Data Analytics & Business Analytics Training
Excel || SQL || Power BI || Tableau || Python || Pandas || NumPy || Statistics || Data Cleaning || Data Visualization || Business Dashboards || Analytics Projects
Course Introduction
In today’s data-driven world, the ability to understand and interpret information is a valuable skill across every industry. Data Analytics and Business Analytics help businesses uncover hidden patterns, measure performance, solve problems, and make smarter decisions. Building these skills can open doors to exciting opportunities in the technology and business sectors.
At NareshIT, our training introduces you to essential analytics concepts and industry-relevant tools such as Microsoft Excel, SQL, Python, Statistics, Power BI, and Tableau. Through practical exercises, hands-on assignments, and project-based learning, you will gain experience in converting complex datasets into clear, actionable insights.
Whether you are starting your career or upgrading your existing skills, our Data Analytics Training in KPHB helps you take confident steps toward becoming a skilled analytics professional.
“Whether you’re a recent graduate or an aspiring job seeker, this course empowers you to confidently apply for developer roles across the globe.”
Course Details:
Course Price:
with out videos : ₹ 20,000/-
with Videos : ₹ 26,000/-
Duration
4 Months
Language:
English
Fill Details
Description
Learn how to turn raw information into valuable business insights with Data Analytics and Business Analytics Training at NareshIT. This course introduces you to important concepts and tools, including Excel, SQL, Python, Statistics, Power BI, and Tableau.
Explore essential data analytics activities such as data collection, cleaning, transformation, analysis, visualization, and report preparation. Develop practical experience by working with datasets, creating dashboards, and completing hands-on assignments and projects.
Our Data Analytics Training in KPHB focuses on building analytical thinking and technical capabilities that can help you prepare for real-world data-related roles.
Course Objectives
By the end of this training, you will develop the ability to:
– Understand core concepts of Data Analytics and Business Analytics.
– Work with datasets and perform data cleaning and transformation.
– Analyze information and generate reports using Microsoft Excel.
– Write SQL queries to retrieve and manage data.
– Use Python libraries such as NumPy and Pandas for data analysis.
– Apply statistical concepts to interpret data accurately.
– Perform exploratory data analysis to discover patterns and trends.
– Build informative dashboards and reports using Power BI.
– Present data visually through Tableau.
– Apply your knowledge through practical assignments and projects.
Prerequisites
No advanced technical background is necessary to get started. Basic computer knowledge, an interest in analyzing information, and a willingness to learn are sufficient. Basic mathematics and logical reasoning can be helpful.
Course Curriculum
• Basic Module
• Introduction to Microsoft Excel
• Installing Excel: Windows / Mac
• Getting Familiar With Excel
• Introduction to Tables
• Input data into cells
• Introduction to Formulas
• Formula Behavior
• Built in Functions
• Combining Data From Two Tables
• Advance Module
• Pivot Tables
• Nested IF statements
• VBA to automate tasks
• Custom Functions
1.Descriptive Statistics
• Data
• Types of Data
• Collection of Data
• Population & Sample
• Sampling Techniques
• Measures of Central Tendency
• Measures of Spread
• Measures of Shape
• Percentiles
• Quartiles
• Inter Quartile Range (IQR)
• Outliers
• Correlation
• Covariance
• Probability
• Probability Distributions
• Calculation of Probability using
• Standard Error
• Central Limit Theorem
• Confidence Intervals
2.Inferential Statistics
• Hypothesis Testing
• Formulation of Null & Alternate Hypothesis
• Type-I error & Type-II error
• P value
• Left tail vs Right tail vs Two tail
• 1 Sample test (Z test & T test)
• 2 Sample test (independent test & paired test)
• ANOVA Test
• Chi-square Test
- Instance Launch (Windows and Linux OS)
- Overview of launching cloud instances using EC2 (AWS), Azure VMs, and Compute Engine (GCP) with both Windows and Linux operating systems.
- Instance Types
- Overview of instance types (General Purpose, Compute Optimized, Memory Optimized) and selecting appropriate types for different workloads across AWS, Azure, and GCP.
- Key Pair Management
- Generating and managing SSH Key Pairs for Linux and RDP Access for Windows instances across all platforms
- Security Groups & Firewalls
- Configuring Security Groups (AWS), NSGs (Azure), and Firewall Rules (GCP) to control network access and secure instances.
- Storage and Snapshots
- Managing Volumes (AWS EBS, Azure Disks, GCP Persistent Disks) and creating Snapshots for backup and recovery
- User Data and Metadata
- Automating instance configurations with User Data and retrieving instance-specific information using Metadata.
- Load Balancers
- Configuring and managing different types of Load Balancers: Elastic Load Balancer (AWS), Azure Load Balancer, and GCP Load Balancer.
- Billing and Monitoring Alarms
- Setting up Billing Alarms to monitor cloud spending and Instance Alarms for tracking performance and health across AWS, Azure, and GCP.
- Auto Scaling
- Implementing Auto Scaling Groups to manage instance scaling automatically based on demand, using AWS Auto Scaling, Azure VM Scale Sets, and GCP Managed Instance Groups
- Scaling Types and Policies
- Understanding Horizontal and Vertical Scaling, and configuring autoscaling policies to handle dynamic workloads.
- Aws,Azure And Gcp Auto-scaling
- Get Started with Auto Scaling Using the Console
- Maintain a Fixed Number of Running EC2 Instances
- Dynamic Scaling
- The lifecycle of autoscaling
- The lifecycle of autoscaling
- EBS (Elastic Block Storage)
- Create EBS volumes
- Delete EBS Volumes
- Attach and detach EBS volumes
- Mounting and unmounting EBS volume
- Creating and deleting snapshots
- Creating volumes from snapshots
- Networking Basics
- Public IPs & Private IPs
- CIDR Range
- Subnet Calculations
- Creating custom VPCs and custom Subnets
- Network ACL’s
- Route Tables & IGW
- What is s3,blob storage,cloud storage
- Storage Classes
- Versioning
- S3 bucket ACL and bucket permission
- S3 data partition
- Data copying and moving
- Cross-region replication / Same Region replication
- Life Cycle Management
- Security & Encryption
- KMS (Key Management Service)
- Static Webhosting with S3 bucket and blob storage container and cloud stoarge
- Events configuration on S3 buckets,blob storage,cloud storage
- Enabling cross account access for S3
- S3 Data management and backup using 3rd Party applications.
- Pre-Signed URLs
- Storage Gateway
- Root Account Vs IAM user
- Multi Factor Authentication for Users
- IAM Password Policies
- Keys Configure and different access approaches
- User, groups and Role concepts
- Creating Customer Managed Policies.
- Auditing User Activity
- Launching a RDS Instances (MySQL, MSSQL & Aurora)
- Multi-AZ & Read Replicas for RDS instances.
- High Availability concept in RDS
- Difference between DB on Ec2 and managed RDS
- RDS Proxy configure process
- Elasticache overview.
- Connection process from RDS ton Ec2
- Connection process from RDS to local machine by using Work Bench process
- Creating DB and tables run the queries
- Configure Read replica with masters and access the tables from read replica.
- DynamoDB overview
- Data Importing option from S3 to RDS
- Database Migration Service
- DB costings and reserved nodes process.
- AWS Secret Manager
- Key Management Services
- AWS Certificate Manager
- AWS Inspector
- AWS Trusted Advisor
- AWS Global Accelerator
- Content Delivery Networks / CloudFront
- AWS Shield and WAF (Web Application Firewall)
- AWS Shield and WAF (Web Application Firewall)
- AWS Config
- Overview of the Lambda
- Configuration with different services
- Creation and automate the service by using lambda boto3
- General configuration environment setup In lambda
- Lambda layers and importance
- Limitations
- DNS Records overview
- Routing Policies
- Hosting sample Website and configuring Policies.
- Simple RP
- Weighted RP
- Failover RP
- Geo Location RP
- Latency based routing
- Cross region high availability
- Configure records A type, C type
- Alias configure to ELB, Static website hosting and Cloud Front
- What is DevOps?
- DevOps Market Trends | DevOps Engineer Skills
- DevOps Delivery Pipeline
- DevOps Ecosystem
- Introduction
- Type of shells
- Variables
- Types of variables
- Operators
- Input arguments
- String comparison
- Athematic comparison
- File conditions
- If, loops, case, functions
- Examples
- Apache webserver
- Installations
- Configuration
- Tomcat application server
- Installation, configuration
- Tomcat manager
- Application management
- Git Introductions
- What is Git
- How it will work
- Features
- Git Workflow
- Working Directory
- Staging
- Local Repository
- Remote Repository
- How to setup GIT
- Installing Git
- First-Time Git Setup
- Getting a Git Repository
- Working with various commands in GIT
- Git Cheat sheet
- Recording Changes to the Repository
- How to check the Status of Your Files
- How to track New Files
- Staging our modified files
- Ignoring Files from GIT
- Viewing Your Un-staged and Staged Changes
- How to commit Your Changes
- Skipping the Staging Area and commit
- Removing Files from GIT
- Viewing the Commit History
- Limiting Log Output
- Using a GUI to Visualize History
- Undoing Things
- Changing Your Last Commit
- Unstaging a Staged File
- Unmodifying a Modified File
- Git Forking
- Repository forking
- Working with Remotes
- Showing Your Remotes
- Adding Remote Repositories
- Fetching and Pulling from Your Remotes
- Pushing to Your Remotes
- Inspecting a Remote
- Removing and Renaming Remotes
- Branching and Merging in Git
- What a Branch Is
- Basic in Branching and Merging
- Branch Management in GIT
- Branching Workflows and its usage
- Remote Branches – create and delete
- Rebasing
- Maven Installation
- Maven Build requirements
- Maven POM Builds (pom.xml)
- Maven Build Life Cycle
- Maven Local Repository (.m2)
- Maven Global Repository
- Group ID, Artifact ID, Snapshot
- Maven Dependencies
- Maven Plugin
- Installation & Configuration of SonarQube
- Initiate code-scan with SonarQube server
- SonarQube dashboard
- Sample project to check code scan
- Maven integration with SonarQube
- Real-time use cases on SonarQube
- Introduction to Jenkins
- Continuous Integration with Jenkins
- Configure Jeins
- Jenkins Management
- Scheduling build Jobs
- POLL SCM
- POLL SCM
- Jenkins Master & Slave Node Configuration
- Jenkins Workspace Management
- Securing Jenkins
- Authentication
- Authorization
- Confidentiality
- Creating Users
- Jenkins Plugins
- Installing Jenkins Plugins
- SCM plugin
- Build and test
- Jenkins configure with below services tools
- Ec2
- Lambda
- S3
- GitHub
- Docker
- Jfrog
- SonarQube
- Kubernetes
- Trivy
- ArgoCD
- Azure CiCd
- Git Lab CiCd
- Docker Introduction
- What is Docker Image
- How to get Docker Image
- Docker Installation
- Working with Docker Containers
- What is Container
- Docker Engine
- Crating Containers with an Image
- Working with Images
- Docker Command Line Interphase
- Docker Compose
- Docker Hub
- Docker Trusted Registry
- Docker swarm
- Docker attaches
- Docker File & Command
- Revisiting Kubernetesz
- Cluster Architecture
- Spinning up a Kubernetes Cluster on Ec2 VMs
- Exploring your Cluster
- Understanding YAML
- Creating a Deployment in Kubernetes using YAML
- Creating a Service in Kubernetes
- Installing Kubernetes Dashboard
- Deploying an App using Dashboard
- Using Rolling Updates in Kubernetes
- Containers and Container Orchestration
- Helm Charts
- Schedulers
- Metric Server deployment
- Kubernetes volume
- RBAC process
- Aks
- Eks
- Helm installation
- Helm configuration
- Helm deployment
- Configuration
- K8s logs
- Installation
- Configurations
- CD
- Introduction to Continuous Monitoring
- Introduction to Grafana
- Installing Grafana
- Grafana Prometheus and Objects
- Grafana Prometheus configure to Kubernetes
- Introduction to Ansible
- Ansible Server Configuration
- Infrastructure Management
- SSH Connection in Ansible Master
- YAML Scripts
- Host Inventory
- Hosts and Groups
- Host Variables
- Group Variables
- Host and Group Specific Data
- Ad-hoc Commands
- Playbooks o Variables
- Conditionals
- Loops o Blocks
- Handlers
- Templates
- Modules
- Core Modules
- Extra Modules
- Ansible Role
- Introduction to terraform
- Infrastructure Automation
- Install Terraform
- Providers
- Resources
- Basic Syntax
- Exercise: Your First Script main.tf
- Getting started with Terraform
- Terraform Plan, show, Apply, Destroy
- Exploring Terraform Registry
- Interpolation
- Tainting and Updating Resources
- Terraform Console and Output
- Terraform Variables
- Breaking Out Our Variables and Outputs
- Lab exercises: Breaking down main.tf into variables.tf, output.tf
- Terraform Modules
- Introduction to Modules
- Module repositories
- First Basic Module
- The Module codes
- Main Terraform Code
- Lab exercises: Modules – The Root Module
- Terraform: Writing in a more organized way
- Maps and Lookups
- Terraform Workspaces
- Breaking Out Our Variable Definitions
- File, Remote -Exec and Local-Exec
- Terraform with AWS: lab
- Setting up the system for AWS
- AWS Storage: The S3 Bucket and Random ID
- AWS Storage: The Root Module
- AWS Compute: AMI Data, Key Pair, and the File Function
- AWS Compute: The EC2 Instance
- AWS Compute: User Data and Template Files
- AWS Compute: The Root Module
- Terraform state
- Remote state
- Data Sources
- Templates
- Conditionals
- Built-in Functions
- Working with state files
- Outputs, count and Join Function
- Terraform with AWS: Lab Part
- AWS Networking: VPC, IGW, and Route Tables
- AWS Networking: Subnets, Security, and the Count Attribute
- AWS Networking: The Root Module
- Terraform Troubleshooting and Testing
- Terraform Plan revisited
- Debugging the script
- Terraform Testing
- Lab: Writing test scripts for Terraform.
- Lab: Testing with Docker
- Terraform Best Practices
- Best practices in writing terraform scripts
- Terraform Workflow
- Terraform projects
- Other Hashi Corp tools
- New features of Terraform Introduction to Terraform
- Introduction to Python
- List, Ranges & Tuples in Python
- Input and Output in Python
- Python Object Oriented
- Python Regular Expressions
- Python Necessity DevOps
Who can learn this course
This course is suitable for anyone interested in developing data analysis skills, including:
– Freshers and graduates planning to enter the analytics industry.
– Engineering and degree students interested in data-related careers.
– IT professionals looking to expand their technical skill set.
– Working professionals interested in business intelligence and reporting.
– Beginners who want to explore opportunities in Data Analytics and Business Analytics.
Enroll Now!
Give your career a data-driven direction with Data Analytics Training in KPHB at NareshIT. Learn industry-relevant tools, strengthen your analytical abilities, and gain practical exposure through hands-on learning.
Take the first step toward your analytics career. Contact NareshIT today and enquire about upcoming batches!
Talk to Advaisor
FAQ's
Data Analytics involves examining data to identify trends, understand performance, and discover useful insights. Organizations use these insights to solve business challenges, improve operations, and make informed decisions.
You will learn important analytics tools and technologies such as Microsoft Excel, SQL, Python, NumPy, Pandas, Statistics, Power BI, and Tableau, along with practical data analysis concepts.
Multi-Cloud DevOps improves CI/CD by:
- Automating Deployments – Using tools like Jenkins, GitHub Actions, and AWS CodePipeline.
- Containerized Applications – Docker and Kubernetes allow seamless deployment across clouds.
- Cross-Cloud Orchestration – Terraform and Ansible manage infrastructure across multiple providers.
- Monitoring and Feedback Loops – Prometheus, CloudWatch, and Azure Monitor provide real-time insights.
- Security & Compliance Integration – HashiCorp Vault and cloud IAM ensure secure deployments.
CI/CD in Multi-Cloud ensures faster releases, minimal downtime, and consistency across different cloud platforms.
Based on your practical skills, project experience, and overall qualifications, you can explore roles such as Data Analyst, Business Analyst, Reporting Analyst, MIS Analyst, and Business Intelligence Analyst.
NareshIT offers Data Analytics training in KPHB, Hyderabad. Contact the institute to learn about the latest course syllabus, available batches, training options, and enrollment process.
