As you will progress in the <a href="https://www.igmguru.com/data-science-bi/python-training/">Data Science with Python training</a> 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

A good amount of content has also been dedicated to Natural Language Processing techniques and various web scraping methodologies. Of late, NLP is gaining a lot of popularity owing to use in our day to day life eg. Mails, tweets, FB posts, WhatsApp chats are ideal input for any NLP based models. You are very likely to experience NLP based openings which are nowadays considered to be a specialty within the Machine Learning branch. These are all instances of applied data science with python