Computer Skills

SOFTWARE:

• R programming Language.

R is the most common programming language among data scientists.

R has the extremely powerful packages that dramatically streamline the data science workflow (dplyr for data wrangling and ggplot2 for data visualization).

R is common( and Increasingly common) in the world best tech companies like Google, Twitter, LinkedIn, Facebook, Apple,...

• Python Programming Language.

It is interpreted high-level programming language for general-purpose programming.

Python features as dynamic type system and automatic memory management.

It is support multiple programming paradigms including; Objects oriented, Imperative, Functional and procedural.

It has a large and comprehensive standard libraries for various activities and projects

Including:

1. LIBRARY FOR DATA SCIENCE:

numpy: is the foundational library for scientific computing in Python, and many of the libraries on this list use NumPy arrays as their basic inputs and outputs.

scipy: builds on NumPy by adding a collection of algorithms and high-level commands for manipulating and visualizing data.

Pandas:adds data structures and tools that are designed for practical data analysis in finance, statistics, social sciences, and engineering. Pandas works well with incomplete, messy, and unlabeled data (i.e., the kind of data you’re likely to encounter in the real world), and provides tools for shaping, merging, reshaping, and slicing datasets.

ipython: extends the functionality of Python’s interactive interpreter with a souped-up interactive shell that adds introspection, rich media, shell syntax, tab completion, and command history retrieval. It also acts as an embeddable interpreter for your programs that can be really useful for debugging. If you’ve ever used Mathematica or MATLAB, you should feel comfortable with IPython.

matplotlib: is the standard Python library for creating 2D plots and graphs.

2. LIBRARY OR MACHINE LEARNING:

scikit-learn: builds on NumPy and SciPy by adding a set of algorithms for common machine learning and data mining tasks, including clustering, regression, and classification.

Theano uses NumPy-like syntax to optimize and evaluate mathematical expressions.

TensorFlow is another high-profile entrant into machine learning, developed by Google as an open-source successor to DistBelief, their previous framework for training neural networks.

3. LIBRARY FOR PLOTTING AND DATA VISUALIZATION

The best and most sophisticated analysis is meaningless if you can’t communicate it to other people. These libraries build on matplotlib to enable you to easily create more visually compelling and sophisticated graphs, charts, and maps, no matter what kind of analysis you’re trying to do.

Seaborn is a popular visualization library that builds on matplotlib’s foundation.

Bokeh makes interactive, zoomable plots in modern web browsers using JavaScript widgets.

Basemap adds support for simple maps to matplotlib by taking matplotlib’s coordinates and applying them to more than 25 different projections.

NetworkX allows you to create and analyze graphs and networks. It’s designed to work with both standard and nonstandard data formats, which makes it especially efficient and scalable.

• Database Management and data analysis with SQL statistical tools especially with SQL Server Management Studio (SSMS) and Postgres SQL.

It is a general purpose and relational database management system and the most advanced open source database system.

It is first database management system that implements multi-version concurrency control(MVCC) features, even before Oracle. The MVCC features is known as snapshot isolation in Oracle.

• STATA Statistical tools.

Data Analysis and Statistical Software for Professionals. Stata is a complete, integrated statistics package that provides everything you need for data analysis, data management, and graphics.

• SPPSS Statistical tools.

IBM SPSS Statistics is software for managing data and calculating a wide variety of statistics.