Data Scientist with Python

このコースでは、データのインポート、クリーニング、操作、可視化など、データの専門家や研究者を目指す人にとって不可欠なスキルを、この汎用性の高い言語によってどのように実現できるかを学びます。インタラクティブな演習を通して、pandas、NumPy、Matplotlibなど、最も人気のあるPythonライブラリを実際に使ってみます。また、実際のデータセットを使って、決定木の学習や自然言語処理（NLP）に必要な統計・機械学習のテクニックを学びます。このコースでPythonのスキルを身につけ、自信を持ってデータサイエンティストになるための旅を始めましょう。

1. Introduction to Python

① Python Basics
②Python Lists
③Functions and Packages
④NumPy

2. Intermediate Python

① Matplotlib
②Dictionaries & Pandas
③Logic, Control Flow and Filtering
④Loops
⑤Case Study: Hacker Statistics

Project① Investigating Netflix Movies and Guest Stars in The Office

3. Data Manipulation with pandas

① Transforming DataFrames
② Aggregating DataFrames
③ Slicing and Indexing DataFrames
④ Creating and Visualizing DataFrames

Project② The Android App Market on Google Play

4. Joining Data with pandas

① Data Merging Basics
② Merging Tables With Different Join Types
③Advanced Merging and Concatenating
④Merging Ordered and Time-Series Data

Project③ The GitHub History of the Scala Language

5. Introduction to Data Visualization with Matplotlib

① Introduction to Matplotlib
② Plotting time-series
③Quantitative comparisons and statistical visualizations
④Sharing visualizations with others

6. Introduction to Data Visualization with Seaborn

① Introduction to Seaborn
② Visualizing Two Quantitative Variables
③ Visualizing a Categorical and a Quantitative Variable
④ Customizing Seaborn Plots

7. Python Data Science Toolbox (Part 1)

① Writing your own functions
② Default arguments, variable-length arguments and scope
③ Lambda functions and error-handling

8. Python Data Science Toolbox (Part 2)

① Using iterators in PythonLand
② List comprehensions and generators
③Bringing it all together!

9. Intermediate Data Visualization with Seaborn

① Seaborn Introduction
② Customizing Seaborn Plots
③ Additional Plot Types
④ Creating Plots on Data Aware Grids

Project④ A Visual History of Nobel Prize Winners

Skill assessment① Data manipulation with python

10. Introduction to Importing Data in Python

① Introduction and flat files
② Importing data from other file types
③ Working with relational databases in Python

11. Intermediate Importing Data in Python

① Importing data from the Internet
② Interacting with APIs to import data from the web
③ Diving deep into the Twitter API

12. Cleaning Data in Python

① Common data problems
② Text and categorical data problems
③ Advanced data problems
④ Record linkage

13. Working with Dates and Times in Python

① Dates and Calendars
② Combining Dates and Times
③ Time Zones and Daylight Saving
④ Easy and Powerful: Dates and Times in Pandas

Skill assessment② Importing & Cleaning Data with Python

14. Writing Functions in Python

① Best Practices
② Context Managers
③ Decorators
④ More on Decorators

Skill assessment③ Python programming

15. Exploratory Data Analysis in Python

① Read, clean, and validate
② Distributions
③ Relationships
④ Multivariate Thinking

16. Analyzing Police Activity with pandas

① Preparing the data for analysis
② Exploring the relationship between gender and policing
③ Visual exploratory data analysis
④ Analyzing the effect of weather on policing

17. Statistical Thinking in Python (Part 1)

① Graphical Exploratory Data Analysis
② Quantitative Exploratory Data Analysis
③ Thinking Probabilistically-- Discrete Variables
④ Thinking Probabilistically-- Continuous Variables

18. Statistical Thinking in Python (Part 2)

① Parameter estimation by optimization
② Bootstrap confidence intervals
③ Introduction to hypothesis testing
④ Hypothesis test examples
⑤ Putting it all together: a case study

Project⑤ Dr. Semmelweis and the Discovery of Handwashing

19. Supervised Learning with scikit-learn

① Classification
② Regression
③ Fine-tuning your model
④ Preprocessing and pipelines

Project⑥ Predicting Credit Card Approvals

20. Unsupervised Learning in Python

① Clustering for dataset exploration
② Visualization with hierarchical clustering and t-SNE
③ Decorrelating your data and dimension reduction
④ Discovering interpretable features

21. Machine Learning with Tree-Based Models in Python

① Classification and Regression Trees
② The Bias-Variance Tradeoff
③ Bagging and Random Forests
④ Boosting
⑤ Model Tuning

22. Case Study: School Budgeting with Machine Learning in Python

① Exploring the raw data
② Creating a simple first model
③ Improving your model
④ Learning from the experts

23. Cluster Analysis in Python

① Introduction to Clustering
② Hierarchical Clustering
③ K-Means Clustering
④ Clustering in Real World