| Data Cleaning | Data Wrangling |
| Definition: Data cleaning specifically focuses on identifying and correcting errors or inaccuracies in the dataset. Tasks: Involves handling missing values, correcting typos, standardizing formats, and removing duplicates to ensure data accuracy. Goal: Improve data quality by eliminating errors, inconsistencies, or outliers that could impact the validity of analysis or machine learning models. Methods: Encompasses techniques like imputation, outlier detection, and standardization to enhance the accuracy and reliability of the data. Tools: Utilizes tools similar to those in data wrangling, with a focus on cleaning and validating the integrity of the dataset. | Definition: Data wrangling involves the overall process of collecting, transforming, and organizing raw data into a more usable and structured format. Tasks: Includes tasks such as merging datasets, handling missing values, reshaping data structures, and extracting relevant features. Goal: Prepare the data for analysis by addressing inconsistencies, transforming variables, and creating a dataset suitable for modeling. Methods: Involves data aggregation, merging, reshaping, and other operations to make data more manageable and conducive to analysis. Tools: Utilizes tools like pandas in Python, dplyr in R, or SQL for data manipulation. |
Do you want to learn?
We are here to help.
We teach programming languages and tools that are used by industries and generate jobs.
Course benefits
- High engagement and outcome-centric learning
- Customized curriculum built with industry leaders, for industry leaders
- Live Projects for industrial expertise
Get certificates on completion of courses. Our certificates have helped many students in getting placed in reputed companies. Students have presented our certificate to top universities to prove their qualification for various programming languages.
Contact Info
- Uttar Pradesh & Madhya Pradesh
- +91 9454241494, 9696820568
- info[@]slidescope.ocm