Data Science

Data Visualization & Data Analysis using R Training Overview

The new oil of the digital economy is data. Companies began to handle data as a corporate asset for the entire organization. Through 2020, digital data is expected to expand at a compound annual growth rate (CAGR) of 42%, predicts IDC Research. Businesses are now concentrating on making money off of the data that is already accessible in order to boost sales and enhance their goods, services, etc. Data consumption with traditional reports and standard charts is very challenging. Data consumption has been greatly impacted by recent developments in data visualization. Data visualization can be used to create new, hidden stories from data in addition to reporting on the status of affairs as it stands right now.

Data Visualization & Data Analysis using R Training Objective

• Recent trends and interesting case studies to understand how data visualization is helping organization
• The techniques in data mining and the art of telling stories from any data
• Principles of exploratory data analysis
• Starting from univariate analysis, multivariate analysis to identifying hidden relationships between different variables using visualization
• Learn the principles behind the traditional charts along with the new advanced charts
• Data narratives and classification of visualizations
• Creating charts, reports and static dashboard using R

Certification

• Associate Certified Analytics Professional (aCAP)
• Certified Analytics Professional (CAP)
• CDP Data Analyst

Prerequisites

Some basic knowledge and prior experience of R would be useful.

Who Should Attend

Aspiring Data Scientists: Individuals looking to start a career in data science and gain foundational skills.

Business Analysts: Professionals who want to enhance their analytical skills and leverage data for decision-making.

Expected Learning Outcome

Understand the core concepts of data science, including data collection, cleaning, exploration, and visualization. Gain proficiency in statistical methods and techniques for analyzing data and drawing meaningful conclusions. Learn to work with various data manipulation tools and libraries (e.g., Pandas, NumPy) to prepare and preprocess data for analysis.