I've applied for a total of 10 development jobs in my life:
- 7 lead to interviews
- Of those 7, 4 was a good fit for me
- Of those 4, 3 offered me the job
- In all 3 cases, I negotiated a better deal
🧵 on what worked for me and what YOU can do to get similar results 👇
If this thread was interesting to yo, I go a bit more in-depth on some of these subjects in a blog post here: https://t.co/AASK9YayR0
More from Machine learning
10 PYTHON 🐍 libraries for machine learning.
Retweets are appreciated.
[ Thread ]
1. NumPy (Numerical Python)
- The most powerful feature of NumPy is the n-dimensional array.
- It contains basic linear algebra functions, Fourier transforms, and tools for integration with other low-level languages.
Ref: https://t.co/XY13ILXwSN
2. SciPy (Scientific Python)
- SciPy is built on NumPy.
- It is one of the most useful libraries for a variety of high-level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization, and Sparse matrices.
Ref: https://t.co/ALTFqM2VUo
3. Matplotlib
- Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
- You can also use Latex commands to add math to your plot.
- Matplotlib makes hard things possible.
Ref: https://t.co/zodOo2WzGx
4. Pandas
- Pandas is for structured data operations and manipulations.
- It is extensively used for data munging and preparation.
- Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage.
Ref: https://t.co/IFzikVHht4
Retweets are appreciated.
[ Thread ]

1. NumPy (Numerical Python)
- The most powerful feature of NumPy is the n-dimensional array.
- It contains basic linear algebra functions, Fourier transforms, and tools for integration with other low-level languages.
Ref: https://t.co/XY13ILXwSN

2. SciPy (Scientific Python)
- SciPy is built on NumPy.
- It is one of the most useful libraries for a variety of high-level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization, and Sparse matrices.
Ref: https://t.co/ALTFqM2VUo

3. Matplotlib
- Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
- You can also use Latex commands to add math to your plot.
- Matplotlib makes hard things possible.
Ref: https://t.co/zodOo2WzGx

4. Pandas
- Pandas is for structured data operations and manipulations.
- It is extensively used for data munging and preparation.
- Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage.
Ref: https://t.co/IFzikVHht4
