Description
Data Preprocessing for Data Analytics and Data Science course. The Data Preprocessing for Data Analytics and Data Science course provides students with a comprehensive understanding of the critical steps involved in preparing raw data for analysis. Data preprocessing is an essential step in the data science workflow as it involves transforming, cleaning, and integrating data to ensure quality and usability for subsequent analysis. In this course, students will learn various techniques and strategies for managing real-world data that is often messy, inconsistent, and incomplete. They will gain hands-on experience with common data preprocessing tools and libraries, such as Python and its data manipulation libraries (e.g. Pandas), and explore practical examples to reinforce their learning. The main topics covered in this course are: Introduction to Data Preprocessing:
- – Understanding the importance of data preprocessing in data analysis and data science.
- – An overview of the data preprocessing pipeline
- Data cleansing techniques:
- Identification and management of missing values:
- – Dealing with outliers and noisy data
- – Correcting inconsistencies and errors in data
- Data conversion:
Feature scaling and normalization:
- – Management of classified variables through coding techniques
- – Methods for dimension reduction (e.g. principal component analysis)
- – Integration and aggregation of data:
- Merge and connect records:
- – Manage data from multiple sources
- – Collect data for analysis and visualization
- – Management of text and time series data:
Text preprocessing techniques (e.g. tokenization, stemming, keyword removal):
- – Time series data cleaning and feature extraction
- – Assessment of data quality:
- Data profiling and exploratory data analysis
- – Data quality criteria and evaluation techniques
- The best methods and tools:
Effective data cleaning and preprocessing strategies:
- – An introduction to popular data preprocessing libraries and tools (e.g. Pandas, NumPy)
What you will learn in the Data Preprocessing for Data Analysis and Data Science course
-
Students acquire in-depth knowledge of exploratory data analysis and data preprocessing
-
We learn about data cleansing and data management.
-
We learn how to deal with duplicate and missing data.
-
Finally, we learn about the types of outlier analysis.
-
We learn scaling and feature transformation techniques
This course is suitable for people who
- This course is aimed at individuals who want to advance their careers in data analytics and data science.
- It is also aimed at professionals who want to improve their understanding of CRISP-ML(Q).
- Students of all backgrounds are invited to apply for this program.
- Students with technical backgrounds are encouraged to use this program to complement their education.
- Anyone who wants to enter the data world and analyze data.
Data Preprocessing for Data Analytics and Data Science Course Specifications
- Editor: Udemy
- Teacher: AISPRY TUTOR
- Training level: beginner to advanced
- Training duration: 8 hours and 51 minutes
- Number of courses: 48
Course headings
Prerequisites for the course “Data Preprocessing for Data Analysis and Data Science”.
- Recognize the role of Python programming in EDA.
- Familiarize yourself with the remaining procedures in the CRISP-ML(Q) data preparation section.
- It is recommended that learners already have knowledge of the CRISP-ML(Q) methodology.
Images of data preprocessing for the Data Analysis and Data Science course.
Sample video of the course
installation Guide
After extracting, you can watch it with your favorite player.
English subtitles
Quality: 720p
Download link
free download software
Size
5.1GB