Data Analytics

Non IT Course

The 120-Hour Data Analytics Program is a comprehensive, project-based learning journey designed to transform beginners with basic…

AdminElevate
20
120h
0
(0)

The 120-Hour Data Analytics Program is a comprehensive, project-based learning journey designed to transform beginners with basic analytical aptitude into job-ready data analysts. This intensive program covers the complete data analytics lifecycle—from data collection and cleaning to analysis, visualization, and insight communication—using industry-standard tools and real-world datasets. Students will graduate with a portfolio of projects demonstrating their ability to extract actionable insights from data.

What Will You Learn?

Master the complete data analytics lifecycle from problem definition to insight communication and deployment
Collect, clean, manipulate, and analyze data from multiple sources using Python, SQL, and Excel
Perform exploratory data analysis (EDA) to discover patterns, anomalies, and relationships in datasets
Build interactive dashboards and compelling visualizations using Power BI and Tableau
Apply machine learning algorithms for predictive analytics including regression, classification, and clustering
Implement deep learning models and natural language processing (NLP) for advanced analytics applications
Leverage generative AI and large language models (LLMs) to automate analysis, generate insights, and boost productivity
Understand principles of responsible AI, ethics, data privacy, and security in analytics workflows

Course Curriculum

Python Essentials

Python setup, IDE configuration (Jupyter, VS Code)
Variables, data types, and operators
Control flow: conditional statements (if-else) and loops (for, while)
Functions: definition, parameters, return values, lambda functions
Error handling with try-except blocks
File handling: reading/writing CSV, JSON, and text files

Python Data Structures

Lists, tuples, and list comprehensions
Dictionaries and sets
Dictionary comprehensions and nested structures
Working with JSON data

NumPy Fundamentals

NumPy arrays: creation and manipulation
Vectorization and broadcasting
Array indexing, slicing, and filtering
Mathematical operations and universal functions
Matrix calculations and linear algebra basics

Pandas Introduction

Pandas Series and DataFrames
Importing data from CSV, Excel, and APIs
Basic DataFrame operations: selection, filtering, sorting
Handling missing data

Project 1: Exploratory Data Analysis on Retail Dataset

Load retail sales dataset using Pandas
Clean data by handling missing values and duplicates
Perform basic statistical analysis
Generate summary statistics and initial insights
Document findings in Jupyter notebook

Advanced Pandas

Data cleaning techniques: handling missing values, duplicates, outliers
Data transformation: mapping, replacing, and applying functions
Groupby operations for aggregation
Merging, joining, and concatenating DataFrames
Pivot tables and cross-tabulations
Working with categorical data
Date/time data handling and resampling

SQL Fundamentals

Relational database concepts and RDBMS
MySQL setup and database creation
Basic queries: SELECT, WHERE, ORDER BY
Joins: INNER, LEFT, RIGHT, FULL OUTER
Aggregations and GROUP BY with HAVING
Subqueries and correlated subqueries
Window functions and analytical queries
Query optimization and indexing

Project 2: SQL Business Analytics Case Study

Design normalized database schema for e-commerce platform
Load sample transaction data
Write queries to answer business questions
Generate insights report with visualizations

Python Visualization

Matplotlib fundamentals: line plots, bar charts, histograms
Figure customization: labels, titles, legends, colors
Subplots and complex layouts
Seaborn for statistical visualizations
Distribution plots, pair plots, heatmaps
Categorical plots and regression plots

Excel for Analytics

Advanced Excel functions: VLOOKUP , HLOOKUP , XLOOKUP
Logical functions and nested formulas
Pivot tables and pivot charts
What-If Analysis and Goal Seek
Data validation and conditional formatting

Power BI Essentials

Power BI architecture and components
Connecting to various data sources
Power Query for data transformation
Data modeling and relationships
DAX fundamentals: calculated columns and measures
Creating visualizations: bar, line, pie, maps, cards, KPIs
Building interactive reports with slicers and drill-downs

Tableau Fundamentals

Tableau interface and data connections
Creating worksheets and dashboards
Calculated fields and table calculations
Maps and geographic visualization
Storytelling with dashboards

Project 3: Sales Dashboard with Power BI

Import sales data from multiple sources (Excel, CSV, SQL)
Clean and transform data with Power Query
Build data model with relationships
Create DAX measures for KPIs
Design interactive dashboard with slicers and drill-downs
Publish to Power BI service and configure sharing

Machine Learning Foundations

What is Machine Learning? Types: supervised, unsupervised, reinforcement learning
ML pipeline and workflow
scikit-learn introduction
Train-test split and cross-validation
Feature engineering techniques
Model evaluation metrics overview

Regression Models

Simple linear regression
Multiple linear regression
Polynomial regression
Regularization: Ridge, Lasso, ElasticNet
Evaluation metrics: MSE, RMSE, MAE, R-squared

Classification Models

Logistic regression
K-Nearest Neighbors (KNN)
Decision trees
Evaluation metrics: accuracy, precision, recall, F1-score, ROC-AUC
Confusion matrix interpretation

Ensemble Methods

Random Forest
Gradient Boosting (XGBoost, LightGBM)
Bagging vs Boosting
Feature importance analysis

Unsupervised Learning

Clustering algorithms: K-Means, Hierarchical, DBSCAN
Principal Component Analysis (PCA) for dimensionality reduction
Cluster evaluation metrics
Customer segmentation applications

Project 4: Multi-Model Predictive Analytics

House Price Prediction (Regression)
Customer Segmentation (Clustering)
Fraud Detection (Classification)

Deep Learning Fundamentals

What is Deep Learning? Neural networks basics
Activation functions: sigmoid, tanh, ReLU
Forward and backward propagation
Deep Learning libraries: TensorFlow, Keras, PyTorch
Building Artificial Neural Networks (ANN)
Batch normalization and dropout

Convolutional Neural Networks

CNN architecture and components
Convolutional layers, pooling, flattening
CNN for computer vision
Image classification with pre-trained models
OpenCV basics for image processing

Natural Language Processing

NLP fundamentals and applications
Text preprocessing: tokenization, stopwords, stemming, lemmatization
Regular expressions for text cleaning
NL TK and spaCy libraries
Word embeddings and vectorization
Bag of Words, TF-IDF, n-grams
RNN, LSTM, and Bidirectional LSTM architectures

Project 5: Sentiment Analysis System

Load movie reviews or social media comments dataset
Perform comprehensive text preprocessing
Build multiple text classification models
Compare model performance
Create simple web interface for real-time sentiment prediction
Generate word clouds and visualization of results

Generative AI Fundamentals

What is Generative AI? Overview and capabilities
Large Language Models (LLMs): GPT, Claude, Llama
Difference between traditional ML and Generative AI
Generative AI for analytics: use cases and applications
Prompt engineering fundamentals
OpenAI API setup and key management

LLM Integration for Analytics

Using LLMs for automated data analysis
Generating insights from data with AI
Natural language to SQL queries
AI-assisted code generation for analytics
Creating summaries and reports automatically

Prompt Engineering for Analytics

Advanced prompt techniques: few-shot, chain-of-thought
Structuring prompts for consistent outputs
Role-based prompting for specialized analysis
Iterative refinement strategies
Evaluating and validating AI-generated insights

Responsible AI & Ethics

Principles of responsible AI in analytics
Data privacy and security considerations
Bias detection and mitigation in AI models
Ethical implications of AI decision-making
Transparency and explainability in AI systems
Legal frameworks and compliance (GDPR, CCPA)

Project 6: GenAI-Powered Insights Dashboard

Build comprehensive analytics dashboard with AI integration
Automated Data Profiling
Natural Language Query Interface
Insight Generation
Code Assistant
Responsible AI Documentation

Final Project

Student Ratings & Reviews

No Review Yet

No Data Available in this Section

A course by

AdminElevate

Top Course

More Courses By AdminElevate

View All Course

IT Courses

Dev SecOps

25
120h
0
(0)

This comprehensive 120-hour curriculum provides a complete journey from DevSecOps fundamentals to advanced AI-powered security operations. The project-based approach ensures…

AdminElevate

₹31,999.00 ~~₹42,999.00~~

IT Courses

Cloud & DevOps

24
120h
0
(0)

This comprehensive 120-hour curriculum provides a complete journey from Cloud and DevOps fundamentals to advanced AI-powered operations. The project-based approach…

AdminElevate

₹25,999.00 ~~₹35,999.00~~

IT Courses

Cloud SecOps

24
120h
0
(0)

This comprehensive 120-hour curriculum provides a complete journey from cloud security fundamentals to advanced AI-powered SecOps. The project-based approach ensures…

AdminElevate

₹31,999.00 ~~₹42,999.00~~

Data Analytics

What Will You Learn?

Course Curriculum

Python Essentials

Python setup, IDE configuration (Jupyter, VS Code)

Variables, data types, and operators

Control flow: conditional statements (if-else) and loops (for, while)

Functions: definition, parameters, return values, lambda functions

Error handling with try-except blocks

File handling: reading/writing CSV, JSON, and text files

Python Data Structures

Lists, tuples, and list comprehensions

Dictionaries and sets

Dictionary comprehensions and nested structures

Working with JSON data

NumPy Fundamentals

NumPy arrays: creation and manipulation

Vectorization and broadcasting

Array indexing, slicing, and filtering

Mathematical operations and universal functions

Matrix calculations and linear algebra basics

Pandas Introduction

Pandas Series and DataFrames

Importing data from CSV, Excel, and APIs

Basic DataFrame operations: selection, filtering, sorting

Handling missing data

Project 1: Exploratory Data Analysis on Retail Dataset

Load retail sales dataset using Pandas

Clean data by handling missing values and duplicates

Perform basic statistical analysis

Generate summary statistics and initial insights

Document findings in Jupyter notebook

Advanced Pandas

Data cleaning techniques: handling missing values, duplicates, outliers

Data transformation: mapping, replacing, and applying functions

Groupby operations for aggregation

Merging, joining, and concatenating DataFrames

Pivot tables and cross-tabulations

Working with categorical data

Date/time data handling and resampling

SQL Fundamentals

Relational database concepts and RDBMS

MySQL setup and database creation

Basic queries: SELECT, WHERE, ORDER BY

Joins: INNER, LEFT, RIGHT, FULL OUTER

Aggregations and GROUP BY with HAVING

Subqueries and correlated subqueries

Window functions and analytical queries

Query optimization and indexing

Project 2: SQL Business Analytics Case Study

Design normalized database schema for e-commerce platform

Load sample transaction data

Write queries to answer business questions

Generate insights report with visualizations

Python Visualization

Matplotlib fundamentals: line plots, bar charts, histograms

Figure customization: labels, titles, legends, colors

Subplots and complex layouts

Seaborn for statistical visualizations

Distribution plots, pair plots, heatmaps

Categorical plots and regression plots

Excel for Analytics

Advanced Excel functions: VLOOKUP , HLOOKUP , XLOOKUP

Logical functions and nested formulas

Pivot tables and pivot charts

What-If Analysis and Goal Seek

Data validation and conditional formatting

Power BI Essentials

Power BI architecture and components

Connecting to various data sources

Power Query for data transformation

Data modeling and relationships

DAX fundamentals: calculated columns and measures

Creating visualizations: bar, line, pie, maps, cards, KPIs

Building interactive reports with slicers and drill-downs

Tableau Fundamentals

Tableau interface and data connections

Creating worksheets and dashboards

Calculated fields and table calculations

Maps and geographic visualization