Advanced Data Handling
- Created By raju2006
- Last Updated November 22nd, 2023
- Overview
- Prerequisites
- Audience
- Audience
- Curriculum
Description:
As it is said, garbage in leads to garbage out. This applies to not only data cleansing but overall, how we handle data using governance, best practices and tools to implement it. Between data acquisition, storage, and quality, one thing is certain: there will always be room for improvement! In this advanced data handling course, get the tools to drive enterprise-wide change in our data practices via data governance and ethics. Then, build upon your knowledge of data wrangling to prepare for advanced model-building with lessons in databases, data cleaning, and feature engineering. The course will then advance to understanding the impact and benefits of data handling for Data Science and AI. Finally learning about industry use cases relevant to data handling with partial hands-on labs.
Course Code/Duration:
BDT249 / 1 – Day
Learning Objectives:
- Understand the role of data governance and ethics in shaping effective data management strategies.
- Develop proficiency in data wrangling, including databases, data cleaning, and feature engineering.
- Explore real-world industry use cases to grasp practical applications of adept data handling.
- Gain hands-on experience to enhance your ability to apply learned concepts in data-centric scenarios.
- Basic level experience in one or more coding languages (preferably: Python)
- For anyone managing data among few titles like Data Analyst, Data Scientist, Data Engineer, Business Intelligence and Analytics Engineer etc.
- For anyone managing data among few titles like Data Analyst, Data Scientist, Data Engineer, Business Intelligence and Analytics Engineer etc.
Course Outline:
Data Management and Handling
- Introduction to core data management area and concepts
- Overview of general data governance best practices
- Data Security and data handling overview
- Conduct ethical data analyses and address unethical data practices
- Connect to various databases and identify appropriate use cases
- Clean and prepare data with attention to noise, outliers, and missing or duplicate data
- Exploration of tools for data cleansing and exploration of data
Data Handling for Data Science
- Understand how data management relates to Big Data and Data Science
- Engineer advanced data features
- Data Preparation
- Data Cleaning
- Dropping Rows
- Imputing Missing Values
- Feature Selection
- Data Transformation
- One-Hot Encoding
- Standardization
- Normalization
- Feature Engineering
Use Case
- Industry Use Case Studies
- Data Science in the real world
- Next steps
