Data Science: Wrangling Training

Live Online & Classroom Enterprise Training

Data Science: Wrangling focuses on cleaning, transforming, and preparing raw data for analysis. It involves handling missing values, reshaping data, and organizing datasets for accurate insights.

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What is Data Science: Wrangling Training about?

Data wrangling is a critical step in the data science workflow, involving the transformation of raw, messy, or incomplete data into a structured format suitable for analysis. This course equips learners with techniques for handling missing values, normalizing data, parsing complex datasets, and integrating multiple sources. By the end, learners will be confident in preparing high-quality datasets that drive accurate and meaningful insights.

What are the objectives of Data Science: Wrangling Training ?

Understand the principles and importance of data wrangling in data science.
Clean, structure, and format raw datasets for analysis.
Handle missing data, outliers, and inconsistencies effectively.
Work with different data formats (CSV, JSON, databases, APIs).
Automate data wrangling processes using Python or R libraries.

Who is Data Science: Wrangling Training for?

Aspiring Data Scientists and Data Analysts.
Business Analysts working with raw or unstructured data.
Machine Learning Practitioners needing high-quality datasets.
Students in statistics, computer science, or related fields.
Professionals transitioning into data science roles.

What are the prerequisites for Data Science: Wrangling Training?

Prerequisites:

Basic understanding of programming (Python or R preferred).
Familiarity with data science concepts.
Knowledge of basic statistics and data types.
Comfort working with spreadsheets or simple datasets.
Curiosity about solving real-world data problems.

Learning Path:

Introduction to Data Wrangling and Its Role in Data Science
Handling Missing Values and Outliers
Data Transformation and Normalization Techniques
Working with Structured and Unstructured Data Sources
Automating Wrangling with Pandas, dplyr, or Similar Libraries

Related Courses:

Introduction to Data Science with Python
Data Visualization with Python
Data Science Research Methods
Applied Machine Learning Fundamentals

Available Training Modes

Live Online Training

2 Days

Course Outline Expand All

Expand All

Module 1- Data Import

Importing Spreadsheets

Paths and the Working Directory

The readr and readxl Packages

Importing Data Using R-base Functions

Downloading Files from the Internet

Module 2- Tidy Data

Reshaping Data

Combining Tables

Web Scraping

Module 3- String Processing

String Processing

Module 4- Dates, Times, and Text Mining

Dates, Times, and Text Mining

Who is the instructor for this training?

The trainer for this Data Science: Wrangling Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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