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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
    20000
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
    15000
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
    15000
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

  • 33 Classes

This is one of the most comprehensive course on any e-learning platform which uses the power of Python to learn exploratory data analysis and machine learning algorithms. You will learn..

  • 2500
    35000
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.