Glory Technologies
Contact Number: +91-8008627755
Subject: Data Science
Duration: 90 Days
Timings: 8am-9am
1.Python
Introduction and Basics of Python
Operators
Conditional Statements
While Loops
Lists
Strings
For Loop
Functions
Dictionary
Tuples
Set
Object-Oriented Programming
File Handling
Exception Handling
Regular Expression
Modules and Packages
Statistics with NumPy
Data Analysis with Pandas
Data Visualization with Matplotlib
What to do Now?
Libraries for Data Science: Pandas, Numpy, SciPy
- Mathematics and Statistics
Probability
Definitions and Basic Concepts
Probability Rules
Conditional Probability
Bayes’ Theorem
Probability Distributions (Discrete and Continuous)
Random Variables and Expected Value
Descriptive Statistics
Measures of Central Tendency (Mean, Median, Mode)
Measures of Dispersion (Range, Variance, Standard Deviation)
Measures of Shape (Skewness, Kurtosis)
Data Visualization (Histograms, Box Plots, Bar Charts)
Data Summarization Techniques (Tabulation, Frequency Distribution)
Inferential Statistics
Sampling and Sampling Distributions
Hypothesis Testing
Confidence Intervals
Regression Analysis
Analysis of Variance (ANOVA)
Chi-Square Test
t-Tests (One-sample, Two-sample, Paired-sample)
- Data Visualization
Power BI
Introduction to Power BI
Power BI Components
Power BI Desktop
Data Sources in Power BI
Data Transformation with Power Query
Power BI Desktop
Data Sources in Power BI
Data Transformation with Power Query
Power BI Service
Visualization Techniques
Creating Reports
Dashboards in Power BI
Power BI Service
Sharing and Collaboration
Power BI Integration with Other Tools
Matplotlib
Seaborn
- Databases and SQL
Introduction to Databases
What is a Database?
Types of Databases (Relational vs. Non-Relational)
Database Management Systems (DBMS)
Relational Databases
Introduction to Relational Databases
Tables, Rows, and Columns
Primary Keys and Foreign Keys
Relationships (One-to-One, One-to-Many, Many-to-Many)
Normalization (1NF, 2NF, 3NF)
Indexes and Constraints
ACID Properties (Atomicity, Consistency, Isolation, Durability)
SQL Querying
Introduction to SQL
Basic SQL Commands (SELECT, INSERT, UPDATE, DELETE)
SQL Clauses (WHERE, ORDER BY, GROUP BY, HAVING)
SQL Functions (Aggregate Functions, String Functions, Date Functions)
- Joins in SQL (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN)
- Joins in SQL (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN)
17.Triggers in SQL
18.Transactions and Rollbacks
19.Indexing and Performance Tuning
20.SQL Security (Permissions, Roles, Encryption)
- Indexing and Performance Tuning
- SQL Security (Permissions, Roles, Encryption
- Exploratory Data Analysis (EDA)
Data Cleaning
Identifying and Handling Missing Data
Dealing with Outliers
Removing Duplicates
Data Type Conversion
Handling Inconsistent Data
Standardizing Data
Data Normalization and Scaling
Addressing Data Entry Errors
Data Profiling
Summary Statistics
Data Distribution Analysis
Correlation Analysis
Data Quality Assessment
Frequency Distribution for Categorical Data
Data Completeness Check
Identifying Relationships in Data
Feature Engineering
Creating New Features
Feature Transformation
Encoding Categorical Variables
Binning and Discretization
Handling Date and Time Features
- Machine Learning
Introduction to Machine Learning – Supervised, unsupervised, and reinforcement learning concepts.
Python for ML – NumPy, Pandas, Matplotlib, and Scikit-learn basics.
Data Preprocessing – Handling missing values, encoding, normalization, and feature scaling.
Supervised Learning Algorithms – Linear regression, Polynomial, logistic regression, Ridge Regression
, decision trees, and SVM,
Unsupervised Learning – Clustering (K-Means, DBSCAN) and dimensionality reduction (PCA).
Model Evaluation – Accuracy, precision, recall, F1-score, confusion matrix, and cross-validation.
Ensemble Techniques – Bagging, boosting, Random Forest, and XGBoost.
Deep Learning Introduction – Neural networks, activation functions, and backpropagation.
Advanced Deep Learning – CNNs, RNNs, LSTMs using TensorFlow/Keras.
- Advanced Machine Learning
Ensemble Methods: Random Forest, Gradient Boosting, XGBoost
Natural Language Processing (NLP)
Project & Deployment – Real-world ML project with model deployment using Flask or Streamlit.
*Model Evaluation: Cross-Validation, AUC-ROC, Confusion
Matrix
- Deep Learning
Introduction to Deep Learning – Understanding neural networks, perceptron, and activation functions.
Model Architecture – Feedforward networks, loss functions, optimizers, and backpropagation.
Convolutional Neural Networks (CNNs) – Image processing, filters, pooling, and architecture design.
Recurrent Neural Networks (RNNs) & LSTMs – Sequence modeling for time series and NLP.
Advanced Topics – Transfer learning, GANs, attention mechanisms, and model deployment with TensorFlow/Keras
9.AI
Introduction to AI – History, types of AI (narrow, general, super), and real-world applications.
Search Algorithms – BFS, DFS, A*, and problem-solving using state-space search.
Knowledge Representation & Reasoning – Logic, inference, ontologies, and expert systems.
Machine Learning & Deep Learning Basics – Integrating ML/DL within AI systems.
Natural Language Processing & Computer Vision – AI applications in speech, text, and image understanding.
10.Cloud Computing (AWS):
Amazon EC2 (Elastic Compute Cloud)
AWS Lambda
Amazon S3 (Simple Storage Service)
- Advance Excel
Advanced Formulas and Functions
Pivot Tables and Pivot Charts
Data Analysis Tools
Data Visualization
- Data Science Projects
Capstone Projects
End-to-End Data Science Workflows
- Soft Skills and Career Preparation
Communication Skills
Collaboration Tool: Git
Resume Writing and Interview Preparation
Weekly tests to monitor progress.
Daily notes and PDFs for thorough revision.
End-to-end data science projects for practical experience.
Resume preparation workshops.
Trainer available for doubts on WhatsApp group.
Interview questions and answers for job readiness
