Data Analytics Course | Data Analytics Online Training Institute

 

Data Cleaning In Data Analytics

Data cleaning, also known as data cleansing or data scrubbing, is a crucial step in the data analytics process. It involves identifying and correcting errors, inconsistencies, and inaccuracies in a dataset. The quality of the data used in analytics significantly impacts the results and insights. - Data Analytics Course

Here are some common tasks and techniques involved in data cleaning:

1. Removing duplicates: Identify and eliminate duplicate records in the dataset. Duplicates can skew the analysis results and create redundancy.

2. Handling missing values: Deal with missing data points by either filling them in with reasonable values (imputation) or removing rows or columns with too many missing values. - Data Analytics Online Training Institute

3. Correcting inaccuracies: Identify and correct errors in data, such as typos, inconsistencies, and outliers. This may involve standardizing formats, fixing incorrect values, or validating data against predefined rules.

4. Standardizing data: Ensure that data is consistent in format and units, particularly when dealing with numeric or date fields. This can involve converting currencies, units of measurement, or date formats to a common standard.

5. Encoding categorical data: Convert categorical variables into a numerical format that can be used in machine learning algorithms, such as one-hot encoding. - Data Analysis Online Course

6. Dealing with outliers: Identify and handle outliers, which can significantly impact statistical analyses and machine learning models. You may choose to remove outliers or transform the data to mitigate their impact.

7. Handling data inconsistencies: Check for inconsistencies and conflicts between different columns or sources of data. Resolving such conflicts may require domain knowledge and additional data sources.

8. Validating data integrity: Ensure that the data follows defined constraints and business rules. This may involve checking for data integrity violations or referential integrity in relational databases.

9. Normalizing data: Transform data to have a consistent scale and distribution, which is important for various analytical techniques. - Data Analytics Online Training

Data cleaning is an iterative process that may require multiple rounds of cleansing and validation. It is essential to document the steps taken during data cleaning and maintain clear records of any changes made to the data. Effective data cleaning can improve the accuracy of your analysis and the reliability of your results, leading to more meaningful insights and better decision-making.

 

Visualpath is the Leading and Best Institute for learning Data Analytics Course in Hyderabad, Hyderabad. We provide Data Analytics Online Training, you will get the best course at an affordable cost.

 Attend Free Demo Call on - +91-9989971070.

Visit : https://www.visualpath.in/data-analytics-online-training.html