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
Expand all sectionsCollapse all sections
- Introduction to Consulting4
- Requirements of Sales Consultant13
- Consulting Process and Frameworks24
- 3.11- Overview of the Consulting Process
- 3.2Discovery and Assessment
- 3.3Analysis and Diagnosis
- 3.4Solution Development
- 3.5Implementation Planning
- 3.6Execution and Monitoring
- 3.7Evaluation and Feedback
- 3.82- Consulting Frameworks
- 3.9SWOT Analysis
- 3.10PESTLE Analysis
- 3.11Porter’s Five Forces
- 3.12BCG Matrix
- 3.133- Problem-Solving Methodologies
- 3.14PDCA (Plan-Do-Check-Act)
- 3.15DMAIC (Define-Measure-Analyze-Improve-Control)
- 3.164- Client Engagement and Relationship Management
- 3.17Active Listening
- 3.18Clear Communication
- 3.19Building Trust
- 3.20Managing Expectations
- 3.21Stakeholder Management
- 3.22Feedback Mechanisms
- 3.23Rest
- 3.24Case Studies
- How to Use SWOT49
- 4.11- Identify the Objective
- 4.2Understanding the Purpose
- 4.3Scope Identification
- 4.4Determining the Subject
- 4.5Setting Objectives
- 4.6Tailoring the Analysis
- 4.7Aligning with Goals
- 4.82- Gather Information
- 4.9Financial Reports
- 4.10Market Research
- 4.11Customer Feedback
- 4.12Competitor Analysis
- 4.13Internal Assessments
- 4.14Stakeholder Input
- 4.153- List Strengths (S)
- 4.16Resources
- 4.17Capabilities
- 4.18Unique Selling Propositions (USPs)
- 4.19Brand Reputation
- 4.20Strategic Alliances
- 4.214- Identify Weaknesses (W)
- 4.22Identify Internal Factors
- 4.23Focus on Areas for Improvement
- 4.24Developing Solutions
- 4.25Documenting and Communicating Weaknesses
- 4.265- Explore Opportunities (O)
- 4.27Market Trends
- 4.28Emerging Technologies
- 4.29Regulatory Changes
- 4.30New Partnerships and Collaborations
- 4.31Untapped Customer Segments
- 4.32Monitoring and Adaptation
- 4.336- Identify Threats (T)
- 4.34Competition
- 4.35Economic Downturns
- 4.36Changing Consumer Preferences
- 4.37Regulatory Changes
- 4.38Technological Disruptions
- 4.39Market Shifts
- 4.40Environmental Factors
- 4.41Social and Cultural Shifts
- 4.42Global Events
- 4.437- Evaluate and Prioritize
- 4.44Assessing Significance
- 4.45Interplay between Factors
- 4.46Focus on Critical Aspects
- 4.47Actionable Strategies
- 4.48Case Studies
- 4.49Rest
- Data Collection and Analysis4
- Predictive Modeling with SPSS9
- Tools and Technologies4
- Data Types and Sources17
- 8.11- Data Types
- 8.2Structured Data
- 8.3Unstructured Data
- 8.42- Data Sources
- 8.5Internal Data Sources
- 8.6External Data Sources
- 8.73- Data Collection Methods
- 8.8Surveys and Questionnaires
- 8.9Observation
- 8.10Interviews
- 8.11Sensor Data
- 8.12Web Scraping
- 8.13Transaction Data
- 8.14Social Media Monitoring
- 8.15Secondary Data Analysis
- 8.16Case Studies
- 8.17Rest
- Cleaning and Transformation25
- 9.11- Dealing with Missing Data
- 9.2Identification of Missing Values
- 9.3Removal of Missing Values
- 9.4Imputation of Missing Values
- 9.5Evaluate the Impact
- 9.6Documentation
- 9.7Iterative Process
- 9.82- Outlier Detection and Treatment
- 9.9Identification
- 9.10Removal
- 9.11Transformation
- 9.12Winsorizing
- 9.133- Data Normalization and Standardization
- 9.14Normalization (Min-Max Scaling)
- 9.15Standardization (Z-score Normalization)
- 9.16Robust Scaling (IQR Scaling)
- 9.174- Feature Engineering Techniques
- 9.18Binning/Discretization
- 9.19One-Hot Encoding
- 9.20Polynomial Features
- 9.21Interaction Terms
- 9.22Feature Scaling
- 9.23Dimensionality Reduction
- 9.24Case Studies
- 9.25Rest
- Exploratory Data Analysis (EDA) with SPSS16
- 10.1Introduction
- 10.21- Basic Statistical Analysis
- 10.3Descriptive Statistics
- 10.4Frequency Distribution
- 10.5Central Tendency and Dispersion
- 10.6Correlation Analysis
- 10.7Inferential Statistics
- 10.82- Data Visualization Techniques
- 10.9Histograms
- 10.10Box Plots
- 10.11Scatter Plots
- 10.12Bar Charts
- 10.13Heatmaps
- 10.14Pie Charts
- 10.15Line Chart
- 10.16When 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
- 13.11- Dashboard and Visualization Design
- 13.21-1 Principles of Effective Data Visualization
- 13.3Clarity and Simplicity
- 13.4Relevance
- 13.5Consistency
- 13.6Interactivity
- 13.7Storytelling
- 13.8Use of Appropriate Visualization Types
- 13.9Color Usage
- 13.10Data Accuracy and Precision
- 13.111-2 Dashboard Development and Best Practices
- 13.12Define Objectives
- 13.13User-Centric Design
- 13.14Performance Optimization
- 13.15Responsive Design
- 13.16Feedback Mechanisms
- 13.17Regular Updates
- 13.18Testing and ValidationC
- 13.192- Reporting Tools
- 13.202-1 Introduction to Reporting Tools
- 13.21Tableau
- 13.22Power BI (Microsoft Power BI)
- 13.232-2 Creating Dynamic and Interactive Reports
- 13.24Data Connection
- 13.25Report Design
- 13.26Interactivity
- 13.27Data Refresh and Automation
- 13.28Collaboration and Sharing
- 13.29Security and Access Control
- 13.30Documentation and Training
- 13.31Case Studies
- 13.32Rest
- Strategy Development18
- 14.11- Strategic Thinking and Analysis
- 14.2Vision and Mission Statement
- 14.3SWOT Analysis
- 14.4PESTLE Analysis
- 14.5Porter’s Five Forces
- 14.62- Competitive Analysis and Benchmarking
- 14.7Identifying Competitors
- 14.8Analyzing Competitor Strategies
- 14.9Benchmarking
- 14.103- Developing Strategic Recommendations
- 14.11Setting Objectives
- 14.12Strategy Formulation
- 14.13Implementation Plan
- 14.14Monitoring and Evaluation
- 14.15Risk Management
- 14.16Communication and Alignment
- 14.17Case Studies
- 14.18Rest
- Project Management in Consulting17
- 15.11- Project Planning and Scheduling
- 15.2Define Project Scope
- 15.3Work Breakdown Structure (WBS)
- 15.4Estimate Time and Effort
- 15.5Create a Project Schedule
- 15.62- Resource Allocation and Management
- 15.7Identify Resources
- 15.8Allocate Resources
- 15.9Resource Leveling
- 15.10Monitor Resource Performance
- 15.113- Risk Management and Mitigation Strategies
- 15.12Identify Risks
- 15.13Assess Impact and Likelihood
- 15.14Develop Mitigation Strategies
- 15.15Monitor and Control Risks
- 15.16Case Studies
- 15.17Rest
- Communication and Presentation Skills4
- Change Management5
- Ethics and Professionalism in Consulting4
- Industry Focus (Optional)4
Requirements
- https://tetra-pillars.com/course/marketing-management-advanced/