CERTIFIED DATA ANALYST COURSE

MODULE 1: DATA ANALYSIS FOUNDATION

• Data Analysis Introduction
• Data Preparation for Analysis
• Common Data Problems
• Various Tools for Data Analysis
• Evolution of Analytics domain

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: CRIP-DM Model

• Introduction to CRIP-DM Model
• Business Understanding
• Data Understanding
• Data Preparation
• Modeling, Evaluation, Deploying,Monitoring

MODULE 4: UNIVARIATE DATA ANALYSIS

• Summary statistics -Determines the value’s center 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.
• 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: BI-VARIATE DATA ANALYSIS

• Scatter Plots
• Regression Analysis
• Correlation Coefficients

MODULE 1: PYTHON BASICS

• Introduction of python
• Installation of Python and IDE
• Python Variables
• Python basic data types
• Number & 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

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 & Properties
• Z Value / Standard Value
• Empherical Rule  and Outliers
• Central Limit Theorem
• Normality Testing
• Skewness & Kurtosis
• Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance
  
• Covariance & 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

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 Breakeven Analysis
• Make or Buy Decision with Break Even
• Preparing Data for Breakeven Analysis
• Hands-on Case Study: Manufacturing

MODULE 5: PARETO (80/20 RULE) ANALSYSIS

• Pareto rule Introduction
• Preparation Data for Pareto Analysis,
• Performing Pareto Analysis on Data
• Insights on Optimizing 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

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: OPTIMIZATION MODELS

• Predictive Analytics with Low Uncertainty;Case Study
• Mathematical Modeling and Decision Modeling
• Product Pricing with Prescriptive Modeling
• Assignment 1 : 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

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 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;
• Classification & Sigmoid Curve
• Hands-on Logistics Regression with ML Tool

MODULE 4: ML ALGO: KNN

• Introduction to KNN; Nearest Neighbor
• Regression with KNN
• Hands-on: KNN with ML Tool

MODULE 5: ML ALGO: K MEANS CLUSTERING

• Understanding Clustering (Unsupervised)
• Introduction to KMeans 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

MODULE 1: DATABASE 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

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 databases
• 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

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

MODULE 3: PYSPARK FOUNDATION

• PySpark Introduction
• Spark 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

MODULE 1: TABLEAU FUNDAMENTALS

• Introduction to Business Intelligence & Introduction to Tableau
• Interface Tour, Data visualization: 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 Visualizations
• 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

After the Data Analyst course, you can pursue roles like Data Analyst, Business Analyst, Marketing analyst, BI Analyst, Reporting Analyst, and HR analyst. These roles are in sectors like IT, finance, healthcare, marketing, and retail.

Choose the Data Analyst course for comprehensive training in data handling, analysis, and visualization. It offers practical skills, industry-relevant tools, live projects, and enhances job readiness in a growing data-driven market.

The Data Analyst course typically lasts 3 to 6 months, depending on the training mode (full-time/part-time). It covers fundamental to advanced data analysis concepts and tools for career readiness.

The cost of the Data Analyst course varies globally. In the United States, it ranges from approximately $500 to $2,500, including the certification exam. In India, the fee for Data Analyst course typically falls between ₹10,000 and ₹2,00,000, depending on the training provider and course format.

Look for institutes with industry-expert trainers, updated syllabus, hands-on projects, placement support, and positive reviews. Verify course content and student feedback for quality Data Analyst training.

According to a McKinsey report, the U.S. healthcare system could potentially save between $300 billion and $450 billion annually by leveraging big data analytics to enhance operational efficiency, improve patient outcomes, and reduce waste. Demand for Certified Data Analysts is rising globally as businesses rely on data-driven decisions. The need spans across IT, finance, healthcare, e-commerce, and government sectors.

The salary for Data Analysts worldwide, varying by experience, location, and industry. Senior roles and specialized skills can command higher salaries.

• United States: In the U.S., Data Analysts have an average annual salary between $55,000 and $86,000.
• United Kingdom: Data Analysts in the UK earn an average salary ranging from £28,000 to £43,000 per year.
• United Arab Emirates (Dubai): In Dubai, Data Analysts earn approximately AED 7,000 to AED 17,000 per month.
• India: Data Analysts in India earn an average salary ranging from ₹5,00,000 to ₹10,50,000 per annum.
• Spain: Data Analysts in the Spain earn an average salary ranging from €26T - €40T per year.
• Australia: Data Analysts in the Australia earn an average salary ranging from A$82T - A$1L per year.
  
  (source: glassdoor)

Anyone with a basic understanding of computers and a keen interest in data can learn Certified Data Analyst course. Both freshers and working professionals from any background can enroll.

Basic skills include statistical knowledge, Excel, SQL, critical thinking, data visualization, and familiarity with analytical tools like Tableau or Power BI.

Essential tools for Data Analysts include Excel for data handling, SQL for database querying, Tableau and Power BI for visualization, Python and R for analysis, plus reporting software to transform data into actionable insights efficiently.

DataMites is a top choice for the Certified Data Analyst Course due to its industry-aligned curriculum, expert mentorship, hands-on learning with real-world projects, and strong placement support. The course is designed to build practical skills in data analytics, enabling students to confidently enter the job market with in-demand competencies.

Yes, the Certified Data Analyst Course at DataMites includes internship opportunities. These internships provide valuable real-time experience, allowing students to apply their knowledge in actual business scenarios and enhance their practical skills in data analytics.

Yes, DataMites provides flexible EMI options for the Certified Data Analyst Course. These payment plans make it easier for students to invest in their education without financial strain, offering convenience and affordability.

The Certified Data Analyst (CDA) course fee varies by provider and region. For instance, DataMites offers the course with fees ranging from ₹55,000 to ₹60,000 for classroom training, and ₹34,000 to ₹34,900 for hybrid learning options. These fees may include certification exams and other resources. It's advisable to check with the specific training provider for the most accurate and up-to-date pricing.

Yes, DataMites offers placement assistance for its Certified Data Analyst Course. The institute connects students with hiring companies, provides job support, and helps candidates prepare for interviews, boosting their chances of securing a role in the data analytics domain.

DataMites follows a transparent refund policy. Students who choose to withdraw from the Certified Data Analyst Course within the initial days are eligible for a partial refund. Full details and conditions are available on the official website.

Students enrolled in the Certified Data Analyst Course at DataMites receive comprehensive learning materials, including e-books, case studies, assignments, live project support, and access to an online learning portal. These resources ensure an in-depth understanding and practical exposure to data analytics.

The instructors at DataMites for the Certified Data Analyst Course are experienced industry professionals with strong backgrounds in data analytics and related fields. Their practical knowledge and teaching expertise ensure that students receive high-quality, industry-relevant training.

Yes, DataMites offers the Certified Data Analyst Course with live project opportunities. You will work on real-world data analysis projects that provide practical exposure, helping you apply theoretical concepts in actual business scenarios and boosting your readiness for industry roles.

The Certified Data Analyst Course at DataMites typically spans 6 months, including over 200 hours of intensive training. The course is designed to thoroughly cover core data analysis concepts, tools, and techniques, ensuring you gain strong analytical skills and industry-relevant knowledge.