I hate Data science on Unisex Hoodie
What our customers are saying normal fit
- Unisex fit tends to sit more tightly on men and more loosely on women. If in doubt, order a size smaller or larger.
- Hood with drawstring and cord
- Kangaroo pouch on the front
- Smooth and durable fabric quality (280 g/m²), fleecy inside
- Material: 80% cotton, 20% polyester, (anthracite: 52% cotton, 48% polyester; heather grey: 75% cotton, 25% polyester)
- In size 4XL, the hoodies are only available in the following colours: black, navy, light grey, bottlegreen
- Brand: AWDis
Fit: normal fit
Find the right size:
Compare the measurements with a product you already have at home. It is best to lay garments flat on the floor to measure them.
- B - Width in in
- C - Length in in
- A - Length in in
| Size | A (in) | B (in) | C (in) |
|---|---|---|---|
| S | 26.38 | 20.08 | 23.23 |
| M | 27.56 | 22.05 | 23.82 |
| L | 28.74 | 24.02 | 24.41 |
| XL | 29.92 | 25.59 | 25.00 |
| XXL | 31.10 | 27.17 | 25.59 |
| 3XL | 32.28 | 28.74 | 26.18 |
| 4XL | 33.86 | 30.71 | 26.97 |
Alina (178 cm)Size: S
Michal (187 cm)Size: M
Alina (178 cm)Size: S
4.5 (307 reviews)
Filter by rating:
Bought as a present for a friend. Very well received , friend was delighted and fit was perfect. Note the site recommended going one size up on this hoody, good advice.
The product is good and warm. Fits perfectly.
Looks good
Absolutely fab quality,, my husband is going to love this thankyou
Quality product. Very pleased
Great!
Really pleased with this product and would certainly recommend it to others.
Great quality, and fit.
It was a Christmas present for my brother,he loved it
The product is broken already my daughter has worn it once and it’s broken on the sleeve the stitching isn’t good
Created by I hate ...
I hate Data science
Created by I hate ...
Geeks are a community of passionate individuals who have a deep interest in technology. what about you?
Tags: data analysis, machine learning, big data, data mining, statistics, data visualization, predictive modeling, artificial intelligence, natural language processing, deep learning, data wrangling, data cleaning, data engineering, data-driven decision making, data warehousing, data governance, data security, exploratory data analysis, data architecture, database management, data integration, data preprocessing, R programming, Python programming, data storytelling.
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