Listen to this Post
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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