In this course we will learn:
Data Preprocessing
1. Data cleaning, transformation, and Data Reduction, Normalization.
2. Handling missing data, outliers, and categorical variables.
3. Data visualization: plotting graphs, histograms, scatter plots, etc.
Curriculum
- 4 Sections
- 16 Lessons
- 10 Weeks
Expand all sectionsCollapse all sections
- Data Preprocessing2
- Data visualizationAs we all knew that there is a huge buzz going over the term data, like Big data, Data science, Data Analysts, Data Warehouse,Data mining etc. which emphasize that, In the current era data plays a major role in influencing day to day activities of the mankind. Everyday we are generating more than 2.5 quintillion( 10¹⁸) bytes of data(Link) ranging from our Text messages, Images, emails, till data from autonomous cars, IOT devices etc. With such huge amount of data being available on hand, leveraging useful information from this data can help each and every organization very much, for getting a clear insight on several areas like, what can bring a boost for their organization’s revenue, which field needs more focus, how to seek more customer’s attention etc. Machine learning(ML), Data science are some of the interrelated areas of Artificial Intelligence(AI) where this task of learning from data is done in a huge extent on these recent days.2
- Plots discussedPlots discussed: The below are the list of plots that I am going to explain in the subsequent topics. i) Scatter plot (B) ii) Pair plot (M) iii) Box plot (U) iv) Violin plot(U) v) Distribution plot (U) vi) Joint plot(U) & (B) vii) Bar chart (B) viii) Line plot(B)8
- Data Preprocessing in Data Mining4