Data Engineering basics - Data Domains, Type of Data / Availability, Data Collection methodologies

1. Python
• Installation
• Variable
• Operator
• Looping Statement
• Conditional Statement
• Pass Statement
• Function
• File Handling
2. MySQL Workbench installation
3. Power Bi Installation
4. Introduction to python libraries for DE
5. Data lifecycle
6. Data quality, reliability, and scalability
7. Data domains definition and Scope
8. Data domains significant role in decision-making and analysis
9. Introduction to Data base & Warehouse
10. Structured , Unstructured , Big data(3Vs')
11. Data collection methodologies & best practice
12. Basic python visualization & Exercises

Data Science Statistics - Principles, Statistics - Hypothesis testing

1. Overview of basic statistical concepts (e.g., mean, median, variance, standard deviation)
2. Introduction to probability distributions (e.g., normal distribution, binomial distribution)
3. Explanation of hypothesis testing fundamentals
4. Overview of null and alternative hypotheses
5. Introduction to statistical significance and p-values
6. Discussion on different types of hypothesis tests (e.g., t-tests, chi-square tests, ANOVA)
7. T test , Z test , A/B Testing

Domain Understanding, Use case identification & problem statement formulation

1. Industry Knowledge
2. Data Sources
3. Regulatory and Compliance Considerations
4. Business Objectives
5. Opportunity Assessment
6. Feasibility Analysis
7. Risk Analysis
8. Defining Objectives
9. Scope Definition
10. Metrics and Key Performance Indicators (KPIs)
11. Data Requirements
12. Constraints and Assumptions Assignments

Data Cleaning & Pre-processing

1. Identify Data Quality Issues.
2. Handle Missing Values
3. Address Outliers
4. Remove Duplicates
5. Feature Engineering
6. Data Transformation
7. Data Integration
8. Data Reduction
9. Data Normalization
10. Handling Data imbalances
11. Data Splitting
12. Python visualizations
13. Correlation Analysis

Data Visualization techniques & tools - Power Bi

1. Different Charts.
2. Trend Analysis
3. Hierarchical Visualization
4. Power Bi Desktop Basics and Examples
5. Dashboard Examples
6. DAX and Power Query
7. Dashboard Exercises
8. Story from your dashboard
9. Data Preparation
10. Table Relations
11. Sharing Dashboards

Machine Learning Models

1. Supervised Learning
*Support Vector Machines and Kernel Methods*
2. Unsupervised Learning
3. Ensemble Learning
4. Performance Metrics for Classification
5. Performance Metrics for Regression*
6. Cross-Validation Techniques
7. Overfitting and Underfitting
8. Performance in Imbalanced Datasets*

Model Quality - Comparison & Evaluation

1. Time-series basics & LSTM
2. Neural Networks and Deep Learning (incl. deep learning arch.)/li>
3. Reinforcement Learning Theory