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Codecademy

Data Scientist: Inference Specialist

via Codecademy Path

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Overview

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Inference Data Scientists run A/B tests, do root-cause analysis, and conduct experiments. They use Python, SQL, and R to analyze data. Includes **Python 3**, **SQL**, **R**, **pandas**, **scikit-learn**, **NumPy**, **Matplotlib**, and more.

Syllabus

  • Welcome to the Data Scientist: Inference Specialist Career Path
    • Discover what you will learn on your journey to becoming a Data Scientist: Inference Specialist!
  • Principles of Data Literacy
    • Discover the world of data in this fully conceptual course where you will learn how to think about, visualize, and analyze data.
  • Learn SQL
    • Learn SQL — a popular language that’s used for communicating with databases and working with data.
  • Python Fundamentals for Data Science (Part I)
    • Build a foundation in programming with Python with a focus on Data Science!
  • Python Fundamentals for Data Science (Part II)
    • Continue building your Python Skills while applying them to real data science challenges including finding and working with real data.
  • Portfolio Project: U.S. Medical Insurance
    • Use your understanding of Python syntax to sort and analyze data about U.S. medical insurance costs!
  • Python Pandas for Data Science
    • Learn how to use the Python pandas library and lambda functions for Data Science.
  • Exploratory Data Analysis in Python
    • Learn about exploratory data analysis (EDA) techniques for Data Science
  • Statistics Fundamentals for Data Science
    • Learn how and when to use the essential statistical tools Data Scientists use to analyze data.
  • Data Visualization Fundamentals with Python
    • If a picture is worth a thousand words, then a visualization is worth more than a thousand data points. Learn how to make them here!
  • Portfolio Project: Data Visualization
    • Use your understanding of data visualization to analyze and plot data about GDP and life expectancy.
  • Data Wrangling, Cleaning, and Tidying
    • Clean, well-structured data is essential to data science but cleaning data requires both a keen eye and technical skills. Develop both here!
  • Communicating Data Science Findings
    • Communication is an important part of your work as a data scientist. Learn best practices for effectively explaining your analysis.
  • Data Science Foundations Portfolio Project
    • Use your knowledge of data analysis to interpret data about endangered animals for the National Park Service.
  • Next Step: The Inference Speciality
    • Get ready for your data science specialization by reviewing how each role works to make meaning from data.
  • Advanced Exploratory Data Analysis
    • Deepen your statistics knowledge!
  • Statistics Fundamentals Part II
    • Continue to build your Hypothesis Testing and Experimental Design skills!
  • Math for Inference Data Science
    • Gain proficiency in the math you will need for Regression and Inference!
  • Regression for Inference Data Science
    • Learn about Linear, Multiple, and Logistic Regression for Inference Data Science.
  • R for Programmers
    • A quick primer on R fundamentals for programmers.
  • Causal Inference Fundamentals
    • Find out when correlation really is causation!
  • Advanced SQL for Data Science
    • Keep building your SQL skills with advanced techniques and hands-on practice.
  • Data Scientist: Inference Final Portfolio Project
    • Show off your knowledge of data analytics by developing your final portfolio project on a topic of your choice.
  • Data Scientist: Inference Specialist Final Review
    • Review what you learned in the Data Scientist: Inference Specialist Career Path and explore ways to deepen your knowledge!

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