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Data Alchemy: Crafting Actionable Insights from Raw Data
Pratyush	Kundu
Pratyush Kundu
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Data Alchemy: Crafting Actionable Insights from Raw Data

Transform raw data into actionable insights and interactive dashboards, while mastering foundational data science techniques such as exploratory data analysis (EDA) & data visualization.

Register today
Apply now
Tuesdays
 at
5:30
P.M.
 ET /
2:30
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

In today's data-driven world, being able to transform raw data into valuable insights is a critical skill. In this Build Project, you’ll wear the hat of a Data Scientist and tackle the challenge of creating interactive dashboards that showcase business insights from raw data. Under the supervision of an experienced industry expert, you will develop essential skills in SQL, Python, machine learning, and data visualization. You’ll become familiar with industry-standard tools like sklearn and git, while applying these skills to a real-world data problem. This project provides a unique opportunity to experience the full data science lifecycle, from raw data to visualization, simulating the operations of a real data science team.

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

  • Store and retrieve data efficiently using SQL.
  • Perform exploratory data analysis using Python.
  • Train and deploy machine learning models using sklearn.
  • Visualize data using Plotly and Streamlit.
  • Manage and version your project with Git.
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
Introduction to Data Science and Git
2
Data Storage and Retrieval with SQL
3
Exploratory Data Analysis with Python
4
Introduction to Machine Learning with sklearn
5
Advanced Machine Learning and Model Optimization
6
Data Visualization with Plotly and Streamlit
7
Dashboard Refinement and Git Management
8
Final Presentations

Prerequisites

  • Basic understanding of Python: You should be familiar with loops, functions, and libraries.
  • Experience with data analysis using libraries like Pandas.
  • Basic knowledge of SQL: You should know the basic syntax of an SQL query.
  • Willingness to learn new tools like Plotly, Streamlit, and Git.

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About the expert

Pratyush Kundu is a Data Science Build Fellow at Open Avenues, where he works with students leading projects in Data Science and Automation. Pratyush is a Trading Operations Analyst at Virtu Financial, where he focuses on analyzing and improving operational efficiencies for the firm’s algorithmic trading infrastructure by dissecting data to derive actionable insights, writing scripts using Python and SQL to build processes and automate analyses and manage operational risk by implementing and improving monitoring tools. Pratyush has over 3 years of experience in applying data science principles to drive business decisions. Prior to joining Virtu Financial, he stretched his wings at Natera and Citibank applying and honing his programming skills and analytical acumen in the disciplines of Biotechnology and Finance. He holds a Bachelor’s Degree in Business with concentrations in Data Science and Statistics. A fun fact about Pratyush is that he knows all the good Vegan spots in NYC, even though he isn’t Vegan.

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