What I learn from this course?
This course provides a comprehensive overview of consulting, starting with an introduction to the consulting in marketing profession and key processes and frameworks. You will learn about data collection, analysis, and predictive modeling using SPSS, alongside various tools and technologies. The curriculum covers essential topics such as data types and sources, data cleaning and transformation, exploratory data analysis (EDA), statistical analysis, and prescriptive analytics with SPSS. Additionally, you will gain skills in business intelligence, strategy development, project management, effective communication, change management, and maintaining ethics and professionalism in consulting, with an optional focus on specific industries.
* To find out the content of the course, click on “Curriculum” above.
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Curriculum
- 19 Sections
- 280 Lessons
- 7 Weeks
- Introduction to Consulting4
- Requirements of Sales Consultant13
- Consulting Process and Frameworks24
- 3.01- Overview of the Consulting Process
- 3.1Discovery and Assessment
- 3.2Analysis and Diagnosis
- 3.3Solution Development
- 3.4Implementation Planning
- 3.5Execution and Monitoring
- 3.6Evaluation and Feedback
- 3.72- Consulting Frameworks
- 3.8SWOT Analysis
- 3.9PESTLE Analysis
- 3.10Porter’s Five Forces
- 3.11BCG Matrix
- 3.123- Problem-Solving Methodologies
- 3.13PDCA (Plan-Do-Check-Act)
- 3.14DMAIC (Define-Measure-Analyze-Improve-Control)
- 3.154- Client Engagement and Relationship Management
- 3.16Active Listening
- 3.17Clear Communication
- 3.18Building Trust
- 3.19Managing Expectations
- 3.20Stakeholder Management
- 3.21Feedback Mechanisms
- 3.22Case Studies
- 3.22Rest
- How to Use SWOT49
- 4.01- Identify the Objective
- 4.1Understanding the Purpose
- 4.2Scope Identification
- 4.3Determining the Subject
- 4.4Setting Objectives
- 4.5Tailoring the Analysis
- 4.6Aligning with Goals
- 4.72- Gather Information
- 4.8Financial Reports
- 4.9Market Research
- 4.10Customer Feedback
- 4.11Competitor Analysis
- 4.12Internal Assessments
- 4.13Stakeholder Input
- 4.143- List Strengths (S)
- 4.15Resources
- 4.16Capabilities
- 4.17Unique Selling Propositions (USPs)
- 4.18Brand Reputation
- 4.19Strategic Alliances
- 4.204- Identify Weaknesses (W)
- 4.21Identify Internal Factors
- 4.22Focus on Areas for Improvement
- 4.23Developing Solutions
- 4.24Documenting and Communicating Weaknesses
- 4.255- Explore Opportunities (O)
- 4.26Market Trends
- 4.27Emerging Technologies
- 4.28Regulatory Changes
- 4.29New Partnerships and Collaborations
- 4.30Untapped Customer Segments
- 4.31Monitoring and Adaptation
- 4.326- Identify Threats (T)
- 4.33Competition
- 4.34Economic Downturns
- 4.35Changing Consumer Preferences
- 4.36Regulatory Changes
- 4.37Technological Disruptions
- 4.38Market Shifts
- 4.39Environmental Factors
- 4.40Social and Cultural Shifts
- 4.41Global Events
- 4.427- Evaluate and Prioritize
- 4.43Assessing Significance
- 4.44Interplay between Factors
- 4.45Focus on Critical Aspects
- 4.46Actionable Strategies
- 4.47Case Studies
- 4.47Rest
- Data Collection and Analysis4
- Predictive Modeling with SPSS9
- Tools and Technologies4
- Data Types and Sources17
- 8.01- Data Types
- 8.1Structured Data
- 8.2Unstructured Data
- 8.32- Data Sources
- 8.4Internal Data Sources
- 8.5External Data Sources
- 8.63- Data Collection Methods
- 8.7Surveys and Questionnaires
- 8.8Observation
- 8.9Interviews
- 8.10Sensor Data
- 8.11Web Scraping
- 8.12Transaction Data
- 8.13Social Media Monitoring
- 8.14Secondary Data Analysis
- 8.15Case Studies
- 8.15Rest
- Cleaning and Transformation25
- 9.01- Dealing with Missing Data
- 9.1Identification of Missing Values
- 9.2Removal of Missing Values
- 9.3Imputation of Missing Values
- 9.4Evaluate the Impact
- 9.5Documentation
- 9.6Iterative Process
- 9.72- Outlier Detection and Treatment
- 9.8Identification
- 9.9Removal
- 9.10Transformation
- 9.11Winsorizing
- 9.123- Data Normalization and Standardization
- 9.13Normalization (Min-Max Scaling)
- 9.14Standardization (Z-score Normalization)
- 9.15Robust Scaling (IQR Scaling)
- 9.164- Feature Engineering Techniques
- 9.17Binning/Discretization
- 9.18One-Hot Encoding
- 9.19Polynomial Features
- 9.20Interaction Terms
- 9.21Feature Scaling
- 9.22Dimensionality Reduction
- 9.23Case Studies
- 9.24Rest
- Exploratory Data Analysis (EDA) with SPSS16
- 10.0Introduction
- 10.11- Basic Statistical Analysis
- 10.2Descriptive Statistics
- 10.3Frequency Distribution
- 10.4Central Tendency and Dispersion
- 10.5Correlation Analysis
- 10.6Inferential Statistics
- 10.72- Data Visualization Techniques
- 10.8Histograms
- 10.9Box Plots
- 10.10Scatter Plots
- 10.11Bar Charts
- 10.12Heatmaps
- 10.13Pie Charts
- 10.14Line Chart
- 10.15When should I use them?
- Statistical analysis with SPSS24
- 11.11- Descriptive Statistics
- 11.21-1 Measures of Central Tendency and Dispersion
- 11.3Mean, Median, and Mode
- 11.4Variability Measures
- 11.51-2 Frequency Distributions
- 11.6Histograms
- 11.7Percentiles and Quartiles
- 11.81-3 Probability Distributions
- 11.9Normal Distribution
- 11.102- Inferential Statistics
- 11.112-1 Hypothesis Testing
- 11.12Formulate Hypotheses
- 11.13Conduct Statistical Tests
- 11.142-2 Confidence Intervals
- 11.153-1 Confidence Intervals
- 11.16Confidence Interval for Mean
- 11.174-1 Regression Analysis
- 11.18Simple Linear Regression
- 11.19Multiple Regression
- 11.205-1 Correlation Analysis
- 11.21Pearson Correlation
- 11.22SPSS Functionality
- 11.23When should I use them?
- 11.24Case Studies
- Prescriptive Analytics with SPSS8
- Business Intelligence and Reporting32
- 13.01- Dashboard and Visualization Design
- 13.11-1 Principles of Effective Data Visualization
- 13.2Clarity and Simplicity
- 13.3Relevance
- 13.4Consistency
- 13.5Interactivity
- 13.6Storytelling
- 13.7Use of Appropriate Visualization Types
- 13.8Color Usage
- 13.9Data Accuracy and Precision
- 13.101-2 Dashboard Development and Best Practices
- 13.11Define Objectives
- 13.12User-Centric Design
- 13.13Performance Optimization
- 13.14Responsive Design
- 13.15Feedback Mechanisms
- 13.16Regular Updates
- 13.17Testing and ValidationC
- 13.182- Reporting Tools
- 13.192-1 Introduction to Reporting Tools
- 13.20Tableau
- 13.21Power BI (Microsoft Power BI)
- 13.222-2 Creating Dynamic and Interactive Reports
- 13.23Data Connection
- 13.24Report Design
- 13.25Interactivity
- 13.26Data Refresh and Automation
- 13.27Collaboration and Sharing
- 13.28Security and Access Control
- 13.29Documentation and Training
- 13.30Case Studies
- 13.31Rest
- Strategy Development18
- 14.01- Strategic Thinking and Analysis
- 14.1Vision and Mission Statement
- 14.2SWOT Analysis
- 14.3PESTLE Analysis
- 14.4Porter’s Five Forces
- 14.52- Competitive Analysis and Benchmarking
- 14.6Identifying Competitors
- 14.7Analyzing Competitor Strategies
- 14.8Benchmarking
- 14.93- Developing Strategic Recommendations
- 14.10Setting Objectives
- 14.11Strategy Formulation
- 14.12Implementation Plan
- 14.13Monitoring and Evaluation
- 14.14Risk Management
- 14.15Communication and Alignment
- 14.16Case Studies
- 14.16Rest
- Project Management in Consulting17
- 15.01- Project Planning and Scheduling
- 15.1Define Project Scope
- 15.2Work Breakdown Structure (WBS)
- 15.3Estimate Time and Effort
- 15.4Create a Project Schedule
- 15.52- Resource Allocation and Management
- 15.6Identify Resources
- 15.7Allocate Resources
- 15.8Resource Leveling
- 15.9Monitor Resource Performance
- 15.103- Risk Management and Mitigation Strategies
- 15.11Identify Risks
- 15.12Assess Impact and Likelihood
- 15.13Develop Mitigation Strategies
- 15.14Monitor and Control Risks
- 15.15Case Studies
- 15.15Rest
- Communication and Presentation Skills4
- Change Management5
- Ethics and Professionalism in Consulting4
- Industry Focus4
Requirements
- https://tetra-pillars.com/course/marketing-management-advanced/
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