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

Course Rating

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Devit Killer

4.7/5.00

January 29, 2021

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Owen Christ

January 29, 2021

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