Here is a selection of my Data Science and Programming skills and tools that turned out to be helpful in my work and I believe are important for any Data Scientist as well.

The last update of this post was done: 2018-05-23.

## Data Science Skills

**Data Manipulation**- Efficient data manipulation in R;

`dplyr`

,`data.table`

,`reshape2`

. - Working with dates and time-series;

`lubridate`

,`xts`

.

- Efficient data manipulation in R;
**Reproducible Analyses**- RStudio IDE, Markdown, LaTeX;

`RMarkdown`

,`knitr`

.

- RStudio IDE, Markdown, LaTeX;
**Databases and Data Formats**- MySQL, MongoDB, JSON, XML, Excel, CSV, shapefiles, Linked Open Data / SPARQL, etc.

`mongolite`

,`jsonlite`

.

- MySQL, MongoDB, JSON, XML, Excel, CSV, shapefiles, Linked Open Data / SPARQL, etc.
**Data Acquisition**- parallel web-scrapping; working with REST API;

`httr`

,`RCurl`

,`rvest`

,`RSelenium`

.

- parallel web-scrapping; working with REST API;
**Text-mining**- regular expressions; stemming with dictionary method;

`stringr`

,`tm`

,`genderizeR`

.

- regular expressions; stemming with dictionary method;
**Qualitative Analyses**`RQDA`

.

**On-Line Surveys (CAWI)**- advanced on-line surveys in
`LimeSurvey`

;

`LimeRick`

.

- advanced on-line surveys in
**Networks Analyses**- visualizing and analyzing networks in
`Gelphi`

;

- programming dynamic networks simulations in
`NetLogo`

;

- visualizing and analyzing networks in
**Data Visualization**- static and interactive data visualization;

`ggplot2`

,`ggvis`

,`waffle`

. - dashboards and data reporting via WWW apps;

`Shiny`

,`shinydashboard`

,`shinyjs`

.

- static and interactive data visualization;
**Communicating Results**- reports and presentations in
`Tableau`

,`PowerPoint`

, and`HTML5`

;

`Rmarkdown`

,`isoslides`

.

- reports and presentations in
**Statistical Analysis & Machine Learning**- multiple linear regression, logistic regression, ordinal logistic regression, Principal Component Analysis (PCA), factor analysis, segmentation analysis, cluster analysis (k-means, hierarchical clustering), supervised / unsupervised learning,
- neural networks, Support Vector Machines (SVM), decision trees, Random Forests, Ridge Regression, LASSO, elastic net regularization, K-fold cross-validation, bootstraping with
`caret`

; - survival analysis with
`survival`

; - time series analysis (ARIMA, TBATS) with
`forecast`

.

**Big Data Tools**- familiarity with
`Spark`

and`sparklyr`

in R; - familiarity with
`MapReduce`

,`Hadoop`

,`Pig`

.

- familiarity with
**Other Data Science Tools**- familiarity with
`SPSS`

,`SAS`

,`Octave`

,`RapidMiner`

,`Excel VisualBasic`

,`R Revolution Enterprise`

/`Microsoft R`

.

- familiarity with

## Programming Skills

**Programming in R**- code versioning with
`RStudio IDE`

,`Git`

,`GitHub`

; - functional programming with
`purrr`

; - reactive programming with
`Shiny`

,`shinyjs`

; - parallel programming with
`parallel`

;

- code versioning with
**R package development**- package distribution with
`GitHub`

,`CRAN`

; - preparing package documentation with
`roxygen2`

; - unit testing with
`testthat`

;

- package distribution with
**Internet technologies**`HTML5`

,`CSS3`

,`JavaScript`

;- familiarity with
`MongoDB`

,`MEAN`

Stack,`Bootstrap`

, and`Linux`

servers;

**Other programming languages**- familiarity with
`Python`

,`C++`

,`Java for Android`

& Android Studio.

- familiarity with