How I'd Learn AI & Data Science in 2025 (If I Had To Start Over)

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THIS VIDEO:
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I'm former Amazon & Sony PlayStation Data Scientist Andrew Jones. I've been in the field for over 15 years, I've written a book, I've got 5 patents to my name, I run the leading global Data Science program Data Science Infinity, and I've built up one of the biggest followings of data professionals online.

If I lost all of this today - how would I start my career again to move quickly toward success in this exciting, lucrative & future-proof field?

Let's run through it all, step by step!

TIMESTAMPS:
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00:00 - Starting Again
01:11 - SQL
02:10 - Python
03:28 - Machine Learning
05:20 - Deep Learning
05:33 - Math & Stats
06:53 - Projects & Portfolios
09:00 - Tableau
09:40 - Github
10:32 - How & Where To Learn
11:37 - How To Choose A Program
12:20 - The Key To Learning
12:55 - More Details

DATA SCIENCE INFINITY:
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The world's leading Data Science program, I've helped thousands move towards incredible roles at top companies.

LET'S CONNECT
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VIDEO CREDITS
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Music: Creative Minds - Benjamin Tissot (BenSound)

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I wrote these notes for me but thought they'd be useful for you guys too:

1. SQL 1:11
1. Query Foundations
2. Merging & Joining
3. Manipulation
2. Python 2:10
1. Base Python
2. Pandas
3. Numpy
4. Scipy
5. Matplotlib
6. Scikit-learn
7. Streamlit
3. Machine Learning 3:28
1. Supervised
1. Linear Regression
2. Logistic Regression
3. Decision Trees
4. Random Forests
5. K-Nearest Neighbors
2. Unsupervised
1. K-Means- used for clustering & segmentations
2. Principle Component Analysis (PCA)
3. Bonus
1. Association Rule Learning- Strength of relationships between data points. i.e. Which products are commonly bought together
2. Causal Impact Analysis- Measures change in a metric after some event has taken place
4. Deep Learning- Important to grow in career 5:20
1. Either Keras or PyTorch
5. Math + Stats 5:33
1. Math concepts:
1. Types of Data
2. Distributions
3. Basic Linear Algebra
2. Stats concepts:
1. Hypothesis tests
2. p-value
3. Sampling & CLT
4. Confidence intervals
6. Projects & Portfolios- Ranges from coding simple algorithms to coding big ML algorithms 6:53
1. Varied Portfolio
2. Easy for hiring manager to see value
3.NOTE: Stand out by exhibiting growth mindset
7. Tableau 9:00
1. Importing data & understanding data roles
2. Customization- Marks Card
3. Applying filters
4. Calculated fields
5. L.O.D Expressions
8. Github 9:40
1. Repositories
2. Branches
3. Pull Requests
4. Merges
5. Pull & Push between github & local machine

# Consistency Is Key

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