1. Automatic learning models: Supervised learning, not monitored learning, strengthening learning, in -depth learning, neural networks, decision -making trees, random forests, vector support machines (SVM), clustering algorithms, etc.
2. AI Techniques: Natural language processing (PNL), computer vision, generative opponent networks (GAN), learning to transfer, etc.
3. Visualization tools: Table, Power Bi, Matplotlib, Seaborn, Plotly, etc.
4. Databases: Snowflake, Teradata, SQL, NOSQL databases.
5. Programming languages: Python (essential), r, sql.
6. Python libraries: Tensorflow, Pytorch, Scikit-Learn, Pandas, Numpy, Keras, Scipy, etc.
7. Data processing: ETL process, data storage, data lakes.
8. Cloud platforms: AWS, Azure, Google Cloud Platform.
