Best Data Analytics Course in Hyderabad

Welcome to Qubit AI Labs, the leading destination for Data Analytics training in
Hyderabad. Whether you are a beginner aiming to build a career in analytics or a professional looking to upskill, our Industry-Oriented Data Analytics Program is designed to make you job-ready with real-world expertise.

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Course Overview

Our instructor-led Data Analytics program provides comprehensive hands-on experience with live datasets and industry-driven projects. Participants will gain expertise in SQL Server/MySQL, Python, Excel, Statistics, and Power BI, enabling them to effectively clean, analyze, visualize, and automate data for strategic business decision-making. With the added advantage of internship opportunities and dedicated placement support, this program is designed to equip learners with the practical skills and confidence required to excel in the data analytics workforce

Key Highlights

Industry-Relevant Skills

Master SQL, Python, Excel, Statistics, and Power BI.

Data Handling & Management

Work with MySQL/SQLServer, Excel , Power Bi and Python to import, clean, and preprocess
large datasets efficiently

Data Analysis & Statistical Techniques

Apply Exploratory Data Analysis (EDA), descriptive & inferential statistics, hypothesis testing, and regression for actionable  insights.

Data Visualization & Dashboards

Build interactive charts, graphs, and dashboards using Excel, Matplotlib, Seaborn, and Power
BI

Automation & Workflow Optimization

Automate reporting and repetitive analytics tasks with Python and Excel integration.

Version Control & Deployment

Understand Git for version control and learn how analytics pipelines are deployed in real-world workflows.

Data Visualization & Dashboards

Build interactive charts, graphs, and  dashboards using Excel, Matplotlib, Seaborn, and Power BI

Key Features

  • Comprehensive Curriculum
  • 100% Practical Training
  • Capstone Project
  • Expert Trainers
  • Business-Oriented Insights
  • Live Projects & Assignments
  • Resume Building & Mock Interviews
  • Placement Assistance
  • Certification of Completion
  • Soft Skills & Communication Training

Skills Covered

Data Handling & Management

Data Analysis & Statistics

Data Visualization & Dashboards

Automation & Optimization

Problem Solving with Data

Soft Skills

Business Intelligence & Reporting

Eligibility

Students and graduates from all IT & Non-IT branches – CSE, EEE, Civil, Mechanical, etc.

Candidates with career gap are welcome

Beginners with basic computer knowledge can join

No CGPA cut-off. Career gap is not a barrier.

Course Curriculum

Course Overview:
  1. This Industry – Oriented Data Analytics program is designed to bridge the gap between academic learning and real-world business requirements.
  2. The curriculum is built with direct input from industry experts and focuses on hands-on projects, case studies, and live datasets used in top companies.
  3. Participants will master MySQL/SQL Server/ Oracle, Python, Excel, Statistics, and Power BI — learning to clean, process, analyze, and visualize data for business decision-making.
  4. Special emphasis is placed on SQL-Python- Excel-Power-Bi connectivity to automate workflows and create data-driven solutions.
  5. Learners will work on end-to-end projects, starting from raw data extraction to generating actionable insights and presenting them via interactive dashboards.
  1. Gain in-demand technical skills aligned with current job market requirements.
  2.  Build a professional portfolio with multiple real-world projects.
  3.  Understand how to solve business problems using data.
  4.  Receive internship opportunities and placement assistance through our industry partnerships.
  1. Live Projects with real datasets from finance, e-commerce, and marketing domains.
  2. Internship Program with partner companies for practical exposure. 
  3. Mock Interviews & Resume Building sessions to ensure placement readiness.
  4. Industry Guest Lectures from experienced Data Analytics professionals and AI Architectures 
  5. Capstone Project simulating an actual company analytics problem. 
  1. Understand core database concepts and relational data models. 
  2. Write optimized SQL queries to support analytical workflows.
  3. Apply advanced SQL techniques for professional-level reporting and automation.
  4. Design analytics-ready databases for real business use cases.

1.Introduction to Databases & MySQL Workbench
  • Relational Database Concepts
  • Installing & Setting Up MySQL Workbench

2.SQL Basics
  • SELECT, INSERT, UPDATE, DELETE
  • Understanding Data Types

3.Filtering & Sorting Data
  • WHERE, ORDER BY, DISTINCT
  • Logical Operators (AND, OR, NOT, BETWEEN, LIKE, IN)
4.SQL Constraints
  • PRIMARY KEY, FOREIGN KEY
  • UNIQUE, NOT NULL
  • DEFAULT, CHECK
  • ON DELETE & ON UPDATE actions
5.Functions
  • Aggregate Functions: SUM, AVG, COUNT, MIN, MAX
  • String Functions: CONCAT, SUBSTRING, LENGTH, TRIM
  • Date Functions: NOW, DATE_FORMAT, DATEDIFF, DATE_ADD
  • Mathematical Functions: ROUND, CEIL, FLOOR, MOD
6.Joins
  • INNER, LEFT, RIGHT, FULL OUTER Joins
  • SELF Join, CROSS Join
  • Real-time Use Cases in Analytics
7.Subqueries
  • Single-row & Multi-row Subqueries
  • Correlated Subqueries for Dynamic Filtering
8.Advanced SQL Techniques
  • Indexing & Query Performance Tuning
  • Views (Updatable & Non-updatable) for Reusable Queries
  • Stored Procedures & Functions for Automation
  • Triggers (BEFORE, AFTER) for Data Integrity
  • Transactions & ACID Properties for Reliable Data Processing
  • Window Functions: RANK, ROW_NUMBER, NTILE for Analytical
Reports
  • Common Table Expressions (WITH Clause) for Complex Query
Management
9.Industry Project:
  • Build an Analytics-Ready Sales Database
  • Write Stored Procedures to generate monthly KPIs
  • Implement Constraints, Triggers, and Views for a real-world
simulation

1.Python Fundamentals for Analytics
  • Python Installation & IDE setup (Colab Jupyter Notebook, VS Code)
  • Syntax, Variables, Data Types (int, float, str, bool, list, tuple, set, dict)
  • Conditional Statements & Loops (if, while, for)
  • Functions (Built-in & User-defined)
  • Exception Handling & Debugging
2.Data Handling in Python
  • File Handling (CSV, Excel, JSON, Text)
  • OS and shutil modules for file automation
  • Working with Dates & Times (datetime module)
3.Python for Data Analysis
  • NumPy: Arrays, Indexing, Slicing, Vectorized Operations,
Aggregations
  • Pandas: Series, Data Frames, Reading/Writing data (CSV, Excel, SQL)
  • Data Cleaning: Handling Missing Values, Duplicates, Data Type
Conversion
  • Data Transformation: Merging, Joining, Grouping, Pivot Tables
  • Data Aggregation & Summary Statistics
4.Data Visualization
  • Matplotlib: Line, Bar, Histogram, Pie, Scatter
  • Seaborn: Heatmaps, Pairplots, Boxplots, Distribution Plots
  • Styling and Customizing Charts
5.Python + MySQL Integration
  • Installing & Using mysql-connector-python
  • Connecting to MySQL from Python
  • Running SQL Queries via Python Scripts
  • Fetching and Processing Data for Analytics
6. Python + Excel Automation
  • Reading/Writing Excel with openpyxl & pandas
  • Creating automated Excel reports from datasets
  • Using Python to clean data and export ready-to-use Excel files
7.Mini-Projects & Industry Use Cases
  • Automating Monthly Sales Reports (MySQL → Python → Excel)
  • Cleaning and Preparing Customer Data for BI Tools
  • Trend Analysis of Sales Data using Pandas & Matplotlib

1. Excel Basics & Data Formatting
  • Excel interface & navigation
  • Data entry, formatting, and cleaning basics
  • Custom formatting for dates, currency, and text
  • Sorting, filtering, and conditional formatting for insights
2. Formulas & Functions for Analytics
  • Basic Math Functions: SUM, AVERAGE, ROUND, MIN, MAX
  • Logical Functions: IF, AND, OR, IFERROR
  • Lookup Functions: VLOOKUP, HLOOKUP, XLOOKUP, INDEX, MATCH
  • Text Functions: LEFT, RIGHT, MID, CONCAT, TRIM
  • Date Functions: TODAY, NOW, DATEDIF, EDATE
3. Data Cleaning & Transformation
  • Removing duplicates & blanks
  • Splitting & merging columns
  • Text-to-columns & flash fill
  • Using formulas for data standardization
  • Data validation & drop-down lists
4. Advanced Excel Tools
  • Pivot Tables & Pivot Charts
  • Grouping, summarizing, and filtering large datasets
  • Creating calculated fields & items in pivot tables
  • Slicers & Timelines for interactivity
5. Data Visualization
  • Creating professional charts: Column, Bar, Pie, Line, Area
  • Conditional formatting with data bars & color scales
  • Combo charts for comparative analysis
  • Designing interactive dashboards
6. Excel Automation & Integration
  • Automating repetitive tasks with Macros (Intro to VBA)
  • Linking Excel with MySQL via ODBC
  • Python + Excel Automation (Generating reports with pandas &
openpyxl)
7. Mini-Projects & Industry Use Cases
  • Sales Performance Dashboard
  • Customer Feedback Summary
  • Inventory & Stock Monitoring Tool

1. Introduction to Statistics
  • Types of data: Qualitative vs Quantitative
  • Scales of measurement: Nominal, Ordinal, Interval, Ratio
  • Population vs Sample concepts
  • Descriptive vs Inferential statistics
2. Descriptive Statistics
  • Measures of Central Tendency: Mean, Median, Mode
  • Measures of Dispersion: Range, Variance, Standard Deviation, IQR
  • Percentiles & Quartiles
  • Data visualization: Histograms, Boxplots, Scatter plots
3. Probability Fundamentals
  • Definition of probability & sample space
  • Independent & dependent events
  • Conditional probability & Bayes’ theorem
  • Probability distributions: Uniform, Binomial, Normal
4. Hypothesis Testing
  • Null & alternative hypotheses
  • Type I & Type II errors
  • p-value & significance levels
  • t-test, Chi-square test, ANOVA
5. Correlation & Regression
  • Pearson & Spearman correlation
  • Simple & multiple linear regression
  • Model interpretation & R² score
  • Predictive modeling basics
6. Industry Applications
  • Market trend analysis
  • Risk analysis in finance
  • Quality control in manufacturing

1. Introduction to Power BI
  • What is Business Intelligence?
  • Power BI Components (Power Query, Power Pivot, Power View, Power
Map, Power BI Service, Power BI Mobile).
  • Installation & Interface Overview.
2. Data Connections & Sources
  • Importing data from Excel, CSV, Web, SQL Server, MySQL, and APIs.
  • Direct Query vs Import Mode.
  • Data Refresh & Gateway setup.
3. Power Query – Data Preparation & Cleaning
  • Data transformation basics (Remove duplicates, Replace values, Merge
queries).
  • Pivot & Unpivot data.
  • Conditional columns & Data type changes.
  • Parameterized queries.
4. Data Modeling
  • Creating relationships between tables.
  • Star & Snowflake schemas.
  • Calculated columns & measures.
5. DAX (Data Analysis Expressions)
  • Basics of DAX syntax.
  • Common functions: SUM, AVERAGE, COUNTROWS, DISTINCTCOUNT.
  • Time Intelligence functions: DATEADD, SAMEPERIODLASTYEAR,
TOTALYTD.
  • IF, SWITCH, and nested logic in DAX.
6. Visualization & Dashboard Design
  • Choosing the right chart for the data.
  • Custom visuals from Marketplace.
  • Conditional formatting & drill-through.
  • Tooltips, slicers, bookmarks, and buttons for interactivity.
7. Publishing & Sharing
  • Publishing reports to Power BI Service.
  • Creating dashboards from multiple reports.
  • Row-Level Security (RLS).
  • Collaboration & sharing best practices.
8. Advanced Topics
  • Paginated reports.
  • AI visuals (Key Influencers, Decomposition Tree).
  • Power BI & Python integration for advanced analytics.
  • Performance optimization techniques.
9. Industry Project
  • Scenario: Create a Sales & Marketing Dashboard
  • Connect to MySQL Sales Database + Excel Targets Sheet.
  • Apply transformations, calculations, and DAX formulas.
  • Create interactive dashboards with KPIs & Trend Analysis.
  • Publish to Power BI Service with scheduled refresh and security roles.

Data Analytics Certification Overview

At Our Qubit Ai Labs Data Analytics validates your skills and expertise in Data Analytics programming, enhancing your credibility and career prospects. It demonstrates proficiency in core and advanced Data Analytics concepts, frameworks, and tools, making you a sought-after professional in the IT industry. Achieving certification equips you with the confidence to tackle real-world challenges and opens doors to high-paying job opportunities globally.