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Predicting Retail Sales with Data Analytics
Nicholas Yuwono
Nicholas Yuwono
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Predicting Retail Sales with Data Analytics

Create a statistical model that predicts future retail store sales to formulate appropriate business strategies.

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Apply now
Fridays
 at
8:30
P.M.
 ET /
5:30
P.M.
PT
8 weeks, 2-3 hours per week
Intermediate
No experience required
No experience required
Some experience required
Degree and experience required

Description

Predictive analytics is crucial for retail companies as it enables them to optimize sales and inventory management, helping to meet customer demand, minimize excess stock, and maximize profits. In this Build Project, you'll wear the hat of a Business Intelligence Analyst at an imaginary retail company and develop a strategy to manage retail inventory and pricing for different seasons. Under the supervision of an experienced industry expert, you'll develop prediction models based on historical datasets and deliver a strategy plan to improve sales in stores. You'll become familiar with Statistical Analysis and Data Science tools like Python and Jupyter Notebook. All this will happen in an environment that simulates the operations of a real Business Intelligence team, supporting a business with insights about their industry so that they can make data-driven decisions.

Session timeline

  • Applications open
    September 5, 2024
  • Application deadline
    September 19, 2024
  • Project start date
    Week of July 8, 2024
    Week of
    October 7, 2024
  • Project end date
    Week of

What you will learn

  • Explore and visualize datasets with different representations in Python
  • Navigate and utilize different functionalities of Jupyter Notebooks
  • Process structured datasets, understand and visualize time series progressions
  • Develop predictive models using statistical methods like regressions and decision trees
  • Interpret quantitative data to derive insights regarding strategy recommendations based on historical patterns

Project workshops

1
Introductions and Set-up
2
Preliminary Statistical Analysis
3
Data Visualization
4
Developing Predictions using Regression
5
Identifying Best Predictor Variables
6
Refine Your Prediction Models
7
Prepare to Present Your Results
8
Final Presentation of Findings and Recommendations

Prerequisites

  • Some first experience with general programming & problem solving (e.g. writing one or two Python scripts in the past or experience with data structures like strings and integers)
  • Some experience and understanding of statistics and probability (e.g. linear regressions, correlation, population and samples)
  • Experience with basic Python (loops, conditions, functions, libraries, simple algorithms)
  • Some basic exposure to Python libraries (matplotlib, pandas)

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About the expert

Nicholas Yuwono

Business Intelligence Fellow
Open Avenues Foundation

I'm originally from Jakarta, Indonesia and am currently based in Miami. I did my undergraduate degree in Computer Science and am currently working at a trading firm. In my free time, I enjoy watching movies and playing badminton.

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