Advance Analytics

  • Predictive Analysis: Anticipating future outcomes using data patterns.
  • Prescriptive Analytics: Offering optimal action plans based on analysis.
  • Machine Learning: Utilizing algorithms to learn and predict trends.

      Predictive Modeling:

  1. Time Series Analysis: Analyzing data collected at regular intervals to forecast future trends, patterns, or behaviors, commonly used in finance, weather forecasting, and sales forecasting.

  2. Survival Analysis: Assessing the time until an event of interest occurs, like customer churn, equipment failure, or disease onset, using techniques like Kaplan-Meier curves or Cox proportional hazards models.

  3. MachineLearning Algorithms:

  1. Deep Learning: Neural network architectures with multiple layers, used for complex pattern recognition tasks like image and speech recognition, natural language processing, and autonomous vehicles.

  2. Reinforcement Learning: Training models to make sequences of decisions by learning through trial and error, often applied in robotics, gaming, and recommendation systems.

     Network Analysis:

  1. Social Network Analysis (SNA): Analyzing relationships and interactions within networks to understand influence, information flow, and community detection, commonly used in social media and sociology.

  2. Graph Theory: Studying relationships and structures among interconnected entities to reveal patterns, like in transportation networks, biological networks, or fraud detection.

   Text and Sentiment Analysis:

  1. Natural Language Processing (NLP): Extracting insights from unstructured text data, including sentiment analysis, topic modeling, and entity recognition, often used in social media monitoring and customer feedback analysis.

  2. Named Entity Recognition (NER): Identifying and categorizing entities within text, such as names of people, organizations, locations, etc.

    Bayesian Analysis:

  1. Bayesian Inference: Using Bayesian statistics to update beliefs about parameters as more evidence or data becomes available, applied in various fields including healthcare, finance, and machine learning.

  2. Probabilistic Graphical Models: Representing and reasoning about uncertainty and dependencies between variables, used in medical diagnosis, speech recognition, and recommendation systems.

    Spatial Analysis:

  1. Geospatial Analytics: Analyzing geographical data to uncover patterns and relationships, aiding in urban planning, logistics, and environmental studies.

  2. Spatial Autocorrelation: Detecting spatial patterns and relationships between nearby locations, used in geography, ecology, and epidemiology.