Die Top 5 Bücher für erfolgreiche Data Science: Unverzichtbare Lektüre für angehende Data Scientists

Welche Data Science Bücher sollten Sie lesen um als Data Scientist erfolgreich zu sein? | Photo Credit: via Sebastian Sikora
Welche Data Science Bücher sollten Sie lesen um als Data Scientist erfolgreich zu sein? | Photo Credit: via Sebastian Sikora

Möchten Sie eine Karriere in Data Science verfolgen und fragen sich, welche Bücher Ihnen auf diesem Weg helfen können? In diesem Blogbeitrag präsentiere ich Ihnen die fünf entscheidenden Bücher, die für Ihre Ausbildung und Ihren beruflichen Werdegang in Data Science unerlässlich sind. 📚

1. Einstieg und Überblick: „Data Science in der Praxis“

Einstieg und Überblick: Data Science in der Praxis

Data Science in der Praxis von Tom Alby bietet Ihnen einen umfassenden Einstieg in die Welt der Daten. Dieses Buch vermittelt Ihnen nicht nur die Grundlagen von Data Science, sondern bietet auch praktische Beispiele und Fallstudien, die Ihnen helfen, das Gelernte anzuwenden und zu vertiefen.

2. Python Crashkurs: „Data Science mit Python“

Python Crashkurs: Data Science mit Python

Python ist die universelle Programmiersprache, die sich hervorragend zur Lösung von Data-Science-Fragestellungen eignet. Mit Data Science mit Python von Jake VanderPlas lernen Sie Python auf effiziente Weise und bereiten sich auf komplexere Data-Science-Aufgaben vor.

3. Der Statistikwerkzeugkasten: „Statistik I und II für Dummies“

Der Statistikwerkzeugkasten: Statistik I und II für Dummies

Statistik ist das Rückgrat von Data Science. Statistik für Dummies und Statisik II für Dummies von Deborah J. Rumsey bieten eine umfassende und leicht verständliche Einführung in die Statistik. Die Bücher decken eine Vielzahl von Themen ab, einschließlich Regression, Varianzanalyse, Chi-Quadrat-Tests und nichtparametrische Verfahren.

4. Für große Datenmengen: „Hadoop: The Definitive Guide“

Für große Datenmengen: Hadoop: The Definitive Guide

Hadoop: The Definitive Guide von Tom White ist unerlässlich, wenn Sie mit großen Datenmengen arbeiten. Dieses Buch führt Sie durch die Komplexitäten von Hadoop und hilft Ihnen, das Potenzial Ihrer Daten voll auszuschöpfen.

5. Mehrwert durch Data Science: „Data Science für Unternehmen“

Dieses #datamustread Buch von Foster Provost und Tom Fawcett zeigt auf, wie Data Science für Unternehmen Mehrwert schafft. Es erklärt die Grundlagen der Data Science und zeigt auf, wie Sie die Prinzipien auf reale Geschäftssituationen anwenden können.

Gewonnene Erkenntnisse visualisieren: Bücher zu Datenvisualisierung

Sie möchten Ihre mit Data Science gewonnenen Erkenntnisse visualisieren und interessieren sich für Bücher zum Thema Datenvisualisierung? Dann werfen Sie gerne einen Blick in meine Bücher!


Haben Sie andere Empfehlungen für Data Science Bücher? Teilen Sie Ihre Gedanken und Kommentare in diesem Tweet:

„Die Top 5 Bücher für erfolgreiche Data Science: Unverzichtbare Lektüre für angehende Data Scientists“ weiterlesen

Meetup #19 – Chart Choice & Anomaly Detection for Warranty Cases

Dilyana's session: Chart Choice - many ways to visualize data
Dilyana’s session: Chart Choice – many ways to visualize data

Recently we had the 19th edition of our Data & AI Meetup. This meetup focused on Chart Choice & Anomaly Detection for Warranty Cases. Let’s have a quick recap!

Agenda:

Meetup discussion: Sven, Alexander, and Shubham
Meetup discussion: Sven, Alexander, and Shubham

  1. Intro & announcements: our 5th anniversary
  2. Chart Choice
    by Dilyana Bossenz, Business Analytics and Enablement Manager at M2.
  3. New Book: Decisively Digital – From Creating a Culture to Designing Strategy
    by Alexander Loth, author & executive advisor at Microsoft
  4. Anomaly Detection for warranty cases with an example of the automotive industry
    by Shubham Agarwal, Lead Data Scientist at ATCS
    and Frank Schlemmbach, Sr. Consultant at ATCS
    and Sven Sommerfeld, Managing Director at ATCS
  5. Wrap-up

Session recording:

Further information:

The next Data & AI Meetup?

The next Data & AI Meetup will be announced on the Data & AI LinkedIn group and on the Data & AI Meetup page. Feel free to join!

If you’ve dreamed of sharing your Data & AI story with many like-minded Data & AI enthusiasts, please submit your session proposal.

How to Research LinkedIn Profiles in Tableau with Python and Azure Cognitive Services in Tableau

Azure Cognitive Services in Tableau: using Python to access the Web Services API provided by Microsoft Azure Cognitive Services
Azure Cognitive Services in Tableau: using Python to access the Web Services API provided by Microsoft Azure Cognitive Services

A few weeks after the fantastic Tableau Conference in New Orleans, I received an email from a data scientist who attended my TC18 social media session, and who is using Azure+Tableau. She had quite an interesting question:

How can a Tableau dashboard that displays contacts (name & company) automatically look up LinkedIn profile URLs?

Of course, researching LinkedIn profiles for a huge list of people is a very repetitive task. So let’s find a solution to improve this workflow…

Step by Step: Integrating Azure Cognitive Services in Tableau

1. Python and TabPy

We use Python to build API requests, communicate with Azure Cognitive Services and to verify the returned search results. In order to use Python within Tableau, we need to setup TabPy. If you haven’t done this yet: checkout my TabPy tutorial.

2. Microsoft Azure Cognitive Services

One of many APIs provided by Azure Cognitive Services is the Web Search API. We use this API to search for name + company + „linkedin“. The first three results are then validated by our Python script. One of the results should contain the corresponding LinkedIn profile.

3. Calculated Field in Tableau

Let’s wrap our Python script together and create a Calculated Field in Tableau:

SCRIPT_STR("
import http.client, urllib, base64, json
YOUR_API_KEY = 'xxx'
name = _arg1[0]
company = _arg2[0]
try:
headers = {'Ocp-Apim-Subscription-Key': YOUR_API_KEY }
params = urllib.urlencode({'q': name + ' ' + company + ' linkedin','count': '3'})
connection = http.client.HTTPSConnection('api.cognitive.microsoft.com')
connection.request('GET', '/bing/v7.0/search?%s' % params, '{body}', headers)
json_response = json.loads(connection.getresponse().read().decode('utf-8'))
connection.close()
for result in json_response['webPages']['value']:
if name.lower() in result['name'].lower():
if 'linkedin.com/in/' in result['displayUrl']:
return result['displayUrl']
break
except Exception as e:
return ''
return ''
", ATTR([Name]), ATTR([Company]))

4. Tableau dashboard with URL action

Adding a URL action with our new Calculated Field will do the trick. Now you can click on the LinkedIn icon and a new browser tab (or the LinkedIn app if installed) opens.

LinkedIn demo on Tableau Public

Is this useful for you? Feel free to download the Tableau workbook – don’t forget to add your API key!

Get More Insights

This tutorial is just the tip of the iceberg. If you want to dive deeper into the world of data visualization and analytics, don’t forget to order your copy of my new book, Visual Analytics with Tableau (Amazon).  This comprehensive guide offers an in-depth exploration of data visualization techniques and best practices.

I’d love to hear your thoughts. Feel free to leave a comment, share this tweet, and follow me on Twitter and LinkedIn for more tips, tricks, and tutorials on Azure Cognitive Services in Tableau and other data analytics topics.

Also, feel free to comment and share my Azure Cognitive Services in Tableau tweet:

Data Science Toolbox: How to use Julia with Tableau

Julia in Tableau: R allows Tableau to execute Julia code on the fly, enhancing your data analytics experience.
Julia in Tableau: R allows Tableau to execute Julia code on the fly, enhancing your data analytics experience.

Michael, a data scientist, who is working for a German railway and logistics company, recently told me during a FATUG Meetup that he loves Tableau’s R integration and Tableau’s Python integration. As he continued, he raised the question of using functions they have written in Julia. Julia, a high-level dynamic programming language for high-performance numerical analysis, is an integral part of the newly developed data strategy in Michael’s organization.

Tableau, however, does not come with native support for Julia. I didn’t want to keep Michael’s team down and was looking for an alternative way to integrate Julia with Tableau.

This solution is working flawlessly in a production environment for several months. In this tutorial, I’m going to walk you through the installation and connecting Tableau with R and Julia. I will also give you an example of calling a Julia statement from Tableau to calculate the sphere volume.

Step by Step: Integrating Julia in Tableau

1. Install Julia and add PATH variable

You can download Julia from julialang.org. Add Julia’s installation path to the PATH environment variable.

2. Install R, XRJulia, and RServe

You can download base R from r-project.org. Next, invoke R from the terminal to install the XRJulia and the RServe packages:

> install.packages("XRJulia")
> install.packages("Rserve")

XRJulia provides an interface from R to Julia. RServe is a TCP/IP server that allows Tableau to use facilities of R.

3. Load libraries and start RServe

After packages are successfully installed, we load them and run RServe:

> library(XRJulia)
> library(Rserve)
> Rserve()

Make sure to repeat this step every time you restart your R session.

4. Connecting Tableau to RServe

Now let’s open the Help menu in Tableau Desktop and choose Settings and Performance >Manage External Service connection to open the External Service Connection dialog box:

TC17 External Service Connection

Enter a server name using a domain or an IP address and specify a port. Port 6311 is the default port used by Rserve. Take a look at my R tutorial to learn more about Tableau’s R integration.

5. Adding Julia code to a Calculated Field

You can invoke Calculated Field functions called SCRIPT_STR, SCRIPT_REAL, SCRIPT_BOOL, and SCRIPT_INT to embed your Julia code in Tableau, such as this simple snippet that calculates sphere volume:


SCRIPT_INT('
library(XRJulia)
if (!exists("ev")) ev <- RJulia()
y <- juliaEval("
4 / 3 * %s * ' + STR([Factor]) + ' * pi ^ 3
", .arg1)
',
[Radius])

6. Use Calculated Field in Tableau

You can now use your Julia calculation as an alternate Calculated Field in your Tableau worksheet:

Using Julia within calculations in Tableau (click to enlarge)
Using Julia calculations within Tableau (click to enlarge)

Feel free to download the Tableau Packaged Workbook (twbx) here.

Further Reading: Mastering Julia

If you want to go beyond this tutorial and explore more about Julia in the context of data science, I recommend the book Mastering Julia. You can find it here.

Further Reading: Visual Analytics with Tableau

Join the data science conversation and follow me on Twitter and LinkedIn for more tips, tricks, and tutorials on Julia in Tableau and other data analytics topics. If you’re looking to master Tableau, don’t forget to preorder your copy of my upcoming book, Visual Analytics with Tableau. (Amazon). It offers an in-depth exploration of data visualization techniques and best practices.

Also, feel free to comment and share my Tableau Julia Tutorial tweet:

Tableau Conference TC17 Sneak Peek: Integrating Julia for Advanced Analytics

Demo: using Julia within calculations in Tableau (click to enlarge)
Demo: using Julia calculations within Tableau (click to enlarge)

We have already seen some love from Tableau for R and Python, boosting Tableau’s Advanced Analytics capabilities.

So what is the next big thing for our Data Science Rockstars? Julia!

Who is Julia?

JuliaJulia logo is a high-level dynamic programming language introduced in 2012. Designed to address the needs of high-performance numerical analysis its syntax is very similar to MATLAB. If you are used to MATLAB, you will be very quick to get on track with Julia.

Compared to R and Python, Julia is significantly faster (close to C and FORTRAN, see benchmark). Based on Tableau’s R integration, Julia is a fantastic addition to Tableau’s Advanced Analytics stack and to your data science toolbox.

Where can I learn more?

Do you want to learn more about Advanced Analytics and how to leverage Tableau with R, Python, and Julia? Meet me at the 2017 Tableau Conferences in London, Berlin, or Las Vegas and join my Advanced Analytics sessions:

Will there be an online tutorial?

Yes, of course! I published tutorials for R and Python on this blog. And I will also publish a Julia tutorial soon. Feel free to follow me on Twitter @xlth, and leave me your feedback/suggestions in the comment section below.

Further reading: Mastering Julia

A German translation of this post is published on the official Tableau blog: Tableau Conference On Tour Sneak Peek: Julia-Integration für Advanced Analytics

Update 11 Oct 2017: The Julia+Tableau tutorial blog post is now published.