https://github.com/sondosaabed/sp.top-data-science-and-analytics
This repository is created as part of the Data Science Coursework Birzeit university by Dr. Hussein Soboh
https://github.com/sondosaabed/sp.top-data-science-and-analytics
assignment birzeit-university course coursework data-analytics data-cleaning data-processing data-science data-visualization data-wrangling datasets eda exploratory-data-analysis linear-regression machine-learning numpy pandas python statistics storytelling
Last synced: 6 months ago
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This repository is created as part of the Data Science Coursework Birzeit university by Dr. Hussein Soboh
- Host: GitHub
- URL: https://github.com/sondosaabed/sp.top-data-science-and-analytics
- Owner: sondosaabed
- License: mit
- Created: 2024-03-07T20:12:53.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-08T00:03:00.000Z (over 1 year ago)
- Last Synced: 2025-03-23T23:42:55.983Z (7 months ago)
- Topics: assignment, birzeit-university, course, coursework, data-analytics, data-cleaning, data-processing, data-science, data-visualization, data-wrangling, datasets, eda, exploratory-data-analysis, linear-regression, machine-learning, numpy, pandas, python, statistics, storytelling
- Language: HTML
- Homepage: https://www.kaggle.com/sondosaabed
- Size: 47.4 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Special Topic: Data Science and Analytics
This course provides a comprehensive introduction to the principles, techniques, and applications of data science. Where we explore the entire data science workflow, from data collection, cleaning, analysis, visualization, and modeling. The course emphasizes hands-on experience using and “Python” programming language with “Notebook” tools, enables developing practical skills in data manipulation, statistical analysis, and machine learning.
## Course Outline
1. Statistics for data science [Veiw Chapter](https://rawcdn.githack.com/sondosaabed/SP.TOP-Data-Science-and-Analytics/4a9cf4f81af54afbdf6bf80eb302d5a9bd62a5bc/Materials/1-Statistics-for-data-science/Statistics%20for%20Data%20Science.html)
2. Python [Veiw Chapter](https://rawcdn.githack.com/sondosaabed/SP.TOP-Data-Science-and-Analytics/4a9cf4f81af54afbdf6bf80eb302d5a9bd62a5bc/Materials/2-Python/Python%20crash%20course.html)
3. Numpy [Veiw Chapter](https://rawcdn.githack.com/sondosaabed/SP.TOP-Data-Science-and-Analytics/4a9cf4f81af54afbdf6bf80eb302d5a9bd62a5bc/Materials/3-Numpy/Numpy.html)
4. Pandas [Veiw Chapter](https://rawcdn.githack.com/sondosaabed/SP.TOP-Data-Science-and-Analytics/4a9cf4f81af54afbdf6bf80eb302d5a9bd62a5bc/Materials/4-Pandas/Pandas.html)
5. Data wrangling and aggregation [Veiw Chapter](https://rawcdn.githack.com/sondosaabed/SP.TOP-Data-Science-and-Analytics/4a9cf4f81af54afbdf6bf80eb302d5a9bd62a5bc/Materials/5-Data-wrangling-and-aggregation/Data%20Wrangling%20and%20aggregation.html)
6. Data cleaning [Veiw Chapter](https://rawcdn.githack.com/sondosaabed/SP.TOP-Data-Science-and-Analytics/4a9cf4f81af54afbdf6bf80eb302d5a9bd62a5bc/Materials/6-Data-cleaning/Data%20Cleaning.html)
7. Data Visualization [Veiw Chapter](https://rawcdn.githack.com/sondosaabed/SP.TOP-Data-Science-and-Analytics/4a9cf4f81af54afbdf6bf80eb302d5a9bd62a5bc/Materials/7-Data-Visualization/Data%20visualization.html) [Part two hands on](https://rawcdn.githack.com/sondosaabed/SP.TOP-Data-Science-and-Analytics/4a9cf4f81af54afbdf6bf80eb302d5a9bd62a5bc/Materials/7-Data-Visualization/Data%20Visualization%20-%20hands-on.html)
8. Machine Learning Intro [Veiw Chapter](https://rawcdn.githack.com/sondosaabed/SP.TOP-Data-Science-and-Analytics/4a9cf4f81af54afbdf6bf80eb302d5a9bd62a5bc/Materials/8-ML-Intro/Intro.html)
9. Machine Learning Preprocessing [Veiw Chapter](https://rawcdn.githack.com/sondosaabed/SP.TOP-Data-Science-and-Analytics/4a9cf4f81af54afbdf6bf80eb302d5a9bd62a5bc/Materials/9-ML-Preprocessing/Preprocessing.html)
10. Regressionmodeling [Veiw Chapter](https://rawcdn.githack.com/sondosaabed/SP.TOP-Data-Science-and-Analytics/4a9cf4f81af54afbdf6bf80eb302d5a9bd62a5bc/Materials/10-Regression-modeling/ML_1.html)## Course Assignments
1. Intro to ipynb: 
2. Minimum price groceries Numpy 
3. Normal Distribution Sampling np 
4. Coffee reviews analysis pandas 
5. Retail queries groupby pd 
6. Happiness score Visualization 
7. 3D prints Roughness Prefiction Linear Regression 