Market Research Rigour with Python: Data Cleaning, Advanced Analytics & Trend Forecasting for Research & Strategy Teams
Venue: Acacia Premier Hotel, Kisumu-Kenya
Date: 6th - 11th July 2026
Cost: Kshs. 86,000 / USD 850
CPD points: 10
Overview
Enhance your market research with Python, the leading language for data analysis. This program covers data cleaning, advanced analytics, and trend forecasting using Python libraries such as Pandas, NumPy, and scikit‑learn. Participants will learn to process messy survey data, perform statistical analyses, and build predictive models to forecast market trends. Through hands‑on exercises with real datasets, attendees will gain practical skills to derive actionable insights and communicate findings effectively. This program is ideal for market researchers, strategy analysts, and data scientists seeking to apply rigorous analytical methods to market intelligence.
Objectives
- Clean and preprocess raw market research data using Pandas
- Perform exploratory data analysis to uncover patterns and outliers
- Apply statistical tests (t‑tests, chi‑square) to validate hypotheses
- Segment markets using clustering techniques (k‑means, hierarchical)
- Build regression models to identify drivers of consumer behavior
- Forecast market trends using time series analysis (ARIMA, Prophet)
- Visualize insights with Matplotlib and Seaborn
- Communicate findings through clear reports and presentations
- Automate recurring analysis tasks with Python scripts
Target Audience
- Market research analysts
- Strategy and insights professionals
- Data analysts in marketing and sales
- Consumer insights managers
- Business intelligence teams
- Consultants involved in market analysis
- Anyone seeking to apply Python to market research
Methodology
- Interactive Python coding sessions in Jupyter notebooks
- Data cleaning workshops with real survey data
- Statistical analysis exercises
- Machine learning model building for segmentation and forecasting
- Case study analyses of market research projects
- Peer review of analytical approaches
- Action planning for integrating Python into research workflows
