Data Analytics
Data Analytics involves examining raw data to uncover trends, patterns, and insights that drive informed decision-making. Using statistical methods, visualization tools, and machine learning, businesses optimize operations, enhance customer experiences, and predict future outcomes. Industries like finance, healthcare, and marketing rely on analytics for strategic growth. Key tools include Python, SQL, Tableau, and Power BI. By transforming data into actionable intelligence, organizations gain a competitive edge, improve efficiency, and innovate faster in today’s data-driven world.
Data Analytics extracts insights from raw data using statistics, visualization, and AI. Businesses use it to optimize decisions, predict trends, and improve performance. Tools like Python, SQL, and Power BI drive actionable results.
Where do we use Data Science
Business and Marketing
Healthcare Industry
Finance and Banking
Retail and E-Commerce
Telecommunications
Manufacturing and Supply Chain
Education Sector
Sports and Entertainment
Course Syllabus
- 🧠 Introduction to Data Analytics
- Excel for Data Analytics
- Statistics for Data Analysis
- SQL for Data Analysis
- Data Visualization
- Python for Data Analytics
- Data Wrangling and Cleaning
- Exploratory Data Analysis (EDA)
- Predictive Analytics with Python
- Deep Learning
- Machine learning
- Business Intelligence Tools
- Big Data Basics
- Cloud Analytics Platforms
- Capstone Projects
- Case Studies and Industry Applications
