Python 50
- Let's try: dbt part 9 - variables
- Let's try: dbt part 8 - DAGs and docs
- Let's try: dbt part 7 - tests
- Let's try: dbt part 6 - snapshots and analyses
- Let's try: dbt part 5 - from Jinja to macros and hooks
- Let's try: dbt part 4 - model materialization
- Let's try: dbt part 3 - seed and source
- Let's try: dbt part 2 - first start
- Let's try: dbt part 1 - prologue
- Let's try: pre-commit before you commit
- Let's try: uv for faster Python packages
- Slack me what you build
- Let's try: Jinja2
- 3 ways for Python string template
- Let's try: Apache Beam part 8 - Tags & Side inputs
- Let's try: Apache Beam part 7 - custom IO
- Let's try: Apache Beam part 6 - instant IO
- Data contracts in action (Python)
- Let's try: Apache Beam part 5 - transform it with Beam functions
- Let's try: Apache Beam part 4 - live on Google Dataflow
- Let's try: Apache Beam part 3 - my own functions
- Let's try: Apache Beam part 2 - draw the graph
- Let's try: Apache Beam part 1 - simple batch
- Paint Terminal with Shell color codes
- Python logging - better than just print
- Well-documented with variable type annotation & Docstring
- Formatting your script with Black
- A private repo for our own Python packages
- File formats I've worked with
- argparse - next level Python parameterization
- Python testing - module pytest
- Python testing - module unittest
- DAG integrity - unit test your DAG before deploying
- Let's try: Apache Airflow 2
- File is too big? Make it chunks.
- Time zone is a distant relationship
- REGEX is sexy
- Let's try: Apache Airflow
- Note of data science training EP 13: Regularization – make it regular with Regularization
- Note of data science training EP 12: skimage – Look out carefully
- Note of data science training EP 11: NLP & Spacy – Languages are borderless
- Note of data science training EP 10: Cluster – collecting and clustering
- Note of data science training EP 9: NetworkX – Map of Marauder in real world
- Note of data science training EP 8: Ensemble – Avenger's ensemble
- Note of data science training EP 7: Metrics – It is qualified
- Note of data science training EP 6: Decision Tree – At a point of distraction
- Note of data science training EP 5: Logistic Regression & Dummy Classifier – Divide and Predict
- Note of data science training EP 4: Scikit-learn & Linear Regression – Linear trending
- Note of data science training EP 3: Matplotlib & Seaborn – Luxury visualization
- Note of data science training EP 1: Intro – unboxing