Greater Insights
Learning simplified!
Master Python for Data Analysis in Bangalore — move into Business Analyst roles paying ₹12–22 LPA, rising to ₹36L at senior level.
Open-house batches for individuals — enroll directly. Training a team? Request an enterprise quote →
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14 Nov – 06 Dec 2026
Saturday
all session dates
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10:00 AM–2:00 PM IST | 🎓 Virtual Instructor-led🗓️ Weekends | Half days | 4 weekends | ₹16,800 ₹28,000 |
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Sat 14 NovSun 15 NovSat 21 NovSun 22 NovSat 28 NovSun 29 NovSat 5 DecSun 6 Dec
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16 Nov – 19 Nov 2026
Monday
all session dates
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9:30 AM–5:30 PM IST | 🎓 Virtual Instructor-led🗓️ Weekdays | Full days | 4 days | ₹16,800 ₹28,000 |
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Mon 16 NovTue 17 NovWed 18 NovThu 19 Nov
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07 Dec – 13 Jan 2027
Monday
all session dates
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8:00 PM–10:00 PM IST | 🎓 Virtual Instructor-led🗓️ Weekdays | Evenings | 16 sessions | ₹16,800 ₹28,000 |
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Mon 7 DecWed 9 DecFri 11 DecMon 14 DecWed 16 DecFri 18 DecMon 21 DecWed 23 DecMon 28 DecWed 30 DecFri 1 JanMon 4 JanWed 6 JanFri 8 JanMon 11 JanWed 13 Jan
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| Enterprise Custom Date |
Custom Schedule Your timing & location |
⚡ Any Mode | Flexible | Custom pricing | |
Can't find a suitable batch? Contact us
Every function in every enterprise is becoming data-driven, and data science remains one of the most reliable on-ramps into high-growth technology careers, with demand consistently outpacing supply.
Explore datasets, clean messy data, and build visualizations to uncover business insights.
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Extract, transform, and analyze data to identify trends and inform strategic decisions.
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Explore datasets, clean messy data, and build visualizations to uncover business insights.
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* Salary figures sourced from AmbitionBox (2026-Q2). Indicative — actual pay varies by city, company and experience.
This beginner-to-intermediate course teaches you to load, clean, transform, and analyse tabular data using Python. You'll work in JupyterLab notebooks to master NumPy arrays for fast numerical computation, pandas DataFrames for data manipulation, and Matplotlib and seaborn for visualisation. The course covers reading CSV, JSON, and SQLite files; handling missing values and duplicates; grouping and aggregating data; parsing dates and resampling time series; and building exploratory analyses that lead to actionable business insights.
Ideal for data analysts, business analysts, junior data scientists, and reporting professionals, this training equips you to design repeatable analytical workflows from hypothesis through recommendation. You'll learn to measure correlations quantitatively, fit linear regression models to detect trends, and compose end-to-end data pipelines that translate raw sources into clear findings.
The job roles this programme is built for.
13 modules · hands-on labs · 1 capstone project · 32 hours
Every batch includes guided labs, case studies and a capstone — applied to real-world problems.
Everything your programme needs, in one connected place.
No install. No config. Just build.
Not email, Zoom links and scattered PDFs. From the moment you enroll, your whole programme lives in one place.
From enrolment to certificate, every step is laid out in order — you always know where you are and exactly what happens next. Nothing lost between tools.
Pre-configured cloud labs. Spin one up in seconds, build on real infrastructure, break things and learn — nothing to install.
Write and run code right in the browser, get AI feedback as you go, and practice against problems that mirror the job — no local setup, ever.
A pre-assessment sets your baseline; a post-assessment proves your uplift. Real, measurable growth — for you, and for the employer looking at your record.
A capstone graded by AI and validated by your trainer — detailed feedback in hours, not weeks, on work that looks like what teams actually ship.
Today's session, pending tasks, resources and progress — organised in one place so you focus on learning, not on chasing links and files.
One verifiable link — the project you built, your before → after scores, the skills you proved. Shareable with any employer.
Earn your Greater Insights Python for Data Analysis certificate — ready to share on LinkedIn.
Verified Google reviews — Greater Insights company-wide
Python for Data Analysis is a live, instructor-led programme that takes learners from their first line of Python code to a complete, portfolio-ready data analysis project. Every session combines explanation with hands-on work inside JupyterLab notebooks, where learners run real code against real datasets. The curriculum moves through Python fundamentals, NumPy, pandas, Matplotlib, seaborn, and scikit-learn before culminating in a guided capstone project in which participants load, clean, analyse, and present findings from an authentic dataset.
Python has become the dominant language for data analysis because it combines readable syntax with an ecosystem purpose-built for working with data. NumPy provides the fast array arithmetic that underpins almost everything else; pandas adds the labelled rows-and-columns structure that analysts already recognise from spreadsheets, together with powerful tools for cleaning, merging, grouping, and reshaping tabular data; Matplotlib and seaborn turn those tables into publication-quality charts; and scikit-learn brings statistical modelling into reach without requiring a mathematics background. Connecting these libraries inside JupyterLab produces a transparent, reproducible workflow — every transformation is visible, every chart is regenerable, and the entire analysis can be shared, audited, or automated in ways that manual spreadsheet work simply cannot match.
Demand for analysts who can move beyond spreadsheets and write repeatable, scalable Python code is accelerating across every sector. Organisations now collect more data than manual tools can realistically handle, and employers routinely prioritise candidates who can query a SQLite database into a pandas DataFrame, automate cleaning routines, and produce seaborn visualisations alongside written recommendations — all in a single JupyterLab notebook. The combination of NumPy numerical computing, pandas data wrangling, and scikit-learn regression gives analysts a toolkit that commands attention at interview, supports genuine self-service analytics, and shortens the distance between raw data and a credible business decision.
By the end you will have built — not just studied — the core systems of the field:
Completing this programme builds directly applicable skills for roles including Data Analyst, Junior Data Scientist, Business Analyst, Reporting Analyst, Operations Analyst, and Financial Analyst. Across industries — retail, finance, healthcare, logistics, and technology — employers are actively seeking practitioners who can write pandas pipelines, query SQLite databases, produce seaborn and Matplotlib visualisations, and communicate findings clearly. The ability to conduct a full exploratory data analysis routine inside JupyterLab, measure correlation, and fit a scikit-learn regression model positions graduates as credible candidates for both specialist and generalist analyst positions. As organisations invest further in data-driven decision-making, analysts who combine Python fluency with sound business communication are among the most consistently sought-after hires in the market.
Across Bangalore's enterprise consulting firms, e-commerce product teams, and fintech companies, the analytical problems you are expected to solve with Python are immediate and consequential. You might build pipelines that aggregate transaction data across millions of daily orders, detect anomalies in payment flows before they reach customers, or construct recommendation models that influence purchasing decisions at scale. In applied AI and data engineering teams, you write the Python logic that transforms raw event logs into structured datasets powering business dashboards, forecasting models, and customer segmentation systems. At SaaS companies operating at scale, your ability to wrangle messy, high-volume data using pandas, NumPy, and scikit-learn determines whether product and growth teams get reliable insights or none at all. These are not exploratory skills for Bangalore employers — they are baseline requirements embedded in job descriptions across the city.
Employers in Bangalore that recruit for Python for Data Analysis skills include Flipkart and Amazon, whose product and data engineering teams employ analysts and engineers who work directly with large-scale behavioural and transactional datasets. Enterprise consulting firms such as Infosys, Wipro, and Accenture hire professionals who apply these skills across client engagements spanning financial services, retail, and supply chain analytics.
Demand for Python for Data Analysis roles in Bangalore grows year on year, reflecting the city's expanding base of data-intensive teams across every major sector. Live online instructor-led batches run in IST, so you can join from anywhere in Bangalore without relocating or commuting.
