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21 September 2018 29 December 2018
The goal is overdue by 2186 days
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Goal abandoned

The author does not write in the goal 6 years 1 month 26 days

Goal author

Lidia

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General

Пройти курс от Гарварда CS109

Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data management to be able to access big data quickly and reliably; exploratory data analysis to generate hypotheses and intuition; prediction based on statistical methods such as regression and classification; and communication of results through visualization, stories, and interpretable summaries.

 Goal Accomplishment Criteria

Все лекции просмотрены, все задания сделаны

  1. Неделя 1

    1. Лекция 1 Course Overview

  2. Неделя 2

    1. Lab 1: Pandas, Python, and Github

    2. Lecture 2: Web Scraping. Regular Expressions. Data Reshaping. Data Cleanup. Pandas

    3. Lecture 3: Exploratory Data Analysis

  3. Неделя 3

    1. Lab 2: Scraping, Pandas, Python, and viz

    2. Lecture 4: Pandas, SQL, and the Grammar of Data

    3. Lecture 5: Statistical Models

  4. Неделя 4

    1. Lab 3: Probability, Distributions, and Frequentist Statistics

    2. Lecture 6: Story Telling and Effective Communication

    3. Lecture 7: Bias and Regression

  5. Неделя 5

    1. Lab 4: Regression, Logistic Regression: in sklearn and statsmodels

    2. Lecture 8: More Regression

    3. Lecture 9: Classification. kNN. Cross Validation. Dimensionality Reduction. PCA. MDS.

  6. Неделя 6

    1. Lab 5: Machine Learning

    2. Lecture 10: SVM, Evaluation.

    3. Lecture 11: Decision Trees and Random Forests

  7. Неделя 7

    1. Lab 6: Machine Learning 2

    2. Lecture 12: Ensemble Methods.

    3. Lecture 13: Best Practices

  8. Неделя 8

    1. Lab 7: Ensembles

    2. Lecture 14: Best Practices, Recommendations and MapReduce

    3. Lecture 15: MapReduce Combiners and Spark

  9. Неделя 9

    1. Lab 8: Vagrant and VirtualBox, AWS, and Spark

    2. Lecture 16: Bayes Theorem and Bayesian Methods

    3. Lecture 17: Bayesian Methods Continued

  10. Неделя 10

    1. Lab 9: Bayes

    2. Lecture 18: Bayesian Methods Continued,Text Data

    3. Lecture BONUS: Interactive Visualization

  11. Неделя 11

    1. Lab 10: Text and Clustering

    2. Lecture 19: Clustering

    3. Lecture 20: Effective Presentations

  12. Неделя 12

    1. Lab 10: Projects, and an example

    2. Lecture 21: Experimental Design

    3. Lecture 22: Deep Networks

  13. Неделя 13

    1. Lecture 23: Guest Lecture: Building Data Science

    2. Lecture 24: Wrapup, and where to go from here.

  • 444
  • 21 September 2018, 17:49

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