What is Data Analytics? — Free Data Analytics Tutorial
Learn What is Data Analytics? in Data Analytics with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn What is Data Analytics? in Data Analytics with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
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What is Data Analytics? in Data Analytics
Data Analytics is the science of examining raw data to draw conclusions, discover patterns, and extract actionable insights. It involves collecting, processing, cleaning, analyzing, and visualizing data to support decision-making. In the modern world, data is the new oil—organizations that master analytics gain competitive advantages. Every business decision from strategy to tactics should be data-driven.
Data Analytics has historical roots in Florence Nightingale's 1854 infographic showing Crimean War deaths (pioneering data visualization). Modern analytics began with statistical analysis in the 1900s, computerized data processing in the 1960s, and business intelligence (BI) in the 1990s. The term "Data Analytics" became mainstream in the 2000s with big data and machine learning. Excel (1985) democratized analytics for millions of office workers.
Data Analytics evolved from simple statistics to a sophisticated field blending statistics, computer science, and domain expertise. Early analytics used spreadsheets and basic charts; modern analytics uses Python, R, SQL, and cloud platforms. Key milestones: Hadoop (2006) enabled big data processing, Spark (2010) made it faster, machine learning became mainstream (2010s), and AI-driven analytics emerged (2020s). Today, analytics is predictive and prescriptive, not just descriptive.
Data Analytics uses Python (Pandas, NumPy, Matplotlib, Seaborn), R (ggplot2, dplyr), SQL, Excel, Tableau, Power BI, Google Analytics, Apache Spark, and cloud platforms (AWS Redshift, Google BigQuery, Azure Synapse). Machine learning libraries (Scikit
What is Data Analytics? — Syntax
# Data Analytics workflow: # 1. Collect data (survey, database, CSV, API) # 2. Clean data (fix errors, remove duplicates) # 3. Explore data (statistics, charts) # 4. Analyse (find patterns, trends, outliers) # 5. Report (communicate findings clearly)
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