This is The Beginning!

Congratulations!

You have reached the conclusion of this course. The time you invested in this course will definitely benefit you in your career advancements. But this is not the end. This is just a beginning for your career. Here is the quick summary of what you have learned in this course:

• Different roles exist in the data science domain.
• The process of data science from identifying the problem to communicating the results.
• Future direction in data science.
• Basics of Python and its popular data science libraries.
• Popular data science tools like KNIME, R, Tableau, Jupyter, Weka, etc.
• Popular data structures including array, list, stack, queue, tree, and hash tables.
• Algorithm techniques with real-world examples.
• Mathematics for machine learning including statistics and probability.
• Ways to handle missing data and different types of features.
• Measuring machine learning model performance and ways to improve it for any problem space.
• Regression techniques with model assessment.
• Classification techniques with all major algorithms and model assessment techniques.
• Unsupervised learning with a focus on clustering methods.
• Neural networks and issues in deep learning
• Recommendation engines and natural language processing.

That’s a lot! So, well done! If you experienced any difficulty in the content, do it again. Practice is the key here. Focus on learning and understanding rather than memorizing.