Live Sessions

Applied Data Analytics

Analyze and visualize data using Python, SQL, Excel, and Power BI to generate business insights.

Tools you’ll work with
Python
R
NumPy
Sagemaker
AWS Lambda
Git
+2 more tools
₹38000
12 weeksBeginner
Instructor
Arun S
MSc AI • 10+ years • Ex - Microsoft
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Course Overview

Description

25 lessons•57 exercises•4 exams•~12 hours

Are you looking for a well-structured data science fundamentals course?

Do you want to gain a clear understanding of the data science field?

This is the perfect course for you.

If terms like traditional data, big data, business intelligence, and machine learning sound confusing, this course will help you understand both meaning and practical application.

25 lessons•57 exercises•4 exams•12 hours

Course Curriculum

A structured, progressive curriculum designed to build depth, intuition, and real-world proficiency over time.

This section introduces key concepts and builds intuition through structured lessons and exercises.

Python fundamentals: variables, data types, loops, functions
Working with NumPy for numerical computing
Pandas for data manipulation & analytics
Data cleaning, transformation & handling missing values
Exploratory Data Analysis (EDA)
Visualization using Matplotlib & Seaborn

This section introduces key concepts and builds intuition through structured lessons and exercises.

Univariate, bivariate & multivariate analytics
Pairplots, correlation analysis, heatmaps
Feature importance & feature selection
Outlier detection (Z-score, IQR, Isolation Forest)
Data transformation & scaling
PCA (Principal Component Analysis)

This section introduces key concepts and builds intuition through structured lessons and exercises.

Regression models: Linear, Ridge, Lasso
Classification models: Logistic Regression, KNN, SVM, Decision Trees
Ensemble methods: Random Forest, Gradient Boosting
Model evaluation metrics & cross-validation
Time series components & decomposition
ARIMA, SARIMA
Prophet for business forecasting
Forecast evaluation (MAPE, RMSE)

This section introduces key concepts and builds intuition through structured lessons and exercises.

Introduction to cloud computing for data science
AWS Lambda basics
Model deployment using AWS Lambda
Introduction to AWS SageMaker
Training, tuning & deploying ML models on SageMaker
CI/CD for ML workflows
Automated monitoring & versioning

This section introduces key concepts and builds intuition through structured lessons and exercises.

EDA
Predictive modeling or forecasting
Deployment on AWS (Lambda or SageMaker)
Dashboard/report generation

How You’ll Learn

This course is designed to help you move beyond tutorials — toward deep understanding, confident implementation, and long-term career growth.

Live Classes
Quizzes
Assignments
Projects
Certification

Build Skills That Scale With You

This course is designed to help you move beyond tutorials — toward deep understanding, confident implementation, and long-term career growth.

Learn deeply. Apply repeatedly.