As you will progress in data science with python certification training program, you will get to know the below things
Statistics for data science
Basic data cleaning techniques for model building
Converting your raw data into a machine consumable format
Working principle of machine learning models and their applicability
Understanding the parameters required for checking model accuracy
Deploying the model to make it available as a service
Maintaining the model over a period of time
With respect to the above steps, you will also learn how to use data science specific libraries in Python eg. Frequently used libraries in data cleaning like NumPy, pandas, spicy, groupby, merge; data plotting libraries like matplotlib, seaborn; machine learning-based modules available inside scikit learn for building various regression and classification based algorithms, libraries to check model accuracy like confusion matrix, MSE, RMSE, Natural Language-based libraries like NLTK, genism, VADER. These will help learners with applied data science with python
