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Name: AI DATA ANALYST
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✅Best AI DATA ANALYST Training Institute [Top-Rated & Certified]

COURSE CURRICULUM

87+ Course Topics • Hands-on Labs • Real-Time Practice

MODULE 1: ANALYTICS & AI FOUNDATIONS

01 Data Analytics vs Data Science vs Business Intelligence Topics

Definitions and scope
Key differences in responsibilities, outputs and tools
When each discipline is used in business

02 Role of an AI Data Analyst Topics

Core responsibilities and daily workflow
AI-assisted analysis and reporting
Stakeholder communication and insight validation

03 Analytics workflow: Data → Analysis → Visualization → Insights → Action Topics

Business problem and data requirements
Analysis, visualization and insight generation
Recommendation and action-oriented decision making

04 Introduction to AI-powered analytics Topics

AI-assisted exploration and querying
Generative AI for analysis and summaries
Human validation and responsible use

05 Business use cases for AI in analytics Topics

Sales, marketing and customer analytics
Operations, finance and product use cases
Automation, anomaly detection and decision support

06 Understanding structured and unstructured data Topics

Tables, relational data and spreadsheets
Text, documents and other unstructured sources
Semi-structured formats such as JSON and XML

MODULE 2: EXCEL FOR DATA ANALYTICS

07 Excel fundamentals for analytics Topics

Workbooks, worksheets, cells and ranges
Tables, sorting, filtering and validation
Formatting and analyst productivity practices

08 Data cleaning and preparation Topics

Missing values and duplicates
Standardization and data consistency
Validation and analysis-ready datasets

09 Formulas and functions Topics

Core arithmetic and statistical functions
Text, date and logical functions
Reusable calculations for business reporting

10 Conditional logic Topics

IF/IFS and logical operators
Business-rule classification
Error handling and conditional calculations

11 Lookup functions Topics

VLOOKUP, XLOOKUP and HLOOKUP
INDEX/MATCH concepts
Exact matching, errors and multi-table analysis

12 Pivot Tables Topics

Grouping and aggregation
Filters, calculated fields and pivot charts
Business summaries and interactive analysis

13 Charts and dashboards Topics

Chart selection and visual hierarchy
KPI reporting and dashboard layout
Interactive business reporting

14 Business reporting Topics

Core concepts and terminology of business reporting
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

15 Automated reporting concepts Topics

Refreshable and repeatable reports
AI-generated summaries and narratives
KPI monitoring and management reporting

MODULE 3: SQL FOR DATA ANALYSTS

16 SELECT, WHERE, ORDER BY, LIMIT Topics

Core concepts and terminology of select, where, order by, limit
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

17 Filtering with AND, OR, NOT, IN, BETWEEN Topics

Core concepts and terminology of filtering with and, or, not, in, between
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

18 DISTINCT and COUNT Topics

Core concepts and terminology of distinct and count
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

19 GROUP BY and aggregation Topics

Core concepts and terminology of group by and aggregation
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

20 HAVING Topics

Core concepts and terminology of having
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

21 INNER, LEFT, RIGHT and FULL JOIN Topics

Core concepts and terminology of inner, left, right and full join
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

22 Self-joins and multiple joins Topics

Core concepts and terminology of self-joins and multiple joins
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

23 Subqueries and CTEs Topics

Core concepts and terminology of subqueries and ctes
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

24 Window functions Topics

Core concepts and terminology of window functions
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

25 CASE statements Topics

Core concepts and terminology of case statements
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

26 Customer segmentation Topics

Core concepts and terminology of customer segmentation
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

27 Cohort and funnel analysis Topics

Core concepts and terminology of cohort and funnel analysis
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

28 Revenue analysis Topics

Core concepts and terminology of revenue analysis
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

MODULE 4: PYTHON FOR DATA ANALYTICS

29 Python environment setup Topics

Python syntax and programming fundamentals
Data structures, functions and reusable code
Analytics-oriented programming workflow

30 Variables, data types and operators Topics

Core concepts and terminology of variables, data types and operators
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

31 Conditions and loops Topics

Core concepts and terminology of conditions and loops
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

32 Strings Topics

Core concepts and terminology of strings
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

33 Lists, tuples and dictionaries Topics

Core concepts and terminology of lists, tuples and dictionaries
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

34 Functions Topics

Core concepts and terminology of functions
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

35 NumPy arrays Topics

Arrays and dimensions
Vectorized mathematical operations
Statistical calculations and reshaping

36 Mathematical and statistical operations Topics

Core concepts and terminology of mathematical and statistical operations
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

MODULE 5: PANDAS & DATA PREPARATION

37 Series and DataFrames Topics

Core concepts and terminology of series and dataframes
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

38 Importing CSV, Excel and SQL data Topics

Relational database concepts and query structure
Filtering, aggregation and joins
Analytical SQL patterns for business questions

39 Data inspection Topics

Core concepts and terminology of data inspection
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

40 Missing-value handling Topics

Core concepts and terminology of missing-value handling
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

41 Duplicate removal Topics

Core concepts and terminology of duplicate removal
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

42 Data-type conversion Topics

Core concepts and terminology of data-type conversion
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

43 Outlier handling Topics

Core concepts and terminology of outlier handling
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

44 Filtering and sorting Topics

Core concepts and terminology of filtering and sorting
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

45 GroupBy and aggregation Topics

Core concepts and terminology of groupby and aggregation
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

46 Pivot tables Topics

Grouping and aggregation
Filters, calculated fields and pivot charts
Business summaries and interactive analysis

47 Merge, join and concatenate Topics

Core concepts and terminology of merge, join and concatenate
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

48 Time-series analysis Topics

Core concepts and terminology of time-series analysis
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

MODULE 6: EDA & STATISTICS

49 Univariate, bivariate and multivariate analysis Topics

Core concepts and terminology of univariate, bivariate and multivariate analysis
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

50 Data distributions Topics

Core concepts and terminology of data distributions
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

51 Mean, median and mode Topics

Core concepts and terminology of mean, median and mode
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

52 Variance and standard deviation Topics

Core concepts and terminology of variance and standard deviation
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

53 IQR and Z-score Topics

Core concepts and terminology of IQR and Z-score
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

54 Probability fundamentals Topics

Core concepts and terminology of probability fundamentals
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

55 Correlation Topics

Core concepts and terminology of correlation
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

56 Hypothesis testing Topics

Core concepts and terminology of hypothesis testing
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

57 Confidence intervals Topics

Core concepts and terminology of confidence intervals
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

58 T-test, Z-test and Chi-square Topics

Core concepts and terminology of t-test, z-test and chi-square
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

59 A/B testing Topics

Core concepts and terminology of A/B testing
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

60 Business significance vs statistical significance Topics

Core concepts and terminology of business significance vs statistical significance
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

MODULE 7: VISUALIZATION & POWER BI

61 Data visualization principles Topics

Visualization principles and chart selection
Static and interactive visualizations
Storytelling and stakeholder communication

62 Matplotlib Topics

Core concepts and terminology of matplotlib
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

63 Seaborn Topics

Core concepts and terminology of seaborn
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

64 Interactive visualization Topics

Core concepts and terminology of interactive visualization
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

65 Power BI dashboards Topics

Data import and Power Query
Data modeling and DAX
Interactive dashboards, KPIs and drill-through

66 KPI reporting Topics

Core concepts and terminology of KPI reporting
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

67 Business dashboards Topics

Core concepts and terminology of business dashboards
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

68 Choosing the right chart Topics

Core concepts and terminology of choosing the right chart
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

69 Data storytelling Topics

Core concepts and terminology of data storytelling
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

70 Presenting insights to stakeholders Topics

Core concepts and terminology of presenting insights to stakeholders
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

MODULE 8: AI-POWERED ANALYTICS & AUTOMATED REPORTING

71 Generative AI for analytics Topics

Generative AI concepts and use cases
LLM capabilities and limitations
Prompting and application patterns

72 AI-assisted data exploration Topics

Core concepts and terminology of AI-assisted data exploration
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

73 AI-generated insights Topics

Core concepts and terminology of AI-generated insights
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

74 Natural-language analytics Topics

Core concepts and terminology of natural-language analytics
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

75 Automated report generation Topics

Core concepts and terminology of automated report generation
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

76 AI-assisted visualization Topics

Core concepts and terminology of AI-assisted visualization
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

77 Insight summarization Topics

Core concepts and terminology of insight summarization
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

78 Business decision support Topics

Core concepts and terminology of business decision support
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

79 Capstone Project Topics

Core concepts and terminology of capstone project
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

80 Build an end-to-end business analytics solution covering: Topics

Data sourcing → Cleaning → SQL → EDA → Statistics → Dashboard → AI insights → Business recommendations.
Relational database concepts and query structure
Filtering, aggregation and joins
Analytical SQL patterns for business questions

81 Example Projects: Topics

Core concepts and terminology of example projects:
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

82 Customer Retention Analysis Topics

Core concepts and terminology of customer retention analysis
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

83 Sales & Revenue Analysis Topics

Core concepts and terminology of sales & revenue analysis
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

84 SaaS User Behaviour Analysis Topics

Core concepts and terminology of SaaS user behaviour analysis
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

85 Marketing Performance Analytics Topics

Core concepts and terminology of marketing performance analytics
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

86 Career Roles Topics

Core concepts and terminology of career roles
Practical implementation, analysis or workflow considerations
Business use cases, best practices and common challenges

87 AI Data Analyst | Data Analyst | BI Analyst | Reporting Analyst | Business Analytics Associate Topics

Security operations workflow
Alert triage and investigation
Incident escalation and response

HANDS-ON LABS YOU WILL WORK ON

SQL Data Analysis

Excel Analytics

Python Data Analysis

Pandas Data Preparation

EDA & Statistics

Power BI Dashboards

AI-Powered Analytics

Business Analytics Projects

24×7 Access to Dedicated Cloud Lab

MEET YOUR TRAINER

Surya Kumar

Data Science Professional

15+ Years of Experience

Senior AI Engineer | Agentic & Generative AI | AWS Cloud | PMP Certified

With 12 years of industry experience, I specialize in deploying AI-powered solutions at scale, driving digital transformation for diverse organizations. My core expertise spans large language models (LLMs), advanced machine learning pipelines, retrieval-augmented generation (RAG), and AI-driven feature engineering.

I bring hands-on experience with full-stack solution development, cloud-native AI deployments on AWS, and proven cross-functional delivery in both startup and enterprise settings. My project portfolio includes designing and implementing state-of-the-art generative AI applications, optimizing ML workflows for production, and leading teams in complex, agentic AI integrations.

As a certified Project Management Professional (PMP), I ensure technical excellence and timely project execution. My training approach blends deep technical knowledge with practical, real-world application—empowering teams to deliver robust and innovative AI systems.

Key Skills:

Generative AI & LLMs (OpenAI, HuggingFace, RAG)
ML Pipeline Design & Feature Engineering
Cloud Deployment (AWS EC2, SageMaker, Lambda, API Gateway)
Full-stack Application Development (Python, JavaScript, REST APIs)
Project Leadership (Agile, Cross-functional Teams, PMP Certified)
AI Solution Architecture & Productionization
Technical Training & Mentorship

Oracle DBA RAC Data Guard ASM Performance Cloud

STUDENT SUCCESS STORIES

Priyanka Student

★★★★★

I had used different AI models before, but I never really understood the difference between using an API, an open-source model and a local model. The sessions on OpenAI, Gemini, Claude, Hugging Face and local models cleared up a lot of that confusion.

Amit Student

★★★★★

Before joining, I mostly knew how to use ChatGPT and similar tools. This course helped me understand the developer side of GenAI — APIs, prompts, LangChain, RAG and building applications. Quite a different experience from just using AI tools.

Sharad Student

★★★★★

I liked the way the course brings different tools together. Instead of learning Excel, SQL, Python and Power BI as separate things, we could see how they are used during the data analysis process. The project work made that connection clearer for me.

Hanuman Student

★★★★★

I came from a non-data background, so I was a little unsure before joining. Now I can work with SQL queries, Python libraries and Power BI with much more confidence. Still have a lot to practise, but the course gave me a proper starting point.

Aditya Student

★★★★★

The course gave me a much better idea of what a data analyst actually does. Earlier I thought it was mostly Excel and charts. Learning SQL, Python, statistics, Power BI and AI together changed that understanding for me.

Supriya Student

★★★★★

I joined this course mainly because I wanted to learn Power BI and SQL properly. Surprisingly, I ended up enjoying the Python and AI part also. The projects were useful because they showed how the different tools are connected.

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AI DATA ANALYST

COURSE OVERVIEW

WHO SHOULD JOIN?

CAREER OUTCOMES

PRACTICE LIKE A REAL PROFESSIONAL

What You Will Learn to Do in Real-World Projects

Analytics & AI Foundations

Excel Data Analytics

SQL for Data Analysts

Python for Data Analytics

Pandas & Data Preparation

Visualization & Power BI

EDA, Statistics & Insights

Visualization & Power BI

COURSE CURRICULUM

HANDS-ON LABS YOU WILL WORK ON

SQL Data Analysis

Excel Analytics

Python Data Analysis

Pandas Data Preparation

EDA & Statistics

Power BI Dashboards

AI-Powered Analytics

Business Analytics Projects

WHAT'S INCLUDED IN THIS PROGRAM

3 months Live Sessions

70% Hands-On Training

200+ hrs Lab Practices

24×7 Lab Access

Resume Building

Job Support

Real-World Projects

Internship Opportunities

Technical Mentorship

Career Guidance

GitHub Portfolio

Interview Preparation

Course Completion Certificate

MEET YOUR TRAINER

Surya Kumar

STUDENT SUCCESS STORIES

PLACEMENT ASSISTANCE

ResumePreparation

MockInterviews

TechnicalInterview Q&A

CompanyReferrals

PlacementSupport

UPCOMING BATCHES

LEARNING MODES

CLASSROOMTRAINING

LIVE ONLINETRAINING

SELF-PACEDLEARNING

FAQs

READY TO BECOME AN ORACLE DBA EXPERT?

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Mock
Interviews

Technical
Interview Q&A

Company
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Placement
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CLASSROOM
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