Module 8: Natural language processing Pt 1Sara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to inquiry@deltanalytics.org .
A lightning talk presentation from Jisc's Focus on the future: new developments in accessible and assistive technologies event held on 16 March 2022 as part of Digifest community fringe.
Speech Recognition: Art of the possible - DigiFest 2022Dominik Lukes
Presentation introducing a panel discussion on the present and future of speech recognition for lecture capture at Digifest 2022 online fringe on Assistive Technologies: https://www.jisc.ac.uk/events/focus-on-the-future-new-developments-in-accessible-and-assistive-technologies-16-mar-2022
Speech Recognition: Art of the possible - DigiFest 2022Dominik Lukes
Presentation introducing a panel discussion on the present and future of speech recognition for lecture capture at Digifest 2022 online fringe on Assistive Technologies: https://www.jisc.ac.uk/events/focus-on-the-future-new-developments-in-accessible-and-assistive-technologies-16-mar-2022
Module 8: Natural language processing Pt 1Sara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to inquiry@deltanalytics.org .
A lightning talk presentation from Jisc's Focus on the future: new developments in accessible and assistive technologies event held on 16 March 2022 as part of Digifest community fringe.
Speech Recognition: Art of the possible - DigiFest 2022Dominik Lukes
Presentation introducing a panel discussion on the present and future of speech recognition for lecture capture at Digifest 2022 online fringe on Assistive Technologies: https://www.jisc.ac.uk/events/focus-on-the-future-new-developments-in-accessible-and-assistive-technologies-16-mar-2022
Speech Recognition: Art of the possible - DigiFest 2022Dominik Lukes
Presentation introducing a panel discussion on the present and future of speech recognition for lecture capture at Digifest 2022 online fringe on Assistive Technologies: https://www.jisc.ac.uk/events/focus-on-the-future-new-developments-in-accessible-and-assistive-technologies-16-mar-2022
Analyzing Arguments during a Debate using Natural Language Processing in PythonAbhinav Gupta
This presentation will guide you through the application of Python NLP Techniques to analyze arguments during a debate and define a strategy to figure out the winner of the debate on the basis of strength and relevance of the arguments.
This is made for PyCon India 2015.
For details : https://in.pycon.org/cfp/pycon-india-2015/proposals/analyzing-arguments-during-a-debate-using-natural-language-processing-in-python/
Contact me : abhinav.gpt3@gmail.com
Let us begin with mentioning that this article is completely non-serious. New Year is coming, holidays are almost there and there is no reason to do anything deliberate. That is why we decided to write an article about, suddenly, statistics.
TwiSent: A Multi-Stage System for Analyzing Sentiment in TwitterSubhabrata Mukherjee
TwiSent: A Multi-Stage System for Analyzing Sentiment in Twitter, Subhabrata Mukherjee, Akshat Malu, Balamurali A.R. and Pushpak Bhattacharyya, In Proceedings of The 21st ACM Conference on Information and Knowledge Management (CIKM 2012), Hawai, Oct 29 - Nov 2, 2012 (http://www.cse.iitb.ac.in/~pb/papers/cikm2012-twisent.pdf)
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/TBJqgvXYhfo.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
Abstract:
Machine learning is at the forefront of many recent advances in science and technology, enabled in part by the sophisticated models and algorithms that have been recently introduced. However, as a consequence of this complexity, machine learning essentially acts as a black-box as far as users are concerned, making it incredibly difficult to understand, predict, or "trust" their behavior. In this talk, I will describe our research on approaches that explain the predictions of ANY classifier in an interpretable and faithful manner.
Sameer's Bio:
Dr. Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine. He is working on large-scale and interpretable machine learning applied to natural language processing. Sameer was a Postdoctoral Research Associate at the University of Washington and received his PhD from the University of Massachusetts, Amherst, during which he also worked at Microsoft Research, Google Research, and Yahoo! Labs on massive-scale machine learning. He was awarded the Adobe Research Data Science Faculty Award, was selected as a DARPA Riser, won the grand prize in the Yelp dataset challenge, and received the Yahoo! Key Scientific Challenges fellowship. Sameer has published extensively at top-tier machine learning and natural language processing conferences. (http://sameersingh.org)
Presented by Ted Xiao at RobotXSpace on 4/18/2017. This workshop covers the fundamentals of Natural Language Processing, crucial NLP approaches, and an overview of NLP in industry.
Webinar: Simpler Semantic Search with SolrLucidworks
Hear from Lucidworks Senior Solutions Consultant Ted Sullivan about how you can leverage Apache Solr and Lucidworks Fusion to improve semantic awareness of your search applications.
Automated Software Requirements LabelingData Works MD
Video of the presentation is available here: https://youtu.be/L6EMnvALYtU
Talk: Machine Learning for Requirements Engineering
Speaker: Jon Patton
This project applies a number of machine learning, deep learning, and NLP techniques to solve challenging problems in requirements engineering.
Le développement du Web et des réseaux sociaux ou les numérisations massives de documents contribuent à un renouvellement des Sciences Humaines et Sociales, des études des patrimoines littéraires ou culturels, ou encore de la façon dont est exploitée la littérature scientifique en général.
Les humanités numériques, qui croisent diverses disciplines avec l’informatique, posent comme centrales les questions du volume des données, de leur diversité, de leur origine, de leur véracité ou de leur représentativité. Les informations sont véhiculées au sein de « documents » textuels (livres, pages Web, tweets...), audio, vidéo ou multimédia. Ils peuvent comporter des illustrations ou des graphiques.
Appréhender de telles ressources nécessite le développement d'approches informatiques robustes, capables de passer à l’échelle et adaptées à la nature fondamentalement ambiguë et variée des informations manipulées (langage naturel ou images à interpréter, points de vue multiples…).
Si les approches d’apprentissage statistique sont monnaie courante pour des tâches de classification ou d’extraction d’information, elles doivent faire face à des espaces vectoriels creux et de dimension très élevées (plusieurs millions), être en mesure d’exploiter des ressources (par exemple des lexiques ou des thesaurus) et tenir compte ou produire des annotations sémantiques qui devront pouvoir être réutilisées.
Pour faire face à ces enjeux, des infrastructures ont été créées telle HumaNum à l’échelle nationale, DARIAH ou CLARIN à l’échelle européenne et des recommandations établies à l’échelle mondiale telle que la TEI (Text Encoding Initiative). Des plateformes au service de l’information scientifique comme l’équipement d’excellence OpenEdition.org sont une autre brique essentielle pour la préservation et l’accès aux « Big Digital Humanities » mais aussi pour favoriser la reproductibilité et la compréhension des expérimentations et des résultats obtenus.
This is an introduction to text analytics for advanced business users and IT professionals with limited programming expertise. The presentation will go through different areas of text analytics as well as provide some real work examples that help to make the subject matter a little more relatable. We will cover topics like search engine building, categorization (supervised and unsupervised), clustering, NLP, and social media analysis.
Yelp challenge reviews_sentiment_classificationChengeng Ma
Using LIBLinear to train SVM on 2015 Yelp Challenge Dataset (~1.5GB) to predict the sentiment of reviewers (above 95% accuracy on validation and testing). And the SVM output weights are used to find 100 most positive and negative words, which are quite consistent with our experience.
Analyzing Arguments during a Debate using Natural Language Processing in PythonAbhinav Gupta
This presentation will guide you through the application of Python NLP Techniques to analyze arguments during a debate and define a strategy to figure out the winner of the debate on the basis of strength and relevance of the arguments.
This is made for PyCon India 2015.
For details : https://in.pycon.org/cfp/pycon-india-2015/proposals/analyzing-arguments-during-a-debate-using-natural-language-processing-in-python/
Contact me : abhinav.gpt3@gmail.com
Let us begin with mentioning that this article is completely non-serious. New Year is coming, holidays are almost there and there is no reason to do anything deliberate. That is why we decided to write an article about, suddenly, statistics.
TwiSent: A Multi-Stage System for Analyzing Sentiment in TwitterSubhabrata Mukherjee
TwiSent: A Multi-Stage System for Analyzing Sentiment in Twitter, Subhabrata Mukherjee, Akshat Malu, Balamurali A.R. and Pushpak Bhattacharyya, In Proceedings of The 21st ACM Conference on Information and Knowledge Management (CIKM 2012), Hawai, Oct 29 - Nov 2, 2012 (http://www.cse.iitb.ac.in/~pb/papers/cikm2012-twisent.pdf)
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/TBJqgvXYhfo.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
Abstract:
Machine learning is at the forefront of many recent advances in science and technology, enabled in part by the sophisticated models and algorithms that have been recently introduced. However, as a consequence of this complexity, machine learning essentially acts as a black-box as far as users are concerned, making it incredibly difficult to understand, predict, or "trust" their behavior. In this talk, I will describe our research on approaches that explain the predictions of ANY classifier in an interpretable and faithful manner.
Sameer's Bio:
Dr. Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine. He is working on large-scale and interpretable machine learning applied to natural language processing. Sameer was a Postdoctoral Research Associate at the University of Washington and received his PhD from the University of Massachusetts, Amherst, during which he also worked at Microsoft Research, Google Research, and Yahoo! Labs on massive-scale machine learning. He was awarded the Adobe Research Data Science Faculty Award, was selected as a DARPA Riser, won the grand prize in the Yelp dataset challenge, and received the Yahoo! Key Scientific Challenges fellowship. Sameer has published extensively at top-tier machine learning and natural language processing conferences. (http://sameersingh.org)
Presented by Ted Xiao at RobotXSpace on 4/18/2017. This workshop covers the fundamentals of Natural Language Processing, crucial NLP approaches, and an overview of NLP in industry.
Webinar: Simpler Semantic Search with SolrLucidworks
Hear from Lucidworks Senior Solutions Consultant Ted Sullivan about how you can leverage Apache Solr and Lucidworks Fusion to improve semantic awareness of your search applications.
Automated Software Requirements LabelingData Works MD
Video of the presentation is available here: https://youtu.be/L6EMnvALYtU
Talk: Machine Learning for Requirements Engineering
Speaker: Jon Patton
This project applies a number of machine learning, deep learning, and NLP techniques to solve challenging problems in requirements engineering.
Le développement du Web et des réseaux sociaux ou les numérisations massives de documents contribuent à un renouvellement des Sciences Humaines et Sociales, des études des patrimoines littéraires ou culturels, ou encore de la façon dont est exploitée la littérature scientifique en général.
Les humanités numériques, qui croisent diverses disciplines avec l’informatique, posent comme centrales les questions du volume des données, de leur diversité, de leur origine, de leur véracité ou de leur représentativité. Les informations sont véhiculées au sein de « documents » textuels (livres, pages Web, tweets...), audio, vidéo ou multimédia. Ils peuvent comporter des illustrations ou des graphiques.
Appréhender de telles ressources nécessite le développement d'approches informatiques robustes, capables de passer à l’échelle et adaptées à la nature fondamentalement ambiguë et variée des informations manipulées (langage naturel ou images à interpréter, points de vue multiples…).
Si les approches d’apprentissage statistique sont monnaie courante pour des tâches de classification ou d’extraction d’information, elles doivent faire face à des espaces vectoriels creux et de dimension très élevées (plusieurs millions), être en mesure d’exploiter des ressources (par exemple des lexiques ou des thesaurus) et tenir compte ou produire des annotations sémantiques qui devront pouvoir être réutilisées.
Pour faire face à ces enjeux, des infrastructures ont été créées telle HumaNum à l’échelle nationale, DARIAH ou CLARIN à l’échelle européenne et des recommandations établies à l’échelle mondiale telle que la TEI (Text Encoding Initiative). Des plateformes au service de l’information scientifique comme l’équipement d’excellence OpenEdition.org sont une autre brique essentielle pour la préservation et l’accès aux « Big Digital Humanities » mais aussi pour favoriser la reproductibilité et la compréhension des expérimentations et des résultats obtenus.
This is an introduction to text analytics for advanced business users and IT professionals with limited programming expertise. The presentation will go through different areas of text analytics as well as provide some real work examples that help to make the subject matter a little more relatable. We will cover topics like search engine building, categorization (supervised and unsupervised), clustering, NLP, and social media analysis.
Yelp challenge reviews_sentiment_classificationChengeng Ma
Using LIBLinear to train SVM on 2015 Yelp Challenge Dataset (~1.5GB) to predict the sentiment of reviewers (above 95% accuracy on validation and testing). And the SVM output weights are used to find 100 most positive and negative words, which are quite consistent with our experience.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
1. Data Science that Vacation.
Using Data Science to find where you should take your next vacation.
WIFI: Eastern Foundry Guest
Password: FoundryGuest@!!
http://bit.ly/ds-event
2. TJ Stalcup
Lead DC Mentor @Thinkful
API Evangelist @WealthEngine
Pokemon Master
About Us
Jennifer
Thinkful Student
Recent Graduate (applause)
5. Online Bootcamp since 2012. We have worked
with over 6000 students around the world
paired up with over 300 mentors.
We get you ready for a career and guarantee
your first job
92% success rate
About Thinkful
Local DC Crew
7. A text analyzer to take your writeup of your dream vacation and find your best match.
To do that we need 3 things:
A set of vacation reviews (we're going to use hotel reviews)
A text based model for hotel matching
Dream vacation descriptions
What we're building:
8. The data tonight is a sample of reviews of 1000 hotels collected by Datafinity, available
on Kaggle .
Has information about the hotel (name, location, etc)
Information about the reviewer
Review Text
Rating
here
The Data
9. Text processing is a slow and involved process
This way we can make a model and perform matching in a relatively quick amount of
time
Why is it slow?
Why only 1000 hotels?
10. Text data is often referred to as 'unstructured data'.
But what is structure data?
Let's talk about text
11. Structured Data
NameName EmailEmail Date of SignupDate of Signup
TJ Stalcup tj@thinkful.com 12/13/2017
... ... ...
This data is nice. It's a table with columns and we know what to
expect.
12. Unstructured Data
This data is not as nice. It's unpredictable, varying in length and we
don't really know what's what. It just kind of looks like one big thing.
The text above (and this text here) is unstructured data....
13. The Problems with Unstructured
Unstructured data gives us a few specific problems:
- What is a data point?
- How do we compare data?
- What parts of the data matter?
14. An example
This is our test sentence.
So what parts of this sentence matter?
What are our data points?
15. An example
This is our test sentence.
The words matter! And whitespace gives us a way to find them.
16. An example
This is our test sentence.
ThisThis isis ourour testtest sentence.sentence.
1 1 1 1 1
17. An example
This is our test sentence.
And this is a second sentence.
ThisThis isis ourour testtest sentence.sentence.
1 1 1 1 1
0 1 0 0 1
We've taken our data and turned it into a table.
We added structure!
18. Bag of words
This is called a 'bag of words' approach. (It's also called vectorizing.)
We took our initial sentence and created a bag for each word.
Count the number of times we found a word that matched.
Words are columns, rows are counts
ThisThis isis ourour testtest sentence.sentence.
1 1 1 1 1
0 1 0 0 1
19. Punctuation and case
However, in looking at our example, something should seem logically
off.
This is our test sentence.
And this is a second sentence.
ThisThis isis ourour testtest sentence.sentence.
1 1 1 1 1
0 1 0 0 1
20. Punctuation and case
Things like 'This' and 'this' are not considered equal because the
computer doesn't see them as the same. The case is a difference.
This is why you (almost) always preprocess text data.
ThisThis isis ourour testtest sentence.sentence.
1 1 1 1 1
0 1 0 0 1
21. Back to the example
This is our test sentence. ---> this is our test sentence
And this is a second sentence. ---> and this is a second sentence
thisthis isis ourour testtest sentencesentence
1 1 1 1 1
1 1 0 0 1
Getting rid of case and punctuation makes comparisons easier and
more effective (particularly on small data)
22. Stop words
But there's more!
Some words don't matter. They don't really tell us anything.
These are called 'stop words'.
Things like 'it', 'is', 'the' are usually just thrown out.
23. Back to the example
This is our test sentence. ---> this our test sentence
And this is a second sentence. ---> this second sentence
thisthis ourour testtest sentencesentence
1 1 1 1
1 0 0 1
Now we have vectors of the essentials for each sentence.
This is something we can build a model on!This is something we can build a model on!
24. The Model
Our model is going to be a Random Forest.
A random forest is an ensemble of decision trees to predict the most
likely class of an outcome variable.
What does that mean?
25. Decision Trees
A set of rules that get us to a prediction, in the form of a tree.
You can think of it like a computer building a version of 20
questions.
27. Random Forest
A random forest builds a lot of different decision trees and then lets
each one vote.
Our questions will be things like "Contains the word 'beach'" or
"Contains the world 'sun' 2 or more times".
28. The Notebook
We're going to use a Google hosted Python to build this
model.
http://bit.ly/ideal-vacationhttp://bit.ly/ideal-vacation
notebook
31. Relative Frequency
Each one scores a 1 for beach.
TFIDF is the answer. It rates each word by its relative frequency.
So the word beach in a ten word sentence counts more than one
mention in 10000 words.
http://bit.ly/tfidf-wiki
32. Context
'I hate beaches and love cities'
vs
'I love beaches and hate cities'
Our model would see these as the same thing.
33. Context - N-Grams
We can get a sense of context with n-grams. Each feature is a set of
words rather than individual words.
So we'd get features like 'love cities' and 'hate beaches' rather than
'love' 'cities' 'hate' 'beaches'.
http://bit.ly/ngram-wiki
34. There's a lot more
This all falls under the banner of Natural Language Processing, or
NLP, one of the largest and most exciting fields of data science and
artificial intelligence.
It's the basis for things like chatbots and Siri and the Turing test itself.
There is a lot of fun to be had in this space.
35. Data Science @ Thinkful
Flexible, project-based curriculum to help you become the data
scientist you want to be
You don’t just learn skills, you get to make things
Mentor support from experts in the industry
Also, there's a job guarantee
36. Link for the third party audit jobs report:
https://www.thinkful.com/bootcamp-jobs-statshttps://www.thinkful.com/bootcamp-jobs-stats
Thinkful Graduates 92%92% Job Placement Rate
38. http://bit.ly/dc-ds-trialhttp://bit.ly/dc-ds-trial
Initial 2-week trial course
Start with Python and Statistics
Unlimited Q&A Sessions
Option to continue with full bootcamp
Financing & scholarships available
Offer valid for tonight onlyOffer valid for tonight only
Aaron LamphereAaron Lamphere
Trial Program ManagerTrial Program Manager
Thinkful Two Week Trial
