Data Analysis Courses Online

About

In this Xcelsius Training course, students will use Xcelsius Present to create interactive visualizations for presenting complex data in a simple way, and to conduct analysis to make critical decisions. Students will also create complete dashboards that present business, project, and human resources information, all consolidated and presented in a user-friendly manner. Finally, students will publish dashboards into various file formats such as Adobe Flash, Microsoft Office PowerPoint, Adobe PDF, and also to the web.

Objectives

Upon successful completion of this course, students will be able to:

• Explore the Xcelsius workspace and an already created dashboard.
• Create simple visualizations.
• Conduct data analysis using Xcelsius components that give dynamic functionality to the specified data.
• Create a Project Management dashboard.
• Create a dashboard to consolidate and present the Human Resources information of an organization.
• Finalize dashboards and export them to different file formats.

About

The course aims to broaden the knowledge of participants about programming using PL / SQL and issues related to the optimization commands. Particular emphasis in this training is the performance of the data collection to ensure a smooth operation for very large amounts of data. In addition, workshops supplement an understanding of the elements necessary to any advanced user of Oracle in their daily work, such as copying and downloading large amounts of information, data modeling, modification of an existing data model and reverse engineering techniques using Oracle tools.

The course is based on the software version 11g XE

Content

• Moving and loading
• Procedural language PL / SQL allows you to expand the analytical capabilities of a SELECT statement
• Improving the performance of SQL queries
• Data modeling and acquisition and modification of the existing data model based on Oracle SQL Modeler

About

This course is for data scientists and statisticians that have some familiarity statistical methods and would like to use the Octave programming language at work.

The purpose of this course is to give a practical introduction in Octave programming to participants interested in using this programming language at work.

About

SPM (Statistical Parametric Mapping) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional imaging data. These ideas have been instantiated in software that is called SPM. The SPM software package has been designed for the analysis of brain imaging data sequences. The sequences can be a series of images from different cohorts, or time-series from the same subject. The current release is designed for the analysis of fMRI, PET, SPECT, EEG and MEG. SPM is made freely available to the [neuro]imaging community, to promote collaboration and a common analysis scheme across laboratories. The software represents the implementation of the theoretical concepts of Statistical Parametric Mapping in a complete analysis package, as a suite of MATLAB (The MathWorks, Inc) functions and subroutines with some externally compiled C routines. 

Content

• Overview
• Installation
  • System Requirements
  • Detailed Installation
  • Old platforms
• Experimental design for fMRI
• Data Formats
• PreProcessing
• Modeling
• Statistical Interface
• Voxel-based Morphometry
• Connectivity Analysis
• Miscellaneous
• Other tools

About

Data visualization is an interdisciplinary field that deals with the graphic representation of data. It is a particularly efficient way of communicating when the data is numerous as for example a Time Series. From an academic point of view, this representation can be considered as a mapping between the original data (usually numerical) and graphic elements (for example, lines or points in a chart). The mapping determines how the attributes of these elements vary according to the data. In this light, a bar chart is a mapping of the length of a bar to a magnitude of a variable. Since the graphic design of the mapping can adversely affect the readability of a chart, mapping is a core competency of Data visualization. Data visualization has its roots in the field of Statistics and is therefore generally considered a branch of Descriptive Statistics. However, because both design skills and statistical and computing skills are required to visualize effectively, it is argued by some authors that it is both an Art and a Science.

Content

• Introduction to data visualization in data science
• NumPyand pandas operations
• Data visualizations using plotting libraries
• Methods to trace geospatial data on a map
• Integrate interactive visualizations

About

Project Jupyter is a web application for interactive data science and scientific computing It is a rich Internet application.

Content

• Introduction to Project Jupyter
• Data Analysis
• Strategy in machine learning classification and classification models.
• Validation curves and dimensionality reduction
• Learn Pandas DataFrames
• Creation of interactive, web-friendly visualizations

About

QGIS is open source desktop geographic information system software, It is a geographic information system. QGIS is developed by QGIS Development TeamSupported by Linux, macOS and Microsoft Windows Operating Systems.

Content

• Loading data in the QGIS
• Types data presented it in a map
• Creation of maps
• Processing toolbox in QGIS 3.4
• Geospatial data and gain quality information
• Customization in QGIS 3.4
• QGIS 3.4 in 3D

About

Qlik Sense is a data analytics platform developed by Qlik It is business intelligence and data visualization software.

Content

•  Introduction to Qlik Sense Interactive dashboards
• Data visualization functions
• Write and use script subroutines
• Custom objects and indicators for your UI requirements
• Qlik Sense dashboard using visualization extension
• Explore Aggr and use for set analysis

About

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.

Data science is a "concept to unify statistics, data analysis and their related methods" in order to "understand and analyze actual phenomena" with data.^[3] It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, domain knowledge and information science. Turing award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge.

Content

• Foundations
  • Relationship to statistics
• Etymology
  • Early usage
  • Modern usage
• Impacts of data science
• Technologies and techniques
  • Techniques
  • Languages
  • Frameworks
  • Visualization Tools
  • Platforms

About

R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Polls, data mining surveys, and studies of scholarly literature databases show substantial increases in popularity; as of January 2021, R ranks 9th in the TIOBE index, a measure of popularity of programming languages.

Content

• History
• Statistical features
• Programming features
• Packages
• Milestones
• Interfaces
• Implementations
• Comparison with SAS, SPSS, and Stata
• Commercial support for R
• Examples
  • Basic syntax
  • Structure of a function
  • Modeling and plotting
  • Mandelbrot set

About

Get to know the learning you need for starter level data science in this involved Python and Jupyter course. You will find most oftentimes utilized libraries that are essential for the Anaconda distribution, and investigate AI models with new data sets to give you the knowledge and the understanding you need. 

Content

• Introduction to Analysis of data
• Understand machine learning strategy and classification
• Validation curves and dimension reduction
• Pandas Dataframes
• Develop User Interface for viewing

About

R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Polls, data mining surveys, and studies of scholarly literature databases show substantial increases in popularity; as of January 2021, R ranks 9th in the TIOBE index, a measure of popularity of programming languages.

Content

• History
• Statistical features
• Programming features
• Packages
• Milestones
• Interfaces
• Implementations
• Communities
• useR! conferences
• The R Journal
• Comparison with SAS, SPSS, and Stata
• Commercial support for R
• Examples
  • Basic syntax
  • Structure of a function
  • Modeling and plotting
  • Mandelbrot set