Leading With Data· a Bicycle Guide

Partial book profile

Data Science Bookcamp Ten case studies MEAP V02

Leonard Apeltsin

A practical, hands-on guide for Python coders to become job-ready data scientists by solving ten real-world case studies using code instead of complex math.

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What it’s about

For Python programmers aspiring to a career in data science, 'Data Science Bookcamp' offers a clear, practical path to success, no advanced math degree required. This book demystifies data science by teaching fundamental concepts like probability, statistics, and machine learning entirely through hands-on Python code. You'll master essential libraries like NumPy, Pandas, and Scikit-Learn by tackling ten engaging, real-world case studies—from analyzing online ad clicks to tracking disease outbreaks. The project-based approach not only builds critical problem-solving skills but also provides a portfolio of work designed to help you land your first high-paying data science job.

The through-line

Who it’s for
A Python coder who wants to transition into a high-paying data science career but feels intimidated by the perceived mathematical complexity and lacks a clear, practical path to acquire job-ready skills.
The problem
The reader lacks the specific skills, portfolio, and demonstrated experience in areas like statistics, machine learning, and data analysis that are required to get hired as a data scientist. The reader feels overwhelmed and anxious about the heavy mathematical and theoretical requirements often presented in traditional data science resources, making them doubt their ability to succeed in the field.
The plan
  1. Work through each of the ten case studies, starting with the open-ended problem statement.
  2. Learn the required skills for each case, including Python libraries and key data science concepts explained through code.
  3. Attempt to solve each case study on your own before comparing your work to the provided solution, thereby building true problem-solving muscle.
The payoff
The reader acquires a full suite of job-ready data science skills, from statistical analysis to machine learning. · They build a strong portfolio of ten completed projects to showcase to employers. · They develop the confidence and practical ability to solve open-ended data problems, leading to a successful career as a high-paying data scientist.

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