SQL Data Scientist
Full-stack data science covers the entire spectrum of data handling, from gathering and manipulating data to backend processing, model deployment, and the implementation of complete machine learning solutions including natural language processing (NLP), computer vision (CV), and traditional machine learning algorithms. This approach integrates the entire pipeline of data science workflows into a cohesive skill set, allowing professionals to manage and execute projects from start to finish.
Skills You Will Gain
Understanding Database Management Systems (DBMS)
Mastery of SQL Syntax and Queries
Data Manipulation and Retrieval Techniques
Database Design Principles
Optimization of SQL Queries
Networking and Storage in Kubernetes
CI/CD in a Containerized Environment
Security Best Practices in Docker and Kubernetes
This course includes
- 1 : 1 Session
- 100% Placement Assistance
- 4 Weeks
- Real TIme Project Training
Syllabus Overview
SQL Data Scientist
SQL is an essential tool for anyone working with data, offering the power to store, manipulate, and retrieve data from relational databases.
- Provide a solid foundation in SQL and data management, crucial for any data scientist.
- Topics Covered:
- Introduction to relational databases and SQL basics.
- Data types, tables, and relationship definitions.
- Basic SQL commands: SELECT, INSERT, UPDATE, DELETE.
- Learning Outcomes:
- Understand and utilize basic SQL syntax for data retrieval and manipulation.
- Design and query databases effectively.
- Assessment:
- Multiple choice tests and hands-on SQL exercises.
- Topics Covered:
- Develop advanced SQL querying skills tailored for data analysis and data science.
- Topics Covered:
- Complex SQL queries: joins, subqueries, window functions.
- Analytical functions: aggregation, ranking, and windowing.
- Integration of SQL with statistical programming languages like Python or R.
- Learning Outcomes:
- Conduct complex data manipulations and analyses using SQL.
- Integrate SQL data retrieval with analytical software for deeper insights.
- Assessment:
- Real-world data manipulation tasks and analysis reports.
- Topics Covered:
- Explore the role of SQL in data warehousing and big data technologies essential for modern data science.
- Topics Covered:
- Data warehousing concepts: ETL processes, OLAP, and data marts.
- Introduction to big data platforms (Hadoop, Spark) and their interaction with SQL.
- NoSQL databases: Use cases and differences compared to traditional SQL.
- Learning Outcomes:
- Design and implement data warehousing solutions.
- Understand and apply SQL within big data frameworks.
- Assessment:
- Project involving the setup and querying of a data warehouse.
- Topics Covered:
- Master the use of SQL for predictive analytics and machine learning, critical components of data science.
- Topics Covered:
- SQL for data preprocessing and feature extraction.
- Using SQL-based tools for predictive analytics (e.g., Microsoft SQL Server Analysis Services).
- Integration of SQL in machine learning workflows.
- Learning Outcomes:
- Prepare datasets for machine learning using SQL.
- Implement basic predictive models using SQL tools.
- Assessment:
- Implementation of a predictive model using SQL-prepared datasets.
- Topics Covered:
- Synthesize all learned skills in a comprehensive, real-world data science project.
- Topics Covered:
- Project conception and data strategy development.
- Extensive data analysis and model building.
- Results interpretation and presentation to stakeholders.
- Learning Outcomes:
- Execute a full-scale data science project from data gathering to model deployment.
- Effectively communicate complex data insights.
- Assessment:
- Capstone project presentation and a detailed technical report.
- Topics Covered:
- Workshops: Regular interactive sessions on advanced SQL topics and data science trends.
- Guest Lectures: Insights from industry leaders in data science.
- Competitions: Participation in data science competitions to enhance practical skills and problem-solving abilities.
Transform Your Skills: Enroll Now
This course includes practical exercises, real-world case studies, and projects to ensure that you not only understand SQL theory but can also apply it effectively in any data environment. By the end of this course, you will have a deep understanding of SQL, empowering you to handle and analyze data proficiently, a skill highly valued in today’s data-driven world.
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