Essential Resources to Level Up Your Data Science and AI Skills

By HackMoN Ai / March 18, 2025

Newsletters:

Data Neighbor Podcast – dataneighbor.substack.com
An Unconventional Machine Learning Career – https://lnkd.in/d3ychpfU
Navigating Career Transitions – https://lnkd.in/dZbNCwMP
Highlighting Impact in Your Data Role – https://lnkd.in/dHEcE736
Data Science Salary – https://lnkd.in/dK-fJNcG

Podcasts & Talks:

5 Steps to Getting Into Data Science Without Experience – https://lnkd.in/dvJR8WYa
Storytelling Skills to 10x Your Career – https://lnkd.in/d-TPi-Zq
How Top Companies Use Data Science – https://lnkd.in/dWTaPWfF
4 Steps to Becoming an AI Engineer – https://lnkd.in/dUYRMJWX
How to Grow Your Data Science Career – https://lnkd.in/dK_v5dh3
How to Master AI for Data Science – https://lnkd.in/dbGU8nqP

You Should Know:

To complement your learning, here are some practical commands and steps to enhance your Data Science and AI skills:

1. Python for Data Science:

Install essential libraries:

pip install numpy pandas matplotlib seaborn scikit-learn

Basic data manipulation with Pandas:

import pandas as pd
data = pd.read_csv('data.csv')
print(data.head())

2. SQL for Data Analysis:

Basic SQL query:

SELECT * FROM table_name WHERE condition;

Aggregation:

SELECT column_name, COUNT(*) FROM table_name GROUP BY column_name;

3. AWS for Data Science:

Set up an S3 bucket:
```
aws s3 mb s3://my-bucket
```
Upload data to S3:
```
aws s3 cp my-data.csv s3://my-bucket/
```

4. Machine Learning with Scikit-Learn:

Train a simple model:
```
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression</li>
</ul>

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = LinearRegression()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
```
5. Linux Commands for Data Scientists:
- Check system resources:
```
top
```
- Monitor disk usage:
```
df -h
```
- Search for files:
```
find /path/to/directory -name "*.csv"
```
What Undercode Say:
Data Science and AI are rapidly evolving fields, and staying updated with the latest resources and tools is crucial. The newsletters and podcasts listed above provide valuable insights and practical advice to help you grow in your career. Additionally, mastering essential tools like Python, SQL, and AWS, along with Linux commands, will significantly enhance your productivity and efficiency. Keep practicing and exploring new technologies to stay ahead in the game.
References:
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