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Data Scientist/Analyst and A.I. developer
Dr. Vytautas Bielinskas
,
Vilnius, Lithuania
Experience
Other titles
Skills
I'm offering
I can provide efficiency and user friendly data analysis with additional features such as automatic data preparation, cleaning, visualization, internal calculations with integrating processes of Machine Learning and Deep Learning based on sufficient techniques of Data Science.
For that I use Python programming language (with all required modules for Data Science), Power BI, Tableau, other ant related tools or services for you task personally.
I suggest a 6 year experience working on very different datasets beginning from online user data to real estate and time-series data. For such projects multiple tools and custom made algorithms has been implement.
I am Data Science and Analytics specialist, currently building deep learning algorithms for human cells and X-rays classification and building data analysis automation systems. As Data Scientist in Ekistics Real Estate Advisors LLP I build build data mining, storage and automation systems with Python. I previously worked as Data Analyst at Gemius Baltics, and as Data Analyst I performed multiple online market analysis for ads campaign optimization. Ex lecturer and researcher of GIS at Vilnius Gediminas technical university.
Co-author of more than 12 articles published in scientific journals.
For that I use Python programming language (with all required modules for Data Science), Power BI, Tableau, other ant related tools or services for you task personally.
I suggest a 6 year experience working on very different datasets beginning from online user data to real estate and time-series data. For such projects multiple tools and custom made algorithms has been implement.
I am Data Science and Analytics specialist, currently building deep learning algorithms for human cells and X-rays classification and building data analysis automation systems. As Data Scientist in Ekistics Real Estate Advisors LLP I build build data mining, storage and automation systems with Python. I previously worked as Data Analyst at Gemius Baltics, and as Data Analyst I performed multiple online market analysis for ads campaign optimization. Ex lecturer and researcher of GIS at Vilnius Gediminas technical university.
Co-author of more than 12 articles published in scientific journals.
Markets
Lithuania
Links for more
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Language
English
Fluently
Lithuanian
Fluently
Ready for
Larger project
Ongoing relation / part-time
Available
My experience
2018 - ?
freelance
Data Analyst
Baltic Asset Management.
Preparing, structuring and cleaning real estate data by using my own custom made web scrapers in well prepared automatic system based on AWS EC2 and Python. In this way 10 separate well cleaned and structured datasets are providing to the client every month.
Data Analysis, Data mining, Data Modeling, Web scraping, Python, Automation
2017 - ?
job
Scientist/Analyst
Ekistics Real Estate Advisors LLP.
> Python programming
> Exploratory and complex data analysis (EDA)
> Data proceeding automation / Data Mining performing using AWS EC2.
> Big Data Mining & Visualization (Python Matplotlib, Seaborn, Bokeh, Folium, Power BI, etc.)
> Build Web scraping apps for different Data sources online.
> Geocoding data on Google Cloud Platform and Google Maps API.
> NoSQL + SQL (MongoDB Atlas Framework) in Python.
> Build ML systems for predicting and classifying real estate prices and properties for investment purposes.
> Time Series Data Analysis (Multivariate Regression Analysis, Prophet, ARIMA, etc.).
> Exploratory and complex data analysis (EDA)
> Data proceeding automation / Data Mining performing using AWS EC2.
> Big Data Mining & Visualization (Python Matplotlib, Seaborn, Bokeh, Folium, Power BI, etc.)
> Build Web scraping apps for different Data sources online.
> Geocoding data on Google Cloud Platform and Google Maps API.
> NoSQL + SQL (MongoDB Atlas Framework) in Python.
> Build ML systems for predicting and classifying real estate prices and properties for investment purposes.
> Time Series Data Analysis (Multivariate Regression Analysis, Prophet, ARIMA, etc.).
Data Science, Machine learning, Google cloud, Analyst, Google Cloud Platform, Apps, Sql, NoSQL, Cloud, Automation, Power BI, Data mining, Big Data, MongoDB, API, AWS, Data Analysis, Python
2019 - 2019
project
A.Based Lungs X-Rays multi-classification for detecting diagsnosis
A.I. based Lungs X-Rays multi-classification and diagnosis detecting algorithm. For this algorithm complex various configuration CNN has been used on powerful GPU. Achieved accuracy is 0.85 on unseed X-Rays data.
Artificial Intelligence, Machine learning, Keras, Python, Google cloud, Big Data, Tensorflow, Algorithms, Data Analysis, DATA ANALYSIS AND VISUALIZATION, Data Science
2019 - 2019
project
Human Cell Classifier to detect infection of Malaria
The project contain folders for labeled infected and non-infected cells by malaria. The code define an 4 layers ANN that learning to recognize infected cells by a given shuffled examples from working directory and provide a learning curve as output. For optimization Adam and MiniBatch methods have been applied (Tensorflow).
First of all, after all images are loaded, every single one is converted from RGB to grayscale, then transformed to the same resolution 134/2 × 134/2 and converting to 2D Numpy arrays. Then all
data manipulations are being performing through algoritm of splitting data to train and test sets by a given proportion and Deep Learning part. For this ANN I used structure Linear->ReLU->Linear->ReLU->Linear->ReLU->Linear->Sigmoid for binary classification.
For optimization I decided to apply Adam and MiniBatch methods.
In total more than 27k examples has been included into the model which has been split to train and test sets.
Number of nodes for each hidden layers are: 30, 15, 7 and 1 (W1–W4).
To train a single mini-batch takes appr. 10 minutes on my laptop.
First of all, after all images are loaded, every single one is converted from RGB to grayscale, then transformed to the same resolution 134/2 × 134/2 and converting to 2D Numpy arrays. Then all
data manipulations are being performing through algoritm of splitting data to train and test sets by a given proportion and Deep Learning part. For this ANN I used structure Linear->ReLU->Linear->ReLU->Linear->ReLU->Linear->Sigmoid for binary classification.
For optimization I decided to apply Adam and MiniBatch methods.
In total more than 27k examples has been included into the model which has been split to train and test sets.
Number of nodes for each hidden layers are: 30, 15, 7 and 1 (W1–W4).
To train a single mini-batch takes appr. 10 minutes on my laptop.
Artificial Intelligence, Machine learning, Computer vision, Python, Image recognition, Tensorflow, Keras, Big Data, Data Analysis, Data Science, Data mining
2018 - 2019
project
Business Intelligence based reports and data analysis with predictions
Ekistics Real Estate Advisors LLP.
Automation of Data reporting Pipeline: (1) Data Scraping from different sources. (2) Data Joining. (3) Set Longitudes and Latitudes. (4) Identifying and removing outliers. (5) Data Cleaning and Pre-processing. (6) Updating existing Data Sets. (7) Build interactive Dashboard with Price Map. (8) Prepare a report.
Used technique: MySQL, Selenium, Google Maps API, Beautiful Soup, Pandas, Numpy,
Power BI.
Used technique: MySQL, Selenium, Google Maps API, Beautiful Soup, Pandas, Numpy,
Power BI.
Power BI, Python, Automation, Data Analysis, DATA ANALYSIS AND VISUALIZATION, Predictive Analytics, Forecasting, Data Science, Data mining, Sql, SQL Databases, Real estate
2016 - 2018
job
Data Analyst for Online Data
Gemius Baltic.
> Users behavior and Internet data Analysis.
> Help desk, technical supporting of adServers.
> Javascript debugging.
> E-commerce KPI and User behavior analysis.
> Technical supporting of data analysis platforms.
> Ad Campaign setuping and reporting via gemiusDirectEffect.
> AdServing, monitoring, reporting, analysis via AdOcean.
> Working with Big data (systemization, analyzing.
> A/B testing, Web Consulting.
> Preparing education material for internal purposes and for clients.
> JIRA.
> Working with clients and partners, business meetings;
> Web analytics with gemiusPrism (alternative of Google Analytics).
> Help desk, technical supporting of adServers.
> Javascript debugging.
> E-commerce KPI and User behavior analysis.
> Technical supporting of data analysis platforms.
> Ad Campaign setuping and reporting via gemiusDirectEffect.
> AdServing, monitoring, reporting, analysis via AdOcean.
> Working with Big data (systemization, analyzing.
> A/B testing, Web Consulting.
> Preparing education material for internal purposes and for clients.
> JIRA.
> Working with clients and partners, business meetings;
> Web analytics with gemiusPrism (alternative of Google Analytics).
Google analytics, Online marketing strategy, Javascript, Data Analysis, Big Data, Jira, E-commerce, Consulting, Analytics, Testing, KPI, Monitoring, Web, Internet, Campaign, Google, Google tag manager
2018 - 2018
project
Exploratory Data Analysis for Real Estate Hypothesis testing
Ekistics Real Estate Advisors LLP.
Geostatistical analysis + EDA on checking economical assumptions on possible investments in Copenhagen. GIS, scrapped and 3rd party and economical data has been combined. Main assumption: what is the probability that a possible sell price of property will raise after metro station open within 500 radius?
Used technique: Hypothesis testing, EDA, GIS, Spatial Join, ML, Time Series Analysis with Feature Engineering, SMA, Matplotlib, Seaborn, Power BI, Pandas, Numpy.
Used technique: Hypothesis testing, EDA, GIS, Spatial Join, ML, Time Series Analysis with Feature Engineering, SMA, Matplotlib, Seaborn, Power BI, Pandas, Numpy.
Python, Data Analysis, DATA ANALYSIS AND VISUALIZATION, Data Science, Business Intelligence, Sql, SQL Databases, Data Visualisation, Google maps, Geodata, Excel, Forecasting, Predictive Analytics, Machine learning, Data mining, Big Data, Real estate
2017 - 2018
project
Machine Learning based the selection of new real estate object for investments
Ekistics Real Estate Advisors LLP.
ML Classification model to automate processes on Data mining and identify Real Estate properties for new long term (4–6 year) investments. Calculating probabilities if a specific property will return a positive ROI for a long term.
Used technique: Scikit-Learn, SciPy, Random Forest, Decision Tree, XgBoost, Pandas, Numpy, Natural Language Processing (NLP).
Used technique: Scikit-Learn, SciPy, Random Forest, Decision Tree, XgBoost, Pandas, Numpy, Natural Language Processing (NLP).
Machine learning, Data Science, Data Analysis, Python, Power BI, Reporting, Excel, Business Intelligence, Data Visualisation, Forecasting, Predictive Analytics
2017 - 2018
project
Automated Balance Report (Python)
Ekistics Real Estate Advisors LLP.
Accounting Python application that read extracted values from PDF, perform internal calculations and update existing Excel file & formulas with keeping whole original formatting for 45 different Real estate properties. Business value: one day work by one click in just few seconds.
Used technique: XlsXwriter, Python, Pandas, Numpy.
Used technique: XlsXwriter, Python, Pandas, Numpy.
Python, Financial Accounting, Accounting, Excel, Calculations, PDF, Programming, Automation, Data Analysis
2017 - 2017
project
Geostatistical Analysis of Urban Brownfields in Liverpool City
The project study analyses major data of the real estate transactions made in Liverpool during the period 2006/2016 with the urban brownfield dissemination and scope. The author has collected and systematized multidimensional data, studied real estate transaction price changes, layout in the space, interrelations and relation with the urban brownfields during the period analysed in order to use them for the conversion or revitalization. In total, 478 transactions and 2.83 km² of brownfields were systematized. By its size and statistical values, the study volume corresponds to the context of Vilnius city. The aim of the study is to define relationship between attribute data of real estate transaction and spatial distribution on urban brownfields. When conducting a study described in the article, the author applied GIS and MS Excel technologies. A spatial data aggregation method and data pairing method have been applied. The author examined statistical data of real estate transactions by the type of sector in time, carried out their statistical and spatial analysis, identified a relationship between a location and distance from the city centre and calculated their correlative connection with the spread of brown-fields in the city.
Liverpool John Moores University
Gis, Data Analysis, DATA ANALYSIS AND VISUALIZATION, Data Science, Data mining, Data Modeling, Data Visualisation, Geodata, Process Mapping, Google maps, Research
My education
2014
-
2019
Vilnius Gediminas Technical University
Doctorate, Civil Engineering
Doctorate, Civil Engineering
Multi-Attribute Decision Making and Data Science applying for Urban Researches (Investments on Urban Brownfields, Classification for the best scenarios of land purpose change).
2012
-
2014
Vilnius Gediminas Technical University
Masters, Engineering
Masters, Engineering
4 scientific articles have been prepared for this thesis.
2008
-
2012
Vilnius Gediminas Technical University
Bachelors, Urban Engineering
Bachelors, Urban Engineering
?
-
2016
Liverpool John Moores University
Doctorate, Internship
Doctorate, Internship
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