Data Science With Python in 2021
from web site
The three main pillars of applied data science with python
- Application of mathematical and statistical concepts
- Expressing them using a programming language or a tool/platform
- Particular business domain
The data science with python training modules focuses on explaining various use cases, some of the very famous applications/services which use Python, and then we gradually move to understand data science workflow using Python theoretically. We will help you understand the basic components of any data science model, right from fetching your data from your database to building a model that is in a deployable form.
What are the key deliverables?
As you will progress in the Data Science with Python online 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
