forked from MartinThoma/LaTeX-examples
-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
6b7078c
commit 3d95a55
Showing
2 changed files
with
253 additions
and
0 deletions.
There are no files selected for viewing
245 changes: 245 additions & 0 deletions
245
presentations/Data-Scientist-Role/Data-Scientist-Role.tex
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,245 @@ | ||
| \documentclass{beamer} | ||
| \usetheme{metropolis} % https://github.com/matze/mtheme | ||
| \usepackage{hyperref} | ||
| \usepackage[utf8]{inputenc} % this is needed for german umlauts | ||
| \usepackage[english]{babel} % this is needed for german umlauts | ||
| \usepackage[T1]{fontenc} % this is needed for correct output of umlauts in pdf | ||
|
|
||
| \usepackage{adjustbox} | ||
| \usepackage{tikz} | ||
| \usetikzlibrary{mindmap,trees} | ||
|
|
||
| \begin{document} | ||
|
|
||
| \title{Data Science} | ||
| \subtitle{Tasks, Tools and Roles} | ||
| \author{Martin Thoma} | ||
| \date{3. September 2019} | ||
| \subject{Computer Science; Business} | ||
|
|
||
| \frame{\titlepage} | ||
|
|
||
| \begin{frame}[plain]{} | ||
| \begin{center}\Huge | ||
| Hi. \uncover<2->{I'm Martin.} | ||
| \end{center} | ||
| \end{frame} | ||
|
|
||
| \begin{frame}[plain]{} | ||
| \begin{center}\huge | ||
| \uncover<1->{I'm a Data Scientist.\\} | ||
| \uncover<2->{Or Machine Learning Engineer?\\} | ||
| \uncover<3->{Or Business Analyst?\\} | ||
| \uncover<4->{Or Data Engineer?} | ||
| \end{center} | ||
| \end{frame} | ||
|
|
||
|
|
||
| \begin{frame}[plain]{} | ||
| \begin{adjustbox}{max totalsize={.99\textwidth}{.99\textheight},center} | ||
| \begin{tikzpicture} | ||
| \path[mindmap,concept color=black,text=white] | ||
| node[concept] {Data Science}; | ||
| \end{tikzpicture} | ||
| \end{adjustbox} | ||
| \end{frame} | ||
|
|
||
| \begin{frame}[plain]{} | ||
| \begin{adjustbox}{max totalsize={.99\textwidth}{.99\textheight},center} | ||
| \begin{tikzpicture} | ||
| \path[mindmap,concept color=black,text=white] | ||
| node[concept] {Data Science} | ||
| [clockwise from=0] | ||
| child[concept color=green!50!black] { | ||
| node[concept] {Data} | ||
| } | ||
| child[concept color=blue] { | ||
| node[concept] {Science} | ||
| }; | ||
| \end{tikzpicture} | ||
| \end{adjustbox} | ||
| \end{frame} | ||
|
|
||
| \begin{frame}[plain]{} | ||
| \begin{adjustbox}{max totalsize={.99\textwidth}{.99\textheight},center} | ||
| \begin{tikzpicture} | ||
| \path[mindmap,concept color=black,text=white] | ||
| node[concept] {Data Science} | ||
| [clockwise from=0] | ||
| child[concept color=green!50!black] { | ||
| node[concept] {Data} | ||
| [clockwise from=135] | ||
| child { node[concept] {access} } | ||
| child { node[concept] {understand} } | ||
| child { node[concept] {clean} } | ||
| child { node[concept] {transform} } | ||
| } | ||
| child[concept color=blue] { | ||
| node[concept] {Science} | ||
| }; | ||
| % child[concept color=red] { node[concept] {technical} } | ||
| % child[concept color=orange] { node[concept] {theoretical} }; | ||
| \end{tikzpicture} | ||
| \end{adjustbox} | ||
| \end{frame} | ||
|
|
||
| \begin{frame}[plain]{} | ||
| \begin{adjustbox}{max totalsize={.99\textwidth}{.99\textheight},center} | ||
| \begin{tikzpicture} | ||
| \path[mindmap,concept color=black,text=white] | ||
| node[concept] {Data Science} | ||
| [clockwise from=0] | ||
| child[concept color=green!50!black] { | ||
| node[concept] {Data} | ||
| [clockwise from=135] | ||
| child { node[concept] {access} } | ||
| child { node[concept] {understand} } | ||
| child { node[concept] {clean} } | ||
| child { node[concept] {transform} } | ||
| } | ||
| child[concept color=red] { | ||
| node[concept] {Engineering} | ||
| } | ||
| child[concept color=blue] { | ||
| node[concept] {Science} | ||
| }; | ||
| % child[concept color=orange] { node[concept] {theoretical} }; | ||
| \end{tikzpicture} | ||
| \end{adjustbox} | ||
| \end{frame} | ||
|
|
||
| \begin{frame}[plain]{} | ||
| \begin{adjustbox}{max totalsize={.99\textwidth}{.99\textheight},center} | ||
| \begin{tikzpicture} | ||
| \path[mindmap,concept color=black,text=white] | ||
| node[concept] {Data Science} | ||
| [clockwise from=0] | ||
| child[concept color=green!50!black] { | ||
| node[concept] {Data} | ||
| [clockwise from=135] | ||
| child { node[concept] {access} } | ||
| child { node[concept] {understand} } | ||
| child { node[concept] {clean} } | ||
| child { node[concept] {transform} } | ||
| } | ||
| child[concept color=red] { | ||
| node[concept] {Engineering} | ||
| [clockwise from=90] | ||
| child { node[concept] {business problem} } | ||
| child { node[concept] {API} } | ||
| } | ||
| child[concept color=blue] { | ||
| node[concept] {Science} | ||
| }; | ||
| % child[concept color=orange] { node[concept] {theoretical} }; | ||
| \end{tikzpicture} | ||
| \end{adjustbox} | ||
| \end{frame} | ||
|
|
||
| \begin{frame}[plain]{} | ||
| \begin{adjustbox}{max totalsize={.99\textwidth}{.99\textheight},center} | ||
| \begin{tikzpicture} | ||
| \path[mindmap,concept color=black,text=white] | ||
| node[concept] {Data Science} | ||
| [clockwise from=0] | ||
| child[concept color=green!50!black] { | ||
| node[concept] {Data} | ||
| [clockwise from=135] | ||
| child { node[concept] {access} } | ||
| child { node[concept] {understand} } | ||
| child { node[concept] {clean} } | ||
| child { node[concept] {transform} } | ||
| } | ||
| child[concept color=red] { | ||
| node[concept] {Engineering} | ||
| [clockwise from=90] | ||
| child { node[concept] {business problem} } | ||
| child { node[concept] {API} } | ||
| } | ||
| child[concept color=blue] { | ||
| node[concept] {Science} | ||
| [clockwise from=90] | ||
| child { node[concept] {Hypothesis Testing} } | ||
| child { node[concept] {Modeling} } | ||
| child { node[concept] {Optimization} } | ||
| child[concept] { | ||
| node[concept] {Linear Algebra} | ||
| [clockwise from=180] | ||
| child { node[concept] {Matrix Multiplication} } | ||
| } | ||
| }; | ||
| % child[concept color=orange] { node[concept] {theoretical} }; | ||
| \end{tikzpicture} | ||
| \end{adjustbox} | ||
| \end{frame} | ||
|
|
||
| \begin{frame}[plain]{} | ||
| \begin{tabular}{l|ll} | ||
| & \textbf{Data Engineer} & \textbf{Data Scientist} \\ | ||
| Buzz Words & Big Data, Data Lake & AI, DL, Neural Networks \\ | ||
| Background & Computer Science & Computer Science \\ | ||
| Languages & Java, Python & Python, R \\ | ||
| Tools & Spark, Hadoop & Tensorflow, Keras, Sklearn \\ | ||
| Solutions & Data Accessible & Predictive Model \\ | ||
| \end{tabular} | ||
| \end{frame} | ||
|
|
||
| \begin{frame}[plain]{} | ||
| \begin{tabular}{l|ll} | ||
| & \textbf{Data Analyst} & \textbf{Data Scientist} \\ | ||
| & \textbf{Business Analyst} & \\ | ||
| Buzz Words & Data Warehouse & AI, Deep Learning, NNs \\ | ||
| Background & Mathematics, economics & Computer Science \\ | ||
| Languages & Excel, Python, R & Python, R \\ | ||
| Tools & Tableau, QlikView & Pandas, Jupyter, Sklearn \\ | ||
| Solutions & Business Decision & Predictive Model \\ | ||
| \end{tabular} | ||
| \end{frame} | ||
|
|
||
| \begin{frame}[plain]{} | ||
| \begin{center} | ||
| \huge ML Engineer | ||
| \normalsize | ||
| \begin{itemize} | ||
| \item Refactor / Productionalize Data Scientists Code | ||
| \item Glorified Software Engineer who stumbled into Data Science | ||
| \end{itemize} | ||
| \tiny | ||
| Sources: | ||
|
|
||
| \begin{itemize} | ||
| \item /r/MachineLearning: \href{https://www.reddit.com/r/MachineLearning/comments/cxhvbd/}{What is the reality of machine learning engineer?} | ||
| \item Tomasz Dudek: \href{https://medium.com/@tomaszdudek/but-what-is-this-machine-learning-engineer-actually-doing-18464d5c699}{But what is this “machine learning engineer” actually doing?} | ||
| \end{itemize} | ||
| \end{center} | ||
| \end{frame} | ||
|
|
||
| \begin{frame}{Use Cases} | ||
| \begin{itemize} | ||
| \item \textbf{Time Series}: How many calls will our call center get? | ||
| \item \textbf{Categorization}: What topic is an e-mail / tweet / a comment about? | ||
| \item \textbf{Recommendations}: What do I want to buy? What should I watch next? | ||
| \item \textbf{Information Retrival}: (Fuzzy) Search, Lookup, Autocomplete | ||
| \end{itemize} | ||
| \end{frame} | ||
|
|
||
| \begin{frame}{Hard Use Cases} | ||
| \begin{itemize} | ||
| \item \textbf{Automatic Speech Recognition}: Speech to Text | ||
| \item \textbf{Speech Synthesis}: Text to Speech | ||
| \item \textbf{Translation} | ||
| \end{itemize} | ||
| \end{frame} | ||
|
|
||
| \begin{frame}{Typical Problems} | ||
| \begin{itemize} | ||
| \item Data Access / Availability | ||
| \item Data Understanding | ||
| \item Dirty Data | ||
| \item Problem Definition / Optimization Metric | ||
| \item When is it good enough? | ||
| \end{itemize} | ||
| \end{frame} | ||
|
|
||
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | ||
| \end{document} |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,8 @@ | ||
| SOURCE = Data-Scientist-Role | ||
|
|
||
| make: | ||
| pdflatex $(SOURCE).tex -output-format=pdf | ||
| make clean | ||
|
|
||
| clean: | ||
| rm -rf $(TARGET) *.class *.html *.log *.aux *.out *.glo *.glg *.gls *.ist *.xdy *.1 *.toc *.snm *.nav *.vrb *.fls *.fdb_latexmk *.pyg |