Business Analytics and AI in Retail
The development of business analytics and AI solutions for the retail industry is a specialist area which has grown in demand with the e-commerce retail boom. Currently “Retail Business Analyst” is one of the most sought after and highly paid jobs in the market.
This course dives into the basics of using Business Analytics and AI tools using Python to develop models on Demand Forecasting, Markdown, Price And Promotion Optimization, Promotional Effectiveness Benchmarking, Dynamic Market Basket Analysis, Consumers Behaviour Analysis, Customers Segmentation and much more.
- Python for Retail
- How to use Numpy and Pandas using Python?
- Functional Programming basics in Python
- SQL SELECT queries
- Excel & Reporting Fundamentals
- Introduction to Business Analytics, Retail and Artificial Intelligence
- Basics of Data Reporting and Analytics in Retail
- How the retail reports look like, and to whom these are reported?
- Basics of Forecasting
- What is regression?
- Kernels in regression
- Linear Regression
- Impact of distribution on Regression models
- How to choose the regression model?
- Building your first Regression Model to make prediction for different areas in Retail
- Exploratory Data Analysis
- Univariate Analysis
- Bivariate Analysis
- Outlier treatment
- Missing value imputation
- Forecasting using Regression Model
- How to interpret Categorical variables?
- Simple Linear Regression
- Multiple Linear Regression
- Model evaluation
- Model Optimization & Tuning
- Handling Special events like Holiday sales
- Normalizing the effect of Day of Week, Month Effect and high sales occasions (like Christmas, Boxing day, Black Friday, etc.)
- Identifying Seasonality & Trend for Forecasting
- Additive or Multiplicative model
- Trend analysis
- Seasonality analysis
- Moving/Rolling Averages
- Do you need to eliminate seasonality to increase prediction accuracy?
- What would the effect of normalizing data?
- Additive or Multiplicative model
- Market Basket Analysis and Lift
- Frequent Itemsets
- Association Mining
- Recency, frequency, and monetary value analysis
- Maximizing profits from promotional campaigns
- Price bundling
- Revenue/Profit maximizing price point analysis
- Customer Lifetime value (CLV)
- Sensitivity and scenario analysis
- Understanding variance in CLV
Price: CA$ 1,950
Course Stream: Data Science
Delivery method: Live instructors through online platforms
No prerequisites required for this course
Flexi pay: Option to pay in installments
Paid internships available for students who successfully pass the course.
- Upcoming start date: August 15, 2022
- Course duration: 20 hours
- Lecture duration: between 2-3 hours per week on evenings and weekends
Students will receive ThinkLogix Certification upon course completion.
Ajitpal is a professional with over 10 years of experience working in data analysis and strategic management. Currently, he works as TimeLabs’s Data Analyst, and work towards building and improving products and services for our customers by using advanced analytics, ad-hoc analysis, consulting, creating data models and workflows, and onboarding compelling new data sets.
His previous experience includes a position as a Sr. Data Analyst at Tara Health Foods, where he worked with marketing, finance and logistics departments to analyze data for their logistics and retail network to look for the bottlenecks in the supply chain, production cycle planning, supplier network, discounting, marketing penetration analysis and financial analysis.