Learn Data Analysis with SQL and Tableau (2 Courses in 1)

Download and install Microsoft SQL Server and use SQL Server Management Studio to write queries and perform data analysis, using the North-Western database for practice.

Install SQL Server Express (2017 or newer) and SQL Server Management Studio by downloading from the official site, accepting the license, and completing the installation to navigate SSMS.

Learn how to install and open SQL Server Management Studio, connect to a server, navigate system databases, switch to a different database, and run queries with new query and execute.

Create and populate the Northwind database using the provided SQL file, connect with SQL Server Management Studio, execute the script, and explore the tables to prepare for data analysis.

Explore the Northwind database to learn table relationships, including one-to-many and many-to-many, and build data analysis skills with customers, orders, products, categories, and employees.

Discover sql, the standard language for manipulating relational databases. Explore the five components—ddl, dml, dcl, dtl, and dql—and how select retrieves data.

Explore the select statement to query data from tables, retrieving all columns or specific columns, and apply where filters like city London or country Germany in the Northwind database.

Learn to use and/or operators in the where clause to refine a select query, filtering orders by ship country Brazil and ship date constraints for flexible results.

order data by a column using the order by clause, with ascending as default and desc for descending, sorting dates oldest to newest, numbers smallest to largest, and strings alphabetically.

Apply the distinct clause to a column with duplicates to retrieve unique values. See how selecting ship country from orders reduces 830 rows to 21 unique countries.

Explore how to use in and not in operators to filter customers by country, replacing multiple or clauses with a single in clause and excluding Germany, France and Italy.

Learn how wildcards enable flexible SQL searches with the like operator in where clauses, using asterisk for multiple characters, ? for a single character, and brackets for options.

Learn how to join tables in SQL to retrieve related data, such as customers and their orders. Explore inner, left outer, right outer, full outer, and self joins with examples.

Master inner join in SQL by selecting rows from two tables where columns match, using on clauses and aliases to avoid ambiguity.

Left outer join returns all records from the left table and only matching records from the right table, shown with customers and orders example with two customers having no orders.

Master the right outer join by selecting all records from the right table and only matching records from the left, swapping table order to mirror the left join.

Master the full outer join by retrieving all records from tables, replacing left or right or inner with full join, illustrated in SQL Server Management Studio with Ordos and customers.

Perform a self join by aliasing the same customers table as t1 and t2 to find different customers in the same city.

Learn how SQL aggregate functions: count, sum, average, max, and min compute values from a products table, using aliases and the upcoming group by for per-product totals.

Apply group by to create aggregations by a column, such as counting customers by country or city. Ensure every selected column appears in the group by clause to avoid errors.

Discover how the having clause filters aggregated data after group by, using examples like country equals usa and countries with three or more customers, combined with where and order by.

Discover sql string functions such as upper, lower, len, concat, and reverse to transform query results, alias columns, and combine names on the fly in Northwind.

Learn essential sql date functions for data analysis, including getdate, year, month, and day. Use date diff to calculate age and date add to shift dates.

Learn SQL conversion functions cast and convert to convert an integer employee ID to character for joining with last name. See examples and run queries in Management Studio against Northwind.

Master subqueries in SQL by contrasting non correlated and correlated queries, and observe how inner and outer queries determine results with practical examples.

Review core sql concepts from this section, including select statements, order by, where, and or, like, joins, aggregation, group by and having, string and date functions, cast and convert.

Identify the top 10 purchased products and the second highest unit price, rank sold products by dense_rank in the USA, using case statements for stock levels and orders per product.

Analyze the top 10 purchased products by name in the Northwind database by joining order details with products, then group by product and sort by units sold in descending order.

Explore a sql solution to identify the top 10 products by units sold by joining order details with products, grouping by product name, and summing quantity.

Identify the product with the second highest unit price from the Products table and retrieve its name and unit price, with the code solution shown in the next tutorial.

Discover how to identify the product with the second highest unit price in Northwind by using a self join and a subquery with distinct unit prices and count.

Apply the dense_rank window function to rank sold products within each city in the USA by quantity, partitioning by city and ordering by descending quantity.

Analyze sold products in USA cities by joining products, order details, orders, and customers; filter to country USA and rank results by quantity sold using dense_rank.

Identify orders shipping over two days after the order date for sales over 10,000, compute days with datediff, and show days, order ID, order date, customer ID, and ship country.

Join orders and order details to compute total sale value and duration to ship with datediff, filter by total sale value greater than ten thousand, and order by duration.

Learn how to use SQL case statements to display conditional values in one column by applying when, then, else, and end, mapping conditions to names or numbers.

Apply a case statement to classify products into three stock levels using unit stock, unit on order, and discontinued status to decide when to order, reorder, or stop selling.

Explore a Northwind database query solution that uses case logic to classify each product's stock status, from no stock and negative inventory to reorder reminders and discontinued items.

Compute the number of orders per product, focusing on the beer product in 2017, using SQL and Tableau.

Execute a left join across products, order details, and orders to count 2017 orders per product, group by product name, and sort by descending order count.

Learn to combine query results with union and union all, ensuring identical column counts, data types, and order, and apply these concepts to data analysis in SQL Server.

Learn how to define a common table expression (cte) as a temporary result set, name the cte, and reference it in a subsequent select while matching column counts.

Create a list of products with current and next month stock requirements for all products, as North Wind Company grows, using union all and city operators.

Create a temporary table with union all to display current stock and next month's stock (current stock plus 20%), pulling data from the products table and excluding zero stock.

Analyze 2017–2018 sales data to compute orders, revenue, and average revenue per order; identify top customers, classify customers by contribution, and generate sql code for each task for tableau-ready insights.

Analyze customers and sales data to compute the number of orders, revenue, and average revenue per order for 2017 using SQL and Tableau.

Analyze 2017 orders by joining orders and order details to compute total orders, revenue, and average revenue per order using count, sum, and avg aggregations.

Identify the top 10 valuable customers in 2018 by city and country, by joining customers, orders, and order details and computing total sales as unit price times quantity.

Join customers, orders, and order details to compute top 10 customers by 2018 sales. Group by company, country, and city, then order by total sale using quantity times unit price.

Find products with 2018 total sales of at least 30,000. Compute sales as unit price times quantity and group by product, joining products, order details, and orders.

Join orders, order details, and products in Northwind database to compute per product units and total sale amount for 2018, using group by, having 30000, and ordering by sale amount.

Classify customers into three levels using a case statement: grade A for total sales >= 30000, grade B for 20000 to 29999, and grade C for less than 20000.

Classify customers by total sales using joins across customers, orders, and order details; compute total as sum(quantity times unit price) and assign A, B, or C by a case statement.

Find customers with total sales above the 2018 average by joining customers, orders, and order details. Calculate sales from unit price and quantity and use a subquery for the average.

Identify customers with above-average total sales in 2018 by inner joining customers, orders, and order details, grouping by company name, city, and country, using a subquery to compute the average.

Learn data analysis with sql and tableau by joining customers, orders, and order details to compute yearly sales per customer, save results in a view, and use pivot tables.

Create a view Sale_Year to compute yearly sales per customer with a pivot. Join customers, orders, and order details to sum unit price times quantity for 2016–2018.

Identify customers who haven't placed orders in the last 20 months. Use datediff with max to find the last order date and getdate as the current date.

join customers with orders on customerID, group by company name to get last order date and compute months since last order, filtering > 20 months to show eight customers.

Join the Customers and Orders tables to list each customer and their number of orders, then review the code solution in the next tutorial.

Identify customers and their order counts using a correlated subquery on the Northwind database, and order by the number of orders in descending order to reveal the most valuable customers.

Learn data analysis with sql and tableau: identify the customer with the third highest sales volume by joining customers, orders, and order details to compute unit price times quantity.

Create a saved query view in the Northwind database to compute sale volume by joining customers, orders, and order details, then identify the customer with the third highest sales volume.

Identify the customer name and country with the third highest sales volume by self-joining the view using cs1 and cs2, and counting distinct sales in a subquery.

Compute the duration in days between consecutive orders using the Orders table, then identify the longest period between two order dates.

Compute the duration between consecutive orders using a self-join on the orders table and the datediff function, revealing days between orders and helping identify valuable customers by order cadence.

Create a view to analyze sales over time using the Northwind database, computing yearly and monthly sales by company, then compare complete months across 2016 and 2017.

Analyze sales data across time by selecting two years and comparing monthly volume to reveal trends; conclude that 2017 outperformed 2016, and 2018 outpaced 2017, showing growth.

Calculate the number of orders per customer for each month in 2017 using a case statement in this task from the Learn Data Analysis with SQL and Tableau course.

Pivot customer order data for 2017 by month using a case statement and an inner join of customers and orders, counting orders per month and listing results by customer name.

Explore how to compute the number of orders by date, week, and day for 2017, and practice writing the code to reveal the solution in this task.

Learn to compute 2017 orders per week and per day from the orders table using convert to date and datepart for week and day, with grouping and counting.

Learn data analysis with SQL and Tableau by computing revenue and revenue percentage per customer, and prepare for the core solution in the next tutorial.

Learn how to compute each customer's revenue and its share of total revenue in 2017 using a scalar SQL variable, left joins, and aggregation across customers, orders, and order details.

Analyze employees to identify high sales and bonuses, count employees by title per city, list work duration in years, and flag employees older than 70.

Identify the top three employees by January 2018 sales, show sales and cities, and concatenate names; join employees, orders, and order details to compute a 0.02 bonus from total sales.

Connect to database, select top 3 employees by sales, concatenate names with city, group by name, city, filter for 2018 January, and apply a 2% bonus, order by total sales.

Learn to count employees by title for each city by querying the employees table, applying SQL techniques to analyze workforce data.

Learn to count employees by title and city using a grouped query, selecting title, city, and count from the Northwind employees table, aliasing the result as number of employees.

Identify the names of employees and their years of employment across every city by querying the employees table.

Select last name, first name, and title, and compute work years using datediff(year, hiredate, getdate()). Optionally filter where city is London and execute the query to view employees.

Identify employees and their ages, focusing on those older than 70 in every city, using the employees table for data analysis with sql and Tableau.

Select last name, first name, and title from the employees table, compute age with datediff between birth date and getdate, and filter for ages 70 and older in Northwind database.

Master data visualization and analytics with Tableau, connect to diverse data formats, build advanced diagrams, dashboards, and stories, and complete a hands-on project with real data.

Visualize data by turning text into diagrams and charts to reveal insights and trends, using bar and line charts and dashboards, with Tableau as a user friendly, no coding required.

Install Tableau Public from the official site to start your data analysis training. Compare public and professional versions, and how data saves locally and in the cloud after account creation.

Explore the Tableau work environment, connect to data sources, and navigate the main page and data pane to create visuals with filters and Show Me diagrams.

Learn how to connect to a data file in Tableau Desktop, choose Microsoft Excel, and understand how Tableau identifies data types and distinguishes dimensions from measures.

Explore Tableau professional data connections, including extra data sources not in the public version, and choose between live connections with automatic updates or extracts for faster diagrams (TWB or TWBX).

Create your first diagram in Tableau by placing states in rows and columns, sorting by sales, and exploring bar charts and heat maps for clear visuals.

Apply color by sales volume to bars, show labels, and format currency in thousands, then explore chart types and titles in Tableau to create clear, well-formatted sales diagrams.

Learn to choose different diagram types in Tableau using the Show Me menu, creating a treemap chart where state size and color reflect sales volume.

Create a horizontal bar chart of profit by state, color red for the lowest profits and blue for the highest, format with dollars and thousands, and add a chart title.

Analyze and present profits per state using a horizontal bar chart in Tableau, sort by profit descending, toggle orientation, apply red-blue diverging colors, and format currency labels.

Create calculated fields in Tableau to compute cost by subtracting profit from sales and to model discount effects, then visualize cost per state with a vertical bar chart.

Filter diagrams by location or by the sum of profit using Tableau's filters feature. Drag and drop location into filters, adjust values, and show filters to shape your chart.

Connect to a flat file of company profits and expenses, then use Tableau to apply aggregation measures—average, sum, max, min—and align labels with profits across Paris, London, Frankfurt, and Rome.

Create a vertical bar chart showing the maximum IT expenses for Company A in London, as part of the Learn data analysis with SQL and Tableau course.

Use data interpreter in Tableau to clean and reshape an Excel file of employee flight data, removing nulls and headers so you can visualize flights per date.

Learn how pivot and unpivot operators transform data: pivot converts rows into columns, unpivot reverts columns back to rows, using an employee flights dataset for reporting and data visualization.

Pivot data to a single date and the number of flights per employee, split the section and employee id, and convert strings to date to prepare the data for Tableau.

Learn to create a bar chart in Tableau to compare employee flight counts, then use date and section filters to reveal trends and top performers.

Learn to manage metadata by hiding and renaming data fields in Excel data sources to streamline visualizations, including selecting sheets, showing hidden fields, and converting fields between dimensions and measures.

Organize data fields by creating folders and hierarchies in Tableau, grouping related fields under folders like customers and building category-subcategory-product hierarchies to improve navigation and space efficiency.

Explore data granularity from continent to city and date, and demonstrate how to analyze sales by country, category, and subcategory in Tableau.

Explore time series analysis by turning date dimensions into continuous scales, visualizing sales trends with line charts, and using trend lines to reveal seasonality across years.

Learn how data bins categorize numbers into ranges for histograms, using an age example to show where most customers fall and how this aids practical data analysis.

Create bins in Tableau to group ages and incomes, convert age into a dimension, and visualize client distributions by age and income in bar charts.

Tackle the homework tasks to compute total orders, identify daily sales over 30000 from 2012–2015, find the max daily sales, and compute monthly average profit by technology category.

Organizes data fields with folders and hierarchies for customers, locations, orders, and projects to streamline analysis and reveal sales above 30,000, maximum daily sales, and monthly profit by technology.