Data analytics converts raw data into actionable insights.It is the process of examining large and varied data sets to uncover patterns, correlations, trends, and other useful information.

Data analytics has multiple facets and approaches,encompassing diverse techniques under a variety of names,and is used in different busniness,science,and social science domains.

It involves the application of statistical analysis, machine learning, and other analytical techniques to interpret data and derive insights that can inform decision-making and optimize processes.

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Syllabus

Syllabus we covered

Module 1

Introduction to Data Analytics

Overview of Data Analytics
Importance of Data Analytics in Business
Data Analytics Lifecycle

Module 2

Data Collection and Data Management

Data Sources and Types
Data Collection Methods
Data Management and Warehousing
Introduction to Big Data

Module 3

Data Preparation and Cleaning

Data Preparation Techniques
Data Cleaning Methods
Handling Missing Data
Data Transformation and Normalization

Module 4

Data Exploration and Visualization

Exploratory Data Analysis (EDA)
Data Visualization Principles
Visualization Tools (Matplotlib, Seaborn, Tableau)
Creating Dashboards

Module 5

Statistical Analysis

Descriptive Statistics
Inferential Statistics
Probability Theory and Distributions
Hypothesis Testing

Module 6

Predictive Analytics

Introduction to Predictive Analytics
Regression Analysis
Linear Regression
Multiple Regression
Classification Techniques
Logistic Regression
Decision Trees
Support Vector Machines (SVM)
Time Series Analysis and Forecasting

Module 7

Machine Learning for Data Analytics

Introduction to Machine Learning
Supervised Learning Techniques
k-Nearest Neighbors (k-NN)
Naive Bayes
Random Forests
Unsupervised Learning Techniques
Clustering (K-means, Hierarchical)
Principal Component Analysis (PCA)
Model Evaluation and Tuning

Module 8

Data Mining

Introduction to Data Mining
Data Mining Techniques
Association Rule Mining
Anomaly Detection

Module 9

Advanced Analytics

Text Analytics and Natural Language Processing (NLP)
Sentiment Analysis
Social Media Analytics
Web Analytics

Module 10

Tools and Technologies for Data Analytics

Data Analytics Tools Overview
SQL for Data Analytics
Python for Data Analytics
R for Data Analytics
Excel for Data Analytics

Module 11

Data Analytics in Business

Business Intelligence (BI)
Data-Driven Decision Making
Case Studies in Data Analytics

Module 12

Data Analytics Project

Defining the Project Scope and Objectives
Data Collection and Preparation
Analysis and Modeling
Interpretation and Reporting
Project Presentation and Documentation