Data Pre-Processing for Data Analytics and Data Science

Pre-Processing for Data Analytics and Data Science


What you'll learn
Students will get in-depth knowledge of Exploratory Data Analysis & Data Pre-Processing
We learn about Data Cleaning & how to handle the data.
We will learn about how to handle Duplicate & Missing Data.
Finally, we will learn a variety of Outlier Analysis Treatment.
We will learn about Features Scaling and Transformation Techniques

Requirements
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.

Description
The Data Pre-processing for Data Analytics and Data Science course provides students with a comprehensive understanding of the crucial steps involved in preparing raw data for analysis. Data pre- processing is a fundamental stage in the data science workflow, as it involves transforming, cleaning, and integrating data to ensure its quality and usability for subsequent analysis.
Throughout this course, students will learn various techniques and strategies for handling real-world data, which is often messy, inconsistent, and incomplete. They will gain hands-on experience with popular tools and libraries used for data pre-processing, such as Python and its data manipulation libraries (e.g., Pandas), and explore practical examples to reinforce their learning.
Key topics covered in this course include:
Introduction to Data Pre-processing:
- Understanding the importance of data pre-processing in data analytics and data science
- Overview of the data pre-processing pipeline
- Data Cleaning Techniques:
Identifying and handling missing values:
- Dealing with outliers and noisy data
- Resolving inconsistencies and errors in the data
- Data Transformation:
Feature scaling and normalization:
- Handling categorical variables through encoding techniques
- Dimensionality reduction methods (e.g., Principal Component Analysis)
- Data Integration and Aggregation:
Merging and joining datasets:
- Handling data from multiple sources
- Aggregating data for analysis and visualization
- Handling Text and Time-Series Data:
Text preprocessing techniques (e.g., tokenization, stemming, stop-word removal):
- Time-series data cleaning and feature extraction
- Data Quality Assessment:
Data profiling and exploratory data analysis
- Data quality metrics and assessment techniques
- Best Practices and Tools:
Effective data cleaning and pre- processing strategies:
- Introduction to popular data pre-processing libraries and tools (e.g., Pandas, NumPy)
Who this course is for:
This course is designed for people who desire to advance their careers in Data Analytics & Data Science.
It is also intended for working professionals who want to improve their grasp of CRISP-ML(Q).
Students of all backgrounds are invited to enroll in this program.
Students with engineering backgrounds are invited to use this program to supplement their education.
Anyone who wants to get into the field of Data and Analyse the Data.

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