Advanced Analytics Course
in Hyderabad

A course on Google Analytics with a focus on somewhat more complex subjects including custom reports, attribution, and helped conversions

Advanced Analytics Course in Hyderabad

Advanced marketing analytics (AMA) uses data analysis to improve marketing strategies. AMAhelps marketers gain insights into their customers’ behavior. preferences, and other information to create better campaigns. It’s important to note that the Advanced Analytics Course in Hyderabad isn’t just limited to marketing. The term also applies to any data collection or analysis. For example, it can include customer service. sales. finance, and operations.

What you’ll learn

Requirements

Curriculm

MODULE 1: DATA ANALYSIS FOUNDATION

Data Preparation forAnalysis

Common Data Problems

Various Tools for Data Analysis

Evolution ofAnaMics domain

MODULE 2: CLASSIFICATION OF ANALYTICS

Four tnes oftheAnaMics

DescriptiveAnalytics

DiagnosticsAnalytics

Predictive Analytics

Prescriptive Analytics

Human Input in Various tljpe ofAnaMics

MODULE 1: MACHINE LEARNING INTRODUCTION

What Is ML? ML vs Al

ML Workflow, Popular ML Algorithms

Clustering, Classification And Regression

Supervised Vs Unsupervised

MODULE 2: ML ALGO: LINEAR REGRESSSION

Introduction to Linear Regression

How it works: Regression and Best Fit Line

Hands-on Linear Regression with ML Tool

MODULE 3: ML ALGO: LOGISTIC REGRESSION

Introduction to Logistic Regression

How it works: Classification & Sigmoid Curve

Hands-on Logistics Regression with ML Tool

MODULE 1: PYTHON BASICS

Introduction of python

Installation of Python and IDE

Python objects

Python basic data types

Number & Booleans, strings

Arithmetic Operators

Comparison Operators

Assignment Operators

Operator's precedence and associativity

MODULE 2: PYTHON CONTROL STATEMENTS

IF Conditional statement

IF-ELSE

NESTED IF

Python Loops basics

WHILE Statement

FOR statements

BREAK and CONTINUE statements

MODULE 1: GIT INTRODUCTION

Purpose of Version Control

Popular Version control tools

Git Distribution Version Control

Terminologies

Git Workflow

Git Architecture

MODULE 2: GIT REPOSITORY and GitHub

Git Repo Introduction

Git Architecture

Create New Repo with Init command

Copying existing repo

Git user and remote node

Git Status and rebase

Review Repo History

GitHub Cloud Remote Repo

MODULE 1: DATA SCIENCE ESSENTIALS

Introduction to Data Science

Data Science Terminologies

Classifications of Analytics

Data Science Project workflow

MODULE 2: DATA ENGINEERING FOUNDATION

Introduction to Data Engineering

Data engineering importance

Ecosystems of data engineering tools

Core concepts of data engineering

MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION

DATABASE Overview

Key concepts of database management

CRUD Operations

Relational Database Management System

RDBMS vs No-SQL (Document DB)

MODULE 2: SQL BASICS

Introduction to Databases

Introduction to SQL

SQL Commands

MY SQL workbench installation

Comments

import and export dataset

MODULE 1: COMPARISION AND CORRELATION ANALYSISY

Data comparison Introduction

Concept of Correlation

Calculating Correlation with Excel

Comparison vs Correlation

Performing Comparison Analysis on Data

Performing correlation Analysis on Data

Hands-on case study 1 : Comparison Analysis

Hands-on case study 2 Correlation Analysis

MODULE 2: VARIANCE AND FREQUENCY ANALYSIS

Concept of Variability and Variance

Data Preparation for Variance Analysis

Business use cases for Variance and FrequencyAnalysis

Performing Variance and FrequencyAnalysis

Hands-on case study 1 : Variance Analysis

Hands-on case study 2: FrequencyAnalysis

MODULE 1: BIG DATA INTRODUCTION

Big Data Overview

Five Vs of Big Data

What is Big Data and Hadoop

Introduction to Hadoop

Components of Hadoop Ecosystem

Big Data Analytics Introduction

MODULE 2: HDFS AND MAP REDUCE

HDFS — Big Data Storage

Distributed Processing with Map Reduce

Mapping and reducing stages concepts

Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort

Hands-on Map Reduce task

MODULE 1: DATA ANALYTICS FOUNDATION

Business Analytics Overview

Application of Business Analytics

Visual Perspective

Benefits of Business Analytics

Challenges

Classification of Business Analytics

Data Sources

Data Reliabiliti and Validity

Business Analytics Model

MODULE 2: OPTIMIZATION MODELS

Prescriptive Analytics with Low Uncertainti

Mathematical Modeling and Decision Modeling

Break Even Analysis

Product Pricing with Prescriptive Modeling

Building an Optimization Model

Case Study 1 : WonderZon Network Optimization

Assignment 1 : KERC Inc. Optimum Manufacturing Quantity

MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION

What is Business Intelligence (B1)?

What Bi Is The Core Of Business Decisions?

Bl Evolution

Business Intelligence Vs Business Analytics

Data Driven Decisions With Bi Tools

The Crisp-Dm Methodology

MODULE 2: Bl WTH TABLEAU: INTRODUCTION

The Tableau Interface

Tableau Workbook, Sheets And Dashboards

Filter Shelf, Rows And Columns

Dimensions And Measures

Distributing And Publishing

Instructor

Raghu Gaddam by raghugaddam
Raghu Gaddam

Digital Marketing

Raghu Gaddam is a digital marketing professional with over 12+ years of experience in the industry. He were worked with leading companies in the e-commerce and technology sectors, developing and executing successful digital marketing strategies. Raghu is known for his practical approach to teaching, combining real-world examples with the latest industry trends. He holds certifications in digital marketing and regularly attends conferences to stay updated on emerging technologies and methodologies.

Advanced Analytics course in Hyderabad by raghugaddam with social media icon

What's included

74.5 hours video

Quizzes

Maximum Students

Certificate

Watch Offline

Lifetime Access

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