To begin the course, you will learn why cleaning data is important. You will solve simple problems such as leading and trailing spaces in strings, unifying formats for flight registrations, combining strings and more. 

Introduction to Cleaning Data

Unifying flight formats I

Unifying flight formats II

Cleaning messy strings

Trimming strings I

Trimming strings II

Unifying strings

Comparing the similarity between strings

SOUNDEX() and DIFFERENCE()

Comparing names with SOUNDEX()

Comparing names with DIFFERENCE()

Starting with Cleaning Data

In this chapter, you will deepen your data cleaning knowledge. You will learn how to deal with missing data,  avoid duplicate data in your datasets, and work with different formats of dates.

Gestire i dati mancanti

Rimuovere i valori mancanti

Rimozione degli spazi vuoti

Riempire i valori mancanti con ISNULL()

Compilare i valori mancanti con COALESCE()

Evitare i dati duplicati

Diagnosticare i duplicati

Gestire i duplicati

Gestire formati di data diversi

Uso di CONVERT()

Usare FORMAT()

CONVERT() vs FORMAT()

Dealing with missing data, duplicate data, and different date formats

In this chapter, you will deal with out of range values and inaccurate data. You will also practice converting data with different types. Finally, you will work on matching patterns to your data to find outliers.

Out of range values and inaccurate data

Out of range values or inaccurate data?

Detecting out of range values

Excluding out of range values

Detecting and excluding inaccurate data

Converting data with different types

Using CAST() and CONVERT()

The series with most episodes

Pattern matching

Characters to specify a patterns

Matching urls

Checking phone numbers

Dealing with out of range values, different data types, and pattern matching

In this final chapter,  you will learn how to combine and aggregate data of some columns into one, split data of one column into more columns,  and transform rows into columns and vice versa.

Combining data of some columns into one column

Combining cities and states using +

Concatenating cities and states

Working with DATEFROMPARTS()

Splitting data of one column into more columns

Using SUBSTRING() and CHARINDEX()

Using RIGHT() , LEFT() and REVERSE()

SUBSTRING() or CHARINDEX()?

Transforming rows into columns and vice versa

PIVOT or UNPIVOT?

Turning rows into columns

Turning columns into rows

Congratulations!

Combining, splitting, and transforming data

Flight statistics dataset and series ratings dataset

Sapevi che data scientist e data analyst dedicano molto tempo a pulire i dati prima di poterli analizzare? Questo perché i dati del mondo reale sono disordinati. Per aiutarti a orientarti tra dati “sporchi”, questo corso ti insegna a pulire i dati archiviati in un database SQL Server. Imparerai a risolvere problemi comuni come ripulire stringhe disordinate, gestire valori mancanti, confrontare la somiglianza tra stringhe e molto altro. Metterai in pratica tutte queste attività usando una vasta gamma di insiemi di dati interessanti e disordinati, tra cui i voli aerei mensili per aeroporto, serie TV e vendite di edicole. Pronto a sporcarti le mani?

Intermediate SQL Server

Sviluppa le competenze per trasformare dati grezzi in intuizioni straordinarie rapidamente e con precisione.

Pulizia dei dati nei database SQL Server

Metti a punto le competenze che ti servono per pulire i dati grezzi e trasformarli in informazioni precise.

CONVERT() vs FORMAT()

Pulizia dei dati nei database SQL Server

Esercizio pratico interattivo