Basic Statistics in Python (Correlations and T-tests)

17개의 평가
Coursera Project Network
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

Clean data, including removing missing data and unnecessary columns

Explore data using descriptive statistics

Create visualizations of our data and analysis

Perform statistical tests, including t-tests and correlation

Clock1 hour 10 mins
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

By the end of this project, you will learn how to use Python for basic statistics (including t-tests and correlations). We will learn all the important steps of analysis, including loading, sorting and cleaning data. In this course, we will use exploratory data analysis to understand our data and plot boxplots to visualize the data. Boxplots also allow us to investigate any outliers in our datasets. We will then learn how to examine relationships between the different data using correlations and scatter plots. Finally, we will compare data using t-tests. Throughout this course we will analyse a dataset on Science and Technology from World Bank. The measures in this dataset are numeric, therefore you will learn how to handle and compare numeric data. This guided project is for anyone with an interest in performing statistical analysis using Python. This could be someone from a social science background with statistics knowledge who wants to advance their analysis, or anyone interested in analysing data.

개발할 기술

  • Statistical Analysis
  • Data Cleansing
  • Summary Statistics
  • Pandas
  • Data Visualization (DataViz)

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  1. Set up a new Google Colab notebook

  2. Load data into notebook

  3. Clean data to remove missing data and unnecessary columns

  4. Perform exploratory data analysis

  5. Create boxplots using for loops

  6. Explore relationships between data using correlation and scatter plots

  7. Perform t-tests to compare data from different years

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.



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