Data Analytics & Business Analytics Training
Turn raw information into meaningful insights. Build practical skills in Excel, SQL, Python, Statistics and business intelligence tools.
Turn Business Questions into Data-Driven Answers
Data Analytics helps organizations identify trends, understand performance and support decisions through evidence. Analysts gather information, check its quality, examine patterns and present results to stakeholders.
Data Analytics and Business Analytics training at Naresh i Technologies, JNTU, Hyderabad, introduces learners to Excel, SQL, Python, Statistics, Power BI and Tableau.
The learning path progresses from spreadsheet fundamentals and data interpretation to database queries, Python-based analysis, visualization and dashboard development.
This course is relevant to students, graduates, working professionals and individuals interested in analytical career opportunities.
Learn the Complete Data Analytics Workflow
Analytics involves more than dashboards. Before interpreting a report, analysts must identify the correct business question, understand the dataset, address missing information and select an appropriate method of analysis.
This course introduces Microsoft Excel, SQL database operations, Python programming, statistical interpretation, data cleaning and Exploratory Data Analysis.
Power BI and Tableau provide additional approaches to visual reporting. Together, these subjects help learners understand how information can be translated into clear, useful business insights.
From Raw Data to Business Intelligence
Follow the process analysts use to transform information into meaningful insights. This is an animated educational illustration.
Collect
Gather information from relevant data sources.
Clean
Prepare records, missing values and duplicates.
Analyze
Discover relationships, trends and patterns.
Visualize
Create dashboards, reports and charts.
Decide
Communicate insights to support decisions.
Skills You Will Develop
Microsoft Excel
Work with formulas, PivotTables and spreadsheet analysis.
SQL
Retrieve, join, filter and summarize relational data.
Python
Use programming for data processing and analytical tasks.
Statistics
Interpret distributions, variability and hypothesis testing.
Power BI
Create interactive reports and understand DAX calculations.
Tableau
Explore charts, dashboards and visual analytics.
Who Can Start Learning Data Analytics?
Beginners can start with foundational spreadsheet concepts and progress toward SQL, programming and data visualization. Previous professional experience in analytics is not essential to study introductory topics.
- Basic computer knowledge and logical thinking.
- Interest in numbers, data and business problem-solving.
- Basic mathematics is useful for statistics.
- Prior Excel, SQL or Python knowledge can be helpful.
- Regular practice and attention to detail.
Data Analytics & Business Analytics Syllabus
Explore all nine major curriculum areas. Open any module to view its topics.
01 / Microsoft Excel
Excel Fundamentals
- Introduction and Installation
- Interface, Rows, Columns and Cells
- Data Entry and Formatting
- Tables and Structured Data
- Formulas and Cell References
- Built-in Excel Functions
- Combining Data from Multiple Tables
Advanced Excel
- PivotTables
- Nested IF Statements
- VBA Task Automation
- Custom Functions
02 / Statistics for Analytics
Descriptive Statistics
- Population, Sample and Sampling Methods
- Types of Data
- Mean, Median and Mode
- Variance, Standard Deviation and Measures of Spread
- Percentiles, Quartiles and IQR
- Outliers
- Correlation and Covariance
Probability and Inferential Statistics
- Probability and Distributions
- Standard Error and Central Limit Theorem
- Confidence Intervals
- Null and Alternative Hypotheses
- P-Values, One-Tailed and Two-Tailed Tests
- Type I and Type II Errors
- Z-Test, T-Test, ANOVA and Chi-Square Test
03 / Python Programming
Python Basics
- Python Introduction and Installation
- Variables and Data Types
- Input and Output Operations
- Lists, Tuples, Sets and Dictionaries
- Operators and Conditional Statements
- Loops and Functions
Advanced Concepts
- Advanced Functions
- File Handling
- Errors and Exception Handling
04 / Python for Data Analysis
NumPy
- Arrays and Attributes
- Creating and Manipulating Arrays
- Indexing and Slicing
- Mathematical and Relational Operations
- NumPy Functions
Pandas
- Series and DataFrames
- Creating and Importing DataFrames
- Selecting, Adding and Deleting Rows and Columns
- Sorting and Filtering
- Data Summarization and Statistical Functions
- Merge, Join and Concatenation
- Indexing and GroupBy
- Importing and Exporting Data
05 / Exploratory Data Analysis (EDA)
Data Exploration
- Univariate Analysis
- Bivariate Analysis
- Multivariate Analysis
Matplotlib
- Histograms
- Bar Charts and Pie Charts
- Line Plots and Scatter Plots
- Box Plots and Subplots
Seaborn
- Bar and Count Plots
- Box and Violin Plots
- Scatter and Line Plots
- Regression Plots
- Pair Plots and Heatmaps
06 / Data Cleaning and Preparation
Data Quality Techniques
- Identifying Incorrect Data
- Correcting Data Types
- Detecting and Removing Duplicates
- Handling Missing Values
- Detecting and Treating Outliers
- Removing Unnecessary Columns
07 / SQL for Data Analytics
Database Fundamentals
- Databases vs Spreadsheets
- DBMS and RDBMS
- SQL vs NoSQL
- MySQL Installation and Data Types
- Primary and Foreign Keys
- Constraints and CRUD Operations
Querying Data
- DDL, DQL, DML, DCL and TCL
- SELECT and SQL Clauses
- Comparison and Logical Operators
- Wildcards and Aggregate Functions
- INNER, LEFT, RIGHT and Other Joins
Advanced SQL
- Normalization and Denormalization
- Subqueries and Common Table Expressions
- SQL Functions and Views
- Stored Procedures
08 / Microsoft Power BI
Power BI Fundamentals
- Introduction to Power BI
- Connectivity Modes and Data Sources
- Power BI Desktop
- Data Transformation
- Report Visualization
- Interactive Dashboards
DAX and Calculations
- DAX Introduction and Syntax
- Data Types and Operators
- Calculated Columns and Measures
- DAX Functions
- DAX Tables and Filtering
09 / Tableau for Business Analytics
Tableau Fundamentals
- Introduction and Data Connections
- Basic Charts and Visual Analytics
- Sorting, Filtering, Grouping and Sets
- Calculations and Functions
- Table Calculations
Advanced Tableau
- Trend Lines and Reference Lines
- Forecasting
- Advanced Plots
- Dashboard Development
Who Should Learn Data Analytics?
Students & Freshers
Start with spreadsheets, SQL and fundamental analytics concepts.
Working Professionals
Explore data interpretation and reporting skills relevant to business roles.
IT Learners
Expand technical understanding with analytical programming and databases.
Business Teams
Learn how dashboards and reports support operational decisions.
Researchers
Explore data cleaning, visualization and statistical analysis.
Career Switchers
Build a foundation for exploring entry-level analytics opportunities.
Career Paths Related to Data Analytics
Depending on skills, qualifications and experience, learners may explore roles involving data interpretation, visualization and business reporting.
- Data Analyst and Junior Business Analyst
- Reporting Analyst
- Junior Business Intelligence Analyst
- MIS Executive
- Dashboard Developer
These are potential career paths, not placement guarantees.
Learn the Tools. Understand the Numbers. Present the Insights.
Contact our JNTU team for current course batches, enrollment guidance and fee details.
Data Analytics Training FAQs
What is Data Analytics and Business Analytics?
Data Analytics examines information to discover patterns and generate insights. Business Analytics uses analytical methods to understand performance and support business decisions.
Can beginners learn Data Analytics without coding?
Beginners can start with Excel, foundational statistics and introductory SQL before moving into Python. Regular hands-on practice is helpful.
Which tools are included in this course?
The curriculum includes Microsoft Excel, Statistics, Python, NumPy, Pandas, SQL, Matplotlib, Seaborn, Power BI and Tableau.
What is the duration of Data Analytics training at JNTU?
The listed duration is 4 months. Contact Naresh i Technologies JNTU to confirm the current batch schedule.
What are the Data Analytics course fees?
The listed fees are ₹20,000 without videos and ₹26,000 with videos. Confirm package inclusions and current enrollment details with the branch.
Why is SQL important for Data Analysts?
SQL is commonly used to retrieve, join, filter and summarize information stored in relational databases.
What is the difference between Power BI and Tableau?
Both are business intelligence and data visualization platforms. Power BI integrates closely with Microsoft's tools, while Tableau provides a wide range of interactive visual analysis capabilities.
What jobs can learners explore after Data Analytics training?
Depending on qualifications and skills, learners may explore Data Analyst, Reporting Analyst, Junior BI Analyst and MIS-related roles. Employment is not guaranteed by course completion.
Where can I learn Data Analytics near JNTU Hyderabad?
Naresh i Technologies provides Data Analytics training at its JNTU branch in Hyderabad. Call +91 99660 45222 to enquire about current batches.