Data Analytics is the process of examining, transforming, and modeling data to extract useful insights, identify trends, and support decision-making. It involves descriptive, diagnostic, predictive, and prescriptive analytics to turn raw data into actionable knowledge.
Week-1. Introduction to Data Analytics
Role of a Data Analyst, Industry Use Cases, Tools Overview
Week-2.Excel for Data Analysis
Basic Formulas, Pivot Tables, Data Cleaning, Advanced Excel Functions
Week-3.SQL for Data Analysis
SQL Basics, Joins, Subqueries, Window Functions, Aggregations
Week-4. Python for Data Analysis
Pandas, NumPy, Data Cleaning, Exploratory Data Analysis
Week-5. Data Visualization with Excel
Charts, Conditional Formatting, Dashboards
Week-6.Tableau for Data Visualization
Connecting Data Sources, Creating Dashboards, Storytelling with Data
Week-7. Power BI for Business Intelligence
DAX Functions, Power Query, Interactive Dashboards
Week-8. Python Data Visualization
Matplotlib, Seaborn, Plotly for Interactive Charts
Week-9.Exploratory Data Analysis (EDA)
Outlier Detection, Missing Data Handling, Feature Engineering
Week-10.Statistics for Data Analysis
Descriptive Statistics, Hypothesis Testing, A/B Testing
Week-11.Regression Analysis
Linear Regression, Multiple Regression, Business Use Cases
Week-12. Classification Models
Logistic Regression, Decision Trees, Model Evaluation
Week-13. Clustering Techniques
K-Means, Hierarchical Clustering, Market Segmentation
Week-14.Business Intelligence & Reporting
Creating Effective Reports, Automating Reports with Python
Week-15.Google Data Studio & Advanced BI Tools
Connecting Data Sources, Creating Interactive Dashboards
Week-16. Data Storytelling & Presentation
Best Practices, Communicating Insights
Week-17. Capstone Project – Part 1
Data Collection, Cleaning, EDA
Week-18.Capstone Project – Part 2
Modeling, Visualization, Dashboard Creation
Week-19. Career Guidance & Resume Building
Interview Preparation, Case Studies, Resume & Portfolio Building