Data science

Introduction to Data Science and Project Cycle:
Understand the fundamentals of data science and the life cycle of data-driven projects, providing a solid foundation for your data journey.

Python Basics:
Dive into Python fundamentals, including data types, conditional statements, operators, loops, ranges, and functions, laying the groundwork for effective data manipulation and analysis.

File Operations:
Learn to handle data by mastering reading and writing operations in Python, essential skills for managing datasets efficiently.

Numpy, Matplotlib, and Pandas:
Gain proficiency in these essential libraries for numerical computation, data visualization, and data manipulation, empowering you to handle complex datasets with ease.

Graph Representations and Hypothesis Testing:
Develop skills in visualizing data and formulating hypothesis statements, which are crucial for making informed, data-driven decisions.

Error Analysis:
Understand Type-I and Type-II errors, P-Value, and significance to enhance the reliability of your statistical analysis and ensure accurate conclusions.

SQL Fundamentals:
Get hands-on with SQL, covering Data Definition Language (DDL), Data Manipulation Language (DML), Data Control Language (DCL), and Data Retrieval Language (DRL) commands to build a strong foundation in database management.

Data Querying:
Master essential SQL querying techniques, including SELECT, WHERE, conditional statements, GROUP BY, ORDER BY, and various aggregate functions, to extract meaningful insights from your data.

Advanced SQL Techniques:
Explore advanced SQL concepts such as joins, unions, correlated queries, and views to effectively manage and analyze relational data, enhancing your data manipulation skills.

Regression Models:
Learn about simple and multiple linear regression, polynomial regression, and logistic regression, which serve as foundational techniques for predictive modeling.

K-Nearest Neighbors (KNN) and SVM:
Understand classification techniques and support vector machines (SVM) for effectively tackling data classification tasks.

Bias-Variance Tradeoff and Regularization:
Delve into the concepts of overfitting and explore regularization techniques like Lasso and Ridge to enhance model accuracy and performance.

Naïve Bayes, Decision Trees, and Ensemble Learning:
Explore various algorithms, including Naïve Bayes and Decision Trees, as well as ensemble learning methods that drive modern data-driven applications.

Clustering Algorithms:
Learn to segment data into meaningful groups using clustering techniques, facilitating insightful analysis and decision-making.

Dashboard Creation:
Begin your journey by creating your first Power BI dashboard, learning how to connect and integrate various data sources effectively.

Visual Interactions and Formatting:
Understand the fundamentals of formatting in Power BI and explore how different visuals interact within a dashboard for a cohesive user experience.

Charts and DAX Functions:
Discover a variety of chart types and delve into DAX functions to create insightful and dynamic visualizations that enhance data storytelling.

Deep Dive into PL/SQL:
Advance your PL/SQL skills by exploring essential topics such as Collections and Records, Error Handling, Subprograms, Packages, and Native Dynamic SQL. Enhance your database operations with flexible and efficient PL/SQL programming techniques that improve performance and maintainability.

PL/SQL Object Types:
Learn to create encapsulated data structures within Oracle databases using PL/SQL Object Types. Enhance modularity and code reusability by leveraging advanced object-oriented programming concepts for more organized and efficient database management.

Database Administration Essentials:
Gain insights into essential Database Administration activities, including performance tuning, backup and recovery, and security management. Equip yourself with the knowledge and skills necessary to effectively manage and secure Oracle databases for optimal performance and reliability.