Data Cleaning is one of the most essential aspects of Data Science or Machine Learning. In order to get the most out of the data, your data must be clean. Uncleaned data even makes it harder for ML Models to get trained. In regard to ML & Data Science, data cleaning generally filters & modify your data making it easier for you to explore, understand and model.
Because of this, a good statistician or a researcher should at least spend 90% of his/her time on collecting or cleaning data for developing a hypothesis and remaining 10% on the actual manipulation of the data for analyzing or deriving the results. Despite this, data cleaning is not commonly discussed or taught in detail in most of the data science or ML courses. With the rise of big data & ML, now data cleaning has also become equally important.
Why you should learn Data Cleaning?
- Improves decision making
- Improves the efficiency
- Increase productivity
- Remove the errors and inconsistencies from the dataset
- Identifying missing values
- Remove duplication
Why you should take this course?
Data Cleaning being an essential part for Data Science & AI, it has become an equally important skill for a programmer. It’s true that you will find hundreds of online tutorials on Data Science and Artificial Intelligence but most of them do not cover data cleaning or just give the basic overview. This online guide for data cleaning includes numerous sections having over 5 hours of video which are enough to teach anyone about all its concepts from the very beginning.
By taking this course, you will develop all the required skills for the same. This course teaches you everything including the basics of Data Cleaning, Data Reading, merging or splitting datasets, different visualization tools, locate or handling missing/absurd values and hands-on sessions where you’ll be introduced to the dataset for ensuring complete learning of Data Cleaning.
Enroll in this course now to learn about data cleaning concepts and techniques in detail!