Become a Data Scientist: SQL, Tableau, ML & DL [4-in-1]
- Description
- Curriculum
- FAQ
- Reviews
If you are a curious learner looking to dive into the exciting world of data science, then this course is tailor-made for you! Do you want to master the essential skills required for a successful career in data science? Are you eager to develop expertise in SQL, Tableau, Machine and Deep Learning using Python? If your answer is a resounding “yes,” then join us and embark on a journey towards becoming a data scientist!
In this course, you will gain a comprehensive understanding of SQL, Tableau, Machine Learning, and Deep Learning using Python. You will develop the necessary skills to analyze data, visualize insights, build predictive models, and derive actionable business solutions. Here are some key benefits of this course:
-
Develop mastery in SQL, Tableau, Machine & Deep Learning using Python
-
Build strong foundations in data analysis, data visualization, and data modeling
-
Acquire hands-on experience in working with real-world datasets
-
Gain a deep understanding of the underlying concepts of Machine and Deep Learning
-
Learn to build and train your own predictive models using Python
Data science is a rapidly growing field, and there is a high demand for skilled professionals who can analyze data and provide valuable insights. By learning SQL, Tableau, Machine & Deep Learning using Python, you can unlock a world of career opportunities in data science, AI, and analytics.
What’s covered in this course?
The analysis of data is not the main crux of analytics. It is the interpretation that helps provide insights after the application of analytical techniques that makes analytics such an important discipline. We have used the most popular analytics software tools which are SQL, Tableau and Python. This will aid the students who have no prior coding background to learn and implement Analytics and Machine Learning concepts to actually solve real-world problems of Data Science.
Let me give you a brief overview of the course
-
Part 1 – SQL for data science
In the first section, i.e. SQL for data analytics, we will be teaching you everything in SQL that you will need for Data analysis in businesses. We will start with basic data operations like creating a table, retrieving data from a table etc. Later on, we will learn advanced topics like subqueries, Joins, data aggregation, and pattern matching.
-
Part 2 – Data visualization using Tableau
In this section, you will learn how to develop stunning dashboards, visualizations and insights that will allow you to explore, analyze and communicate your data effectively. You will master key Tableau concepts such as data blending, calculations, and mapping. By the end of this part, you will be able to create engaging visualizations that will enable you to make data-driven decisions confidently.
-
Part 3 – Machine Learning using Python
In this part, we will first give a crash course in python to get you started with this programming language. Then we will learn how to preprocess and prepare data before building a machine learning model. Once the data is ready, we will start building different regression and classification models such as Linear and logistic regression, decision trees, KNN, random forests etc.
-
Part 4 – Deep Learning using Python
In the last part, you will learn how to make neural networks to find complex patterns in data and make predictive models. We will also learn the concepts behind image recognition models and build a convolutional neural network for this purpose.
Throughout the course, you will work on several activities such as:
-
Building an SQL database and retrieving relevant data from it
-
Creating interactive dashboards using Tableau
-
Implementing various Machine Learning algorithms
-
Building a Deep Learning model using Keras and TensorFlow
This course is unique because it covers the four essential topics for a data scientist, providing a comprehensive learning experience. You will learn from industry experts who have hands-on experience in data science and have worked with real-world datasets.
What makes us qualified to teach you?
The course is taught by Abhishek (MBA – FMS Delhi, B. Tech – IIT Roorkee) and Pukhraj (MBA – IIM Ahmedabad, B. Tech – IIT Roorkee). As managers in the Global Analytics Consulting firm, we have helped businesses solve their business problems using Analytics and we have used our experience to include the practical aspects of business analytics in this course. We have in-hand experience in Business Analysis.
We are also the creators of some of the most popular online courses – with over 1,200,000 enrollments and thousands of 5-star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman – Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet, or anything related to any topic, you can always post a question in the course or send us a direct message.
Don’t miss out on this opportunity to become a data scientist and unlock your full potential! Enroll now and start your journey towards a fulfilling career in data science.
-
2Installing PostgreSQL and pgAdmin in your SystemVideo lesson
In Lecture 2 of Section 2 on "Installation and getting started," we will focus on installing PostgreSQL and pgAdmin in your PC. We will begin by discussing the importance of PostgreSQL as a powerful, open-source relational database management system commonly used by data scientists. We will guide you through the step-by-step process of downloading and installing PostgreSQL on your PC, ensuring you have the necessary tools to store and manage your data effectively.
Following the installation of PostgreSQL, we will move on to installing pgAdmin, a comprehensive database design and management tool that works seamlessly with PostgreSQL. We will provide detailed instructions on how to download and set up pgAdmin on your PC, giving you access to a user-friendly interface for interacting with your PostgreSQL databases. By the end of this lecture, you will have successfully installed both PostgreSQL and pgAdmin, laying the foundation for your journey to becoming a proficient data scientist with the necessary tools for data storage and management. -
3This is a milestone!Video lesson
-
4If pgAdmin is not opening...Text lesson
In this lecture, we will be discussing what to do if pgAdmin, a popular open-source database management tool, is not opening properly. We will cover troubleshooting steps to ensure that you can successfully launch pgAdmin and begin working with your databases. From checking for updates to verifying that your firewall settings aren't blocking the program, we will walk you through the process of getting pgAdmin up and running.
Additionally, we will provide tips on optimizing pgAdmin for better performance and functionality. By proper configuration and settings adjustments, you can enhance your experience with this powerful tool and make the most out of its features. We will also discuss common errors that may occur when using pgAdmin and how to resolve them effectively. By the end of this lecture, you will have the knowledge and resources to successfully troubleshoot any issues with pgAdmin and start utilizing it to manage your databases efficiently. -
5Course resourcesText lesson
-
6CREATEVideo lesson
In Lecture 6 of Section 3: Fundamental SQL statements, we will be focusing on the CREATE statement in SQL. The CREATE statement is used to create a new table within a database. We will go over the syntax of the CREATE statement and discuss the different data types that can be used when creating a table. Additionally, we will cover how to define primary keys and foreign keys within a table using the CREATE statement.
Furthermore, we will delve into the importance of properly naming columns and tables when using the CREATE statement. We will also discuss best practices for creating tables in SQL, including ensuring data integrity and efficiency. By the end of this lecture, students will have a solid understanding of how to use the CREATE statement to create tables in SQL and will be able to apply this knowledge to their own data science projects. -
7Exercise 1: Create DB and TableVideo lesson
In this lecture, we will be focusing on the fundamental SQL statements that are essential for creating and managing databases and tables. We will go over the basics of SQL syntax, including how to create a new database and table, as well as how to insert, update, and delete data within a table. By the end of this lecture, you will have a solid understanding of how to use SQL to manipulate data in a structured and efficient manner.
For the exercise portion of this lecture, we will be creating a new database and table from scratch using SQL commands. You will be guided through the process of setting up the database and defining the table structure, including specifying data types and constraints for each column. By completing this exercise, you will gain hands-on experience in creating and managing databases, which will be crucial for your success as a data scientist. -
8Solutions to all ExercisesText lesson
In Lecture 8 of Section 3 of the course "Become a Data Scientist: SQL, Tableau, ML & DL [4-in-1]," we will be covering the solutions to all exercises related to fundamental SQL statements. This lecture will focus on demonstrating how to write SQL queries to retrieve specific data from a database, including SELECT, WHERE, ORDER BY, GROUP BY, and JOIN statements. By understanding these fundamental SQL statements, you will be able to manipulate and analyze data effectively, which is crucial for becoming a successful data scientist.
Throughout this lecture, we will walk through step-by-step solutions to the exercises provided in the course materials. These exercises are designed to help you practice applying SQL statements in real-world scenarios, allowing you to improve your skills and knowledge in working with databases. By the end of this lecture, you will have a solid understanding of how to use SQL to retrieve, filter, sort, group, and join data, empowering you to work with complex datasets and derive valuable insights as a data scientist. -
9INSERTVideo lesson
In this lecture, we will be focusing on the fundamental SQL statement INSERT. We will go over the syntax and usage of the INSERT statement, which is used to insert new records into a database table. We will also discuss how to specify the column names and values when inserting data, as well as common pitfalls to avoid when using the INSERT statement.
Additionally, we will cover how to use the INSERT statement in conjunction with other SQL commands such as SELECT and UPDATE. We will explore how to insert data into a table based on the results of a SELECT query, as well as how to update existing records using the INSERT statement. By the end of this lecture, students will have a solid understanding of how to effectively use the INSERT statement in SQL to add new data to their database tables. -
10Import data from FileVideo lesson
In Lecture 10 of Section 3 of our course on becoming a Data Scientist, we will be covering how to import data from a file into SQL. We will explore the different methods and tools available to efficiently import data from various file formats such as CSV, Excel, JSON, and text files. Understanding how to import data is crucial as it forms the foundation for data analysis and manipulation within SQL.
Additionally, we will delve into the best practices for importing data, including how to handle common data quality issues that may arise during the import process. By the end of this lecture, you will have a comprehensive understanding of how to effectively import data from files into SQL, setting you on the path towards becoming a proficient Data Scientist with the necessary skills to excel in the field. -
11Exercise 2: Inserting and Importing dataVideo lesson
In Lecture 11 of Section 3 of our course, we will focus on Exercise 2, which will cover the process of inserting and importing data using SQL. We will start by discussing the importance of properly inserting data into a database and how it can affect the overall performance of the system. We will then move on to practical examples, where we will walk through the steps of inserting data into the appropriate tables using SQL commands.
Next, we will explore the process of importing data from external sources into our database using SQL. We will discuss the various methods available for importing data, such as bulk insert and import/export wizards. We will also cover best practices for importing data efficiently and accurately, as well as potential pitfalls to avoid. By the end of this lecture, students will have a solid understanding of how to insert and import data using SQL, and will be able to apply these concepts to real-world scenarios as data scientists. -
12SELECT statementVideo lesson
In Lecture 12 of Section 3 on Fundamental SQL statements, we will be diving deep into the SELECT statement, one of the most important and fundamental statements in SQL. We will cover the syntax of the SELECT statement, how to use it to retrieve data from a database, and the different clauses that can be used in conjunction with SELECT such as WHERE, ORDER BY, GROUP BY, and HAVING. We will also explore the use of aggregate functions like COUNT, SUM, AVG, and MIN/MAX within the SELECT statement.
Additionally, we will discuss the concept of subqueries and how they can be used within the SELECT statement to retrieve more complex and specific data from a database. We will demonstrate various examples of using subqueries in conjunction with the SELECT statement to solve real-world data problems and optimize data retrieval. By the end of this lecture, you will have a solid understanding of the SELECT statement and be able to confidently use it to retrieve, filter, and manipulate data in SQL databases. -
13Quick coding exercise on Select StatementQuiz
-
14SELECT DISTINCTVideo lesson
In this lecture, we will be diving into the SELECT DISTINCT statement in SQL. We will be learning how to retrieve unique values from a table using the DISTINCT keyword in our queries. We will explore how to use SELECT DISTINCT with single and multiple columns, as well as how to combine it with other SQL clauses to filter and sort our data effectively.
Additionally, we will cover examples of how to use SELECT DISTINCT to eliminate duplicate entries in our result sets. We will discuss best practices for using the DISTINCT keyword in SQL queries, and how it can be combined with other SQL commands to extract valuable insights from our database. By the end of this lecture, you will have a solid understanding of how to use SELECT DISTINCT to manipulate and analyze data in SQL effectively. -
15Quick coding exercise on Distinct CommandQuiz
-
16WHEREVideo lesson
In Lecture 14 of Section 3 of our course on becoming a Data Scientist, we will dive into the fundamental SQL statement of WHERE. This statement is crucial in filtering data from a table based on specific conditions. We will learn how to use WHERE to select rows that meet certain criteria, such as filtering for data that falls within a specific date range or belongs to a particular category.
Additionally, we will explore how to use operators like =, <>, >, <, >=, and <= in conjunction with WHERE to further refine our queries. By the end of this lecture, you will have a solid understanding of how to leverage the power of the WHERE statement to extract meaningful insights from your data using SQL. Join us as we deepen our knowledge of SQL and take our data analysis to the next level. -
17Quick coding exercise on Where StatementQuiz
-
18Logical OperatorsVideo lesson
In Lecture 15 of Section 3, we will be covering Logical Operators in SQL. Logical Operators are used to combine multiple conditions in a query to produce a more targeted result set. We will dive into the three main Logical Operators in SQL: AND, OR, and NOT, and show you how to use them effectively in your queries.
We will discuss how to use Logical Operators to filter data based on multiple conditions, allowing you to retrieve the right information from your databases efficiently. By the end of this lecture, you will have a solid understanding of how Logical Operators work in SQL and be able to apply them to your own data analysis projects. -
19Quick coding exercise on Logical OperatorsQuiz
-
20Exercise 3: SELECT, WHERE & LogicalVideo lesson
In Lecture 16 of Section 3: Fundamental SQL statements, we will be covering Exercise 3 which focuses on using the SELECT, WHERE, and Logical operators in SQL queries. We will delve into how to extract specific data from a database by using the SELECT statement, which allows us to choose the columns we want to display. Then, we will learn about the WHERE clause, which is used to filter the results based on specific conditions, allowing us to retrieve only the data that meets our criteria.
Additionally, we will explore how to use logical operators such as AND, OR, and NOT to further refine our SQL queries. These operators allow us to combine multiple conditions in a single query to retrieve more accurate and relevant results. By the end of this lecture, you will have a better understanding of how to use these fundamental SQL statements and operators to manipulate data effectively in a database environment. -
21UPDATEVideo lesson
In this lecture, we will dive into the fundamentals of SQL statements, specifically focusing on the UPDATE statement. The UPDATE statement is used to modify existing records in a database table. We will learn how to use the UPDATE statement to change values in specific columns based on certain conditions, allowing us to update data in our tables efficiently and accurately.
We will explore various examples of using the UPDATE statement, including updating single and multiple records at once. We will also discuss best practices for using the UPDATE statement, such as always including a WHERE clause to specify which records should be updated. By the end of this lecture, you will have a solid understanding of how to effectively use the UPDATE statement in SQL to manipulate data in your databases. -
22Quick coding exercise on Update CommandQuiz
-
23DELETEVideo lesson
In Lecture 18 of Section 3 of our course on becoming a Data Scientist, we will cover the fundamental SQL statement DELETE. We will explore how to use the DELETE statement to remove one or more rows from a table in a database. We will also discuss the importance of using the WHERE clause with the DELETE statement to specify which rows should be deleted based on certain conditions.
Additionally, we will delve into the potential risks of using the DELETE statement, such as accidentally deleting important data if not used correctly. We will provide examples and practical exercises to help you gain a better understanding of how to effectively use the DELETE statement in SQL to manipulate and manage data in relational databases. By the end of this lecture, you will have a solid grasp of how to use the DELETE statement and avoid common pitfalls when deleting data in SQL. -
24Quick coding exercise on Delete CommandQuiz
-
25ALTER Part - 1Video lesson
In Lecture 19 of Section 3 of the course "Become a Data Scientist: SQL, Tableau, ML & DL [4-in-1]," we will be exploring the topic of ALTER statements in SQL. This lecture will cover the fundamentals of ALTER statements, including how to add, modify, and drop columns in a table. We will also learn about the different types of alterations that can be made to a table structure, such as changing the data type of a column or adding constraints to columns.
Additionally, in Part 1 of this lecture, we will delve into the syntax and usage of the ALTER TABLE statement in SQL. We will discuss how to alter the structure of a table by adding new columns, modifying existing columns, and dropping columns. By the end of this lecture, you will have a solid understanding of how to use ALTER statements effectively to make changes to your database tables in SQL. -
26ALTER Part - 2Video lesson
In Lecture 20 of Section 3 on Fundamental SQL statements, we will be diving deeper into the topic of ALTER statements in SQL. This lecture will cover the different aspects of altering a table in SQL, such as adding columns, dropping columns, changing data types, and modifying constraints. Understanding how to properly use ALTER statements is crucial for data scientists as they often need to make changes to existing database tables to support their analysis and modeling tasks.
Additionally, we will explore more advanced ALTER statements, like renaming tables and columns, adding default values, and setting column constraints. These advanced techniques will help data scientists efficiently manage their database schema and ensure data integrity. By the end of this lecture, students will have a solid understanding of how to use ALTER statements effectively in SQL to manipulate database tables according to their analytical needs. -
27Quick coding exercise on Alter CommandQuiz
-
28Exercise 4: Update, Delete and Alter TableVideo lesson
In Lecture 21 of Section 3 of our course, we will be diving into Exercise 4 where we will focus on three important SQL statements - Update, Delete, and Alter Table. We will begin by learning how to use the Update statement to modify existing data in a database table. We will cover the syntax for updating specific columns and rows, as well as how to use conditions to update only certain data based on specific criteria. This is a crucial skill for data analysts and data scientists to manipulate data efficiently and accurately.
Next, we will move on to the Delete statement, which allows us to remove specific rows from a database table. We will explore the syntax for deleting rows based on certain conditions, as well as how to delete all rows from a table. Finally, we will conclude the lecture by discussing the Alter Table statement, which is used to add, modify, or delete columns in a database table. We will cover how to add a new column, modify the data type of an existing column, and delete a column from a table. By mastering these fundamental SQL statements, you will be well-equipped to manipulate data effectively and confidently in your data science projects. -
29QuizQuiz
-
30Restore and Back-upVideo lesson
In Lecture 22 of Section 4: Restore and Back-up, we will be covering the importance of data backups in the field of data science. We will discuss the various methods and tools available for backing up crucial data to ensure its safety and accessibility in case of unexpected events such as system failures or data corruption. Additionally, we will explore the best practices for creating and implementing data backup strategies to minimize the risk of data loss and to facilitate efficient data recovery processes.
Furthermore, we will delve into the process of restoring data from backups, including the steps involved in recovering lost or corrupted data. We will also address common challenges and troubleshooting techniques related to data restoration, as well as the significance of testing backup systems to ensure their reliability and effectiveness. By the end of this lecture, students will have a comprehensive understanding of the importance of data backups in data science and the necessary skills to create and implement effective backup and restoration strategies in their own projects. -
31Debugging restoration issuesVideo lesson
In this lecture, we will delve into the importance of restoring and backing up data in the field of data science. We will discuss the various techniques and best practices for restoring and backing up data to ensure the integrity and security of the data. We will explore how to handle restoration issues, such as data corruption, failed backups, and other common problems that may arise during the restoration process. By understanding these issues and learning how to debug them effectively, data scientists can ensure that their data is consistently and accurately restored and backed up.
Furthermore, we will cover the tools and technologies that can be used to debug restoration issues, such as SQL queries, Tableau dashboards, and machine learning and deep learning algorithms. We will provide hands-on demonstrations and examples of how to troubleshoot and resolve restoration issues using these tools. By the end of this lecture, students will have a solid understanding of how to effectively restore and back up data, as well as the skills to debug any issues that may arise during the process. -
32Creating DB using CSV filesVideo lesson
In this lecture, we will delve into the process of creating a database using CSV files. We will start by discussing the importance of having a solid backup and restore strategy in place to ensure the integrity of your data. We will then walk through the steps of importing data from CSV files into a new database, covering key concepts such as data types, column headings, and delimiter options.
Next, we will explore best practices for creating a database schema based on the structure of the CSV files. We will discuss how to map CSV columns to database tables and fields, optimizing the layout for efficient querying and data manipulation. By the end of this lecture, you will have a solid understanding of how to leverage CSV files to build a robust and well-organized database for your data science projects. -
33Debugging summary and Code for CSV filesText lesson
In Lecture 25 of Section 4: Restore and Back-up, we will delve into a thorough summary of debugging techniques for data science projects. We will cover common errors and issues that may arise while working with SQL, Tableau, ML, and DL, and how to effectively debug and troubleshoot them. In addition, we will provide hands-on examples and tips for identifying and fixing errors in your code to ensure smooth data analysis and visualization processes.
Furthermore, in this lecture, we will explore the process of working with CSV files in data science projects. We will discuss the importance of properly formatting and cleaning CSV files for optimal data analysis and visualization outcomes. Additionally, we will showcase various code snippets and best practices for manipulating CSV files using SQL, Tableau, ML, and DL tools. By the end of this lecture, students will have a solid understanding of how to work with CSV files in their data science projects and be well-equipped to handle and analyze large datasets efficiently. -
34Exercise 5: Restore and Back-upVideo lesson
In this lecture, we will discuss the importance of restoring and backing up your data when working as a data scientist. We will learn how to create a backup of our databases and tables in order to prevent data loss and ensure data integrity. We will also explore different methods for restoring data, such as using backups or snapshots, to recover information in case of errors or system failures.
Additionally, we will cover best practices for creating and maintaining backups, including scheduling regular backups, storing backups in multiple locations, and testing backups to ensure they can be successfully restored. By the end of this lecture, you will have a comprehensive understanding of the importance of data restoration and backup in the field of data science, as well as the practical skills needed to effectively manage and protect your data.
-
35INVideo lesson
In this lecture, we will be covering the concept of selection commands in SQL, specifically focusing on filtering data using the IN operator. The IN operator is a powerful tool that allows us to specify a list of values that we want to include in our query results. We will learn how to use the IN operator to retrieve data that matches specific criteria, making our queries more precise and efficient.
Furthermore, we will also discuss advanced filtering techniques such as using subqueries in combination with the IN operator to further refine our data selection process. By the end of this lecture, students will have a solid understanding of how to effectively filter data using the IN operator in SQL, and be able to apply this knowledge to enhance their data analysis skills. -
36Quick coding exercise on IN operatorQuiz
-
37BETWEENVideo lesson
In this lecture, we will focus on selection commands in SQL, specifically the BETWEEN operator. The BETWEEN operator is used to filter rows based on a range of values in a specified column. We will discuss how to use the BETWEEN operator in conjunction with other selection commands to refine our queries and extract the desired data from a database.
We will also explore practical examples of how to use the BETWEEN operator in real-world scenarios. By the end of this lecture, you should have a solid understanding of how to apply the BETWEEN operator effectively to filter data and ensure that your queries return the necessary information for your analysis. Additionally, we will discuss best practices for using the BETWEEN operator to improve the efficiency and accuracy of your data analysis processes. -
38Quick coding exercise on Between OperatorQuiz
-
39LIKEVideo lesson
In Lecture 29 of Section 5 of the course "Become a Data Scientist: SQL, Tableau, ML & DL [4-in-1]," we will be diving into the topic of LIKE in SQL. LIKE is a powerful operator that allows you to perform pattern matching on text data in your database. We will learn how to use LIKE to filter our data based on specific patterns or characters, allowing us to retrieve only the information we need for our analysis.
We will cover different wildcard characters that can be used in conjunction with the LIKE operator, such as % and _. These characters can help us match patterns more accurately and efficiently in our SQL queries. By the end of this lecture, you will have a solid understanding of how to use the LIKE operator to filter and retrieve data from your database, making you more proficient in SQL and better equipped to analyze and manipulate data as a data scientist. -
40Quick coding exercise on Like operatorQuiz
-
41Exercise 6: In, Like & BetweenVideo lesson
In Lecture 30 of Section 5 of the course "Become a Data Scientist: SQL, Tableau, ML & DL [4-in-1]", we will be covering exercises related to selection commands in SQL. Specifically, we will focus on the usage of the IN, LIKE, and BETWEEN operators for filtering data in SQL queries. These operators are essential for narrowing down datasets and extracting only the relevant information for analysis.
Throughout this lecture, we will walk through various exercises that will require students to apply the IN, LIKE, and BETWEEN operators to filter data based on specific criteria. By practicing these exercises, students will gain a deeper understanding of how to use these selection commands effectively in SQL queries and enhance their data analysis skills. Additionally, we will discuss the importance of proper data filtering techniques in data science projects and how these operators can help in simplifying the process of data extraction and analysis.
-
42Side Lecture: Commenting in SQLVideo lesson
In this lecture, we will discuss the importance of commenting in SQL. Commenting is a crucial aspect of writing SQL queries as it helps to document and explain the purpose of the code to other developers or to your future self. We will learn how to add comments in SQL using double dashes (--), multi-line comments /* */, and naming conventions for comments to make the code more readable and maintainable.
Additionally, we will explore the selection commands in SQL, focusing on the ORDER BY clause. We will learn how to sort the results of a query in ascending or descending order based on a specific column or expression. Understanding how to use ORDER BY will allow us to organize and analyze our data effectively in SQL, enhancing our ability to extract valuable insights from databases. -
43ORDER BYVideo lesson
In this lecture, we will be diving deep into the selection commands in SQL, specifically focusing on the ORDER BY clause. We will discuss how the ORDER BY clause can be used to sort the result set of a query based on one or more columns in ascending or descending order. Understanding this clause is crucial for data scientists as it allows for the customization of the output to better analyze and interpret the data.
Additionally, we will cover various examples and scenarios where the ORDER BY clause can be applied effectively. By learning how to use this clause, you will be able to present your data in a more organized and meaningful way, making it easier to draw insights and conclusions from your analysis. Understanding the nuances of ordering data is a key skill for any data scientist, and this lecture will provide you with the knowledge and tools to master this essential concept. -
44Quick coding exercise on Order by ClauseQuiz
-
45LIMITVideo lesson
In Lecture 33 of Section 6 of our course "Become a Data Scientist: SQL, Tableau, ML & DL [4-in-1]," we will be diving into the topic of LIMIT in SQL. LIMIT is a powerful command that allows you to control the number of rows returned by a query. We will explore how to use the LIMIT command to specify a maximum number of rows to be retrieved from a database table, helping you efficiently manage large datasets and optimize query performance.
Additionally, we will cover how to combine the LIMIT command with other SQL selection commands such as ORDER BY to sort the results before limiting the number of rows. Understanding how to use these commands together will enable you to extract specific subsets of data for analysis, reporting, or visualization in tools like Tableau. By the end of this lecture, you will have a solid understanding of how to leverage the LIMIT command to streamline your data analysis and access the information you need quickly and effectively. -
46Quick coding exercise on Limit CommandQuiz
-
47Exercise 7: SortingVideo lesson
In this lecture, we will be discussing the selection commands in SQL, focusing on the ordering of data. We will cover how to use the ORDER BY clause to sort data in ascending or descending order based on specific columns in a table. By understanding how to use ordering commands effectively, you will be able to manipulate and analyze data more efficiently in your data science projects.
Additionally, we will be delving into an exercise focused on sorting data using SQL commands. Through this hands-on exercise, you will have the opportunity to practice implementing sorting techniques in SQL to arrange data in a desired order. By completing this exercise, you will gain valuable experience in using sorting commands to enhance your data manipulation skills as a data scientist. -
48QuizQuiz
-
49ASVideo lesson
In Lecture 35: AS, we will be diving into the concept of aliases in SQL. An alias allows us to give a temporary name or shorthand to a column, table, or expression in our SQL queries. We will learn how to use the AS keyword to create aliases and make our queries more readable and concise.
We will also explore how to use aliases in conjunction with other SQL commands such as SELECT, WHERE, GROUP BY, and ORDER BY. By the end of this lecture, you will have a solid understanding of how aliases can be used to improve the clarity and efficiency of your SQL queries, making you a more effective and efficient data scientist. -
50Quick coding exercise on AS operatorQuiz
-
51COUNTVideo lesson
In this lecture, we will be diving into the topic of aggregate commands, specifically focusing on the COUNT function. We will learn how to use the COUNT function in SQL to determine the number of rows in a table that meet certain criteria. This will be a crucial skill for any data scientist, as counting the number of records within a dataset is a common task in data analysis.
We will also explore the various ways in which the COUNT function can be used to count the number of non-null values in a specific column, as well as how to count the distinct values in a column. Understanding how to use the COUNT function effectively will allow us to gain valuable insights from our data and make informed decisions based on the information we gather. By the end of this lecture, you will have a solid grasp of how to use the COUNT function in SQL to manipulate and analyze data efficiently. -
52Quick coding exercise on Count functionQuiz
-
53SUMVideo lesson
In Lecture 37 of Section 8 on Aggregate Commands in the course "Become a Data Scientist: SQL, Tableau, ML & DL [4-in-1]," we will be covering the important topic of SUM function. We will discuss how to use the SUM function in SQL to calculate the total of a specific column in a table. This function is essential for analyzing and aggregating large datasets to gain insights and make informed decisions based on the data.
Additionally, we will explore different scenarios where the SUM function can be applied effectively in real-world data science projects. We will learn how to use the SUM function in conjunction with other SQL commands to retrieve meaningful results and derive valuable information from the data. By the end of this lecture, students will have a solid understanding of how to use the SUM function to perform calculations on data sets and extract relevant information for further analysis and visualization in Tableau. -
54Quick coding exercise on Sum functionQuiz
-
55AVERAGEVideo lesson
In Lecture 38 of Section 8 on Aggregate Commands, we will be diving into the AVERAGE function in SQL. This crucial function allows data scientists to easily calculate the average value of a specified column in a database table. We will first discuss the syntax of the AVERAGE function and how it can be used in conjunction with other commands to obtain valuable insights from large datasets.
Furthermore, we will explore real-world examples where the AVERAGE function is utilized to analyze data effectively. By the end of this lecture, students will have a solid understanding of how to leverage the AVERAGE function in SQL to derive meaningful conclusions from complex datasets. Join us as we uncover the power of aggregation commands in data science and enhance your skills as a budding data scientist. -
56Quick coding exercise on Average functionQuiz
-
57MIN & MAXVideo lesson
In this lecture, we will explore the MIN and MAX aggregate commands in SQL. These commands are used to find the minimum and maximum values in a given dataset. We will learn how to apply these commands to our SQL queries to retrieve specific information, such as the oldest or youngest entry in a table.
Next, we will discuss how to use the MIN and MAX functions in Tableau to create visualizations that showcase the highest and lowest values in our data. We will also cover practical examples of how these commands can be combined with other SQL functions to generate insightful reports and dashboards. By the end of this lecture, you will have a solid understanding of how to utilize the MIN and MAX aggregate commands to analyze and present data effectively in both SQL and Tableau. -
58Quick coding exercise on MIN & MAX functionQuiz
-
59Exercise 8: Aggregate functionsVideo lesson
In Lecture 40 of Section 8, we will be focusing on the exercise related to aggregate functions in SQL. We will explore how to use aggregate commands such as COUNT, SUM, AVG, MIN, and MAX to perform calculations on groups of data in a database. By the end of this lecture, students will have a better understanding of how to summarize and analyze data using these powerful functions.
Additionally, we will discuss how aggregate functions can be combined with other SQL commands to generate more complex queries. We will walk through examples of how to calculate averages, totals, and counts of specific data points within a dataset. By practicing these exercises, students will improve their skills in data analysis and gain a deeper understanding of how to utilize aggregate functions effectively in SQL. -
60QuizQuiz
-
61GROUP BYVideo lesson
In Lecture 41 of Section 9: Group By Commands, we will delve into the concept of grouping data in SQL using the GROUP BY command. We will learn how to organize and group data based on specific columns in a dataset, allowing us to analyze and aggregate information more effectively. By understanding how to employ the GROUP BY command, we will be able to generate insightful reports and summaries from our data sets.
Furthermore, we will explore the importance of using GROUP BY in combination with other SQL functions such as COUNT, SUM, AVG, and MAX. By utilizing these functions alongside the GROUP BY command, we can perform calculations and analyses on grouped data sets, providing us with valuable insights and actionable information. Through practical examples and hands-on exercises, we will gain a comprehensive understanding of how to effectively use GROUP BY commands to manipulate and analyze data in SQL. -
62Quick coding exercise on Group By ClauseQuiz
-
63HAVINGVideo lesson
In this lecture, we will dive into the topic of HAVING in SQL. The HAVING clause is used in conjunction with the GROUP BY clause to filter groups based on specific conditions. We will explore how HAVING allows us to apply conditions to groups of rows, similar to the WHERE clause which applies conditions to individual rows.
We will cover the syntax of the HAVING clause and discuss its usage in various scenarios. We will also walk through examples to demonstrate how HAVING can be used to filter and aggregate data in a more efficient and effective way. By the end of this lecture, you will have a solid understanding of how to leverage the HAVING clause in SQL to analyze and manipulate data at the group level. -
64Quick coding exercise on Having ClauseQuiz
-
65Exercise 9: Group ByVideo lesson
In Lecture 43 of Section 9 of the course "Become a Data Scientist: SQL, Tableau, ML & DL [4-in-1]", we will be covering the topic of Group By commands. This lecture will focus on how to use the Group By command in SQL to group data based on specific criteria. We will discuss how to apply the Group By command to aggregate functions such as Sum, Count, Avg, etc., in order to analyze and summarize data more effectively.
During this exercise, we will practice using the Group By command in SQL to group data and perform various aggregate functions on the grouped data. Students will have the opportunity to apply what they have learned in previous lectures on SQL queries, as well as learn new techniques for summarizing and analyzing data using the Group By command. By the end of this lecture, students should have a solid understanding of how to use the Group By command effectively in SQL to group and summarize data for further analysis in their data science projects. -
66QuizQuiz
-
67CASE WHENVideo lesson
In Lecture 44 of Section 10 on Conditional Statements in our course "Become a Data Scientist: SQL, Tableau, ML & DL [4-in-1]," we will be diving into the function CASE WHEN in SQL. We will learn how to use this powerful feature to create conditional logic in our SQL queries, allowing us to perform different actions based on specific conditions. We will cover syntax, examples, and best practices for using CASE WHEN statements to manipulate and transform data efficiently.
Additionally, we will explore how to combine CASE WHEN statements with other SQL functions and clauses to enhance our data analysis capabilities. By the end of this lecture, students will have a solid understanding of how to use CASE WHEN statements to solve complex problems, make data-driven decisions, and create meaningful insights from their datasets. -
68Quick coding exercise on CASE WHEN StatementQuiz

External Links May Contain Affiliate Links read more