HomeUncategorizedPlacement Spotlight Series | Data Science Academy — Thiruvananthapuram & Kochi – April 2026 Update
Placement Spotlight Series | Data Science Academy — Thiruvananthapuram & Kochi – April 2026 Update
DSA Operations
April 29, 2026
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From Our Classrooms to Real Careers: Placement Stories from Data Science Academy, Kerala
Over 7 years, 80+ batches, and 5,000+ students trained, Data Science Academy has maintained a 95% placement rate across its campuses in Thiruvananthapuram and Kochi. The vast majority of our alumni prefer not to be featured publicly. Many work at companies where sharing salary details or employment information externally requires prior HR approval, and we respect that without exception.
What you will find on this page is different from what most institutes publish. We do not display a wall of first names and company logos. We publish honest, detailed accounts — the background each person came from, the specific skills they built during the course, the role they landed, and what the first weeks on the job actually looked like. Every claim on this page is specific because vague claims are not useful to anyone making a serious career decision.
This page is a monthly spotlight series. New stories are added every month from recent batches. The six people featured here represent a range of backgrounds, courses, and outcomes — chosen specifically to reflect the diversity of students who train with us, from fresh graduates with non-technical degrees to working professionals making a mid-career switch.
Spotlight 1 — Arjun M.
Junior Data Analyst | Data Analytics Tools and Techniques | Batch: 2025 | Campus: Thiruvananthapuram
Background
Arjun completed his BCom from a college in Thiruvananthapuram in 2022. He spent fourteen months after graduation attempting to crack government competitive examinations. By the time he joined Data Science Academy, he had no programming background, had never opened a Python interpreter, and was genuinely uncertain whether someone from a commerce stream could survive a technical program. His father, a retired government officer, asked the institute directly during the orientation session whether this was realistic for a student with no mathematics background beyond Class 12. That is an honest question and it deserved an honest answer. We told him it was realistic with consistent effort, not guaranteed without it.
What He Learned and How
Arjun began with the Advanced Excel module and found immediate footing. The logic of VLOOKUP, INDEX-MATCH, array functions, and Pivot Tables came naturally to someone who had spent months organising study material systematically. Python was harder. He struggled with Pandas for nearly three weeks, specifically with the shift from thinking in rows to thinking in columns, and from loops to vectorised operations. The turning point came during the live project component, where he had to clean a messy real-world sales dataset — missing values across multiple columns, inconsistent date formats, duplicate transaction entries, and a category field with over thirty spelling variants. He used Pandas for cleaning and Power Query for the final transformation step. Solving a genuine problem rather than a textbook exercise shifted how he thought about the tools. By the end of the Power BI module, he had built an interactive sales dashboard with DAX-calculated KPIs including month-over-month revenue variance and a rolling 90-day average, with drill-through pages by product category and region. That dashboard became his primary portfolio piece.
Placement Outcome
Arjun joined a Kochi-based B2B SaaS startup in the retail intelligence space. The company, approximately 40 people and Series A funded, builds a product that helps regional retail chains in South India understand inventory movement, demand patterns, and supplier performance. His role is Junior Data Analyst on a three-person analytics team. His day-to-day work involves writing SQL queries against a PostgreSQL database to extract weekly sales and inventory data, cleaning and transforming it using Python and Pandas, and publishing Power BI reports that the client-facing team presents to retail chain owners every Monday morning. Starting salary: ₹3.4 LPA.
The hiring process involved two rounds. The first was a live SQL test — he was given a dataset and asked to write queries identifying the top five products by revenue per region and flagging inventory items where stock had fallen below a dynamic threshold. The second round was a dashboard walkthrough — he presented his Power BI capstone project and was asked to explain every DAX measure he had written. He answered every question because he had built the dashboard himself from scratch.
For the parent reading this: Arjun’s father attended his son’s first day at work. He had been the most skeptical person in the room at orientation. He is no longer skeptical.
Spotlight 2 — Meera S.
Business Intelligence Analyst | Data Analytics Tools and Techniques | Batch: 2025 | Campus: Thiruvananthapuram
Background
Meera holds a BSc in Computer Science from a college in Kollam. She graduated in 2023 with consistent academic scores but no industry exposure. She had learned C and Java in college but had never worked with Python, SQL in a professional context, or any business intelligence tool. She applied to 27 companies in the six months following graduation and received zero interview calls. She joined Data Science Academy after her mother found the institute through a Google search for data analytics courses near Technopark Thiruvananthapuram.
What She Learned and How
Meera moved through the Python fundamentals quickly because of her prior programming exposure. The Pandas and Matplotlib modules gave her the data manipulation vocabulary she had been missing — she was particularly focused on groupby operations, merge functions, and time-series resampling because she had seen these mentioned in the job descriptions she had been unsuccessfully applying to. She spent the most hours on the SQL module, specifically T-SQL stored procedures, window functions, and Common Table Expressions, because she had read that SQL proficiency is the most consistently tested skill in BI analyst interviews. In Power BI, she concentrated on DAX time intelligence functions — Year-to-Date, Month-to-Date, and month-over-month variance calculations — because entry-level BI job descriptions in India almost universally list these. Her capstone project was a sales performance dashboard for a simulated e-commerce dataset, with drill-through pages by region, product category, and time period, and a KPI summary card row at the top of every page.
Placement Outcome
Meera joined an early-stage fintech startup based in Kochi, approximately 25 people and pre-Series A, that is building a digital lending platform for small businesses across Kerala and Tamil Nadu. She is the only dedicated analytics person at the company, which means her work directly informs decisions made by the founding team. She extracts loan disbursement data, repayment patterns, and customer acquisition metrics from a PostgreSQL backend using SQL, models the data in Power BI, and presents a weekly dashboard to the founders every Friday. Starting salary: ₹3.8 LPA.
This role type — sole junior BI analyst at an early-stage fintech startup — is genuinely common in the Indian startup ecosystem. Companies at this stage routinely hire one junior analytics person to own internal reporting before they have the budget for a data engineering team. The tools Meera uses daily are exactly what any hiring manager building this function would specify.
For the data science aspirant reading this: The gap between Meera before the course and after was not intelligence or aptitude. She had always been capable. The gap was structured, tool-specific knowledge and a portfolio piece she could demo under interview conditions. Twenty-seven rejections became a job offer within eight weeks of completing the program.
Spotlight 3 — Rahul V.
Python Developer | AI and Applied Data Science with Python — Level 1 | Batch: 2025 | Campus: Thiruvananthapuram
Background
Rahul is a 2024 engineering graduate — B.Tech in Electronics and Communication — from a college in Thrissur. He had strong foundations in mathematics and signal processing but no meaningful exposure to data science tooling or Python beyond what he described as one semester of barely-taught programming. He joined Data Science Academy specifically because he wanted to build machine learning projects and could not navigate the gap between college-level Python and something an employer would actually pay for.
What He Learned and How
Rahul moved through Python fundamentals quickly but encountered his first real obstacle in the NumPy and Pandas modules — not because the concepts were difficult but because he kept approaching data manipulation with a C programmer’s instinct, writing loops where vectorised operations were the correct tool. His mentor gave him a specific corrective exercise: rewrite every loop-based solution using NumPy array operations and benchmark the execution time of both approaches. That exercise changed his mental model permanently. In the supervised learning module, he built a customer churn classification model on a simulated telecom dataset, starting with Logistic Regression as a baseline, then training a Random Forest, evaluating both using ROC-AUC curves, precision-recall analysis, and a confusion matrix, and writing a structured comparison of the two. He also completed the Introduction to Deep Learning module, building a regression neural network using TensorFlow and Keras, which became a specific talking point in two of his placement interviews. The model deployment section — serving a trained scikit-learn model through a Flask REST API — was the skill that ultimately separated him from other candidates at his level.
Placement Outcome
Rahul joined an AI-first health-tech startup, approximately 60 people, with Kerala co-founders and a distributed team, that is building diagnostic support tools for smaller hospitals and clinics across South India. His role is Python Developer (Data), a position that sits practically between a data analyst and a junior ML engineer. He writes Python scripts for data ingestion, cleaning, and feature extraction from clinical data sources, and contributes to the preprocessing pipeline that feeds the company’s core ML models. Starting salary: ₹4.2 LPA.
For the hiring manager reading this: This role profile — entry-level Python developer supporting an ML pipeline at a health-tech or agri-tech startup — is one of the most actively hired positions in India’s Tier 2 startup ecosystem right now. The specification is consistent: clean Python, solid data structure knowledge, enough ML literacy to communicate effectively with the modelling team, and at least one deployed model in the portfolio. Rahul meets every point of that specification precisely.
Spotlight 4 — Divya K.
DATA ANALYST, OPERATIONS | DATA ANALYTICS TOOLS AND TECHNIQUES | BATCH: 2025 | CAMPUS: THIRUVANANTHAPURAM
Background
Divya worked for three years as an operations executive at a logistics company in Trivandrum after completing her BBA. She was effective at her job — she managed vendor data, tracked delivery KPIs in Excel, and produced weekly reports for her manager. But she had reached a ceiling. Advancement at her company required either a postgraduate management degree or a technical analytics capability she did not have. She joined Data Science Academy as a working professional attending weekend batches, which meant she was studying while holding down a full-time job with real accountability.
This story is included specifically for working professionals considering the course, and it is written without flattery. Divya missed two sessions during a particularly demanding quarter at work. She fell behind on the Power BI module for ten days. She submitted her capstone project at 11 PM on the night of the deadline. She is not an outlier. This is what upskilling alongside full-time employment actually looks like, and the program is structured to accommodate it.
What She Learned and How
Because Divya already understood business operations at a practical level, her advantage was that she immediately understood what questions the data should answer. When she studied SQL joins, she thought instinctively about vendor master tables and shipment transaction tables — structures she had been working with manually in Excel for three years. When she learned T-SQL GROUP BY aggregations and window functions, she immediately applied them to delivery performance analysis scenarios she had lived through. Her Power BI capstone project was a logistics operations dashboard showing on-time delivery rate by route and vendor, vendor performance ranking by quarter, cost-per-kilometre trend, and anomaly flags for routes with delivery time variance above a defined threshold. Her mentor noted it was the most operationally grounded dashboard produced by that batch.
Placement Outcome
Divya was hired by a Trivandrum-based supply chain software startup, approximately 35 people, bootstrapped, that serves logistics companies across South India. Her role is Data Analyst on the Operations team. She helps the company’s clients interpret the analytics output of the software product — writing SQL queries against client databases, building custom Power BI reports, and presenting findings in monthly business reviews. Her salary moved from ₹2.8 LPA to ₹4.1 LPA. She did not apply for this role. She was contacted by a client who had seen her present an analysis during a business review meeting and asked her employer for a referral.
For the parent reading this: Divya was 26 years old with a non-technical degree and a full-time job when she enrolled. She completed the weekend batch program without quitting her job, without relocating, and without taking any financial risk beyond the course fee. The outcome was a 46% salary increase and a role she did not have to interview for.
Spotlight 5 — Vishnu R.
JUNIOR ML ENGINEER (INTERN TO FULL-TIME) | ADVANCED AI AND APPLIED DATA SCIENCE WITH PYTHON — LEVEL 2 | BATCH: 2025 | CAMPUS: KOCHI
Background
Vishnu holds an MCA from a university in Kerala, completed in 2023. He had foundational exposure to Python and database concepts from his postgraduate program but described his college-level ML education as theoretical to the point of being useless in a technical interview. He had attempted two ML engineer interviews before joining Data Science Academy and failed both at the first technical screening stage. He could define logistic regression accurately. He could not implement it in scikit-learn, tune its hyperparameters, or interpret a precision-recall curve under timed conditions. That gap — between knowing and doing — is what Level 2 was designed to close.
What He Learned and How
Level 2 was the appropriate entry point for Vishnu because it assumed foundational Python competence and moved directly into the full supervised learning stack without spending time on basics he had already covered. He worked through K-Nearest Neighbors, Ridge and Lasso Regression, Support Vector Machines, Decision Trees, Random Forest, and Gradient Boosted Decision Trees — not as theoretical exercises but as sequential model-building sessions on real datasets, each followed by formal model evaluation using confusion matrices, ROC-AUC curves, and precision-recall analysis. He spent significant independent time on the model evaluation module, specifically understanding why accuracy is a misleading metric on imbalanced class distributions and how to make a defensible choice between models using the right evaluation criteria for the business problem. He also completed the Unsupervised Learning module including KMeans clustering and PCA-based dimensionality reduction. The skill that directly determined his placement outcome was the model serving module — wrapping a trained scikit-learn model in a Flask REST API and exposing it as a deployable endpoint. At the time of his interviews, most other candidates at his level had trained models. Vishnu had deployed one.
Placement Outcome
Vishnu joined a Bangalore-based B2B AI startup, approximately 80 people, Series B funded, that builds recommendation and personalisation infrastructure for e-commerce companies. He began as a paid ML intern and was confirmed to a full-time Junior ML Engineer position after 90 days on the basis of performance review. His work involves Python-based data preprocessing and feature engineering, running model experiments using scikit-learn, and contributing to the Flask-based model serving layer that the company’s clients access via API. Full-time starting salary: ₹5.2 LPA.
For the data science aspirant reading this: The intern-to-full-time conversion path is the most common and most reliable route into ML engineering at Indian product startups right now. Series B companies hire paid interns specifically to evaluate real-world performance before committing to a full-time cost. The candidate who arrives with a deployed Flask API in their portfolio — not just a Jupyter notebook — is consistently the one who gets the internship offer and subsequently the conversion.
Spotlight 6 — Anjali T.
DATA OPERATIONS ANALYST | DATA ANALYTICS TOOLS AND TECHNIQUES | BATCH: 2025 | CAMPUS: THIRUVANANTHAPURAM
Background
Anjali holds a BSc in Mathematics from a college in Thiruvananthapuram, graduated in 2022. Analytically she was strong — probability, statistics, and linear algebra were familiar territory from her degree. But she had no coding background, no industry experience, and no portfolio. She had considered pursuing an MSc but her family’s financial situation made a two-year full-time program difficult to justify without a clear employment outcome at the end. She found Data Science Academy through a recommendation and noted that its location directly opposite Technopark Phase 1 in Kazhakuttom gave her a specific confidence that the training would be calibrated to what Technopark companies and their supply chain of startups actually hire for.
What She Learned and How
For a student with a mathematics background, the Python statistics applications were immediately accessible — Anjali was producing distribution plots with Matplotlib and Seaborn and writing Pandas-based summary statistics before most of her batch had finished the fundamentals module. Her genuine challenge was SQL. Relational database design, normalisation, stored procedures, transactions, and especially TRY-CATCH error handling and triggers were entirely new concepts. She requested additional practice datasets from her mentor and spent independent hours writing CTEs, subqueries, and window functions on three different practice schemas before she felt confident enough to use SQL fluently in an interview setting. Her capstone project was a complete end-to-end analytics workflow: SQL queries to extract structured data from a simulated relational database, Python and Pandas for exploratory data analysis and anomaly detection, and a Power BI dashboard delivering the summary findings. The project required all three tools in sequence and produced a single, coherent output — which is what real analytics work looks like.
Placement Outcome
Anjali joined a data services company based in Thiruvananthapuram, approximately 50 people, that provides analytics and reporting services to international clients in the healthcare and insurance verticals. Her role is Data Operations Analyst. She works on structured data quality workflows — writing SQL queries to validate incoming data from client pipelines, using Python scripts to flag statistical anomalies and format violations, and generating Power BI reports that are delivered directly to the end client on a defined schedule. Starting salary: ₹3.6 LPA. Her hiring manager subsequently told the institute’s placement coordinator that she was the strongest analytical hire at entry level the team had made that year.
For the parent reading this: A BSc in Mathematics from a Kerala college, with no coding background and no work experience, became a full-time data operations role at a Thiruvananthapuram company serving international clients — within five months of completing a 4-month weekday course. The mathematics degree was not a liability. It was an accelerator. Anjali understood why the analytical methods worked while other candidates simply used them. That difference was visible in the interview.
Frequently Asked Questions About Data Science Placements in Kerala
What jobs can a fresher get after completing a data analytics course in Kerala?
After completing the Data Analytics Tools and Techniques program at Data Science Academy, freshers are prepared for entry-level roles including Data Analyst, Business Intelligence Analyst, SQL Analyst, Power BI Developer, Python Developer (Data), and Data Operations Analyst. These roles exist across IT services companies, SaaS startups, fintech companies, logistics firms, and data services organisations operating in Thiruvananthapuram, Kochi, and across India. Entry-level salaries in Kerala for these roles range from ₹3.0 LPA to ₹5.0 LPA depending on company type and the candidate’s demonstrated skill level.
What salary can a fresher expect after a data science or data analytics course in Thiruvananthapuram?
Based on placements completed by Data Science Academy students from Thiruvananthapuram, entry-level data analyst roles at startups and mid-size companies in Kerala typically start between ₹3.0 LPA and ₹4.5 LPA. Roles involving Python and machine learning skills, such as Python Developer (Data) or Junior ML Engineer, command starting salaries between ₹4.0 LPA and ₹6.0 LPA. Students who join Bangalore-based startups through the placement network have secured starting salaries between ₹4.2 LPA and ₹5.5 LPA at entry level. Salary outcomes depend on the course level completed, the quality of the portfolio project, and performance in technical interviews.
Is Data Science Academy good for placements?
Data Science Academy has maintained a 95% placement rate across 7 years of operations and 80+ batches from its Thiruvananthapuram and Kochi campuses. The institute provides resume preparation, mock technical interviews, and active placement support through its industry network. Alumni from Data Science Academy are employed at companies including Infosys, TCS, UST Global, KPMG, EY, Tata Elxsi, Quantiphi, InApp, QBurst, Cognizant, PwC, Amazon, Deloitte, and a range of funded startups across India. Prospective students can request direct contact with a Data Science Academy alumnus before enrolling by writing to trainings@datascience.one.
How long does it take to get placed after completing the course at Data Science Academy Thiruvananthapuram?
Most students who actively engage with the placement support process — attending mock interviews, completing their portfolio project, and applying consistently — receive their first job offer within 60 to 90 days of course completion. Students who complete internships as part of the live project component sometimes receive full-time offers from their internship companies before the course formally ends. The timeline varies by the role type being targeted, the student’s prior background, and the quality of the portfolio they have built during training.
Can someone from a non-engineering or non-technical background get a data science or analytics job in Kerala?
Yes. Data Science Academy has placed students from BCom, BBA, BSc Mathematics, BSc Computer Science, and Arts backgrounds into data analyst and business intelligence roles. The curriculum for the Data Analytics course begins from foundational Excel and Python concepts with no prior technical knowledge assumed. Students from commerce and mathematics backgrounds have consistently performed well in analytics roles because their comfort with numbers and structured thinking complements the tool-based training. The placement stories on this page include a BCom graduate from Thiruvananthapuram who is now a Junior Data Analyst at a funded startup in Kochi.
Does Data Science Academy Kochi have placement support?
Yes. Data Science Academy operates campuses in both Thiruvananthapuram and Kochi. Placement support — including resume review, mock technical interviews, LinkedIn profile optimisation, and job referrals through the institute’s industry network — is provided to students from both campuses. Alumni from the Kochi campus have been placed at fintech startups, IT services companies, and product firms operating in Kochi and across India.
What companies hire data analytics graduates from Kerala?
Data Science Academy alumni work at companies including Infosys, TCS, UST Global, KPMG, EY, Tata Elxsi, Quantiphi, InApp, QBurst, Cognizant, PwC, Amazon, Deloitte, Hyreo, Clap Research, Infoblox, Arcadis, IQVIA, and Resnova, among others. Entry-level hiring for data analytics roles also comes from the Kerala startup ecosystem — particularly companies in or adjacent to Technopark Thiruvananthapuram and Infopark Kochi — and from Bangalore-based startups that actively recruit from Kerala training institutes. Sectors that consistently hire entry-level data analysts include IT services, fintech, health-tech, logistics, retail technology, and data services.
Is a data analytics course worth it for someone already working in a non-data role in Thiruvananthapuram?
For working professionals in Thiruvananthapuram and Kochi, the Data Analytics Tools and Techniques program is available in weekend batch format specifically to accommodate existing employment. Based on outcomes from working professional students, the average salary increase achieved after transitioning to a data analytics role has been between 30% and 50% above the pre-course salary. The more significant benefit for many working professionals is access to a new role category — analytical roles tend to offer clearer promotion paths, broader industry transferability, and higher salary ceilings than operational roles at the same experience level. The decision is worth evaluating against the specific ceiling you are currently facing in your career.
What is the difference between the Data Analytics course and the AI and Data Science course at Data Science Academy?
The Data Analytics Tools and Techniques program covers Advanced Excel, Python for data analysis, SQL and T-SQL, Power BI, and an introduction to machine learning concepts. It is designed for students targeting analyst and BI roles and can be completed in 3 to 4 months on weekdays or 5 to 6 months on weekends. The AI and Applied Data Science with Python program series (Levels 1 through 4) covers the full machine learning and AI stack including supervised and unsupervised learning, deep learning, NLP, computer vision, generative AI, LLMs, and cloud deployment, progressing to increasingly advanced specialisations. It is designed for students targeting data science, ML engineering, and AI engineering roles. Both programs include live project internships and placement support.
What tools will I learn at Data Science Academy that employers actually ask about in interviews?
The most frequently tested tools and skills in data analytics interviews are SQL query writing, Power BI dashboard building, and Python-based data manipulation using Pandas. For data science and ML engineering roles, interviewers typically assess scikit-learn model building, model evaluation methodology, and — increasingly at product startups — the ability to serve a trained model through a Flask or FastAPI endpoint. The Data Science Academy curriculum covers all of these specifically, not as introductory overviews but as hands-on modules with live project implementation.
About Data Science Academy — 7 Years of Placement-Focused Training in Kerala
Data Science Academy is Kerala’s longest-established specialist data science and AI training institute, with campuses in Thiruvananthapuram and Kochi. Founded 7 years ago and located opposite Technopark Phase 1 at Kazhakuttom, the institute has trained more than 5,000 students across 80+ batches in data science, data analytics, machine learning, artificial intelligence, and cloud technologies. The curriculum is designed and delivered by instructors with active industry experience, including PhD-qualified practitioners with applied backgrounds in AI, analytics, robotics, and business intelligence. Every program includes live project internships and structured placement support. Certificates are issued with unique QR-verified authentication codes.
To speak with our placement team, discuss which course is right for your background, or request a free career counselling session, contact us below.
