Summary
Highlights
The video begins by introducing data analytics as a buzzword and aims to provide an accessible introduction for beginners. It clarifies terms like business analytics (analytics focused on business decision-making), data analytics (an overarching term for analyzing data in various fields like healthcare, customer research, and education), business intelligence (a mix of analytics and subject matter expertise), and data science (a vast term combining multiple skills).
The concept of 'big data' is explained as any data beyond a system's capacity to process. Key characteristics of big data are highlighted: velocity (high speed of generation), volume (large quantity), variety (data from multiple sources and formats), and veracity (accuracy of data). Big data is noted to be more technical due to storage and processing speed requirements.
The video emphasizes the significance of data analytics, citing reports from Gartner and McKinsey. Gartner states that data and analytics are crucial for innovation and disrupting business models, requiring adaptation of architecture. McKinsey predicts a significant talent shortage in data analytics by 2018, with hundreds of thousands of deep analytic skills needed and millions of managers requiring data analysis know-how. The Harvard Business Review even calls data analytics the 'sexiest job of the 21st century,' underscoring its future demand and career prospects.
The surge in demand for data analytics is attributed to the explosion of digital footprints, social media, and the widespread use of smartphones and digital devices. This has led to an exponential increase in data availability, with projections of 35 zettabytes by 2020. The video notes that traditional education systems and businesses have been slow to adapt to this data influx, creating a gap in skilled professionals, but it also assures viewers that it's never too late to acquire these valuable skills.
IBM predicts a 28% increase in demand for data scientists by 2020. A data scientist is defined as a blend of three skills: coding, statistics/mathematics, and business acumen. The video clarifies that it's not necessary to be an expert in all three, and individuals can leverage their strengths. A PwC report suggests that companies will seek business people with analytics skills rather than just analysts. The video also highlights the comfortable and growing salary potential in data analytics roles globally, increasing significantly with experience.
'Analytics' originates from the Greek word 'analytikos,' meaning 'involving analysis.' The key difference between analysis and analytics is that analytics always involves data. Two definitions for analytics are provided: the analysis of large datasets using mathematics, statistics, and computer software, and the science of using data to build models for better decision-making that adds value. The three types of analytics are descriptive (what has occurred), predictive (what will probably happen), and prescriptive (what course of action should be taken).
Initially, advanced analytics tools were expensive and proprietary, limiting access. Now, ample open-source solutions supported by global communities are available. The two most popular languages are R and Python, both offering integrated software facilities for data manipulation, calculation, and graphical display. The video suggests choosing one and focusing on it, as both are effective.
The video presents several practical applications of data analytics. Text analytics is used to analyze customer sentiment from unstructured data like chat logs, identifying negative keywords to proactively address customer issues. HR analytics helps predict employee attrition using key indicators, allowing companies to retain staff more effectively. Customer lifetime value, often tracked through loyalty programs, helps businesses understand purchasing behavior and the impact of promotional campaigns. Additionally, wildlife crime analytics has been used in India to identify tiger poaching hotspots, and healthcare analytics, exemplified by the Framingham Heart Study, helps predict an individual's risk of cardiovascular disease based on their characteristics. These examples demonstrate the broad applicability of analytics across industries to solve real-world problems and improve lives.