< Home
Week 1: DataScience with FabLab Workshop¶
I am Dawa Tshering Sherpa from Damphu CS attending FabLab-Data Science workshop sponsored by our DGI from 18-11-2025. I am pretty excited about this since I am very much interested in learning DataSci with python. I love to see forward a great learning week ahead though I am quite busy with year-end academic tasks pressure. I would sincerely that our DGI and particularly sir ANith Ghalley for helping us through struggles as many of us are new to Jupiter and python programming.
DAY 01 : INTRODUCTION TO FABLAB DATASCIENCE FOR DGI TEACHERS
My Brief Notes on Day-01
I was very much excited to learn new things today and before its too late, i would like to thank the host especially Prof Neil who is very kind enough to lend us his most precious time to tutor us. THe first session was really interesting to know about DataScience - all about how large dataset can be analysed and visualized to know hiddle facts about anything.
WHAT IS DATASCIENCE ?¶
- Math (to find patterns)
- Programming (to work with data)
- Domain knowledge (to understand the real-world problem)
Python code : I am quite familiar with Pthon + Jupiter Notebook/Lab.¶
print('hello, I am Dawa Tshering Sherpa')
hello, I am Dawa Tshering Sherpa
I am learning more syntax on markdown from this site : https://www.markdownguide.org/cheat-sheet/
DATASCIENCE - DATASET
**WHAT IS DATASET?**A dataset = a table of information. It usually has: a) Rows → each row represents one item, person, event, or observation. b) Columns → each column represents a type of information (feature), such as name, age, price, temperature, etc.
# **STEP 1:**
import pandas as pd
import numpy as np
# Load Titanic CSV
df = pd.read_csv("datasets/titanic.csv")
# Quick look at the data
print(df.head())
print(df.info())
print(df.describe())
PassengerId Survived Pclass \
0 1 0 3
1 2 1 1
2 3 1 3
3 4 1 1
4 5 0 3
Name Sex Age SibSp \
0 Braund, Mr. Owen Harris male 22.0 1
1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1
2 Heikkinen, Miss. Laina female 26.0 0
3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1
4 Allen, Mr. William Henry male 35.0 0
Parch Ticket Fare Cabin Embarked
0 0 A/5 21171 7.2500 NaN S
1 0 PC 17599 71.2833 C85 C
2 0 STON/O2. 3101282 7.9250 NaN S
3 0 113803 53.1000 C123 S
4 0 373450 8.0500 NaN S
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 PassengerId 891 non-null int64
1 Survived 891 non-null int64
2 Pclass 891 non-null int64
3 Name 891 non-null object
4 Sex 891 non-null object
5 Age 714 non-null float64
6 SibSp 891 non-null int64
7 Parch 891 non-null int64
8 Ticket 891 non-null object
9 Fare 891 non-null float64
10 Cabin 204 non-null object
11 Embarked 889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB
None
PassengerId Survived Pclass Age SibSp \
count 891.000000 891.000000 891.000000 714.000000 891.000000
mean 446.000000 0.383838 2.308642 29.699118 0.523008
std 257.353842 0.486592 0.836071 14.526497 1.102743
min 1.000000 0.000000 1.000000 0.420000 0.000000
25% 223.500000 0.000000 2.000000 20.125000 0.000000
50% 446.000000 0.000000 3.000000 28.000000 0.000000
75% 668.500000 1.000000 3.000000 38.000000 1.000000
max 891.000000 1.000000 3.000000 80.000000 8.000000
Parch Fare
count 891.000000 891.000000
mean 0.381594 32.204208
std 0.806057 49.693429
min 0.000000 0.000000
25% 0.000000 7.910400
50% 0.000000 14.454200
75% 0.000000 31.000000
max 6.000000 512.329200