1 Month Course: 4 days a week, for two hours per day
Start date: July 6, 2020
Rs. 10,000/- for early registation, before June 30
All students and professionals who successfully complete the course will recieve a certificate of completion from QLeap Academy
Data is one of the most important resources available with the companies. It is the ability to derive actionable insights from a complex set of data for business decisions is key for the success of the businesses.
Today, industrial processes generate large amounts of data that can be used to predict supply and demand, reduce costs and machine downtime, improve production efficiency in the industrial environment. Data science is also a foundation for mastering machine learning and artificial intelligence. More and more companies are building Data Science capabilities. Its application is becoming an essential ingredient for companies across the sectors. The knowledge and mastery of data science concepts, tools and practices for real world business problems offers a great opportunity for both industry professionals and students.
This course is intended to provide you with a solid foundation on the mathematics and programming concepts used in data science. The course will consist of interactive live sessions from our experienced faculty, and will help you through every step of the data science pipeline - from statistical concepts to visualizing big data. Through case studies and examples, you will be able to derive approaches to solve real world data science problems in the industry.
This course does not require any prior knowledge of Python or the mathematical concepts in data science. We will make programming simple by walking you through live coding sessions, and teach you data science algorithms using simple illustrative examples. In the end, you will be able to write your own Python code to solve challenging problems.
1. Introduction to Python and libraries related to data science
2. Basic mathematics and data operations using Numpy
3. Data manipulation using Pandas
4. Data visualization using Matplotlib
5. Basic mathematics required for data analysis: Matrix analysis, Statistics
6. Examples demonstrating data visualization and analysis
7. Introduction to machine learning techniques
8. Introduction to machine learning library scikit-learn
9. Examples demonstrate machine learning algorithms using scikit-learn
10. Case studies to demonstrate application of data science to industrial applications