Data Analytics
Data Analytics is the process of examining, cleaning, transforming, and interpreting data to uncover useful information, draw conclusions, and support decision-making. It plays a crucial role in various industries by helping organizations understand patterns, trends, and relationships within their data. Through techniques such as statistical analysis, machine learning, and data visualization, businesses can optimize operations, enhance customer experiences, and gain a competitive edge. As the volume of data continues to grow, data analytics has become an essential tool for driving innovation and strategic planning.
Introduction to Data Analytics
What is data analytics?
Business applications
Types of analytics: Descriptive, Diagnostic, Predictive, Prescriptive
Data Collection & Cleaning
Data types & sources
ETL (Extract, Transform, Load)
Data wrangling using Python or Excel
Statistics & Probability
Descriptive stats
Hypothesis testing
Regression analysis
Machine Learning Basics
Machine Learning Basics
Models: Linear regression, KNN, decision trees
Python or R (for analytics)
Visualization tools
Descriptive stats
Dashboards and storytelling
Tools: Tableau, Power BI, Matplotlib, Seaborn
Programming for Data Analytics
Programming for Data Analytics
Libraries: pandas, NumPy, scikit-learn
Tools: Tableau, Power BI, Matplotlib, Seaborn
Real-world datasets
End-to-end project work
Data Visualization
Dashboards and storytelling
Tools: Tableau, Power BI, Matplotlib, Seaborn
Tools You May Learn
• Languages: Python, R, SQL
• Languages: Python, R, SQL
• Visualization: Tableau, Power BI
• Spreadsheet: Excel/Google Sheets
• Databases: MySQL, PostgreSQL
Skills You Gain
• Data cleaning & analysis
• Visual storytelling
• Statistical thinking
• Predictive modeling
• Business problem-solving
Career Outcomes
• Data Analyst
• Business Analyst
• Marketing Analyst
• Financial Analyst
• Product Analyst
• Data Scientist (with further specialization)
🚀 Start Your Data Journey
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