|
|
|
|
| LEADER |
00000cam a2200000Ia 4500 |
| 001 |
12608042 |
| 005 |
20210813213023.0 |
| 006 |
m o d |
| 007 |
cr |n||||||||| |
| 008 |
201015s2020 xxu o 000 0 eng d |
| 019 |
|
|
|a 1202454811
|a 1204151507
|a 1206408907
|a 1225895779
|a 1227400138
|
| 020 |
|
|
|a 9781484258293
|q (electronic bk.)
|
| 020 |
|
|
|a 1484258290
|q (electronic bk.)
|
| 020 |
|
|
|z 1484258282
|
| 020 |
|
|
|z 9781484258286
|
| 024 |
7 |
|
|a 10.1007/978-1-4842-5829-3
|2 doi
|
| 035 |
|
|
|a (OCoLC)1200306447
|z (OCoLC)1202454811
|z (OCoLC)1204151507
|z (OCoLC)1206408907
|z (OCoLC)1225895779
|z (OCoLC)1227400138
|
| 035 |
|
9 |
|a (OCLCCM-CC)1200306447
|
| 037 |
|
|
|b Springer
|
| 040 |
|
|
|a YDX
|b eng
|c YDX
|d EBLCP
|d SFB
|d UKAHL
|d OCLCF
|d DCT
|d ERF
|d GW5XE
|d OCLCO
|d N$T
|d SNK
|d OCL
|
| 049 |
|
|
|a MAIN
|
| 050 |
|
4 |
|a QA76.9.I52
|
| 072 |
|
7 |
|a UMP
|2 bicssc
|
| 072 |
|
7 |
|a COM051380
|2 bisacsh
|
| 072 |
|
7 |
|a UMP
|2 thema
|
| 100 |
1 |
|
|a Wade, Ryan.
|
| 245 |
1 |
0 |
|a Advanced analytics in power BI with R and Python :
|b ingesting, transforming, visualizing /
|c Ryan wade.
|
| 260 |
|
|
|a [United States] :
|b Apress,
|c 2020.
|
| 300 |
|
|
|a 1 online resource
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 347 |
|
|
|a text file
|
| 505 |
0 |
|
|a Part I. Creating Custom Data Visualizations using R -- 1. The Grammar of Graphics -- 2. Creating R custom visuals in Power BI using ggplot2 -- Part II. Ingesting Data into the Power BI Data Model using R and Python -- 3. Reading CSV Files -- 4. Reading Excel Files -- 5. Reading SQL Server Data -- 6. Reading Data into the Power BI Data Model via an API -- Part III. Transforming Data using R and Python.-7. Advanced String Manipulation and Pattern Matching -- 8. Calculated Columns using R and Python -- Part IV. Machine Learning & AI in Power BI using R and Python -- 9. Applying Machine Learning and AI to your Power BI Data Models -- 10. Productionizing Data Science Models and Data Wrangling Scripts. .
|
| 520 |
|
|
|a This easy-to-follow guide provides R and Python recipes to help you learn and apply the top languages in the field of data analytics to your work in Microsoft Power BI. Data analytics expert and author Ryan Wade shows you how to use R and Python to perform tasks that are extremely hard to do, if not impossible, using native Power BI tools without Power BI Premium capacity. For example, you will learn to score Power BI data using custom data science models, including powerful models from Microsoft Cognitive Services. The R and Python languages are powerful complements to Power BI. They enable advanced data transformation techniques that are difficult to perform in Power BI in its default configuration, but become easier through the application of data wrangling features that languages such as R and Python support. If you are a BI developer, business analyst, data analyst, or a data scientist who wants to push Power BI and transform it from being just a business intelligence tool into an advanced data analytics tool, then this is the book to help you to do that. You will: Create advanced data visualizations through R using the ggplot2 package Ingest data using R and Python to overcome the limitations of Power Query Apply machine learning models to your data using R and Python Incorporate advanced AI in Power BI via Microsoft Cognitive Services, IBM Watson, and pre-trained models in SQL Server Machine Learning Services Perform string manipulations not otherwise possible in Power BI using R and Python.
|
| 630 |
0 |
0 |
|a Microsoft .NET Framework.
|0 http://id.loc.gov/authorities/names/n2017043838
|
| 630 |
0 |
7 |
|a Microsoft .NET Framework.
|2 fast
|0 (OCoLC)fst01020083
|
| 650 |
|
0 |
|a Information visualization.
|0 http://id.loc.gov/authorities/subjects/sh2002000243
|
| 650 |
|
0 |
|a Python (Computer program language)
|0 http://id.loc.gov/authorities/subjects/sh96008834
|
| 650 |
|
0 |
|a R (Computer program language)
|0 http://id.loc.gov/authorities/subjects/sh2002004407
|
| 650 |
|
0 |
|a Big data.
|0 http://id.loc.gov/authorities/subjects/sh2012003227
|
| 650 |
|
7 |
|a R (Computer program language)
|2 fast
|0 (OCoLC)fst01086207
|
| 650 |
|
7 |
|a Python (Computer program language)
|2 fast
|0 (OCoLC)fst01084736
|
| 650 |
|
7 |
|a Information visualization.
|2 fast
|0 (OCoLC)fst00973185
|
| 650 |
|
7 |
|a Big data.
|2 fast
|0 (OCoLC)fst01892965
|
| 650 |
|
7 |
|a Microsoft software.
|2 fast
|0 (OCoLC)fst01020088
|
| 655 |
|
0 |
|a Electronic books.
|
| 655 |
|
4 |
|a Electronic books.
|
| 776 |
0 |
8 |
|i Print version:
|z 1484258282
|z 9781484258286
|w (OCoLC)1138673483
|
| 903 |
|
|
|a HeVa
|
| 929 |
|
|
|a oclccm
|
| 999 |
f |
f |
|i cfb75e63-a2ad-5d55-9fe7-676cc71cd093
|s 934124a2-e168-57b5-a1f1-664044d7a70f
|
| 928 |
|
|
|t Library of Congress classification
|a QA76.9.I52
|l Online
|c UC-FullText
|u https://link.springer.com/10.1007/978-1-4842-5829-3
|z Springer Nature
|g ebooks
|i 12623650
|
