SQL for data analysis
Tags: sql, dataanalysis, etlprocesses, olapcubes
Published: 2023-04-04 03:05:15

SQL (Structured Query Language) is a powerful tool for data analysis, allowing users to access and manipulate data stored in relational databases. In this course, we'll cover the basics of SQL and dive into more advanced topics, enabling you to perform a wide range of data analysis tasks.

### **Module 1: Introduction to SQL**
The course starts with an introduction to SQL, where you'll learn the basics of querying relational databases. We'll cover topics such as selecting data from tables, filtering data with WHERE clauses, sorting data with ORDER BY clauses, and grouping data with GROUP BY clauses.

### **Module 2: Advanced SQL Queries**
In module 2, we'll cover more advanced SQL queries, including joining tables with INNER JOIN, LEFT JOIN, and RIGHT JOIN. We'll also cover aliasing tables and columns, aggregating data with functions (COUNT, SUM, AVG, MAX, MIN), and using subqueries and nested queries. Additionally, we'll look at conditional expressions with CASE statements and working with dates and timestamps.

### **Module 3: Data Manipulation with SQL**
Module 3 focuses on data manipulation with SQL, covering how to insert data into tables, update data in tables, delete data from tables, and create and alter tables. Additionally, we'll cover constraints and data integrity, including primary keys, foreign keys, and indexes.

### **Module 4: Data Analysis with SQL**
In module 4, we'll dive into data analysis with SQL. We'll cover topics such as data exploration and visualization with SQL, pivot tables and crosstab queries, window functions and ranking functions, common table expressions (CTEs), and recursive queries. Additionally, we'll explore statistical analysis with SQL, including correlation and regression analysis.

### **Module 5: Advanced Data Analysis with SQL**
Module 5 covers advanced data analysis techniques with SQL. We'll cover topics such as preprocessing data, data cleaning, data transformation, feature engineering, dimensionality reduction, model selection, cross-validation, hyperparameter tuning, ensemble learning, and evaluation metrics for regression (MAE, MSE, RMSE, R-squared). Additionally, we'll explore how to handle missing values and deal with imbalanced datasets.

### **Module 6: Time Series Analysis and Anomaly Detection with SQL**
Module 6 focuses on time series analysis and anomaly detection with SQL. We'll cover window functions for time series data, moving averages and trend analysis, seasonality and cyclicality analysis, and methods for detecting anomalies, including the Z-score and standard deviation method, moving median method, and exponential smoothing method.

### **Module 7: Recommender Systems and Natural Language Processing with SQL**
In module 7, we'll cover recommender systems and natural language processing (NLP) with SQL. We'll explore collaborative filtering and content-based filtering for recommender systems, matrix factorization and singular value decomposition (SVD), and introduce NLP concepts such as tokenization, stemming, stop words, n-grams, and TF-IDF. Additionally, we'll cover sentiment analysis with SQL and topic modeling with SQL.

### **Module 8: Advanced Topics in SQL**
The final module covers advanced topics in SQL, including transactions and concurrency control, understanding ACID properties, indexing strategies for large databases, creating and managing user-defined functions and stored procedures, querying JSON and XML data with SQL, understanding SQL injection attacks and preventative measures, working with NoSQL data sources (MongoDB, Cassandra, etc.), and understanding new SQL technologies (CockroachDB, TiDB, etc.). Additionally, we'll introduce data warehousing with SQL, including star schema and snowflake schema, ETL processes with SQL, OLAP cube analysis with SQL, implementing row-level security and auditing with SQL