The document outlines a data science accelerator program by Godatadriven, featuring interactive lectures and hands-on hacking sessions focused on various data science topics. Each of the 12 sessions covers essential programming concepts, machine learning models, data visualization, and big data handling using R and Python, culminating in student-led projects. The program aims to equip students with practical skills to analyze data, implement algorithms, and effectively communicate insights within a short timeframe.

1. GoDataDriven
PROUDLY PART OF THE XEBIA GROUP
Data Science
Accellerator Program

2. How we teach
Each session we teach will be interactive. Every month we
give you one interactive lecture and one hackersession.
Both the lecture and hackersession require students to code.
The main distinction is that the lectures have more focus on
theory whereas the hackersessions have more focus on
getting your hands dirty with code.
The hackersessions are meant to be fun and engaging while
giving the students much freedom. We often notice that the
hackersessions end up being the most educational part.

3. Lecture1: Rstudio stack
The first session is meant to reintroduce programming by introducing students to the Rstudio stack. It
will immediately teach them to use the new dplyr syntax and introduce them to a proper work
environment.The session will conclude with a visit to github and showing students how to create
sharable documents.
- intro to programming
- explain the concept of a dataframe
- r ggplot
- r dplyr
- rmarkdown
- ChickWeight
- git
hackersession: webscraping with R
- rvest + html
- type casting
- ggplot
- dplyr
Use R to scrape some video game websites and figure out which heroes of the storm character makes
the most sense.When this is done we scrape funda and task people to find the best house in their home
town.

4. Lecture 2: Simulation
The second session will focus on a review of statistics and probability. We
keep the math light and invest much time in simulation exercizes.
• explain basic probability distributions (normal)
• what is wrong with the mean of a distribution?
• what is a correlation.
• PCA/Covariance
• explain simulation cases (birthday problem, casino)
• explain bayesian thinking
• basic hypothesis testing
hackersession banditproblem
The users get an online and offline version of the bandit problem. We turn it
into a game. One bandit problem is offline, one is online. We give users keys
and we ask them to figure out which banner is best.

5. Lecture 3: iPython stack
During this session we explore the python stack. Python is an all purpose
language that does more than just data science. We first cover how to
write concise elegant code before we delve further into how to do data
science with it.
• command line
• notebooks
• python
• jupyter
• pandas + numpy
• matplotlib
hackersession: build a flask app with pandas
The idea is to build a website that you can query, pandas as a backend.

6. Lecture 4: Linear Models
In this session students will be exposed to the theory behind classical linear
models as well as more modern machine learning models. The focus will be to
understand how these models work and to get a feeling of when to use which
model. We will show how to run all the models in both R and python so people
understand that you can work independant of the language.
• reminder of statistics + assumptions
• how to measure models: training + test
• linear regression
• logistic regression
• tree models
hackersession: automation in regression + codereview
Build your own automation script that applies many algorithms to many datasets
and benchmark it through brute force. You can use a tool that automates this like
caret or just build it yourself. The goal is to find the most robust algorithm.

7. Lecture 5: Optimisation Science
Any self respecting course on data science should spend a least a day on the
science of optimisation. Operational Research is the backbone of many machine
learning algorithms as well as a tool to automate decision making. Today will be
a day we talk about decision engineering.
• the maths behind optimisation
• closed form optimisation
• gradient descent
• linear programming
• stochastic gradient descent
• heuristic/genetic approach
hackersession: TSP
We'll play a game. Whoever gets the best travelling salesman solution wins. Pick
any tool you like. Just do whatever and fix this problem within a day. We give
multiple instances of TSP such that students can learn that not every algorithm
will work all the time.

8. Lecture 6: Non-Linear Models
Although very popular, linear models often fail. The main reason is that not
every dataset is linearly seperable and in this lecture we will delve very
deeply into this.
• linear seperability problem
• support vector machines
• factorization machines
• neural network
hackersession: automation in regression + codereview
Build your own automation script that applies many algorithms to many
datasets and benchmark it through brute force. You can use a tool that
automates this like caret or just build it yourself. The goal is to find the
most robust algorithm. At the end of this

9. Lecture 7: Clustering & Ensemble
In this session we discuss two distinct but important methods in machine learning:
clustering and ensemble models. Clustering involves classifying unlabbeled data such that
we can predict phenomenon without having labels. Ensemble models are models that
combine multiple models together to create a better one. An example of an ensemble
model is a random forest but it is easy to create your own.
• hierarchical clustering
• kmeans clustering
• HMM clustering
• normalisation
• ensemble theory
• random forests
hackersession: outperforming ensembles
We will do a repeat of the basic algorithms dataset but now it is your job to build an
ensemble that outperforms a randomforest. More difficult datasets will also be handed out,
the goal is to have the students realise that just looking at the data is equally important.

10. Lecture 8: Natural Language
During this session the students will learn about the basics of data mining texts and
NLP algorithms. Texts are a very different data structure with a lot of different usecases
than we usually have. The end goal of this session is to explain how to make a
language detector with basic technqiues.
• cleaning text
• regular expressions
• nltk
• tf/idf
• bayesian filter
• word2vec
• clustering documents
hackersession: Markov Generator of Lyrics
We will scrape different websites containing song text and we will then try to train a
markov chain to create random sentences. We will also have other texts available for
the students to play with.

11. Lecture 9:Time series
The problem of prediction in data science becomes different if time is involved. During this
session we discuss how to benefit from taking a time series approach, what common
methods are and we try to create models that can change over time such that they can fit a
real time setting.
• lag variables
• log transform
• autocorrelation
• moving average (window models)
• moving variance
• arma/arima
• real time algoritms
hackersession: stock prediction
We're gonna play a game. Whoever can predict the stockmark the best wins. Use whatever
method, you can only pick a portfolio of max 3 stocks.

12. Lecture 10: Visualisation
Being able to communicate data clearly is important for a data scientist.
The goal of this session is to explain what makes good visualisations
informative and bad visualisations feel like clutter. We will also discuss
how to make interactive visualisations with d3 and how to connect it to
your own backend.
• review of ggplot2 + tufte theory
• interactivity with Shiny
• basics of front end webdev
• d3
• connecting frontend + backend
hackersession: building a custom dashboard
We will give you four interesting datasets and we will leave it up to you to
turn the dataset into an interesting app.

13. Lecture 11: Making things scale
Sofar we have only discussed how to handle files that fit on one computer. In
this session we will discuss Hadoop and Spark and how to use them to handle
big datasets. We will focus more on the Spark API because it is more relevant
for data scientists and we will spend a significant amount of time explaining
when to handle something as a big data problem and when you want to avoid it.
• when to refer to big data
• bootstrapping techniques
• hadoop ecosystem/tools (briefly)
• big data spark syntax
hackersession
We let the students datamine a large dataset on a cluster that they will have
• provisioning
• R syntax
• python syntax

14. Lecture 12:The group determines
There are many possible advanced topics that could be discussed but we would like to leave
the subject of the last lecture open. Preferably the students will find consensus in a new
technology (which surely there is one by now). Otherwise, one of the following subjects can be
chosen:
advanced topics
• feature creation
• computer vision
• bayesian graphical models
• neo4j vs sql vs nosql
• deep learning
• ethical considerations
• legal considerations
• julia
hackersession:
For the last week, each student can work on any project. We are there to help them with
anything.

15. End Goal
After the course the following tasks should be a no-brainer for students:
• get basic insights out of a .csv within a day even if it is a dirty dataset with either python or
R. this includes things like outlier detection, type casting
• when given a clean dataset, candidate will be able to run three different algorithms in a day
with train/test for regression, clustering or classification.
• recognise when a dataset is too big to handle
• be able to estimate when a project will take a week (shiny app) vs a few months (django
app)
• be able to attend a pydata conference and understand half the talks to the degree that they
can summerize it in a few sentences
• turn a .csv file into dashboard as a an microservice with an api within a day