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REQUIREMENT_ID_233 β€’ 3-DAY_ACTIVE_POLICY

Data Analyst

Company
Company HBSPL
Type
Opportunity Type Internship
Salary
Stipend / Salary β‚Ή3 LPA – β‚Ή8 LPA
Location
Location Bangalore, Karnataka, India
Posted Date
Posted Date Today
MS Excel SQL Power BI Tableau Data Visualization Statistical Analysis Problem Solving Communication
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Aptitude Practice Questions Open Resource β†—
Helps you prepare for the online aptitude test commonly used in HBSPL's first round.
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Interview Preparation Guides Open Resource β†—
Covers typical technical and HR questions asked in data analyst interviews, useful for HBSPL's second and third rounds.
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HBSPL Data Analyst Interview Guide Open Resource β†—
Specific insights, sample questions and experiences shared by candidates who interviewed for this role at HBSPL.
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Algorithm & Data Structure Problems Open Resource β†—
Strengthens logical reasoning and problem‑solving skills that are often tested in technical assessments.

Any graduate or postgraduate (B.Sc, B.Com, BBA, B.Tech, M.Sc, MBA, etc.) with a minimum of 60% aggregate. Candidates from Data Science, Statistics, Computer Science, Economics, Business Analytics or related streams are preferred. No active backlogs are allowed. Freshers with 0‑1 year of experience are eligible. Batch year can be 2023‑2025.

1
Round 1: Online Aptitude Test
2
Round 2: Technical Interview (SQL, Excel, BI tools, case study)
3
Round 3: HR Interview
HBSPL (Human Capital Technology) is a fast‑growing data‑driven solutions provider headquartered in Bengaluru. The company focuses on delivering analytics, technology, and strategic decision‑making support to clients across diverse industries such as finance, retail, healthcare and logistics. With a culture that encourages continuous learning, HBSPL invests heavily in up‑skilling its workforce through mentorship programs, hands‑on projects and exposure to the latest BI tools. The firm’s vision is to become a trusted partner for businesses seeking actionable insights, and it has built a reputation for innovative problem solving and collaborative teamwork. The Data Analyst – Fresher role is designed for recent graduates who are eager to start a career in data analytics and business intelligence. As a junior analyst, you will work closely with senior analysts and domain experts to transform raw data into meaningful visualizations and reports that drive strategic decisions. You will be part of a hybrid work model, spending time both in the office and remotely, which offers flexibility while maintaining strong team cohesion. This position provides a solid foundation in data handling, statistical analysis, and dashboard creation, preparing you for rapid growth into senior analytical roles. Key Responsibilities: 1. Gather, clean, and organize large datasets from multiple internal and external sources. 2. Perform exploratory data analysis to identify trends, patterns, and anomalies. 3. Develop and maintain interactive dashboards using Excel, Power BI or Tableau. 4. Write SQL queries to extract, transform and load data into analytical models. 5. Collaborate with cross‑functional teams to understand business requirements and translate them into analytical solutions. 6. Prepare concise reports and presentations for senior management and stakeholders. 7. Ensure data accuracy, consistency, and confidentiality throughout the analysis lifecycle. 8. Assist in building predictive models and statistical reports under guidance. 9. Participate in data governance initiatives and documentation of data pipelines. 10. Continuously learn and adopt new analytical tools and techniques. Tech Stack: MS Excel, SQL, Power BI, Tableau, basic Python/R for statistical tasks, and data visualization best practices. The role offers a clear growth path: junior analyst β†’ analyst β†’ senior analyst β†’ analytics lead, with opportunities to specialize in data engineering or data science based on performance and interest. Joining HBSPL means exposure to real‑world business problems, mentorship from seasoned professionals, and a supportive environment that values curiosity and innovation. The competitive salary range of β‚Ή3‑8 LPA, flexible hybrid work model, and structured training make this an attractive launchpad for a rewarding analytics career.

HBSPL β€” Data Analytics & SQL Interview Guide

Previously asked questions, exam syllabus, coding benchmarks & round strategy.

APTITUDE & LOGIC πŸ“–
Aptitude Practice Questions & Online Mock Tests

Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.

Open Resource β†—
COMPANY GUIDE 🎯
HBSPL Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for HBSPL and off-campus tech roles.

Open Resource β†—
MOCK TESTS & PAPERS πŸ“
Comprehensive Interview Prep Resources & Syllabus

Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.

Open Resource β†—
CODING PRACTICE πŸ’»
Algorithm and Data Structure Problem Set

Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.

Open Resource β†—
Round 1: Online Assessment (OA)
Aptitude, Quantitative Logic & 2 Coding Problems
Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1
Core Tech Stack (MS Excel, SQL, Power BI, Tableau, Data Visualization, Statistical Analysis, Problem Solving, Communication) & Live Code Tracing
Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving
Database Schemas, APIs & Architecture Basics
Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion
HBSPL Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
What is the difference between WHERE and HAVING clauses in SQL? Answer β–Ό
Model Answer: WHERE filters rows before any groupings are applied, while HAVING filters aggregated groups after GROUP BY has executed.
Explain SQL Window functions: ROW_NUMBER(), RANK(), and DENSE_RANK(). Answer β–Ό
Model Answer: ROW_NUMBER() assigns unique sequential integers. RANK() assigns identical ranks to ties and skips ranks. DENSE_RANK() assigns identical ranks to ties without skipping rank numbers.
How do you handle NULL and missing values during data cleaning in Python/Pandas? Answer β–Ό
Model Answer: Use .isna().sum() to identify missing values. Impute with mean/median using .fillna() or remove with .dropna(subset=[...]) depending on variance impact.
What is the difference between Star Schema and Snowflake Schema in Data Warehousing? Answer β–Ό
Model Answer: Star Schema has denormalized dimension tables directly connected to the central Fact table. Snowflake Schema normalizes dimension tables into sub-dimensions to minimize redundancy.
How do you calculate MoM (Month-over-Month) growth in SQL? Answer β–Ό
Model Answer: Use LAG(revenue, 1) OVER (ORDER BY month) to fetch the previous month's revenue and compute (revenue - prev_revenue) / prev_revenue * 100.
Why do you want to join HBSPL as a Data Analyst?
Preparation Tip: Highlight HBSPL's market reputation, recent tech innovations, and how your skills in MS Excel directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Preparation Tip: Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Preparation Tip: Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.

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