I am an experienced data scientist with experience in various predictive methods and text analysis tools. I also have qualifications in Financial Analysis and visualizations. We work on gathering data, creating models and extracting insight to help you make better decisions. Experience with Python, R and AWS using cutting edge techniques and methods, we strive to simplify our analysis to enable anyone to harness to power modern data science techniques
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I started as a physics teacher after my graduation from university but I always had in mind to become a scientist and if possible to combine that with teaching. Also, as I like biology it was always my goal as a scientist to apply physics to the study of biology. During my MSc and PhD research projects, both on plant physiology, I extented further my knowledge on plant sciences, namely plant physiology & biochemistry, plant anatomy and the experimental methods used in these disciplines. Over the last 6 years I have especialized on mathematical modelling of plant transport systems (membrane transport, phloem and xylem transport) for which I used Mathcad as my main tool for solving differential equations. Also, on the experimental level I have experience on isotope labelling, electrophysiology and the standard methods and equipment used in those fields.
I am a data scientist/analyst. My primary tool is R, which I extensively use in conjunction with Python, MySQL, PostgreSQL, NoSQL (e.g. MongoDB), and Linux commands. This is my blog: feelosophy13.github.io. What I do in general: - Data visualization (e.g. interactive dashboards) - Report automation - Machine learning and modeling - Data extraction, cleaning, feature selection, and preprocessing - Data mining and aggregation - API integration Prediction modeling: - Multivariate linear and logistic regression - Classification and regression trees (i.e. decision trees) - Linear and quadratic discriminant analysis (LDA and QDA) - Random forest and variable selection Clustering and segmentation: - Data normalization - Hierarchical and k-means clustering Visualization: - Visualizations via ‘ggplot2’, 'googleVis', and 'rCharts' packages - Interactive dashboard/application using 'shiny' package - Maps visualization
I have 14+ years of programming experience and a Master's degree in Bio- and Medical Informatics. I'm good at User Friendly GUIs, Data Reorganization, File Format Conversions (including text and binary data formats), Web Scrapping, Desktop Applications (Visual FoxPro, C# recently), Databases (a lot of experience), Bioinformatics, Statistical Analysis, R-project (including C code compiled for R to speed up some time consuming computations), SQL, Microsoft Office Excel Automation (VBA code sent from a desktop application to Excel to make it do things), Programs to Automatically fill Acrobat PDF Forms, Text Parsing, Mapple, C, C++, C#, Audio/Video formats conversion, Molecular Biology, Genetics and more. Keywords: Bioinformatics, Desktop Applications, Molecular Biology, Genetics, C, C++, C#, Mapple, PDF Forms, edgelist, edge list, Databases, database, Statistical Analysis, R-project, VBA, Automation, Medical Informatics, Algorithms, algorithm, File Convert, files conversion, Text Parsing, parse, user friendly graphical interface, GUI, Audio, Video, website scrap, scrapping, wordcloud, wordclouds, word cloud, word clouds, C#.
I work with clients on defining, implementing and managing data analysis and data management projects. My expertise lies at the intersection of Statistics, Computer Science and Business acumen. Specific areas of interest to me are education, healthcare, finance, real estate, and energy. Analysis: R (statistics), Python (data munging), Java+Scala (machine learning / big data), VB.NET/Excel (financial modelling) ** Apache Hadoop, Hive, Spark with MLlib (big data ecosystem) ** MongoDB, Neo4j (NoSQL) + MySQL, Monetdb (SQL) ** Gephi (networks/social graph processing) ** Weka (machine learning) Visualization: Tableau (rapid dashboarding), Shiny (R package for interactive statistical dashboards) Environments: Linux, Windows, and Mac (least experience) Productivity: Git (source code control) Web Applications: Play 2 Framework (modern Java/Scala-based, reactive websites) I believe the key qualification to be a Data Science practitioner is an analytic and curious mindset. That said, many hiring decisions seem to hinge on specific skills. Let me know if there's a tool or technology you don't see on my list, and I'll let you know if I've worked with it before.
Over the last 12 years, I worked as research assistant/associate in data analysis. I developed algorithms in processing and performing statistical analysis using R, Perl, Java. I am proficient in various Microsoft Office Applications especially MS Excel, MS Word, and MS Powerpoint. I have strong oral and written communication skills. I am a strong self-starter and able to work well independently. I am hardworking and can work effectively as fast as I can.
Qualification: B.Sc. (H) Statistics from University of Delhi M.Sc. Statistics from University of Pune Academic Project: Fractal Trade Duration (Software used: R ). Known statistical programming language : R, SPSS, Minitab. Other known programming languages: C, C++ , Ms Excel Good command over R-programming Cleared probability 'P/1' paper from Society of Actuaries , USA Work Experience: Worked as an e-tutor (statistics) for two years.