Who am I
I was a Physics Degree and Artificial Intelligence master student who started his professional career as a Data Scientist. I’ve worked as a freelance or salaried, both working in team with different levels of autonomously. My interests are described in the section of interests. If you want to know more, visit my collection of notes.
Languages
| Languages | Reading | Listening | Writing | Speaking | Comments |
|---|---|---|---|---|---|
| Spanish | Native | Native | Native | Native | Native language. |
| Catalan | C1 | C1 | C1 | C1 | School and studies language. |
| Galician | Native | Native | B1 | Native | Native family language. |
| English | C1 | C1 | C1 | C1 | Toefl C1. |
| French | B1 | A2 | A1 | A1 | Studied language for 5 years. |
| German | A2 | A1 | A1 | A1 | New language to learn. |
| Portuguese | B1 | B1 | A1 | A1 | Basic and limited communication skills. |
Technical skills
This is an exercise of the always subjective task to rank and score my different skills related with my technical knowledge of my professional activities. Of course, this is a cumulative catalog and it would not be a surprise to find in it very old technologies that I eventually used. In order to help me to do this catalog, I will put into context every skill in how I acquired the knowledge. The next table is the summary dictionary code for that experience context.
| Experience code | Experience description |
|---|---|
| Ed | Education acquired knowledge |
| PP | Personal projects |
| Prof | Professional experience |
| Complete | Knowledge acquired by education, personal projects and professional experience. |
Programming languages
| Languages | Level | Experience1 | Description of knowledge |
|---|---|---|---|
| Python | 8.5/10 | Complete | Able to design and code large and complex structures with good defensive programming skills applied to process data, applying machine learning algorithms, solving mathematical optimization problems and other heterogeneous experiences applying it to very different problems. I use to code in python to process data, model that data using machine learning techniques , solving mathematical optimization problems, administrative coding to connect with DB or build Backend products, and even setting up cloud infrastructure. |
| SQL | 8.5/10 | Prof | Managing the DB by keeping order in the schema and the types of each data column in tables for proper indexing, ensuring correct insertions of data and running complex queries to obtain the desired data. Also, maintain complex data flows in a DWH, in order to proper layer. |
| Javascript | 4/10 | PP + Prof | Creating scripts to support front-end environments. |
| Scala | 2.5/10 | Prof | Contributing in repositories to create utils for Data Pipelines and translating code from them to python. |
| HTML | 5/10 | PP + Prof | Creating simple front-ends for data visualization (dashboards) within python, Jinja2, Bootstrap and Javascript. |
| R | 7/10 | PP + Ed | The use of the language to code scripts to explore data, transform it by creating new features, visualize data and the use of packages as plyr for data processing or caret, randomforest, Rankcluster among others to apply machine learning to problems. Able to code easily small structures and programs of less than 2500 lines. |
| Matlab/Octave/Scilab | 7.5/10 | Prof + Ed | Linking to DBs, processing images for image segmentation, object detection or other applications of Computer Vision, processing data and the application to Business problems by coding a predictive models using Machine Learning in a back-end product. |
| Fortran | 6/10 | PP + Ed | Coding programs in order to do simulations of multi-body systems (solid, gases, planetary systems) or quantum systems, in some cases using MPI parallelization framework. |
| Java | 4/10 | Ed | OOP coding an AI planner for solving generic Hanoi tower problem or multi-agent systems to help to optimize MAS grid-like problems. |
| SAS | 6/10 | Prof | Predictive models using ordinal logistic regression to rank customers possibility to buy a product. |
| Bash | 4/10 | PP | Using bash for automatic tasking some administration of the system from simple launching processes, sending mails through SMTP protocols or running scripts. |
| C | 4/10 | Ed + PP | Coding basic Computer Science algorithms for activities or high performance computing in personal projects. |
| C++ | 2/10 | PP | Small administrative snippets of code to manage and format some data. |
| Mathematica | 4/10 | Ed | Coding small programs in order to do some computations and calculus, specially to solve ODEs and SDEs. |
| Sage | 3/10 | Ed | Symbolic programming for solve algebraic and geometric problems. |
| Netlogo (Logo) | 3/10 | Ed | Coding for presentations and show the emergence of complexity from simple rules. |
| Lua | 2/10 | PP | Scripts to play with telegram and raspberry pi with domestic purposes. |
| Prolog | 1/10 | Ed | Exercises of logic. |
| Processing | 4/10 | PP | Exercises and used in educational purposes. Simulations of multi-agent systems, and interactive visualizations with dynamical systems. |
Software programs
One of the most important skills that in any role you can have is the good selection of your tools. Sitting on top on good tools it means sitting on top of giants. The main source of efficiency of any worker is very much linked to the tools that he is using. An extensive but not exhaustive list of tools that I used and I have confidence with them can be found in:
Programming packages
The first lesson for a rookie developer is, “don’t reinvent the wheel”. Open source community is very active. It is difficult to find a problem that was never though or face by others.
Knowing how to ensamble code using very different
Data skills
As a professional in the data field, my main skills to offer in the job market are related in how to manage data in order to extract its value, how to use it to get insights, boost performance in the organization, and produce prescriptive actions.
By type of data
By evaluating different type of data processed:
| Type of data | Level | Experience1 | Description of knowledge |
|---|---|---|---|
| Tabular data | 8/10 | Complete | Manipulating data, doing feature engineering and creating MLOps processes around it. |
| Images | 2/10 | Ed | Manipulating image collections by processing and filtering noise, patching images using RANSAC with SIFT descriptors, object detection using Machine Learning techniques (SVM and ANN). |
| Text | 4/10 | Ed + PP | Ability to process text data and extracting usable features. I faced problems of Named Entity Segmentation, Topic Recognition and Sentiment Analysis. |
| Time Series | 7/10 | Complete | Study and modeling stochastic and structural properties of a system of time series. Using Machine learning algorithms (Random Forest and ANN) to create predictions of future values in the timeseries. |
| Network data | 7/10 | Complete | Inferring network structures from other type of data and perform structural analysis in the micro, meso and macro scale and apply community detection algorithms. |
| Spatial data | 7/10 | Complete | Specially experience with geospatial data (points, lines and polygons). Dealing with geojson and topojson files and with geospatial oriented databases as PostGIS and SpatialLite or the use of Carto and QGIS for data visualization and spatial intelligence. |
By type of problem
By evaluating the different type of problems trying to solve with data we can list:
| Type of data problem | Level | Description of knowledge |
|---|---|---|
| Performance management | 7/10 | Creating measurable performance-based KPIs (Customer Support and Sales) and data products to help the interested teams to improve them. |
| Business Intelligence | 9/10 | Building KPIs that tracks high-level performance of the business. |
| Sales analytics | 9/10 | Tracking sales performance, studying incentives of sales processes and creating attribution of success on different sales sources. |
| Product analytics | 7/10 | Study of product behavioral data in order to understand patterns of usage in the application, create attributions on feature usage and identifying usage trends. |
| Compliance and data governance | 7.5/10 | Working with legal and other departments to ensure the right use of the data in agreement with the most up-to-date legal and data governance standards. |
| Marketing Intelligence | 5/10 | Creating targeting for email campaigns on existing customers and triggering in-product communication and blocking campaigns to free users. |
Curious?
Are you interested to collaborate with me in some project? Interested on assessment with data problems? Don’t hesitate and contact me: antoniogquintela@gmail.com
Experience codes: Described in the table of its main section. ↩ ↩2