Overview of Data Science and its applications
Role of a Data Scientist and key skills required
Introduction to the Data Science lifecycle and methodologies
Ethical considerations in Data Science

DQL, DDL, DML
Joins in SQL, Sub query , set operations
CRUD operations              
Database connectivity with python, Power BI , tableau and Studio                                 

Variables, Datatype, flow control, for, if-else, while
Lists, tuples, sets, dictionary,
Strings, functions
Case studies, problem statements and quzzies

Identifying and obtaining relevant data sources
Data collection techniques (web scraping, APIs, databases, etc.)
Data quality assessment and pre-processing
Handling missing data and outliers
Data wrangling and feature engineering
Reinforcement learning algorithms

DQL, DDL, DML
Joins in SQL, Sub query , set operations
CRUD operations
Database connectivity with python, Power BI , Tableau and Studio

Overview of supervised, unsupervised, and reinforcement learning
Linear regression and regularization techniques
Classification Algorithms (logistic regression, decision trees, random forests, etc.)
Clustering Algorithms (k-means, Hierarchi-cal clustering, etc.)
Evaluation metrics for Machine Learning models.

Model selection techniques (train-test split, cross-validation, etc.)
Overfitting, underfitting, and regularization techniques
Hyperparameter tuning and model optimization
Ensemble methods and model stacking
Performance evaluation and model interpretation

Support vector machines and kernel methods         
Neural networks and deep learning
Natural Language Processing (NLP) techniques                   
Time series analysis and forecasting            

Introduction to big data and distributed computing    
Processing large-scale data with Apache Spark         
Working with distributed file systems (Hadoop HDFS, AWS S3, etc.)                               
Scalable data storage and querying (Apache Hive, Apache Cassandra, etc.)
Implementing big data analytics pipelines

Techniques for feature selection and importance ranking
Handling categorical variables and feature encoding
Feature scaling and normalization
Feature extraction from text and images
Handling imbalanced datasets

Fundamentals of deep learning & neural networks        
Building and training deep neural networks     
Convolutional Neural Networks (CNN) for image processing    
Recurrent Neural Networks (RNN) for sequence data
Transfer learning & fine-tuning pre-trained models   

Effective data visualization  
principles and best practices     
Using visualization Tools (Tableau, PowerBI, etc.)           
Interactive dashboards & storytelling with data
Communicating insights and findings to non-technical stakeholders                                
Ethical considerations in data visualization
and communication                   

Overview of cloud computing and its benefits for Data Science
Building scalable data infrastructure on cloud platforms (AWS, Azure, GCP)
Leveraging cloud-based analytics services (AWS Athena, Google Big Query, etc.)
Containerization and deployment of Data Science models