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Social Signals: Unlocking E-commerce Trends
Shubham Dubey
Shubham Dubey
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Social Signals: Unlocking E-commerce Trends

Build a data pipeline that aggregates and analyzes e-commerce and social media data to provide actionable insights for AI startups, empowering them to enhance customer engagement and drive strategic growth.

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Apply now
Wednesdays
 at
6:00
P.M.
 ET /
3:00
P.M.
PT
8 weeks, 2-3 hours per week
Expert
No experience required
No experience required
Some experience required
Degree and experience required

Description

As e-commerce and social media become increasingly intertwined, startups face the challenge of extracting meaningful insights from vast amounts of data to inform their strategies. In this Build Project, you'll step into the role of a Data Engineer, tasked with building a comprehensive data pipeline that aggregates and analyzes customer behavior and market trends. Under the guidance of an experienced industry expert, you’ll develop Python scripts for data extraction, transform data for analysis, and load it into a structured database. You’ll become familiar with essential industry tools and methodologies like SQL and data visualization techniques, all within an environment that simulates the operations of a real data analytics team. This hands-on experience will equip you with valuable skills that are highly sought after in today’s data-driven landscape, making you a standout candidate for future roles in data engineering and analytics.

Session timeline

  • Applications open
    December 1, 2024
  • Application deadline
    January 15, 2025
  • Project start date
    Week of July 8, 2024
    Week of
    February 3, 2025
  • Project end date
    Week of

What you will learn

  • Build a robust data pipeline that integrates e-commerce and social media data for comprehensive analysis.
  • Analyze customer behavior and product trends using SQL queries and data visualization techniques.
  • Implement Python scripts for data extraction, transformation, and loading (ETL) processes from multiple sources.
  • Evaluate insights derived from the data to make strategic recommendations for AI startups.
  • Document your findings and methodologies in a professional GitHub repository and a compelling Medium article.

Build Projects are 8-week experiences that operate on a rolling basis. Selected participants engage in weekly live workshops with a Build Fellow and 2-15 other students.

Project workshops

1
Project Kickoff
2
Data Source Exploration
3
Data Extraction Techniques
4
Data Cleaning and Transformation
5
Database Setup
6
Data Analysis
7
Build the ETL Pipeline
8
Final Presentation

Prerequisites

  • Basic knowledge of Python programming: You should be comfortable writing loops, conditionals, functions, and using libraries such as Pandas for data manipulation.
  • Basic understanding of SQL: You should be able to write simple queries to interact with relational databases (e.g., SELECT, JOIN, and WHERE statements).
  • Familiarity with APIs and data extraction: You should understand how to retrieve data from web APIs and perform basic web scraping.
  • Some exposure to data visualization tools: You should have experience using libraries like Matplotlib or Seaborn to create basic plots and graphs.
  • Good communication skills: You will need to effectively explain your findings and present your project both in written and verbal form.

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About the expert

Shubham is a Data Science Build Fellow at Open Avenues, where he works with students leading projects in data engineering. Shubham is a data engineer at Covetrus, where he focuses on designing and implementing scalable data pipelines, optimizing ETL processes, and developing event-driven data management systems. He works extensively with technologies like Apache Kafka, DBT, and cloud platforms to ensure efficient data flow and analysis across the organization. Shubham has over 4+ years of experience in the data engineering field. His career journey has taken him through impactful roles at leading organizations like Pfizer and NJIT, where he honed his skills in data optimization and analytics. He's particularly passionate about leveraging data to enhance efficiency and support strategic decision-making. He holds a master's in computer science from the New Jersey Institute of Technology. A fun fact about Shubham is that he's an avid Formula 1 fan who loves to travel to different countries to watch races live, combining his passion for data with the thrill of high-speed motorsports.

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