| Parameters | RDS | VDI |
| Each user gets a separate VM | No. Each user gets a session in the same VM | Yes |
| Creates complexity for the IT staff, wherein they have to manage multiple instances of the OS, updating and patching them. | Yes | Easy |
| Multiple users use the same VM and server OS | Yes | Multiple user uses different VM specific to them |
| Does not provide complete administrative rights, as resources have to be shared between multiple users | Yes. | Since it's a single user, you can get admin privilege |
| Cost and complexity is a concern | Yes | No |
| Managing and updating the software is easy | Yes | Yes |
| Less CPU and memory resources are utilized, which facilitates having more users per system | Yes | Obviously need more CPU and Memory in comparison to RDS |
| Local Computer | Need | No need to local computer. |
| Software Requirements | Same set of software used for all users | Different sets of software can be used for different set of users |
| Performance | One heavy user performance will impact other user's performance | Each will work independantly |
| Locally attached USB drives | Not accessible | Accssible. |
Aspect Data Wrangling (Data Preprocessing) Exploratory Data Analysis (EDA) Objective Prepare raw data for modeling by cleaning, transforming, and formatting it appropriately. Explore and understand the data to gain insights, identify patterns, and make decisions on data handling and modeling. Order Typically performed as a preliminary step before EDA. Usually conducted after data wrangling to further investigate data characteristics. Data Handling Focuses on data cleaning, filling missing values, encoding categorical variables, and scaling features. Involves data visualization, statistical analysis, and summary statistics to uncover patterns, relationships, and anomalies. Techniques Techniques include imputation, outlier detection, feature scaling, and one-hot encoding. Techniques include histograms, scatter plots, box plots, correlation matrices, and descriptive statistics. Data Transformation Involves structural changes to the dataset, such as feature engineering, data normaliz...
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