The demand and the supply gap for a data scientist are ever-increasing. In fact, in one of its surveys, IBM predicts increment in data science jobs to be 364,000 to 2720,000 in 2020 which is only going upwards in the subsequent years. Python, as a programming language, is immensely popular for building data science-based applications owing to its simplicity, and large community support and ease of deployment.
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 Python certification for Data Science 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
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