Combining and Shaping Data

Focused View

Janani Ravi

3:27:37

46 View

01 - Course Overview

01 - Course Overview.mp4

01:52

02 - Exploring Techniques to Combine and Shape Data

02 - Module Overview.mp4

01:02

03 - Prerequisites and Course Outline.mp4

01:19

04 - Connecting the Dots - Combining and Shaping Data.mp4

04:45

05 - Wide Form and Long Form Data.mp4

02:50

06 - Joins.mp4

05:09

07 - Aggregations.mp4

02:42

08 - Summary.mp4

01:32

03 - Combining and Shaping Data Using Spreadsheets

09 - Module Overview.mp4

01:11

10 - Load Data and Join Tables.mp4

08:40

11 - Aggregation Operations.mp4

01:56

12 - Join Tables Using Power Query.mp4

05:36

13 - Convert CSV Data to Table Format.mp4

02:35

14 - Convert Wide Form Data to Long Form.mp4

02:22

15 - Pivot Tables for Summary Statistics.mp4

05:15

16 - Unpivot Tables.mp4

03:45

17 - Calculating Cumulative Sum and Ranks on Partitioned Data.mp4

06:59

18 - Summary.mp4

01:27

04 - Combining and Shaping Data Using SQL

19 - Module Overview.mp4

01:17

20 - Introducing the Azure SQL Database.mp4

02:05

21 - Loading Data to Azure Blob Storage.mp4

03:47

22 - Creating an Azure SQL Database and Tables.mp4

03:46

23 - Loading Data Using Azure Data Factory Pipelines.mp4

09:33

24 - Querying Data in SQL Using Aggregations.mp4

03:06

25 - Performing Join Operations in SQL.mp4

03:26

26 - Performing Pivot and Unpivot Operations in SQL.mp4

03:13

27 - Summary.mp4

01:23

05 - Combining and Shaping Data Using Python

28 - Module Overview.mp4

01:24

29 - Data Cleaning - Missing Data and Outliers.mp4

04:28

30 - Getting Started with Azure Notebooks.mp4

02:14

31 - Combining and Shaping Data Using Pandas.mp4

03:18

32 - Identifying and Coping with Outliers.mp4

05:04

33 - Detecting Outliers Using Z-scores.mp4

04:15

34 - Handling Missing Values.mp4

05:05

35 - Cleaning Data.mp4

04:35

36 - Working with Imbalanced Data.mp4

04:07

37 - Handling Imbalanced Data with Scikit Learn.mp4

07:20

38 - Summary.mp4

01:31

06 - Integrating Data from Disparate Sources into a Data Warehouse

39 - Module Overview.mp4

01:12

40 - Transactional and Analytical Processing on Azure.mp4

05:47

41 - Creating an Azure SQL Data Warehouse and Uploading Files to Azure Blob Storage.mp4

04:54

42 - Loading Data from Blob Store to the Data Warehouse.mp4

03:49

43 - Loading Data from GCP Cloud Storage Buckets to the Azure SQL Data Warehouse.mp4

07:05

44 - Enabling Resource Permissions to an Azure Active Directory App.mp4

03:41

45 - Python Script to Transform Data Using Pyspark.mp4

02:21

46 - Transform Data Using a Spark Activity.mp4

07:31

47 - Copying Data from a Blob Storage Folder.mp4

03:05

48 - Summary.mp4

01:25

07 - Working with Streaming Data Using a Data Warehouse

49 - Module Overview.mp4

01:07

50 - Batch and Streaming Data.mp4

05:01

51 - Types of Windows.mp4

05:57

52 - Watermarks and the Notion of Time.mp4

04:18

53 - Creating a Stream Analytics Job and a Power BI Pro Account.mp4

03:56

54 - Configuring Input and Output for the Stream Analytics Job.mp4

04:34

55 - Visualizing Streaming Data Using Power BI.mp4

04:30

56 - Summary and Further Study.mp4

01:30

Description

This course covers both conceptual and practical aspects of pulling together from different data sources, with different schemas and orientations, into a cohesive whole using Excel, Python, and various tools available on the Azure cloud platform.

What You'll Learn?

Connecting the dots between data from different sources is becoming the most sought-after skill these days for everyone ranging from business professionals to data scientists.

In this course, Combining and Shaping Data, you will gain the ability to connect the dots by pulling together data from disparate sources and shaping it so that extracting connections and relationships becomes relatively easy.

First, you will learn how the most common constructs in shaping and combining data stay the same across spreadsheets, programming languages, and databases.

Next, you will discover how to use joins and vlookups to obtain wide datasets, and then use pivots to shape that into long form. You will then see how both long and wide data can be aggregated to obtain higher level insights. You will work with Excel spreadsheets and SQL as well as Python.

Finally, you will round out the course by integrating data from a variety of sources and working with streaming data, which helps your enterprise gain real-time insights into the world around you.

When you are finished with this course, you will have the skills and knowledge to pull together data from disparate sources, including from streaming sources, to construct integrated data models that truly connect the dots.

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Excel

Excel Analytics

Janani Ravi

Instructor's Courses

Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing high-quality content for technical skill development. Loonycorn is working on developing an engine (patent filed) to automate animations for presentations and educational content.

Pluralsight

View courses Pluralsight

Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.