Two sides of data science learning: the mathematical and the applied.
Mathematical courses cover probability, statistics, and machine learning. The applied courses cover the use of specific toolkit and languages such as Python, Numpy, Matplotlib, pandas and Scipy, the Jupyter notebook environment and Apache Spark to delve into real world data.
You will learn how to collect, clean and analyses big data using popular open source software will allow you to perform large-scale data analysis and present your findings in a convincing, visual way. When combined with expertise in a particular type of business, it will make you a highly desirable employee.
Python which once was considered as general programming language has emerged as a star of the Data Science world in recent years, owing to the flexibility it offers for end to end enterprise wide analytics implementation. This data science training covers data handling, visualization, statistical modelling and machine learning effectively with practical examples and case studies.
Anyone can take up the course who is interested in learning data science. Even experienced professionals can learn and improve their career.
One should have experience in python programing and also Microsoft excel skills.
You get hired as data scientist, data analyst in IT companies like Zoox, Nauto, Draw Bridge, APPIER and more.
The main concepts covered in the courses are Python Data Science, Python Data Science Introduction, Python Data Science Environment Setup, Python Pandas, Python Numpy, Python SciPy, Python Matplotlib, Python Data Processing, Python Data, Operations, Python Data cleansing, Python Processing CSV Data, Python Processing JSON Data, Python Processing XLS Data, Python Relational databases, Python NoSQL Databases
Introduction
Statistical Data Analysis