Data Science & Analytics
Turn raw data into insights, forecasts and decisions that move a business.
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What is Data Science & Analytics?
Data scientists and analysts collect, clean and analyse data to answer questions like 'Why did sales drop?', 'Which customers will leave?' or 'What should we stock next month?'. Analysts focus on SQL, dashboards and clear business storytelling; data scientists add statistics, experiments (A/B tests) and predictive models. Both roles exist in almost every industry — banking, telecom, e-commerce, health, garments, NGOs and government.
Why it's trending now
- The US Bureau of Labor Statistics projects data scientist jobs to grow 35% from 2025 to 2035 — one of the fastest rates of any occupation.
- Big data specialists top the WEF Future of Jobs Report 2025 list of fastest-growing jobs.
- AI tools made analysis faster, so companies now expect analysts to answer more questions, automate reporting and work with AI-generated insights — raising demand for people who can judge whether the numbers are right.
- In Bangladesh, telecom operators, MFS providers (bKash, Nagad), banks and e-commerce firms run data teams for churn, credit and fraud analytics.
Job roles
Common job titles you'll see in listings, and what each one actually does.
Data Analyst
Writes SQL, builds dashboards and reports, and explains trends to business teams.
Data Scientist
Uses statistics and machine learning to predict outcomes, segment customers and design experiments.
Business Intelligence (BI) Developer
Builds the data models and dashboards (Power BI, Tableau, Looker) that managers use every day.
Product Analyst
Measures how people use an app or website, runs A/B tests and recommends product changes.
Analytics Engineer
Transforms raw warehouse data into clean, tested, documented tables using tools like dbt.
Decision Scientist / Quant Analyst
Applies causal inference, forecasting and optimisation to pricing, supply chain or risk decisions.
Skills from beginner to advanced
Tick skills as you learn them — your progress is saved in this browser.
1Beginner
Start here2Intermediate
Job-ready core3Advanced
Stand outMost-requested languages & tools
What employers mention most often in current job listings for this field.
Languages
Tools & platforms
SQL is the single most requested skill in analytics listings, followed by Python and one BI tool (Power BI is especially common in Bangladesh and South Asia).
30/60/90-day roadmap
About 1–2 hours a day. Tick goals as you complete them.
Days 1–30
SQL and spreadsheets
Milestone: A short report answering a real question with SQL and charts.
Days 31–60
Python and dashboards
Milestone: A live dashboard plus a notebook-based analysis in your portfolio.
Days 61–90
Modelling and storytelling
Milestone: Three polished portfolio projects: one SQL, one dashboard, one predictive model.
Free learning resources
All free to start. Some courses offer paid certificates — auditing is free.
- Google Data Analytics Professional CertificateGoogle / Coursera · Course · coursera.orgFree to audit; financial aid available
- SQLBolt interactive lessonsSQLBolt · Practice · sqlbolt.comFree
- Mode SQL TutorialMode / ThoughtSpot · Course · mode.comFree
- Kaggle Learn: Python, pandas, Data VisualizationKaggle · Practice · kaggle.comFree
- Microsoft Learn — Power BI trainingMicrosoft · Course · learn.microsoft.comFree
- Tableau Free Training VideosTableau · Video · tableau.comFree
- Python for Data Analysis (3rd ed., open access)Wes McKinney · Book · wesmckinney.comFree online
- OpenIntro StatisticsOpenIntro · Book · openintro.orgFree PDF
- Data Analysis with Python certificationfreeCodeCamp · Course · freecodecamp.orgFree
- StrataScratch / LeetCode SQL practiceLeetCode · Practice · leetcode.comFree tier
Portfolio project ideas
Build these in order. Put each one on GitHub with a clear README and a short demo.
Personal expense or mobile-data usage dashboard
easyTrack 2–3 months of your own spending or app usage and build a dashboard with monthly trends and insights.
Bangladesh public data explorer
easyAnalyse an open dataset (e.g. World Bank indicators for Bangladesh, BBS census tables) and publish a Tableau Public story.
E-commerce SQL analysis
mediumUse a public e-commerce dataset to answer 10 business questions with SQL (retention, top products, cohort revenue).
Telecom churn prediction
mediumBuild a churn model, explain top drivers, and estimate the revenue saved by a targeted retention campaign.
A/B test analysis and experiment platform
hardSimulate or use real experiment data, compute significance and power, handle multiple metrics, and build a reusable analysis report generator.
10 interview questions
Try answering out loud first, then open the short model answer.
1What is the difference between WHERE and HAVING?
WHERE filters rows before grouping; HAVING filters groups after GROUP BY, usually on aggregated values.
2Explain a p-value in plain language.
The probability of seeing results at least as extreme as yours if there were really no effect. A small p-value suggests the observed difference is unlikely to be pure chance.
3How would you design an A/B test?
Define one primary metric and hypothesis, compute required sample size, randomise users, run for a full business cycle, avoid peeking, then check significance and practical impact.
4What is a window function?
A SQL function that computes a value across related rows without collapsing them — e.g. ROW_NUMBER, running totals or moving averages using OVER (PARTITION BY ... ORDER BY ...).
5How do you handle missing data?
First understand why it's missing. Then drop, impute (mean/median/model-based), or flag it, depending on the amount and whether the missingness is random.
6Correlation vs causation?
Correlation means two variables move together; causation means one changes the other. Experiments or causal methods are needed to claim causation.
7How would you explain a model to a non-technical manager?
Focus on the decision it supports, how accurate it is in business terms, the top drivers, and its limits — avoid jargon and show examples.
8What is overfitting and how do you detect it?
When a model memorises training data and fails on new data. Detect it with a large gap between training and validation scores; fix with simpler models, regularisation or more data.
9Which chart would you use to show change over time?
A line chart for continuous time series; a bar chart for a few discrete periods. Avoid pie charts for trends.
10Revenue dropped 10% last week. How do you investigate?
Check data quality first, then break the metric down by segment (region, channel, product, new vs returning), compare to seasonality, and look for product, pricing or external changes.
Resume bullet templates
Replace the [brackets] with your real numbers. Quantified results stand out to recruiters.
Built a [Power BI/Tableau] dashboard used by [N] managers weekly, replacing [X] hours of manual reporting.
Analysed [N] million transactions with SQL to identify [insight], leading to [business result, e.g. 8% revenue lift].
Developed a churn model with [X] AUC that targeted retention offers to the top [Y]% at-risk customers, saving an estimated [amount].
Designed and analysed [N] A/B tests, informing product changes that increased conversion by [X]%.
Automated a monthly KPI pipeline in Python, cutting report preparation from [X] days to [Y] hours with zero manual errors.
Formula: action verb + what you built + tools + measurable result. Only claim numbers you can explain in an interview.
Salary & demand
Ranges vary by company, city, experience and negotiation. Always check the linked sources for the latest figures.
Bangladesh
Tk 17,193 – Tk 62,823
gross per month
Paylab Data Scientist range (10th–90th percentile, gross monthly). Business Intelligence Specialists are listed at BDT 16,485–57,681. Senior data scientists at large telecoms, banks and MNCs can earn above this range.
Demand
High for data analysts and BI roles in telecom, banking, MFS, e-commerce, pharma and development organisations. Power BI + SQL is the most common combination in local postings.
Remote (from Bangladesh)
$25,000 – $70,000
per year
Estimate. Arc reports an average of $42,875 per year for Bangladesh-based remote data scientists; remote analyst roles usually pay less, senior data scientists more.
Demand
High but competitive. Analysts with strong English communication, SQL and a domain specialty (e-commerce, marketing, finance) win remote contracts most easily.
Global benchmark
$120,230
US median, Data Scientists (BLS, 2025)
Data analysts typically earn less than data scientists; product and decision scientists at tech firms often earn more. Projected growth: +35% (2025–35, much faster than average).
Demand
Very high. BLS projects 35% growth for data scientists, about 24,800 openings per year in the US alone.
How to read these numbers: Bangladesh ranges are Paylab's 10th–90th percentile gross monthly salaries (top product companies often pay more). Remote ranges are CareerPulse estimates informed by Arc's Bangladesh remote-developer data. Global figures are US medians from the Bureau of Labor Statistics (May 2025 data, 2025–35 projections) unless stated otherwise.
Start today checklist
Small, concrete actions you can finish today. Momentum beats perfect plans.