Project information

Март 5, 2021

Содержание

2. Why doing a project? to be able to prepare data, analyze data and provide meaningful answers
3. Parts of the Project data collection data understanding data preparation data description data analysis (hypothesis testing)
4. 1. Data Collection kaggle https://www.kaggle.com/ own research UCI Machine Learning Repository https://archive.ics.uci.edu/ml/index.php
5. 2. Data Understanding one of the most critical parts necessary for further data analysis and hypothesis
6. 3. Data Preparation variables coding value labels data transformation/recoding missing values analysis extremes and outliers
7. 4. Data Description descriptive statistics graphs
8. 5. Data Analysis (hypothesis testing) comparing an average value between male/female, age categories, regions test scores
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Слайд 2

Why doing a project?
to be able to prepare data, analyze data and

Why doing a project? to be able to prepare data, analyze data

provide meaningful answers to given questions based on the statistical analysis results

Слайд 3

Parts of the Project
data collection
data understanding
data preparation
data description
data analysis (hypothesis testing)

Parts of the Project data collection data understanding data preparation data description data analysis (hypothesis testing)

Слайд 4

1. Data Collection
kaggle
https://www.kaggle.com/
own research
UCI Machine Learning Repository
https://archive.ics.uci.edu/ml/index.php

1. Data Collection kaggle https://www.kaggle.com/ own research UCI Machine Learning Repository https://archive.ics.uci.edu/ml/index.php

Слайд 5

2. Data Understanding
one of the most critical parts
necessary for further data analysis

2. Data Understanding one of the most critical parts necessary for further

and hypothesis testing
both qualitative and quantitative variables need to be included

Слайд 6

3. Data Preparation
variables coding
value labels
data transformation/recoding
missing values analysis
extremes and outliers

3. Data Preparation variables coding value labels data transformation/recoding missing values analysis extremes and outliers

Слайд 7

4. Data Description
descriptive statistics
graphs

4. Data Description descriptive statistics graphs

Слайд 8

5. Data Analysis (hypothesis testing)
comparing an average value between male/female, age categories,

5. Data Analysis (hypothesis testing) comparing an average value between male/female, age

regions
test scores between male/female
expenditures between families from different regions
time spent on social networks between age categories
min of 2 hypotheses (1 member in the team)
min of 3 hypotheses (2 and 3 members in the team)