Data Analytics Course in Hyderabad with AI
Join the top-rated Data Analytics Course in Hyderabad. Get practical training, resume support, and job assistance to launch your data career.
Data Analytics Course in Hyderabad
Data Analytics Course: Prepare for your career with our Data Analytics course in Hyderabad, designed to help you transform raw data into valuable business insights. Data Analytics is the process of examining raw data to find functional patterns, trends, and insights. It helps companies make more intelligent decisions by understanding what’s happening in their business. Our course includes essential tools and hands-on projects, providing a solid foundation for launching your career in the data industry. Our course will help you learn how to collect, clean, analyse, and visualise data using Excel, SQL, Power BI, Python, and other tools. Our Data Analytics training is perfect for fresh graduates and working professionals looking to transition into data Analytics roles. Our course starts with the basics, where you will learn about Excel, and later you get into advanced topics and tools like Python, Pandas, and NumPy for data analysis.
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Key Features of Our Data Analytics Course
- We provide a comprehensive curriculum for our Data Analyst training program. Our course includes Excel, SQL, Power BI, Python for Data Analysis, Data Visualization, Statistics, and Business Intelligence tools.
- You will get hands-on practice exposure on tools like Excel, SQL, Power BI, Python, Jupyter Notebook, Pandas, NumPy, and basic Tableau. These tools allow you to implement your hands-on practice in real time and enhance your skill set.
- You will get hands-on practical experience in Excel, SQL, Power BI, Python, Jupyter Notebook, Pandas, NumPy, and a basic Tableau overview to give you a well-rounded skill set.
- Our Data Analytics course features 100% placement assistance. You will get resume building, mock interviews, and job referrals from our placement team to help you land a job after the course.
- Our hands-on Training approach will help you learn through real-time case studies and datasets to gain practical experience. Each of our modules includes hands-on practical exposure that helps you better understand.
- Our trainer will help you with live projects, which will allow you to work on real-time business problems like sales reporting, customer segmentation, and trend analysis.
- Our Data Analytics training is led by Industry experts with 7+ years of real-time work experience. Their Data Analytics expertise helps you quickly learn complex concepts and focuses on practical teaching over theory.
- We offer both online and Classroom Data Analytics training, allowing you to choose between them at your convenience. Moreover, we also have weekday and weekend batches, which are specially designed for working professionals.
Why Choose Code Zen Eduversity for Data Analytics Course?
Guaranteed Placement Assistance for Job-Ready Candidates
- 120+ hours of training from real-time industry professionals.
- Small batch sizes for personalized learning approach
- Job-focused course with hands-on live projects.
- Dedicated career counselor and placement team for job assistance.
- Working professional trainers with 7+ years of experience.
- 100% placement support after completing the course.
- Back-up class recording with complete course materials.
- Industry level tools covered during the Data Analytics course.
- Exclusive lifetime access to our resources and community.
Data Analytics Training in Hyderabad
Course Curriculum Overview
- Definition of data analysis
- Role of a data analyst
- Data lifecycle overview
- Introduction to AI in analysis
- AI vs. traditional analysis
- Key tools for analysts
- Industry applications in 2025
- Debugging basic data issues
- Responsibilities of an analyst
- Best practices for starting
- Python introduction and setup
- Variables and data types
- Operators (arithmetic, logical)
- Conditional statements (if-else)
- Loops (for, while)
- Functions and parameters
- Lists and list operations
- Debugging Python scripts
- Benefits of Python in analysis
- Best practices for Python coding
- NumPy introduction
- Creating NumPy arrays
- Array operations (math, stats)
- Indexing and slicing arrays
- Pandas introduction
- Series vs. DataFrames
- Importing/exporting data
- Debugging NumPy/Pandas errors
- Advantages of these libraries
- Best practices for data manipulation
- Statistics overview
- Measures of central tendency
- Measures of dispersion
- Probability fundamentals
- Normal distribution concepts
- Sampling techniques
- Confidence intervals intro
- Debugging statistical calculations
- Role of stats in analysis
- Best practices for statistical analysis
- Visualization introduction
- Matplotlib setup and basics
- Creating line plots
- Building bar charts
- Generating scatter plots
- Using subplots
- Customizing plots (labels, colors)
- Debugging plot errors
- Benefits of visualization
- Best practices for Matplotlib
- Seaborn introduction
- Statistical plot types
- Creating heatmaps
- Generating pair plots
- Building box plots
- Using violin plots
- Applying themes and styles
- Debugging Seaborn issues
- Seaborn’s advantages
- Best practices for advanced viz
- Excel introduction
- Data entry and formatting
- Basic formulas (SUM, AVERAGE)
- Pivot tables introduction
- VLOOKUP and HLOOKUP
- Creating charts
- Conditional formatting basics
- Debugging Excel errors
- Excel’s role in analysis
- Best practices for Excel
- Advanced formulas (INDEX, MATCH)
- Power Pivot introduction
- Data modeling in Excel
- Macros and VBA basics
- Dynamic chart creation
- What-if analysis tools
- Debugging VBA scripts
- Optimizing Excel workflows
- Advanced Excel use cases
- Best practices for advanced Excel
- SQL introduction
- Database concepts (tables, rows)
- Writing SELECT queries
- Using WHERE clauses
- ORDER BY and LIMIT
- Basic JOIN operations
- Aggregations (COUNT, SUM)
- Debugging SQL queries
- SQL’s importance in analysis
- Best practices for SQL
- Window functions introduction
- Common Table Expressions (CTEs)
- Complex multi-table joins
- Indexing for performance
- Query optimization techniques
- Writing stored procedures
- Debugging advanced SQL
- Performance tuning strategies
- Advanced SQL use cases
- Best practices for advanced SQL
- EDA introduction
- Data profiling techniques
- Descriptive statistics
- Identifying outliers
- Correlation analysis
- Handling missing data
- Visualization in EDA
- Debugging EDA processes
- Benefits of EDA
- Best practices for EDA
- AI overview for analysts
- AI’s role in data analysis
- Machine learning basics
- AI tools for analysts
- AutoML introduction
- AI-driven use cases
- Debugging AI outputs
- Benefits of AI in analysis
- Overview of AI tools
- Best practices for AI adoption
- Predictive analytics introduction
- Linear regression concepts
- Scikit-learn setup
- Training a predictive model
- Evaluating model performance
- Feature selection techniques
- Debugging prediction errors
- Benefits of predictive analytics
- Tools for prediction
- Best practices for predictive models
- NLP introduction
- Text preprocessing steps
- Tokenization techniques
- Removing stop words
- Sentiment analysis basics
- NLTK library setup
- Debugging NLP tasks
- NLP benefits for analysts
- NLP tools overview
- Best practices for NLP
- Power BI introduction
- Importing data sources
- Data transformation basics
- DAX language introduction
- Creating basic visuals
- Building dashboards
- Debugging Power BI issues
- Benefits of Power BI
- Power BI tools ecosystem
- Best practices for BI
- Advanced DAX functions
- Data modeling techniques
- Managing relationships
- Using custom visuals
- Power Query advanced features
- Sharing and collaboration
- Debugging advanced BI issues
- Optimizing Power BI reports
- Advanced BI use cases
- Best practices for advanced BI
- Tableau introduction
- Connecting to data sources
- Creating worksheets
- Building charts and graphs
- Applying filters
- Dashboard creation basics
- Debugging Tableau issues
- Benefits of Tableau
- Tableau tools overview
- Best practices for Tableau
- Calculated fields in Tableau
- Level of Detail (LOD) expressions
- Advanced filtering techniques
- Interactive dashboards
- Data blending concepts
- Tableau Server introduction
- Debugging advanced Tableau
- Optimizing Tableau performance
- Advanced Tableau use cases
- Best practices for advanced Tableau
- AWS introduction for analysts
- S3 bucket setup for data
- AWS QuickSight introduction
- Importing data into QuickSight
- Creating visuals in QuickSight
- Building cloud dashboards
- Debugging cloud analytics
- Benefits of cloud analysis
- AWS analytics tools
- Best practices for cloud analytics
- Data storytelling introduction
- Structuring a narrative
- Visualization for storytelling
- Understanding audience needs
- Tools for storytelling
- Presentation techniques
- Debugging story flow
- Benefits of storytelling
- Storytelling use cases
- Best practices for storytelling
- Time series introduction
- Time series data with Pandas
- Identifying trends
- Seasonality analysis
- Moving average calculations
- Decomposition techniques
- Debugging time series data
- Benefits of time series analysis
- Time series tools
- Best practices for TS analysis
- Anomaly detection introduction
- Statistical anomaly methods
- ML-based anomaly detection
- Autoencoder introduction
- Visualizing anomalies
- Evaluating anomaly models
- Debugging anomaly detection
- Benefits of anomaly detection
- Anomaly detection tools
- Best practices for anomalies
- Data cleaning introduction
- Handling missing values
- Data type conversions
- Removing duplicates
- Outlier treatment
- Normalization techniques
- Debugging preprocessing
- Benefits of clean data
- Preprocessing tools
- Best practices for preprocessing
- Version control introduction
- Git setup and basics
- Creating repositories
- Committing changes
- Branching fundamentals
- Merging branches
- Debugging Git conflicts
- Benefits of version control
- Git tools (GitHub)
- Best practices for Git
- Data pipeline introduction
- ETL process overview
- Extracting data sources
- Transforming data
- Loading data to destinations
- Pipeline automation intro
- Debugging pipelines
- Benefits of pipelines
- Pipeline tools
- Best practices for pipelines
- Project planning and setup
- Collecting sales data
- Cleaning data with Pandas
- EDA with Seaborn
- Visualizing in Power BI
- Statistical analysis
- Generating insights
- Debugging analysis issues
- Creating a report
- Preparing a presentation
- Project setup and goals
- Collecting text data
- Preprocessing with NLP
- Sentiment analysis with NLTK
- Dashboard in Tableau
- Deriving sentiment insights
- Debugging NLP processes
- Building a report
- Presentation preparation
- Final tweaks and review
- Defining project architecture
- Integrating multi-source data
- Performing EDA and preprocessing
- Developing AI predictive models
- Building dashboards (Power BI, Tableau)
- Deploying on AWS cloud
- Crafting a storytelling presentation
- Debugging the pipeline
- Finalizing deployment
- Delivering the presentation
Skill Covered
- You will learn to collect, clean, and prepare structured and unstructured data for analysis using Python, Pandas, Excel, and SQL.
- You will get an understanding of core statistical concepts like mean, variance, probability, and distributions to support data-driven decisions.
- With tools like Numpy and Pandas, you can use Python to automate analysis, manipulate datasets, and create reusable functions.
- Able to build impactful charts and dashboards using Matplotlib, Seaborn, Power BI, Tableau, and AWS QuickSight.
- Get the ability to write and optimise SQL queries, perform joins, use window functions, and analyse large datasets efficiently.
- You can e-sign real-time interactive dashboards and reports for business users using Power BI, Tableau, and QuickSight.
- Use Scikit-learn to build, train, and evaluate models like linear regression for forecasting and business predictions.
- Using text cleaning, tokenization, and NLTK-based NLP techniques, you can analyse customer feedback and sentiment.
- You can quickly analyse trends and seasonality, and identify outliers using AI-powered models and decomposition techniques.
- Learn to use AWS services like S3 and QuickSight to manage and visualise data in cloud-based environments.
- Convert raw insights into compelling visual stories tailored to different audiences using visualization and structured reporting.
- Apply everything you’ve learned in live projects, like sales analysis, customer sentiment dashboards, and AI-powered BI systems.
Flexible Learning Options
Online Training
- 1 Hours Training
- 100+ Hrs
- Online Interaction with Trainer
- LMS Access
- Guaranteed Placement Assistance
- Weekly Assessments
- Morning & Evening Slots Available
- Weekdays & Weekend Training
Classroom Training
- 1 Hours Training + 1 Hours of Practice Session
- 100+ Hrs
- In-Personal Interaction with Trainer
- LMS Access
- Guaranteed Placement Assistance
- Weekly Assessments
- Only Morning Slots Available
- Weekdays Training Only
Placement Support and Career Opportunities
At Code Zen Eduversity, we offer 100% placement assistance opportunities after our Data Analytics course in Hyderabad. We help you prepare with mock interviews, resume building, and career guidance. Career mentorship and guidance from our team will help you gain access to job openings in MNCs and startups.
Job Roles You Can Target
- Data Analyst
- Junior Business Analyst
- Business Intelligence (BI) Analyst
- Data Visualization Executive
- Python Data Analyst
- Data Operations Associate
Pre Requisites
To learn the Data Analytics course with AI, you don’t need any coding background. Basic knowledge of Excel and logical thinking is sufficient. Therefore, whether you have a B.Tech, B.Com, B.Sc, or even a non-IT background, you can learn Data Analytics from the ground up. Everything is taught step by step, starting with the basics, making it ideal for both freshers and working professionals.
Who Should Enroll in Data Analytics Training?
- Students Pursuing Graduation
- Working professionals looking to change their domain to Data Analytics
- Any Degree - B. Tech, BSc, B.Com, BBA, etc.
- Anyone passionate about learning Data Analytics
Testimonials from our Alumni
I joined the Data Analytics course at Code Zen Eduversity with no coding background. The way they explain concepts is simple and clear. I am now confidently working on real-time data projects.
Raju Babu
The trainers here are experienced and care about your growth. I liked how they used real industry examples to explain things like SQL, Power BI, and Python.
Aman
I am from a non-technical background, but I never felt lost. They started the training from scratch and helped me build a strong base. I highly recommend Code Zen Eduversity for Data Analytics for freshers.
Revathi
From trainers to the support team, everyone is helpful. If you have any doubts, you can reach out anytime, and they will explain until you understand. The trainers are real-time working professionals and help me learn industry-relevant topics.
Ravi Kiran
Certificate Process
Code Zen Eduversity would provide a course completion certificate to the students who finish their training in Data Analytic.
The certificate would be offered to students within a week after completing the training program.
The certification will be given to the students who have completed their projects and assignments on time.
Frequently asked questions
We keep our fees affordable without compromising on course content. For the current offer price, you can contact us by phone or walk to our Madhapur location.
Yes, we do. We offer online and classroom training, where you can choose the training mode at your convenience.
Our data Analytics course at our institute does not require prerequisites. It is open to recent graduates or working professionals looking to switch careers.
The total duration of the course is 3 months, during which we teach you everything from the basics.
Yes, our Data Analytics training in Hyderabad features placement assistance. Our dedicated Team will help you build a resume and prepare for mock interviews.
Yes, our Data Analytics course includes real-time project exploration. Our trainer will also help you build your personal portfolio.
You can submit your details on our website or visit our offline location in Madhapur.
Yes, we offer course completion certification, which boosts your resume.