SQL for Data Analytics

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Datascience & Analytics
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Unlock the power of data with our “SQL for Data Analytics” course. This hands-on training is designed for individuals seeking to harness the potential of SQL (Structured Query Language) to analyze and manipulate data effectively. Whether you’re a data enthusiast, aspiring data analyst, or someone looking to enhance their database skills, this course provides a solid foundation in SQL tailored for data analytics.

Key Learning Objectives:

  1. Introduction to SQL: Gain a comprehensive understanding of SQL, from the basics to advanced concepts.
  2. Data Retrieval: Learn how to retrieve and filter data using SQL queries, enabling you to extract valuable insights from databases.
  3. Data Manipulation: Master the art of manipulating data through SQL, including adding, modifying, and deleting records.
  4. Data Aggregation: Explore advanced SQL functions for aggregating and summarizing data, essential for analytical tasks.
  5. Joining Tables: Understand how to combine data from multiple tables, a crucial skill for analyzing complex datasets.
  6. Data Visualization: Learn to leverage SQL queries for data visualization, facilitating clearer communication of insights.
  7. Real-world Applications: Apply SQL skills to real-world scenarios and datasets, ensuring practical proficiency.

Who Should Enroll:

  • Aspiring Data Analysts
  • Business Intelligence Professionals
  • Database Administrators
  • Anyone keen on leveraging SQL for data-driven decision-making

Prerequisites:

No prior knowledge of SQL is required. Basic familiarity with data concepts is recommended but not mandatory.

Course Format:

The course combines engaging lectures, practical exercises, and real-world projects to ensure a hands-on learning experience. Participants will have the opportunity to apply their SQL skills in various scenarios, reinforcing their understanding of data analytics.

Outcome:

Upon completion, participants will be equipped with the essential SQL skills needed for effective data analysis. Whether you’re looking to enhance your career prospects or dive into the world of data analytics, “SQL for Data Analytics” is your gateway to unlocking the insights hidden in databases.

Introduction to databases, SQL & MySQL

SQL Theory

Basic database terminology

Installation of Mysql workbench and server

First steps in SQL

Mysql contraints

SQL select statement

SQL insert

SQL update

SQL delete

Aggregate function

SQL joins

Self Joins

SQL sub queries

SQL views

Stored routines

Advance SQL

Combining SQL with Tableau

Data Visualization with Tableau

SQL, or Structured Query Language, is a programming language designed for managing and querying relational databases. In data analytics, SQL is used to extract, manipulate, and analyze data stored in databases. Analysts can use SQL to retrieve specific data sets, apply filters, aggregate information, and perform calculations, enabling them to gain valuable insights from the data.
SQL provides powerful tools for filtering and sorting data. The “where”clause is used to filter rows based on specified conditions, allowing analysts to extract only the relevant data. Sorting is achieved using the ORDER BY clause, which arranges results based on one or more columns in ascending or descending order. These features help analysts focus on specific subsets of data and present results in a meaningful way.
SQL aggregate functions, such as SUM, AVG, MIN, MAX, and COUNT, enable analysts to perform calculations on groups of data. These functions summarize and provide insights into data patterns, trends, and distributions. For instance, SUM calculates the total of a numeric column, AVG computes the average, MIN and MAX find the minimum and maximum values, and COUNT tallies the number of rows meeting specified conditions. Aggregate functions are essential for summarizing and drawing conclusions from large datasets.
Yes, SQL can perform various data transformations. The SELECT statement can be used to create new columns using calculations, combining multiple columns, or applying functions to existing data. Joins allow analysts to combine data from multiple tables, enabling complex analysis by connecting related information. Additionally, CASE statements provide conditional logic for transforming data based on specified criteria. These capabilities allow analysts to shape data to match their analytical needs.
SQL supports data exploration and visualization by enabling analysts to retrieve and organize data for further analysis. Analysts can use SQL queries to extract subsets of data, generate summary statistics, and uncover patterns. Once the data is prepared, it can be imported into data visualization tools or libraries to create charts, graphs, and dashboards. By combining SQL's querying capabilities with visualization tools, analysts can communicate insights effectively to stakeholders and make informed decisions based on the data.
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Enrolled: 10 students
Duration: 36 hours
Level: Beginner
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Working hours

Monday 9:30 am - 7.30 pm
Tuesday 9:30 am - 7.30 pm
Wednesday 9:30 am - 7.30 pm
Thursday 9:30 am - 7.30 pm
Friday 9:30 am - 7.30 pm
Saturday 10:00 am - 7.00 pm
Sunday 10:00 am - 7.00 pm