Certified Data Analyst

Why should you take CERTIFIED DATA ANALYST course?

• Industry-relevant curriculum covering Excel, SQL, Power BI, and Statistics
• Globally recognised IABAC certification for international credibility
• Hands-on projects and case studies to build real-world experience
• Career support including internships, job assistance, and resume guidance
• Beginner-friendly learning with expert-led sessions, no prior experience needed

Who should take CERTIFIED DATA ANALYST course?

• Freshers and working professionals

Curriculum

• MODULE 1
  • DATA ANALYSIS FOUNDATION
    • Data Analysis Introduction
    • Data Preparation for Analysis
    • Common Data Problems
    • Various Tools for Data Analysis
    • Evolution of Analytics

• MODULE 2
  • CLASSIFICATION OF ANALYTICS
    • Four types of the Analytics
    • Descriptive Analytics
    • Diagnostics Analytics
    • Predictive Analytics
    • Prescriptive Analytics
    • Human Input in Various Type of Analytics

• MODULE 3
  • CRISP-DM Model
    • Introduction to CRISP-DM Model
    • Business Understanding
    • Data Understanding
    • Data Preparation
    • Modeling, Evaluation, Deploying, Monitoring

• MODULE 4
  • UNIVARIATE DATA ANALYSIS
    • Summary Statistics - Determines the Value’s Centre and Spread
    • Measure of Central Tendencies: Mean, Median and Mode
    • Measures of Variability: Range, Interquartile Range, Variance and Standard Deviation
    • Frequency Table - This Shows How Frequently Various Values Occur
    • Frequency Table - Histogram in Excel
    • Charts - A Visual Representation of the Distribution of Values

• MODULE 5
  • DATA ANALYSIS WITH VISUAL CHARTS
    • Line Chart
    • Column/Bar Chart
    • Waterfall Chart
    • Tree Map Chart
    • Box Plot

• MODULE 6
  • BIVARIATE DATA ANALYSIS
    • Scatter Plots Part
    • Regression Analysis
    • Correlation Coefficients

• STATISTICS ESSENTIALS

• MODULE 1
  • OVERVIEW OF STATISTICS
    • Introduction to Statistics
    • Descriptive and Inferential Statistics
    • Basic Terms of Statistics
    • Types of Data

• MODULE 2
  • HARNESSING DATA
    • Random Sampling
    • Sampling with Replacement and without Replacement
    • Cochran's Minimum Sample Size
    • Types of Sampling
    • Simple Random Sampling
    • Stratified Random Sampling
    • Cluster Random Sampling
    • Systematic Random Sampling
    • Multi Stage Sampling
    • Sampling Error
    • Methods of Collecting Data

• MODULE 3
  • EXPLORATORY DATA ANALYSIS
    • Exploratory Data Analysis Introduction
    • Measures of Central Tendencies: Mean, Median and Mode
    • Measures of Central Tendencies: Range, Variance and Standard Deviation
    • Data Distribution Plot: Histogram
    • Normal Distribution and Properties
    • Z Value/Standard Value
    • Empirical Rule and Outliers
    • Central Limit Theorem
    • Normality Testing
    • Skewness and Kurtosis
    • Measures of Distance: Euclidean, Manhattan and Minkowski Distance
    • Covariance and Correlation

• MODULE 4
  • HYPOTHESIS TESTING
    • Hypothesis Testing Introduction
    • P-Value, Critical Region
    • Types of Hypothesis Testing
    • Hypothesis Testing Errors: Type I and Type II
    • Two Sample Independent T-test
    • Two Sample Relation T-test
    • One-way ANOVA Test
    • Application of Hypothesis Testing

• DATA ANALYSIS ASSOCIATE

• MODULE 1
  • COMPARISION AND CORRELATION ANALYSIS
    • Data Comparison Introduction
    • Performing Comparison Analysis on Data
    • Concept of Correlation
    • Calculating Correlation with Excel
    • Comparison vs. Correlation
    • Hands-on Case Study: Comparison Analysis
    • Hands-on Case Study: Correlation Analysis

• MODULE 2
  • VARIANCE AND FREQUENCY ANALYSIS
    • Variance Analysis Introduction
    • Data Preparation for Variance Analysis
    • Performing Variance and Frequency Analysis
    • Business use cases for Variance Analysis
    • Business use cases for Frequency Analysis

• MODULE 3
  • RANKING ANALYSIS
    • Introduction to Ranking Analysis
    • Data Preparation for Ranking Analysis
    • Performing Ranking Analysis with Excel
    • Insights for Ranking Analysis
    • Hands-on Case Study: Ranking Analysis

• MODULE 4
  • BREAK EVEN ANALYSIS
    • Concept of Break-Even Analysis
    • Make or Buy Decision with Break-Even
    • Preparing Data for Break-Even Analysis
    • Hands-on Case Study: Manufacturing

• MODULE 5
  • PARETO (80/20 RULE) ANALYSIS
    • Pareto Rule Introduction
    • Preparation Data for Pareto Analysis
    • Performing Pareto Analysis on Data
    • Insights on Optimising Operations with Pareto Analysis
    • Hands-on Case Study: Pareto Analysis

• MODULE 6
  • TIME SERIES AND TREND ANALYSIS
    • Introduction to Time Series Data
    • Preparing Data for Time Series Analysis
    • Types of Trends
    • Trend Analysis of the Data with Excel
    • Insights from Trend Analysis

• MODULE 7
  • DATA ANALYSIS BUSINESS REPORTING
    • Management Information System introduction
    • Various Data Reporting Formats
    • Creating Data Analysis Reports as Per the Requirements

• ADVANCED DATA ANALYTICS

• MODULE 1
  • DATA ANALYTICS FOUNDATION
    • Business Analytics Overview
    • Application of Business Analytics
    • Benefits of Business Analytics
    • Challenges
    • Data Sources
    • Data Reliability and Validity

• MODULE 2
  • OPTIMISATION MODELS
    • Predictive Analytics with Low Uncertainty; Case Study
    • Mathematical Modeling and Decision Modeling
    • Product Pricing with Prescriptive Modeling
    • Assignment: KERC Inc, Optimum Manufacturing Quantity

• MODULE 3
  • PREDICTIVE ANALYTICS WITH REGRESSION
    • Mathematics behind Linear Regression
    • Case Study: Sales Promotion Decision with Regression Analysis
    • Hands-on Regression Modeling in Excel

• MODULE 4
  • DECISION MODELING
    • Predictive Analytics with High Uncertainty
    • Case Study: Monte Carlo Simulation
    • Comparing Decisions in Uncertain Settings
    • Trees for Decision Modeling
    • Case Study: Supplier Decision Modeling - Kickathlon Sports Retailer

• PREDICTIVE ANALYTICS USING ML

• MODULE 1
  • MACHINE LEARNING INTRODUCTION
    • What is ML? ML vs. AI
    • ML Workflow, Popular ML Algorithms
    • Clustering, Classification and Regression
    • Supervised vs. Unsupervised

• MODULE 2
  • ML ALGO: LINEAR REGRESSION
    • 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
    • Classification and Sigmoid Curve
    • Hands-on Logistics Regression with ML Tool

• MODULE 4
  • ML ALGO: KNN
    • Introduction to KNN: Nearest Neighbour
    • Regression with KNN
    • Hands-on: KNN with ML Tool

• MODULE 5
  • ML ALGO: K-MEANS CLUSTERING
    • Understanding Clustering (Unsupervised)
    • Introduction to K-means and How it works
    • Hands-on: K-means Clustering

• MODULE 6
  • ML ALGO: DECISION TREE
    • Decision Tree and How it Works
    • Hands-on: Decision Tree with ML Tool
  • MODULE 7
    • ML ALGO: SUPPORT VECTOR MACHINE (SVM)
      • Introduction to SVM
      • How It Works: SVM Concept, Kernel Trick
      • Hands-on: SVM with ML Tool
  • MODULE 8
    • ARTIFICIAL NEURAL NETWORK (ANN)
      • Introduction to ANN, How It Works
      • Back Propagation, Gradient Descent
      • Hands-on: ANN with ML Tool
  • DATABASE: SQL AND MONGODB
  • MODULE 1
    • DATABASE INTRODUCTION
      • DATABASE Overview
      • Key Concepts of Database Management
      • Relational Database Management System
      • CRUD Operations
  • MODULE 2
    • SQL BASICS
      • Introduction to Databases
      • Introduction to SQL
      • SQL Commands
      • MYSQL Workbench Installation
  • MODULE 3
    • DATA TYPES AND CONSTRAINTS
      • Numeric, Character, Date Time Data Type
      • Primary Key, Foreign Key, Not Null
      • Unique, Check, Default, Auto Increment
  • MODULE 4
    • DATABASES AND TABLES (MySQL)
      • Create Database
      • Delete Database
      • Show and Use Database
      • Create Table, Rename Table
      • Delete Table, Delete Table Records
      • Create New Table from Existing Data Types
      • Insert Into, Update Records
      • Alter Table
  • MODULE 5
    • SQL JOINS
      • Inner join, Outer Join
      • Left join, Right Join
      • Self Join, Cross join
      • Windows Functions: Over, Partition, Rank
  • MODULE 6
    • SQL COMMANDS AND CLAUSES
      • Select, Select distinct
      • Aliases, Where clause
      • Relational operators, Logical
      • Between, Order by, In
      • Like, Limit, null/not null, group by
      • Having, Sub queries
  • MODULE 7
    • DOCUMENT DB/NO-SQL DB
      • Introduction of Document DB
      • Document DB vs. SQL DB
      • Popular Document DBs
      • MongoDB Basics
      • Data Format and Key Methods
      • MongoDB Data Management
  • BIG DATA FOUNDATION
  • 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 MapReduce
      • Mapping and Reducing Stages Concepts
      • Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort
  • MODULE 3
    • PYSPARK FOUNDATION
      • PySpark Introduction
      • PySpark Configuration
      • Resilient Distributed Datasets (RDD)
      • Working with RDDs in PySpark
      • Aggregating Data with Pair RDDs
  • MODULE 4
    • SPARK SQL and HADOOP HIVE
      • Introducing Spark SQL
      • Spark SQL vs. Hadoop Hive
  • PYTHON FOUNDATION
  • MODULE 1
    • PYTHON BASICS
      • Introduction of Python
      • Installation of Python and IDE
      • Python Variables
      • Python Basic Data Types
      • Number and Booleans, Strings
      • Arithmetic Operators
      • Comparison Operators
      • Assignment Operators
  • MODULE 2
    • PYTHON CONTROL STATEMENTS
      • IF Conditional Statement
      • IF-ELSE
      • NESTED IF
      • Python Loops Basics
      • WHILE Statement
      • FOR Statements
      • BREAK and CONTINUE Statements
  • MODULE 3
    • PYTHON DATA STRUCTURES
      • Basic Data Structure in Python
      • Basics of List
      • List: Object, Methods
      • Tuple: Object, Methods
      • Sets: Object, Methods
      • Dictionary: Object, Methods
  • MODULE 4
    • PYTHON FUNCTIONS
      • Functions Basics
      • Function Parameter Passing
      • Lambda Functions
      • Map, Reduce, Filter Functions
  • CERTIFIED BI ANALYST
  • MODULE 1
    • TABLEAU FUNDAMENTALS
      • Introduction to Business Intelligence and Introduction to Tableau
      • Interface Tour, Data visualisation: Pie Chart, Column Chart, Bar Chart
      • Bar Chart, Tree Map, Line Chart
      • Area Chart, Combination Charts, Map
      • Dashboards Creation, Quick Filters
      • Create Table Calculations
      • Create Calculated Fields
      • Create Custom Hierarchies
  • MODULE 2
    • POWER BI BASICS
      • Power BI Introduction
      • Basics Visualisations
      • Dashboard Creation
      • Basic Data Cleaning
      • Basic DAX FUNCTION
  • MODULE 3
    • DATA TRANSFORMATION TECHNIQUES
      • Exploring Query Editor
      • Data Cleansing and Manipulation
      • Creating Our Initial Project File
      • Connecting to Our Data Source
      • Editing Rows
      • Changing Data Types
      • Replacing Values
  • MODULE 4
    • CONNECTING TO VARIOUS DATA SOURCES
      • Connecting to a CSV File
      • Connecting to a Webpage
      • Extracting Characters
      • Splitting and Merging Columns
      • Creating Conditional Columns
      • Creating Columns from Examples
      • Create Data Model

Tools you will learn in CERTIFIED DATA ANALYST course?

• Power BI
• Excel
• Tabeleau