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Data & Architecture

Sub Categories

Data & Architecture Courses

  • 85 Classes

Our Hadoop training Online also includes concepts for Map Reduce Training and Big Data Training. Modern companies estimate that only 12% of their accumulated data is analyzed, and IT pr..

  • 2500
What you will learn
  • Mastering Big Data and Hadoop
  • Data Architect Training
  • Virtual Machine (VM)
  • MapReduce Concepts in detail
  • Data Warehousing
  • Parallel Processing

  • 13 Classes

Web analytics is the measurement, collection, analysis, and reporting of web data to understand and optimize web usage. Web analytics is not just a process for measuring web traffic but..

  • 2500
What you will learn
  • What Web Analytics is
  • Google Analytics and Web Analytics
  • Optimizely and KISS Metrics
  • Key Metrics in Web Analytics
  • Data Source in Web Analytics
  • Process of Segmentation
  • Web Analytics and Dashboard
  • Web Analytics Tools and more.

  • 24 Classes

Learn the Microservices overall Architecture, Building Blocks, Key Advantages, Challenges and Industry Case Studies

  • 2500
What you will learn
  • Challenges of the Traditional Monolithic Software Development
  • Main Building Blocks of a single Micro-Service
  • The Concept of a Microservices Architecture
  • Cloud-native Application
  • Key Advantages
  • Implementation Challenges
  • Netflix - Industry Case Study

  • 110 Classes

This is one of the most comprehensive courses on Data Science that you can find on the web. The complete Data Science course uses the power of Python to learn exploratory data analysis..

  • 2500
What you will learn
  • Python to analyze data, create state of the art visualization and use of machine learning algorithms to facilitate decis...
  • Python for Data Science and Machine Learning
  • NumPy for Numerical Data
  • Pandas for Data Analysis
  • Plotting with Matplotlib
  • Statistical Plots with Seaborn
  • Interactive dynamic visualizations of data using Plotly
  • SciKit-Learn for Machine Learning
  • K-Mean Clustering, Logistic Regression, Linear Regression
  • Random Forest and Decision Trees
  • Principal Component Analysis (PCA)
  • Support Vector Machines
  • Natural Language Processing and Spam Filters and more.