Description
Data Pre-Processing for Data Analytics and Data Science course. The Data Preprocessing for Data Analysis and Data Science course provides students with a comprehensive understanding of the critical steps 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. During this course, students learn various techniques and strategies for managing real-world data, which is often chaotic, inconsistent, and incomplete. They will gain hands-on experience with popular tools and libraries used for data preprocessing, such as Python and its data manipulation libraries (e.g. pandas), and explore practical examples to reinforce their learning. The key 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 cleaning techniques:
- Identification and management of missing values:
- – Dealing with scattered and noisy data
- – Fixing inconsistencies and errors in data
- – Data conversion:
Feature scaling and normalization:
- – Management of classified variables through coding techniques
- Dimensionality reduction methods (eg, principal component analysis)
- – Integration and aggregation of data:
- Merging and joining datasets:
- – Manage data from multiple sources
- – Collect data for analysis and visualization
- – Management of text and time series data:
Text preprocessing techniques (eg, tokenization, stemming, keyword removal):
- – Time series data cleaning and feature extraction
- – Evaluation 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 Pre-Processing for Data Analytics and Data Science course
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Students will gain in-depth knowledge of exploratory data analysis and data preprocessing
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We learn about data cleansing and how to manage data.
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We will learn about how to handle duplicate and missing data.
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Finally, we will learn the types of outlier analysis treatment.
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We will learn about scaling and feature transformation techniques
This course is suitable for people who
- This course is designed for individuals who wish to advance their careers in data analytics and data science.
- It is also intended for professionals who want to improve their understanding of CRISP-ML(Q).
- Students from any background are invited to enroll in this program.
- Students with engineering backgrounds are encouraged to use this program to supplement their education.
- Anyone who wants to get into the data realm and analyze data.
Data Pre-Processing for Data Analytics and Data Science course specifications
- Publisher: Udemy
- teacher: AISPRY TUTOR
- Training level: beginner to advanced
- Training duration: 8 hours and 51 minutes
- Number of courses: 48
Course headings
Prerequisites of the Data Pre-Processing for Data Analytics and Data Science course
- Recognize the role of Python programming in EDA.
- Understand the remaining procedures in the CRISP-ML(Q) data preparation section.
- It is recommended that learners have a prior grasp of the CRISP-ML(Q) Methodology.
Images of Data Pre-Processing for Data Analytics and Data Science course
Sample video of the course
Installation guide
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English subtitle
Quality: 720p
download link
File(s) password: www.downloadly.ir
Size
5.1 GB
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