Data Science Course

1. Understand the main goal and intermediate objectives of the project.
2. Collect data from various sources.
3. Organize the data at your disposal: cleans, filters, and arranges.
4. Construct a model that theoretically should achieve the project's goals.
5. Train the system on the data portion and evaluate the learning results.
6. Validate the model to achieve the goals with unfamiliar data.
7. In case the system did not sufficiently achieve the goals, start again from the first stage.

1. Hardware Dependence: Deep learning requires more 'powerful' hardware to perform learning within a reasonable time. Machine learning is less dependent on hardware.
2. Learning Time: Deep learning requires a longer learning time compared to the learning time of machine learning.
3. Task Execution Time: After the learning process is completed, deep learning performs the required task much faster than machine learning.
4. A machine learning system operates based on a certain algorithm, whereas a deep learning system operates without one.

Data scientists have knowledge and experience working with:
1. Machine Learning algorithms, such as SVM (Support Vector Machine), Decision Trees, etc.
2. Various types of databases (relational and/or non-relational), like SQL, MongoDB, etc.
3. Different programming languages, such as Python, R, etc.
4. Messy, outdated, and missing data.
5. Complex and multifaceted functions.