Data Analytics
- 300+ Hours of training
- 450+ Hiring Partners
- Classroom & Live Online Training Available
- Hands-on training with assignments and capstone projects
- Next batch starts 10 Nov 2026 · Cohort Nanda Devi
DedicatedPlacement Assistance
About the Data Analytics course
Focused squarely on the analytics side of data work — querying, visualization, and storytelling with data — this program takes you from raw spreadsheets and databases to dashboards stakeholders actually use. You'll practice writing SQL queries against real datasets, designing clear and honest visualizations, and structuring analysis so the "so what" is obvious to a non-technical audience. By the end, you'll be comfortable owning a reporting workflow end-to-end: pulling data, cleaning it, analyzing it, and presenting findings in a way that drives a decision rather than just describing numbers.
Learn From The Real Forward Deployed Engineers And AI Experts
Guest lectures and mentorship from Forward Deployed Engineers working at leading companies.
Companies Hiring In This Field
Companies that recruit for roles in this field.
AmazonWho Should Do The Data Analytics?
Who this program is built for.
- Working professionals looking to upskill or switch domains01
- M.Tech / M.E. graduates looking to specialize or pivot into a new tech track02
- B.Tech / B.E. graduates or final-year students in CS, IT, ECE, or related branches03
- BCA, MCA, or B.Sc / M.Sc (Computer Science, IT, or related) graduates04
- 05M.Com, B.Com, MBA, or BBA graduates
- 06Diploma holders in Computer Science, IT, or Engineering
- 07No prior coding experience required for beginner-track courses
- 08Final-year students and recent graduates from any stream
Data Analytics Learning Path
Learn → Build → Deploy → Solve Real Problems → Get Placed
Tech Stack
Technologies & Tools
Placement Path
From your first class to your first offer letter, here is how we take you there.
Career Opportunities with Data Analytics
Tap or hover a card to see what the role looks like day to day. Pay ranges are indicative market figures, not a guarantee of salary or placement.
Reporting AnalystIndicative market pay: ₹3.5 – 5.5 LPA
Reporting Analyst
A role you can aim for after Data Analytics. We help you prepare with projects, mock interviews and resume reviews.
Data Analytics AssociateIndicative market pay: ₹4 – 6.5 LPA
Data Analytics Associate
A role you can aim for after Data Analytics. We help you prepare with projects, mock interviews and resume reviews.
Business AnalystIndicative market pay: ₹5 – 8 LPA
Business Analyst
A role you can aim for after Data Analytics. We help you prepare with projects, mock interviews and resume reviews.
BI DeveloperIndicative market pay: ₹6 – 9 LPA
BI Developer
A role you can aim for after Data Analytics. We help you prepare with projects, mock interviews and resume reviews.
Data Visualization SpecialistIndicative market pay: ₹7 – 11 LPA
Data Visualization Specialist
A role you can aim for after Data Analytics. We help you prepare with projects, mock interviews and resume reviews.
Senior Business AnalystIndicative market pay: ₹9 – 13 LPA
Senior Business Analyst
A role you can aim for after Data Analytics. We help you prepare with projects, mock interviews and resume reviews.
Everything That You Get by Enrolling in Data Analytics
We've built this program with one goal — to guide, support, and uplift you at every stage of your career journey. From the first class to your first placement and beyond, every component has been created with your growth, confidence, and success in mind.
EMI Options Available
Placement Assistance
Collaborative Learning through Teamwork
Course Completion Certificate
Expert Career Guidance
Flexible Learning
ISO 9001:2015 Certified Institute
Hands on Project
Corporate-Ready Curriculum
Industry Expert Sessions
Internship programs
Mock Interview
Placement Mentorship
Resume Preparation Guidance
Recognised worldwide
ISO
IAF
MSME
DPIIT
Accreditation and Recognition from Top Industry Bodies
Our programs and certifications are affiliated with, and recognised by, globally respected standards and skill-development bodies.
Data Analytics Curriculum
Comprehensive modules designed to build Data Analytics skills step by step.
Understand the analyst role and master Excel as the universal analytics tool.
Key Topics
- Role of a data/business analyst; BA vs DA vs product analyst; KPIs & OKRs
- Requirement gathering: BRD, PRD, user stories, as-is/to-be mapping, RCA, SWOT, 5-Why
- Excel: functions, lookups (VLOOKUP, XLOOKUP, INDEX-MATCH), pivot tables & charts, conditional formatting, what-if analysis
- Domain analytics: finance, marketing, HR, supply chain, e-commerce
- A/B testing for business: experiment design, significance, stakeholder interpretation
- Mini projects: sales performance analysis, e-commerce revenue case study, HR attrition dashboard
Industry Relevance
Translate business questions into analysisDocument requirements clearlyAnalyze finance and marketing dataCommunicate to stakeholdersBuild a strong Python foundation for analytics work.
Key Topics
- Dev environment: Jupyter, VS Code, PyCharm, Anaconda, Google Colab
- Data types, operators, strings, lists, tuples, sets & dictionaries
- Control flow, match-case, loops & comprehensions
- Functions, *args/**kwargs, lambda, modules & packages
- Iterators, generators & decorators
- File handling, logging, error & exception handling
- OOP: classes, inheritance, access modifiers, polymorphism, abstraction
Industry Relevance
Automate repetitive analysis tasksWrite clean, reusable codeDebug and log real programsUse AI coding assistants responsiblyMaster data wrangling, visualization and statistical reasoning for confident, data-driven decisions.
Key Topics
- NumPy & Pandas: arrays, groupby, pivot, merges, missing-data handling (MCAR/MAR/MNAR)
- Matplotlib & Seaborn: subplots, pair plots, heat maps, catplots
- EDA: univariate, bivariate, multivariate analysis, outliers & missing values
- Unstructured data: regular expressions, regex with Pandas
- Descriptive vs inferential statistics, central tendency, dispersion, skewness, covariance & correlation
- Probability & distributions: binomial, Poisson, normal, Bernoulli, uniform
- Hypothesis testing: t-test vs z-test, chi-square, ANOVA, CLT, confidence intervals, p-values
- A/B testing: methodology, sample size, statistical significance
- Plotly: interactive visualizations
- NumPy broadcasting, vectorized operations & linear algebra
Industry Relevance
Clean and reshape messy datasetsExplain patterns with clear visualsTest business hypotheses with evidenceDesign and read A/B experimentsExtract, transform and analyze business data with SQL, from basics to advanced analytics.
Key Topics
- SQL fundamentals: SELECT, WHERE, ORDER BY, GROUP BY, HAVING, joins, keys, DML/DDL
- Advanced SQL: subqueries, CTEs, window functions (RANK, DENSE_RANK, ROW_NUMBER, LEAD, LAG)
- Date/time handling, data cleaning in SQL, CASE WHEN, string functions
- SQL for data intelligence: cohort, funnel, revenue trend, CLV, retention & churn analysis
- Stored procedures, views, indexes & query optimization
- Connecting SQL to Python (SQLAlchemy, psycopg2)
- Mini projects: revenue & funnel analysis, business reporting system
Industry Relevance
Build cohort and funnel reportsCalculate CLV and churnAutomate recurring reportingFeed SQL output into BI toolsApply supervised, unsupervised and forecasting models to business problems.
Key Topics
- ML foundations: lifecycle, metrics, bias-variance, cross-validation, responsible ML
- Regression: linear, polynomial, Ridge/Lasso/ElasticNet, logistic; MAE, MSE, RMSE, R²
- Classification: Naive Bayes, decision tree, random forest, SVM, KNN, XGBoost, AdaBoost, SMOTE
- Model evaluation & tuning: confusion matrix, ROC-AUC, GridSearchCV, sklearn pipelines, feature importance
- Unsupervised: K-Means, hierarchical, DBSCAN, PCA/t-SNE/UMAP, anomaly detection, Apriori
- Forecasting: time-series basics, ARIMA, Prophet, forecast accuracy metrics
- Tools: scikit-learn, XGBoost/LightGBM/CatBoost, statsmodels, SHAP/LIME, MLflow, Optuna
- Projects: customer churn prediction, customer segmentation with K-Means, credit risk scoring
Industry Relevance
Predict churn and credit riskSegment customersExplain models to managersCompare models fairlyBuild interactive, enterprise-grade dashboards with Power BI.
Key Topics
- Interface, data loading, data modeling, star schema, Power Query & M basics
- DAX basics: calculated columns vs measures, aggregations, filter & row context, date tables
- Visuals: matrix, maps, slicers, drill-throughs, tooltips, bookmarks, custom visuals
- Advanced DAX: CALCULATE, ALL/ALLEXCEPT, SWITCH, time intelligence (YTD, MTD, SAMEPERIODLASTYEAR), RANKX, TOPN
- Row-level & object-level security, what-if parameters, decomposition tree, key influencers, Smart Narrative
- Power BI Service: publishing, scheduled refresh & gateway, workspaces & apps, Power Automate, mobile layouts
- Mini projects: sales insights, company performance, retail sales & forecasting dashboards
Industry Relevance
Model multi-table dataWrite time-intelligence DAXSecure reports per regionPublish and refresh in the ServiceBuild interactive data stories and executive dashboards with Tableau.
Key Topics
- Workspace, data sources, dimensions & measures, filters, parameters, sets & groups
- Table calculations, calculated fields, LOD expressions (FIXED, INCLUDE, EXCLUDE)
- Geo-spatial analysis, dual-axis charts, reference lines, trend lines & forecasting, extracts vs live
- Dashboard design, storyboarding, actions, responsive layouts, Tableau Prep
- Publishing to Tableau Public/Server; embedding
- Storytelling: story points, annotations, color theory & accessibility
- Projects: HR analytics overview, customer retention & cohort dashboard, executive review dashboard
Industry Relevance
Build cohort dashboards with LODPresent to executivesDesign accessible dashboardsPublish to Tableau ServerSupercharge analytics with AI, from automated EDA to natural-language dashboards.
Key Topics
- AI-assisted analysis: ChatGPT/Claude, ydata-profiling, SweetViz, D-Tale, AI data cleaning
- AI-powered SQL: text-to-SQL, Vanna AI, Copilot for SQL, LLM query optimization
- AI in BI: Power BI Copilot, Tableau AI, Looker AI, Looker Studio + Gemini
- AutoML: H2O, TPOT, PyCaret, auto-feature engineering
- GenAI workflows: prompt engineering for data, conversational analytics, LangChain for structured data
- AI governance: bias, privacy & GDPR, explainability, audit trails
Industry Relevance
Automate EDA and reportingChat with your dataUse Copilot inside BI toolsApply AI governance in analyticsBuild resume-ready analytics projects and a full enterprise capstone.
Key Topics
- Power BI project: retail sales & forecasting (complex DAX, RLS, scheduled refresh)
- Tableau project: customer retention & cohort dashboard (LOD, storyboard, publish)
- SQL project: business reporting system (window functions, CTEs, funnel analysis)
- ML project: predictive customer analytics with SHAP and Power BI dashboard
- Capstone: enterprise performance intelligence system
- Deliverables: BRD, SQL queries & views, Python ML pipeline, Power BI & Tableau dashboards, AI insights report, GitHub portfolio, executive deck
Industry Relevance
Deliver an end-to-end analytics systemWrite professional BRDsCombine SQL, Python and BIPresent to stakeholdersBe job-ready with a polished portfolio and interview confidence.
Key Topics
- Interview fundamentals: BA/DA rounds, STAR method, case studies, statistics & ML concept questions
- Technical prep: 50 must-know SQL queries, dashboard walkthroughs, Python, Excel, Power BI & Tableau scenarios
- Resume, LinkedIn & GitHub portfolio, project READMEs, networking, job search strategy
- Mock interviews (SQL + BI + case study), feedback loop, salary negotiation, first 90 days, learning roadmap
Industry Relevance
Crack SQL and BI interviewsPresent dashboards confidentlyNegotiate offersPlan your first 90 days
The L&L AI Solutions Placement Mentorship Program
At L&L AI Solutions, we treat every learner as a professional in progress. Our Placement Mentorship Program pairs you with dedicated mentors who guide, support, and push you through every stage of your journey — from your first module to your final interview — so you leave not just trained, but genuinely ready to build a career in Data Analytics.
Stage 01
Sharpen Your Fundamentals & Interview Confidence
You'll work closely with mentors who pressure-test what you know and flag exactly where to focus next. Through regular check-ins, simulated interviews, and guided practice, you'll build the technical clarity and communication confidence to hold your own in any interview — technical or HR.
Skill Gap Assessments
Confidence Coaching
Technical Interview Simulations
Weekly Progress Scorecards
HR Round Simulations
Concept Refresher Sessions
Communication Skill Coaching
Key Support
Stage 02
Map Your Career Path & Build Your Professional Profile
Get complete clarity on how placements actually work — the roles open to you, what employers expect, and where you're the strongest fit. With a mentor by your side, you'll rebuild your resume and LinkedIn profile around the roles you're targeting, so your profile reads as capable as you actually are.
Career Path Walkthroughs
What to Expect from Placements
Resume Rebuild Support
LinkedIn Profile Makeover
Role Eligibility Reviews
Personal Branding Guidance
Extra Upskilling Support
Key Support
Stage 03
Apply, Interview & Land the Role
This is where preparation turns into real opportunity. Our placement team matches your profile to live openings, preps you for each specific interview, coordinates directly with employers, and stays with you — feedback and all — until an offer is in hand.
Live Job Opportunities
Profile-to-Role Matching
Role Briefings
Application Support
1:1 Interview Prep
Interview Scheduling Help
Post-Offer Support
Key Support
Data Analytics — FAQs
Everything you need to know about the course and admissions.
Classes are held live online and at our campus: 5th Floor, Draper Startup House Building, Rajiv Gandhi Nagar, Gachibowli, Hyderabad 500032.
Turn raw data into dashboards and decisions.
Eligibility depends on your background. See the "Who Should Do The Data Analytics?" section on this page for the details.
The program runs for 6 Months.
The next cohort starts on 10 Nov 2026. Contact our admissions team to confirm seats and timings.
Yes. You receive a certificate of completion once you finish the program requirements.
Yes. We provide placement assistance, including mentorship, resume support and mock interviews. Placement assistance is not a guarantee of a job.
Yes. The course is built around hands-on practice and projects, ending with a capstone project you can add to your portfolio.
Yes. Mentors review your work and clear doubts throughout the program, not only during the capstone.
A laptop with a stable internet connection. Setup for the tools used is covered at the start of the course.
Our refund and cancellation terms are published on the Refund Policy page of this website. Please read them before you enrol, or ask our admissions team.
