Full Stack Data Analyst

This program transforms you into a data-driven expert, bridging the gap between mathematical foundations and business intelligence. You will master Python, PySpark, and Exploratory Data Analysis before advancing to complex Supervised and Unsupervised Machine Learning. The curriculum integrates a deep dive into Power BI for advanced data storytelling and DAX modeling. Finalizing with a Capstone Project and interview coaching, you’ll graduate ready to build, optimize, and deploy end-to-end data pipelines for enterprise-scale decision-making.

Topics Covered

Module 0

Data Science Fundamentals

Module 01

Python for Data Science

Module 02

Exploratory Data Analysis

Module 03

Supervised Machine Learning – Regression

Module 04

Supervised Machine Learning – Classification

Module 05

Unsupervised Learning

Module 06

Power BI

Module 07

Capstone Projects: End-To-End Machine Learning

Module 08

Interview Preparation Module

Training Content

Module 0: Data Science Fundamentals

What Is DS, AI, ML, AND DL? Key Differences.
Data Mining Process: CRISP – DM
Real-World Applications (Healthcare, Finance, Etc.).
Types Of ML: Supervised, Unsupervised, Reinforcement Learning.
DS Workflow Overview.
Quant for DS:
Linear Algebra
Calculus
Statistics
Bias-Variance Tradeoff, Overfitting/Underfitting., Cross-Validation (K-Fold, LOOCV).
Model Evaluation Metrics (MSE, Accuracy, Precision/Recall, ROC-AUC)

Module 1: Python for Data Science

Python Basics (Syntax, Data Types, Loops, Functions).
Numpy (Arrays, Operations).
Pandas (DataFrame, Data Manipulation).
Exception Handling
Creating User Defined Functions
Object Oriented Programming: Class, Object, Attributes, Methods
Object Oriented Programming: Inheritance, Polymorphism, Encapsulation
Data Ingestion with Pandas
Different File Types
How To Handle Large Data
Handling Large Datasets with Pyspark

Module 2: Exploratory Data Analysis

Data Quality Check
Handling Messy Missing Values
Handling Outliers
Descriptive Statistics
Data Visualization with Matplotlib, Seaborn and Plotly
Inferential Statistics: Test of Hypothesis

Module 3: Supervised Machine Learning - Regression

Loss Function
Linear Regression: OLS, Assumptions, Gradient Descent
Model Diagnostics: R Square, Adj R Square, AIC, BIC
Polynomial Regression.
Ridge, Lasso and Elastic Net Regularization
Decision Trees for Regression.
Feature Engineering
Model Evaluation
Pipelining
Selecting The Best Model

Module 4: Supervised Machine Learning - Classification

Logistic Regression.
K Nearest Neighbor
Naive Bayes
Support Vector Machines (Kernel Methods)
Decision Trees
Handling Class Imbalance
Model Evaluation (Confusion Matrix, F1-Score, AUC – ROC).
Pipelining
Selecting The Best Model

Module 5: Unsupervised Learning

Clustering Techniques: K-Means, DBSCAN, Agglomerative Clustering
Dimensionality Reduction: PCA, Factor Analysis, Singular Value Decomposition, T-Sne
Anomaly Detection and Applications in Outlier Identification
Market Segmentation and Customer Profiling

Module 6: Power BI

What is Business Intelligence: Self-service BI vs Traditional BI
Power BI ecosystem: Power BI Desktop, Service, Mobile
Workflow in Power BI: Data → Transform → Model → Visualize → Publish → Share
Connecting to different data sources: Excel, CSV, Web, SQL Server, SharePoint
Power Query Editor deep dive: Data cleaning (Power Query)
Data Cleaning:
o Remove duplicates
o Change data types
o Merge & Append Queries
o Pivot & Unpivot
o Column extraction and transformation
Star Schema vs Snowflake Schema
Fact and Dimension tables
Relationship types: one-to-many, many-to-one
Cardinality & cross-filter direction
Data modelling best practices
Calculated Columns vs Measures
Intro to DAX: SUM, COUNT, DISTINCTCOUNT
Row context vs Filter context
DAX Measure vs Columns
DAX Deep Dive
Power BI Standard Visuals Deep Dive
Conditional formatting
Drill-through, drill-down, bookmarks
Decomposition tree
Scatter and bubble charts
Field parameters
What-If Analysis
Row-Level Security (RLS)
Object-Level Security (OLS)
Publishing reports

Module 6: Capstone Projects: End-To-End Machine Learning

Business Understanding and Project Scope Defining
Data Gathering
Data Understanding
Data Preparation
Presenting Insights

Module 6: Interview Preparation Module

How to introduce yourself
How to explain your project
How to explain complex topics in a simple way
Topic wise interview prep sessions
Curated interview questions
Mock interviews