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About Novris Technologies

Novris Technologies (NTPL Training & Development Private Limited) is an education platform since 2022 providing rigorous industry-relevant programs designed and delivered in collaboration with world- class faculty and industry for Data Analytics.

Course Description

This course provides a comprehensive introduction to data analytics, equipping participants with the knowledge and techniques to extract valuable insights from data and make data-informed decisions This module provides an overview of data analytics, its applications, and the role it plays in various industries. Students gain an understanding of the data analytics lifecycle and the essential tools and technologies used in the field.


  • Analytical Skills
  • Statistical Knowledge
  • Programming Skills
  • SQL and Database Skills
  • Knowledge of Machine Learning
  • Data Wrangling and Preprocessing
  • Domain Knowledge
  • Communication and Presentation Skills
  • Continuous Learning

This module focuses on acquiring, cleaning, and transforming raw data into a structured format suitable for analysis. Students learn techniques to handle missing data, deal with outliers, and ensure data integrity.

Students learn how to explore and visualize data using statistical techniques and data visualization tools. They gain insights into data distributions, correlations, and trends, enabling them to identify patterns and anomalies.

Module 1: Introduction

  • Overview
    • Introduction to Data Analytics
    • What is Data Structures
    • Data Collection and Preparation
    • Exploratory Data Analysiss
    • Statistical Analysis and Hypothesis Testing
    • Predictive Analytics:
    • Predictive Analytics
    • Big Data Analytics
    • Ethical and Legal Considerations
    • Case Studies and Projects
  • Installation
    • Installation
    • Data Cleaning and Preparation
    • Exploratory Data Analysis
    • Statistical Analysis and Modeling
    • Predictive Analytics
    • Data Visualization and Reporting
    • Big Data Analytics

Module 2: Architecture, Scenarios, and Admin Tools Big Data Analytics

  • Architecture & scenarios
    • Data Sources
    • Data Ingestion
    • Data Storage
    • Data Processing
    • Image Classification
    • Object Detection
    • Model Training and Tuning
    • Model Interpretability and Explainability

Module 3:Data Collection and Data Types Data Analytics

  • Variables
    • Data sources and data acquisition methods
    • structured, unstructured, and semi-structured
    • Data quality assessment and data governance
  • Module 4: Data Preprocessing and Data Cleaning Data Analytics

    • Recurrent Neural Networks (RNNs)
    • Long Short-Term Memory (LSTM) networks
    • Applications in natural language processing and speech recognition

    Module 5:Exploratory Data Analysis (EDA) Data Analytics

      • Descriptive statistics: measures of central tendency and variability
      • Data visualization techniques: histograms, scatter plots, box plots, etc.
      • Exploring relationships and correlations in data
      • Hypothesis testing and statistical inference

    Module 6: Predictive Analytics and Regression Analysis Data Analytics

      • Introduction to predictive analytics
      • Linear regression and multiple regression analysis
      • Model evaluation and selection
      • Feature selection and engineering

    Module 7: Classification and Machine Learning Data Analytics.

    • Introduction to classification techniques
    • Decision trees and random forests
    • Support Vector Machines (SVM)
    • Model evaluation metrics: accuracy, precision, recall, F1-score

    Module 8: : Cluster Analysis and Unsupervised Learning Data Analytics.

    • Introduction to clustering techniques
    • K-means clustering
    • Hierarchical clustering
    • Evaluation of clustering results

    Module 9:Text Analytics and Natural Language Processing (NLP) Data Analytics.

    • . Basics of text analytics and NLP
    • Text preprocessing techniques: tokenization, stemming, stop-word removal
    • Sentiment analysis and text classification
    • Topic modeling and named entity recognition (NER)

    Module 10: Time Series Analysis Data Analytics.

    • Database Backup and Replication
    • Introduction to time series data
    • Time series visualization and descriptive analysis
    • Forecasting techniques: moving averages, ARIMA, exponential smoothing
    • Seasonality and trend analysis

    Module 11 Big Data Analytics Data Analytics.

    • Introduction to big data and its characteristics
    • Overview of distributed computing frameworks: Hadoop and Spark
    • . Techniques for processing and analyzing big data
    • Real-time and streaming analytics

    Module 12 Ethics and Privacy in Data Analytics Data Analytics.

    • . Data ethics and responsible data usage
    • Privacy considerations and data protection
    • Legal and regulatory aspects in data analytics

    Module 13 Data Visualization and Communication.

    • . Principles of effective data visualization
    • Choosing appropriate visualizations for different types of data
    • Interactive and dynamic visualizations
    • Communicating data insights effectively

    Module 14: Placement Guide

    • Tips to clear an Interview
    • Common Interview questions and answers
    • Data Analytics Interview Questions and Answers
    • Resume Building Guide
    • Career roadmap and certifications
    • Attempt for Data Analytics Certification Exam
    • Start applying for Jobs

Owen Christ

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Course Rating

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