This is my first upload, and I am a data scientist, not a talking head, so sorry for any lack of quality on the production or communications. But hopefully at least one of these tips will be helpful as you look to begin a career in data science.

Tips reviewed in this video:
1. Become a good data science problem solver

2. Get a breadth of experience and knowledge

3. Study the most useful content
Key areas to study:
Supervised vs Unsupervised Learning
Feature engineering and feature selection (PCA, tree-based, etc.)
Understanding and interpreting accuracy (confusion matrices, p-values, confidence intervals, AUC, etc.)
Data Insights
Forecasting (at least ETS and ARIMA, fbprophet is a great starter package)
Prediction vs causal inference (focus on target variable vs focus on feature variable effects, possibly most crucial and underdeveloped in data scientists today) -    • Susan Athey: Counterfactual Inference...  
IML (Interpretable machine learning)
Deep Learning, only if you are interested in wide data problems like computer vision or NLP

4. Don’t neglect soft skills

Link for 15 hours of simple but thorough Machine Learning lectures for a great overview from top stats experts: http://tagteam.harvard.edu/hub_feeds/...


Disclaimer: this video is my own and does not necessarily reflect the positions, strategies, or opinions of 84.51° or our parent company, The Kroger Co.