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.

Our Data Science with Python online course has been designed keeping in mind about learners who have zero to some level of exposure to Python. Any ideal session in this course would dedicate a good amount of time to understanding the theoretical part after which we will be moving on to the application of theoretical concepts by doing hands-on these statistical techniques. You would be provided with a lot of data set to practice things during the session and also to practice later on in the form of self-study which will help you in your journey of applied data science with python.

The three main pillars of applied data science with python

1. Application of mathematical and statistical concepts
2. Expressing them using a programming language or a tool/platform
3. 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

As you will progress in the Data Science with Python 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

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