AI-Data Analyst
AI-Data Analyst – A fast track to the success.
- Industry-Relevant Curriculum
- Hands-On Training
- Internship Opportunities
- Certification Preparation
- Placement Assistance
About Program
The role of Data Analyst has become very critical in today’s world with huge data acquired by companies. Data Analyst are expected to inspect, clean, transform and model various kind of data from Databases to Cloud to build various analysis using Statistical Analysis, Data Visualization and Machine Learning Algorithms. They specialize in understanding the business requirement, work with data and build compelling stories which would help business executives to make better decision. They assist Data Scientist in their complex model development work by performing exploratory data analysis.
Course Curriculum
1. Python Programming-1
| Days | Live Online Session |
|---|---|
Day1 | Introduction to Python and Basic Syntax |
Day2 | Control Structures |
Day3 | Functions |
Day4 | Data Structures |
Day5 | File Handling |
2. Python Programming-2
| Days | Live Online Session |
|---|---|
Day6 | Modules, Libraries |
Day7 | Exception Handling |
Day8 | Object-Oriented Programming |
Day9 | Python programming with Databases |
Day10 | Hands-On Practice and Hands On Guided Project |
3. Prompt Engineering
| Days | Live Online Session |
|---|---|
Day11 | Introduction to Prompt Engineering and AI Language
Models |
Day12 | Crafting Effective Prompts |
Day13 | Prompt Engineering for Specific Applications |
Day14 | Ethical Considerations and Advanced Techniques |
Day15 | Hands-on Practice with AI Tools |
4. AI Coding Assistants
| Days | Live Online Session |
|---|---|
Day16 | Introduction to AI-Powered Coding Assistants |
Day17 | AI-Powered Code Generation |
Day18 | Debugging and Code Optimization with AI |
Day19 | Advanced Features of AI Coding Assistants |
Day20 | Hands on practice with GitHub Copilot, Amazon Q,
Google Gemini |
5. Linux Basics
| Days | Live Online Session |
|---|---|
Day21 | Introduction to Linux and Basic Commands |
Day22 | User and File Permissions Management |
Day23 | Process Management and Shell Scripting Basics |
Day24 | Package Management and Disk Management |
Day25 | Networking Basics and System Monitoring |
6. Networking Essentials
| Days | Live Online Session |
|---|---|
Day26 | Introduction to Networking |
Day27 | TCP/IP Model and IP Addressing |
Day28 | Subnetting and DHCP |
Day29 | Application Layer Protocol |
Day30 | Network Troubleshooting and Basic Security |
7.Databases
| Days | Live Online Session |
|---|---|
Day31 | Introduction to SQL/NoSQL Databases |
Day32 | SQL Querying |
Day33 | Database Normalization & Indexing |
Day34 | NoSQL Databases |
Day35 | Hands-on Guided Project Using Python & MongoDB |
8. Web Development using Flask
| Days | Live Online Session |
|---|---|
Day36 | Introduction to Flask and Application Setup |
Day37 | Working with Templates and Static Files |
Day38 | Handling Forms and User Input |
Day39 | Database Integration with Flask-SQLAlchemy |
Day40 | Authentication and Deploying Flask Applications |
9. Introduction to Cloud Computing
| Days | Live Online Session |
|---|---|
Day41 | Introduction to Cloud Computing,Core Cloud
Services |
Day42 | Core Cloud Services |
Day43 | Introduction to Cloud DevOps |
Day44 | Advanced Cloud Computing |
Day45 | Hands-on Guided Project & Career Pathways and
Hands-On Labs |
10. Introduction Prominent & Cloud Orchestration Platforms
| Days | Live Online Session |
|---|---|
Day46 | Introduction to Cloud Orchestration – Kubernetes |
Day47 | GKE |
Day48 | EKS |
Day49 | AZURE |
Day50 | Run Web Application on each cloud platform |
11. Data Analysis
| Days | Live Online Session |
|---|---|
Day51 | Introduction to Data Analysis and Tools |
Day52 | Data Cleaning and Preprocessing |
Day53 | Data Analysis Using SQL |
Day54 | Data Visualization Techniques |
Day55 | Advanced Data Analytics and Reporting |
12. Data Analytics with Power BI
| Days | Live Online Session |
|---|---|
Day56 | Course Introduction and Overview of Power BI |
Day57 | Understanding Power BI Interface and Basics |
Day58 | Data Sources and Data Importing |
Day59 | Data Transformation with Power Query |
Day60 | Advanced Power Query & Sharing Insights |
13. Power BI Advanced Concepts
| Days | Live Online Session |
|---|---|
Day61 | Data Modelling and Basic Visualization |
Day62 | Advanced Visualizations in Power BI |
Day63 | Introduction to DAX (Data Analysis Expressions) |
Day64 | Intermediate DAX |
Day65 | Data Refresh and Scheduling |
14. Machine Learning Models using PowerBI
| Days | Live Online Session |
|---|---|
Day66 | Create a dataflow with the input data |
Day67 | Create and train a machine learning model |
Day68 | Review the model validation report |
Day69 | Apply the model to a dataflow entity |
Day70 | Using the scored output from the model in a Power
BI report |
1. Program Orientation & Pre-Project Development Courses
| Days | Live Online Session |
|---|---|
Day1 | Course orientation ,Evaluation metrics,Evaluation |
Day2 | Agile principle,Scrum Framework,User stories |
Day3 | Repositories and Branching |
Day4 | User-focused approach to solving problems |
2. Pre -Requisites of AI
| Days | Live Online Session |
|---|---|
Day5 | Overview of Data Visualization using Matplotlib |
Day6 | Data Preprocessing -outlier detection and Removal |
Day7 | Basic statistics and Probability |
Day8 |
3. Introduction to Data Quality
| Days | Live Online Session |
|---|---|
Day9 | Definition and Importance of Data Quality |
Day10 | Data Quality Dimensions: Accuracy, Completeness |
Day11 | Impact of Poor Data Quality , Introduction to Data
Quality Management (DQM) |
Day12 |
4. Introduction to Data Quality
| Days | Live Online Session |
|---|---|
Day13 | Data Collection Methods , Data Sources |
Day14 | Effective Data Acquisition Techniques- Data Governance
Policies and Procedures |
Day15 | Regulatory Compliance: GDPR, CCPA, etc.- Data Privacy
and Security Concerns- Data Lifecycle Management |
Day16 |
5. Data Preprocessing and Cleaning
| Days | Live Online Session |
|---|---|
Day17 | Overview of Data Preprocessing, Data Normalization |
Day18 | Data Transformation , Handling Missing Values |
Day19 | Data Cleaning Processes , Data Validation |
6. Data Preprocessing and Cleaning
| Days | Live Online Session |
|---|---|
Day20 | Automated vs. Manual Data Cleaning Tools , Data Handling Data Quality Issues in Big Data Environments, Data Cleaning for Different Data Types |
Day21 | |
Day22 | |
7. Data Quality Metrics and Measurement
| Days | Live Online Session |
|---|---|
Day23 | Introduction to Data Quality Metrics, Data Quality |
Day24 | Data Profiling and its Use in Measuring Quality |
Day25 | Validity Metrics and How to Assess Data Integrity |
8. Data Quality Metrics and Measurement
| Days | Live Online Session |
|---|---|
Day26 | Advanced Techniques for Quality Measurement |
Day27 | Tools and Software for Data Quality Measurement
Benchmarking Data Quality and Continuous
Improvement |
Day28 |
9. Quality in Data Engineering and Machine Learning
| Days | Live Online Session |
|---|---|
Day29 | Data Quality in Data Engineering Pipelines |
Day30 | Data Engineering Tools and Platforms for Quality |
Day31 | Impact of Data Quality on Machine Learning Models, |
10. Quality in Data Engineering and Machine Learning
| Days | Live Online Session |
|---|---|
Day32 | Techniques for Ensuring Quality in Training Data |
Day33 | Data Quality Monitoring in Machine Learning |
Day34 | Advanced Enhancement Techniques- Leveraging AI |
11. Advanced Data Quality Strategies
| Days | Live Online Session |
|---|---|
Day35 | Innovations in Data Quality Automation, Frameworks |
Day36 | Integrating Quality into Business Processes, Real-time
Monitoring and Management |
Day37 |
12. Project Work
| Project Work |
|---|
13. Project Work
| Project Work |
|---|
14. Project Work
| Project Work |
|---|
| Sessions | Topics |
|---|---|
Session1 | Importance of Employability Skills Changing Workplaces and Related Skills |
Session2 | Greetings and Introductions Read English Text With Appropriate Articulation |
Session3 | Effective Phone Conversations Making Requests |
Session4 | Participating in Buyer Seller Interactions Saying No or Refusing Politely |
Session5 | Construct Meaningful Sentences Describe Personal Experiences and Thoughts |
Session6 | Write Effective Notes Write Effective Resumes and Reports |
Session7 | Making a Great First Impression Non-verbal Communication |
Session8 | Emotional Intelligence Know Yourself |
Session9 | Positive Attitude Personal Values & Ethics |
Session10 | Balance Your Body & Mind: The Power of Nutrition and
Physical Activity Plan and Manage Tasks Within a Timeline |
Session11 | Conflict Management – An Introduction Understanding Perspectives |
Session12 | Resolve Conflicts to Maintain Relationships Negotiation in Action Getting to YES |
Session13 | Communicate Effectively to Gain Acceptance Compare Features and Benefits of Products & Services |
Session14 | Collaborate Across Different Teams Collaborate to Achieve Team Goals |
Session15 | Introduction to Innovation Introduction to Critical Thinking |
Session16 | Introduction to Decision Making Apply Design Thinking |
Session17 | Understand Change Introduction to Result Orientation |
Session18 | Introduction to Quality Understand the Impact of Errors |
Session19 | Values and Beliefs – Make Ethical Decisions Cultural Fitment & Diversity |
Session20 | Prevention of Sexual Harassment (POSH) Act Behave Appropriately Towards People with Disabilities |
Session21 | Types of Customers – II Build a Customer-Focused Mindset |
Session22 | Respond Effectively to Customers Introduction to CRM Systems |
Session23 | Windows Operating System and File Management Create Documents Using MS Word |
Session24 | Excel Skills to Boost Your Productivity Useful Excel Features for the Workplace |
Session25 | Useful MS PowerPoint Features for the Workplace Effective Information Search Online |
Session26 | Communicate using Email Features of Online Communication Tools |
- Mind set to success in data science
- Industry trends over Data science
- Essential Skills to success in Data Science
- Different roles in Data Science
- Different between Data Science, Data Engineering, Data Analytics, Machine Learning
- Data Science Process
- Data Science Portfolios
- Story around Data science usage case
Converting your experience into Data Science field: One Use Case – To Build a story
- Setting up free Jupyter notebook on Google
- How to use Jupyter notebook
- Variables in Python
- Python Integer Data Type
- String Data Type
- Taking Input
- Python Boolean Data Type
- Python Blocks
- if else statement
- if elif else
- Boolean Logic
- While Loop
- Python Lists
- Python List Operations – Append, Index, Max. Min
- Python Range
- Python Functions
- Passing variable arguments to functions
- Python Modules
- Python Exceptions
- Python File Handling
- None Data Type
- Python Dictionaries
- Tuples
- List Slices
- List Comprehensions
- Python String Functions
- Python List Functons – Any
- Python List All – Function
- Numpy – Add, Subtract, Multiply
- Numpy Dot Product
- Numpy Slicing
- Mixing Integer Indexing And Slice Indexing
- Numpy Array Indexing
- More Array Indexing
- Boolean Array Indexing
- Numpy Sum
- Numpy Reshape
- More Numpy Reshape
- Numpy Tensors 1D, 2D,3D
- Numpy Transposing
- Numpy Broadcasting
- Pandas
- Pandas Series
- Pandas Series Index
- Pandas Advantage Over Numpy
- Pandas Loc and iLoc
- Pandas example – Finding Max
- Pandas Series Addition
- Pandas Apply Function
- Pandas Dataframes
- Pandas DataFrames Introduction
- Pandas DataFrame Index, Loc and ILoc
- Pandas Sum Along Axis
- Pandas DataFrame Addition
- Pandas DataFrame ApplyMap
- Pandas Reading A CSV File
- Database Fundamentals
- SQL Queries – Basic
- SQL Queries – Advanced
- Real World Examples of SQL based Analytics
- MYSQL
- Concept of Data Quality
- Metadata Management
- Data Lineage Concept & Application
- Master Data Management
- Data Stewardship as a new role
- Permutations and combinations
- Bayes Theron
- Central Limit Theorem
- Cumulative distribution function (CDF)
- Probability Density Function (PDF)
- Expected value of Discrete Random variable
- Properties of Means and Variance
- Binomial Probabilities
- Mean, Variance, SD of Binomial Distribution
- Negative Binomial Distribution
- Geometric Distribution
- Poisson Distribution
- Uniform Distribution
- Exponential Distribution
- Normal Distribution
- Population mean with known and unknown population standard deviations
- Determining sample size
- Confidence intervals for population proportions
- Quantify minimum sample sizes to achieve certain margin of error in predictions
- Z – Test
- t – Test
- Chi-square Test
- F-Test
- ANOVA
- Type-1 and Type-2 error
- Interpretation of Confidence level
- Significance level and power of test
- Computation and interpretation of P-value
- Determine the sample size and significance level for a given hypothesis test
- Data Types
- Notation and Definitions
- What is Population, Sample, and census?
- What is different scale of measurements?
- What is Simple Random Sample?
- Measures of Central Tendency
- Measures of Variability
- Histogram: Relative Frequency, Frequency Distribution and Cumulative Distribution.
- Skewness and Kurtosis.
- Relations between Mean and SD
- Box Plot
- Bar chart
- Pie Chart
- Time plot
- Scatter plot
- Regression Models – Linear
- Regression Model – Logistics
- Decision Tree Model
- Unsupervised Machine Learning
- Time Series Analysis
- Text Analytics – Basics
- Basic Data Visualizations – Bar Chart, Line Chart, Histogram
- Intermediate Data Visualizations – Tree Map & Geospatial Analysis
- Basic Dashboard Development in Tableau
- Excel Data Import
Tools & Softwares
Salary Scale
Job Role
- Data Analyst
- AI Engineer
- Data Scientist
- Data Engineer
- Quantitative Analyst
- Big Data Analyst
- Data Visualization Specialist
- Analytics Consultant
Enroll Now
Knowledge Center
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Frequently Asked Questions
It is a training program designed to teach skills in data analysis, including statistical analysis, data visualization, and machine learning algorithms.
Basic knowledge of programming and statistics is recommended. Suitable for B.E./B.Tech. graduates, MCA, MBA, and working professionals in IT.
Topics include Data Architecture, Data Management, Statistics, Machine Learning, Time Series, Text Analytics, and Data Visualization.
Certifications from recognized industry bodies like NASSCOM, NSDC, and Skill India.
Yes, the course includes practical projects for real-world experience.
Tools like Python, Jupyter Notebook, SQL, Tableau, and various machine learning libraries are covered.
Roles such as Data Analyst, Business Analyst, AI Developer, and Data Scientist.
Yes, Rooman Technologies offers free demo sessions to give prospective students a preview of the course content and teaching methodology
The instructors are industry experts with extensive experience working with leading technology companies such as CISCO, WIPRO, and Infosys
You can enroll by visiting the Rooman Technologies website, filling out the necessary details, and following the enrollment procedure. For guidance, you can also contact their support team