Data science encompasses a wide range of areas, topics, and sub-domains such as Big Data, Machine & Deep learning (ETL, TensorFlow, Keras), Data Mining/Visualization (EDA), BI, Predictive Analytics, Statistical Analytics, etc.
-
Machine Learning: This involves creating algorithms and models that can learn from and make predictions or decisions based on data.
-
Data Mining: Data mining is the process of discovering patterns and relationships in large datasets.
-
Big Data: Big data refers to the analysis of large and complex datasets that traditional data processing applications are unable to handle.
-
Data Visualization: Data visualization is the graphical representation of data to help people understand the significance of data by placing it in a visual context.
-
Predictive Analytics: Predictive analytics involves using statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data.
-
Natural Language Processing (NLP): NLP is a field of artificial intelligence that focuses on the interaction between computers and humans through natural language.
-
Deep Learning: Deep learning is a subset of machine learning that uses neural networks to model and understand complex patterns in large datasets.
-
Business Intelligence (BI): BI involves the use of data analysis tools and techniques to help organizations make informed business decisions.
-
Data Engineering: Data engineering involves designing and building the systems and infrastructure that enable data collection, storage, and analysis.
-
Statistical Analysis: Statistical analysis involves using statistical methods to analyze and interpret data.
-
Data Cleaning: Data cleaning involves identifying and correcting errors or inconsistencies in datasets to improve the quality of the data.
-
Feature Engineering: Feature engineering is the process of creating new features or variables from existing data that can improve the performance of machine learning models.
-
Time Series Analysis: Time series analysis involves analyzing and modeling data that is collected over time to identify patterns and make forecasts.
-
Anomaly Detection: Anomaly detection is the process of identifying outliers or unusual patterns in data that do not conform to expected behavior.
-
Cluster Analysis: Cluster analysis involves grouping similar data points together in order to identify underlying patterns or structures in the data.
-
Reinforcement Learning: Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment and receiving rewards or penalties.
-
Causal Inference: Causal inference involves determining the causal relationship between variables in observational data.
-
Bayesian Statistics: Bayesian statistics is a framework for statistical inference that uses Bayes' theorem to update the probability of hypotheses as more evidence or information becomes available.
-
Genomic Data Analysis: Genomic data analysis involves analyzing and interpreting genetic data to understand the genetic basis of traits and diseases.
-
Social Network Analysis: Social network analysis involves analyzing the structure of social networks to understand how individuals are connected and how information flows within the network.
-
Spatial Analysis: Spatial analysis involves analyzing data that has a spatial or geographic component. It is used to understand patterns and relationships in spatial data.
-
A/B Testing: A/B testing is a method of comparing two versions of a web page, app, or product to determine which one performs better. It is used to make data-driven decisions about design and marketing strategies.
The above areas, topics, and sub-domains highlight the diverse and multidisciplinary nature of data science, encompassing fields such as statistics, computer science, and domain-specific knowledge. The additional areas further demonstrate the broad applications and interdisciplinary nature of our research across various domains and industries, and is rapidly evolving with new areas and topics emerging all the time.
Will be adding notebooks soon... thank you for your patience!