Trends_in_IT_Big_Data_Analytics_Presentation

1.

Trends in Information
Technologies Using Big Data
Analytics
Insights from StackExchange and
GitHub Data

2.

Objectives of the Study
• - Analyze trends in information technologies
(IT)
• - Utilize big data sources such as
StackExchange and GitHub
• - Provide insights for decision-makers and IT
professionals

3.

Data Sources
• - StackExchange:
• - A network of Q&A websites on diverse
topics
• - Provides data on technology-related
discussions
• - GitHub:
• - A platform for hosting and collaborating on
software projects
• - Offers data on programming languages and

4.

Methodology
• - Data importation into local repositories
• - Data cleaning and preprocessing:
• - Tokenization
• - Stemming
• - Dimensionality reduction
• - Extraction of keywords and their
relationships
• - Selection of key knowledge areas:
• - 20 programming languages

5.

Exploratory Analysis
• - Cluster analysis using Gephi
• - Visualization of programming language data
• - Identification of patterns and relationships

6.

Time Series Analysis
• - Creation of time series for selected keywords
• - Forecasting trends using R's forecast library
• - Evaluation of forecast accuracy:
• - Mean Magnitude of Relative Error (MMRE)
• - Median Magnitude of Relative Error
(MdMRE)

7.

Key Findings
• - Identification of emerging and declining
technologies
• - Insights into programming languages,
databases, cloud services, and mobile OS
trends
• - Recommendations for IT professionals on
skill development

8.

Implications for Decision-Makers
• - Data-driven decisions on technology
investments
• - Strategic planning based on technology
adoption trends
• - Resource allocation for training and
development

9.

Conclusion
• - Big data analytics provides valuable insights
into IT trends
• - Continuous monitoring of platforms like
StackExchange and GitHub is essential
• - Informed decisions can lead to competitive
advantages in the IT industry

10.

References
• - Ozkuran, M. A. (2017). A Study on Trends in
Information Technologies using Big Data
Analytics. arXiv preprint arXiv:1703.09664.
• - StackExchange Data Explorer:
https://data.stackexchange.com/
• - GitHub Archive: https://www.gharchive.org/