Introduction to Data Science I & II
Introduction, introduction #.
Dan L. Nicolae , Michael J. Franklin , Amanda R. Kube Jotte , Evelyn Campbell, Susanna Lange, Will Trimble, and Jesse London
Forthcoming…
Acknowledgements #
Jupyter Books was originally created by Sam Lau and Chris Holdgraf with support of the UC Berkeley Data Science Education Program and the Berkeley Institute for Data Science .
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Introduction to Data Science in Python
Description.
This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.
This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.
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Applied Data Science with Python
U-M Credit Eligible
Christopher Brooks
Associate Professor of Information
School of Information
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This repository includes course assignments of Introduction to Data Science in Python on coursera by university of michigan
Coursera | Introduction to Data Science in Python (University of Michigan) These may include the latest answers to Introduction to Data Science in Python's quizs and assignments. You can see the link in my blog or CSDN.
This repository contains Ipython answers of assignments of the course introduction to data science in python, part of Applied Data Science using Python Specialization from University of Michigan offered by Coursera.
SKILLS YOU WILL GAIN* Understand techniques such as lambdas and manipulating csv files* Describe common Python functionality and features used for data scie...
I'm taking this course on Coursera, and I'm running some issues while doing the first assignment. The task is to basically use regular expression to get certain values from the given file. Then, the function should output a dictionary containing these values:
Introduction to Data Science in PythonUniversity of Michigan | Assignment 1 answer |#courserasolutions #coursera #courseraanswersGitHub link Assignment 1: ht...
This course will teach you how to leverage the power of Python and artificial intelligence to create and test hypothesis. We'll start for the ground up, learning some basic Python for data science before diving into some of its richer applications to test our created hypothesis.
Introduction Part I: Exploring Data 1. What is Data Science? 2. Data Science Case Study 3. Programming in Python Operations Assignment Statements Data Types Comparisons Functions Built-In Functions and Methods User-Defined Functions 4. Collections of Data 4.1 Lists 4.2 Dictionaries
The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively.
#!/usr/bin/env python # coding: utf-8 # # Assignment 1 # For this assignment you are welcomed to use other regex resources such a regex "cheat sheets" you find on the web. # # # Before start working on the problems, here is a small example to help you understand how to write your own answers.