SQL Analyst
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
Machine Learning
Deep Learning
SQL
DJango
NLP
Wordcloud
ANN
Python
LSTM
Database
Docker
Flask
Redis
Kafka
Numpy
Image Processing
CNN
This course includes
- 1 : 1 Session
- 100% Placement Assistance
- 35 Weeks + Projects
- Real TIme Project Training
Syllabus Overview
SQL Analyst
- Introduce SQL and its foundational role in data analysis.
- Topics Covered:
- Introduction to relational databases and SQL.
- Basic SQL commands: SELECT, INSERT, UPDATE, DELETE.
- Data filtering, sorting, and basic aggregations.
- Learning Outcomes:
- Execute basic SQL queries to retrieve and manipulate data.
- Understand data structures and database design.
- Assessment:
- Multiple choice tests and practical SQL assignments on sample databases.
- Topics Covered:
- Develop intermediate skills necessary for complex data analysis.
- Topics Covered:
- Advanced SQL functions: complex joins, subqueries, and window functions.
- Data aggregation and transformation techniques.
- Introduction to analytical functions and their applications in SQL.
- Learning Outcomes:
- Apply advanced SQL techniques to analyze complex datasets.
- Create reports and dashboards using SQL queries.
- Assessment:
- Case studies requiring the analysis of real-world data scenarios using SQL.
- Topics Covered:
- Explore the concepts of data warehousing and how SQL is used in business intelligence.
- Topics Covered:
- Basics of data warehousing: schemas, ETL processes, and data modeling.
- Introduction to business intelligence tools that integrate with SQL (e.g., Power BI, Tableau).
- Using SQL for OLAP (Online Analytical Processing) tasks.
- Learning Outcomes:
- Understand the architecture and operation of data warehouses.
- Use SQL in conjunction with popular BI tools to create insightful data visualizations.
- Assessment:
- Project involving the design of a data warehouse and generating BI reports.
- Topics Covered:
- Master advanced analytics techniques and optimize SQL queries for performance.
- Topics Covered:
- Predictive analytics and machine learning basics in SQL environments.
- SQL query optimization and performance tuning.
- Indexing strategies and their impact on query performance.
- Learning Outcomes:
- Implement predictive models using SQL-based machine learning tools.
- Optimize SQL queries for better performance in large-scale data environments.
- Assessment:
- Optimization project where students improve the performance of existing SQL queries.
- Topics Covered:
- Apply all learned skills in a comprehensive real-world project.
- Topics Covered:
- Project planning and data strategy formulation.
- Comprehensive data analysis using all the SQL and BI skills acquired.
- Presentation of findings to stakeholders.
- Learning Outcomes:
- Execute a full-scale data analytics project from data extraction to insight generation.
- Prepare and deliver professional-level data analysis reports.
- Assessment:
- Capstone project presentation and a detailed analysis report.
- Topics Covered:
- Seminars and Workshops: Regular sessions on new trends in data analytics and emerging technologies.
- Guest Lectures: Invitations to industry experts in data analytics to provide insights and discuss career opportunities.
- Practical Challenges: Use of platforms like Kaggle for practice and real-world problem-solving.
