data analysis
- 108.10.05 Data Analysis - Timing for data analysis projects
- 108.10.10 Data Analysis - Step 1 smart questions and proper data
- 108.10.10.10 Data Analysis - Client working relationship
- 108.10.10.20 Data Analysis - Data request
- 108.10.10.20.10 Data Analysis - Data request details
- 108.10.10.30 Data Analysis - Pre-mortem
- 108.10.20 Data Analysis - Step 2 ingest clean validate
- 108.10.20.10 Data Analysis - Ingest raw data into database
- 108.10.20.10.10 Data Analysis - Receiving raw data
- 108.10.20.10.20 Data Analysis - Standardized process to ingest raw data
- 108.10.20.10.20.10 Data Analysis - Moving data from disk to S3
- 108.10.20.10.20.20 Data Analysis - Commands to move data from S3 to Redshift
- 108.10.20.20 Data Analysis - Clean and map data
- 108.10.20.20.10 Data Analysis - Raw data problems
- 108.10.20.20.20 Data Analysis - Describe the data in words
- 108.10.20.30 Data Analysis - Summary statistics and confirmation
- 108.10.20.30.10 Data Analysis - External data validation
- 108.10.30 Data Analysis - Step 3 exploration and structural analysis
- 108.10.40 Data Analysis - Step 4 model creation and prediction
- 108.10.50 Data Analysis - Step 5 outputs and presentations
- 108.10.60 Data Analysis - Project post-mortem
- 108.10.70 Data Analysis - More data analysis best practices
- 108.10.70.10 Data Analysis - Keep one source of data truth
- 108.10.70.20 Data Analysis - Teamwork for data cleaning mapping analysis
- 108.20 Data Analysis - Customer analysis
- 108.20.05 Data Analysis - Attributes of customer analysis
- 108.20.07 Data Analysis - Customer analysis primary objectives
- 108.20.10 Data Analysis - Optimize
- 108.20.10.10 Data Analysis - Optimize pricing packaging
- 108.20.10.20 Data Analysis - Optimize advertising channels
- 108.20.10.30 Data Analysis - Optimize email
- 108.20.10.40 Data Analysis - Optimize coupons discounts
- 108.20.10.40.10 Data Analysis - Which customer segment to give discounts
- 108.20.10.50 Data Analysis - Optimize retention
- 108.20.20 Data Analysis - Make decisions
- 108.20.30 Data Analysis - Customer lifetime value
- 108.20.30.10 Data Analysis - Benefits of LTV analysis
- 108.20.30.20 Data Analysis - Calculating LTV
- 108.20.30.30 Data Analysis - How NOT to calculate LTV
- 108.20.40 Data Analysis - Customer analysis for different business models
- 108.20.40.10 Data Analysis - Customer analysis for retail brick and mortar
- 108.20.40.20 Data Analysis - Customer analysis for b2b
- 108.20.40.30 Data Analysis - Customer analysis for consumer
- 108.20.40.30.10 Data Analysis - Customer analysis for ecommerce with attachable subscriptions
- 108.20.50 Data Analysis - Acquitention
- 108.20.50.10 Data Analysis - Acquitention Heuristic
- 108.20.50.20 Data Analysis - Acquitention Phase II
- 108.30 Data Analysis - Categories of data analysis
- 108.30.10 Data Analysis - Structural analysis
- 108.30.10.10 Data Analysis - Segmentation is used on every project
- 108.30.10.10.10 Data Analysis - Stratified sampling
- 108.30.10.20 Data Analysis - Structural analysis outputs
- 108.30.10.20.10 Data Analysis - How to read a cohort triangle
- 108.30.10.20.20 Data Analysis - Segments and metrics for outputs
- 108.30.10.20.20.10 Data Analysis - Segments - RFM
- 108.30.10.20.20.10.10 Data Analysis - Segments - RFM - How to segment RFM
- 108.30.10.20.20.10.20 Data Analysis - Segments - RFM - variations
- 108.30.10.20.30 Data Analysis - Structural analysis deliverables
- 108.30.10.30 Data Analysis - Structural analysis data required
- 108.30.10.40 Data Analysis - Structural analysis steps
- 108.30.20 Data Analysis - Predictive analysis
- 108.30.20.10 Data Analysis - Predictive analysis best practices
- 108.30.30 Data Analysis - Prescriptive analysis
- 108.40 Data Analysis - Data analysis software tools and environment
- 108.40.05 Data Analysis - Tools should be generically useful
- 108.40.10 Data Analysis - Working environment
- 108.40.10.10 Data Analysis - Standardized working environment
- 108.40.10.20 Data Analysis - Excel is amazing and it sucks too
- 108.40.10.30 Data Analysis - Data science computing infrastructure
- 108.40.20 Data Analysis - Tools
- 108.40.20.10 Data Analysis - Output generator
- 108.40.20.20 Data Analysis - Raw data understanding and summary statistics
- 108.40.20.30 Data Analysis - An idea for raw data mapping and validation
- 108.40.20.40 Data Analysis - Jupyter notebooks
- 108.40.20.40.10 Data Analysis - Install Jupyter Notebook locally
- 108.40.20.50 Data Analysis - Data quality tool
- 108.40.30 Data Analysis - Tools and environment best practices
- 108.40.30.10 Data Analysis - Data output validation is vital
- 108.40.30.20 Data Analysis - Unit tests everywhere all the time
- 108.60 Data Analysis - Accounting analysis
- 108.60.10 Data Analysis - Accounting terms