All core STA course units.
This course unit builds upon all the statistical concepts and methodologies you have learned so far.
It will evaluate your ability to integrate and apply these techniques cohesively to analyze and solve complex real-world problems.
Formative assessment
Summative assessment
Soft Skills/ Interpersonal Skills
Technical Skills
Analytical and Problem-Solving Skills
Effective Communication Capability to explain technical findings to non-technical stakeholders, bridging the gap between data and decision-making.
Collaboration skills to work in multidisciplinary teams.
Adaptability to changing project needs and fast-paced environments.
A growth mindset to continuously learn new techniques and tools.
Expertise in statistical methods (e.g., regression, hypothesis testing, time series, machine learning).
Proficiency in programming languages like R, Python, and tools like SQL, SAS, or SPSS.
Experience in data cleaning, wrangling, and visualization.
Knowledge of reproducible documentation workflows and version control tools like Git.
Ability to translate complex business problems into statistical models.
Creativity in selecting or designing methodologies to solve unique challenges.
Critical thinking to interpret results and provide actionable insights.
Attention to detail for accuracy and reliability in analyses.
tools and methods you used
how you communicated your findings
In one of my recent projects, I worked with a retail client to forecast product demand across multiple locations.
The challenge was that the sales were influenced by seasonality, promotional events, and external factors like weather.
I used R to analyze this problem and develop a predictive model.
I first used the dplyr and tidyr packages to clean the sales data, handling missing values, duplicates, and outliers. Then, I used lubridate to parse dates and create time-based features for seasonality.
I applied ARIMA and Exponential Smoothing models to forecast future sales. I used the forecast package to fit the models and validate their accuracy.
I visualized the forecasted data with the ggplot2 package to make the results more understandable for the client.
Your turn: Write the response that you would give to this interview question.
Relationship between parental involvement and its association with the social and academic development of kindergarten children
How you would approach data collection for this project?
The early years of a child’s life are critical for their cognitive, social, and emotional development. Research has consistently shown that parental involvement plays a significant role in shaping a child’s academic success and behavioral development. Parents who are knowledgeable about child development and effective parenting strategies are better equipped to support their child’s growth in a nurturing and productive manner.
In response to this, an organization has introduced a parent knowledge improvement program designed to enhance parents’ understanding of child development, effective parenting techniques, and strategies to foster their child’s academic and behavioral growth.
What is the effect of an organization-developed parent knowledge improvement program on the academic performance and behavioral outcomes of kindergarten children?
How you would approach data collection for this project?
How do university students’ learning styles influence their academic performance over the course of their degree program?
How you would approach data collection for this project?
Identify birds behavior, and habitat preferences in Sinharaja Forest.
What are the behavioral patterns of birds (Feeding, Nesting, Flight patterns, Interaction with other species) in this ecosystem?
How do environmental factors (e.g., vegetation type, altitude, human activity) influence bird habitat preferences in Sinharaja Forest?