No experience, no coding, no math degree needed. This course was built from the ground up for complete beginners — 60 hours of structured content, 3 real projects, and dedicated placement support to get you your first analytics job.
Yes — and this is one of the most beginner-friendly fields you can enter in tech. Unlike software development where you write complex logic from scratch, data analytics is about asking the right questions and reading the answers from data. The tools are visual, the skills are learnable in months (not years), and most entry-level roles actively want candidates who can think clearly rather than candidates who already know every tool.
Let's address the myths directly:
Myth: You need a Computer Science degree
Reality: Most working analysts come from commerce, arts, or non-engineering backgrounds. Employers care about SQL, Excel, and critical thinking — not your degree subject.
Myth: You need to be good at math
Reality: You need basic arithmetic and the ability to understand ratios and percentages. The course teaches you statistics in plain English — no calculus, no advanced algebra.
Myth: Learning takes years
Reality: The core stack — SQL, Excel, Power BI, Python basics — can be learned in 8 focused weeks. Thousands of our students went from zero to their first analytics job in under 3 months.
Myth: Coding is required
Reality: SQL is not traditional coding. Power BI is drag-and-drop. Excel needs no code at all. The Python you need for entry-level analytics is basic and readable — more like writing sentences than writing programs.
SQL is the language you use to ask databases questions. You start with simple SELECT statements and build up to JOINs, aggregations, and subqueries. By the end, you can pull any data a business needs — no prior database experience required. This is the single most in-demand skill for every analytics role.
Most beginners have opened Excel before. We take you from basic spreadsheets to VLOOKUP, INDEX-MATCH, pivot tables, and charts. Advanced Excel is used daily in finance, marketing, and operations analytics — and it is the fastest skill to apply immediately in any job.
Power BI is drag-and-drop dashboard building. You connect to data, drag fields onto a canvas, and create interactive charts and reports. No coding at all. You will build 2 real dashboards during the course — the kind hiring managers actually want to see in your portfolio.
You do not learn all of Python — you learn exactly the parts data analysts use: reading data files, cleaning messy columns, summarising with Pandas, and creating charts with Matplotlib. First-timers consistently say our Python module is the least intimidating they have ever encountered.
We cover mean, median, distributions, correlation, and A/B testing — not through proofs, but through real business examples. "Did this marketing campaign actually work?" is a statistics question. You will know how to answer it with data by the end of this module.
8 weeks, 60 hours of content. Every week builds on the previous one — no gaps, no assumed knowledge. You go from opening Excel for the first time to presenting a full data analysis to a mock hiring panel.
What is data analytics? How do analysts actually spend their time? Overview of the tools and the job market. You set up your environment (Excel, Power BI, Python) and run your first SQL query by end of week.
Sorting, filtering, VLOOKUP, IF statements, and pivot tables. You work with a messy real-world sales dataset — cleaning dates, removing duplicates, standardising formats. This is 80% of what a junior analyst does daily.
SELECT, WHERE, GROUP BY, ORDER BY, and aggregate functions. You query an e-commerce database to answer business questions like "which product category has the highest return rate?" No prior database experience needed.
INNER JOIN, LEFT JOIN, subqueries, CTEs, and window functions. You combine multiple tables to build a customer purchase history report — the classic SQL interview problem, solved step by step.
Connecting to data, creating visuals, building calculated columns with DAX basics. Project: build a sales performance dashboard from scratch. By the end you have a portfolio-ready Power BI file.
Descriptive statistics (mean, median, standard deviation), distributions, correlation, and basic hypothesis testing. All taught through business examples — not textbook theory. Enough statistics to pass any analyst interview.
Python setup, Pandas for data manipulation, Matplotlib for charts. You replicate your Week 2 Excel analysis in Python to see the difference. Focus: reading CSV files, groupby, merging DataFrames, and visualising trends.
End-to-end capstone: you receive a raw dataset and a business brief. You clean it, analyse it in SQL and Python, visualise it in Power BI, and present your findings. Mock interview session with mentor feedback on your answers and portfolio.
8 weeks from today you could be interviewing for your first analyst role.
SQL · Excel · Power BI · Python · Statistics — zero prerequisites needed.
You graduate with three end-to-end projects on GitHub and in Power BI — each based on real business data. These are what you show hiring managers instead of a certificate.
Our placement team reviews your LinkedIn profile and resume. We know exactly what recruiters at analytics-heavy companies look for and help you speak their language.
Mock SQL interviews, business case practice, and walkthrough sessions for Power BI portfolio questions. We cover the 50 most common data analyst interview questions asked by Indian companies.
Access to our hiring partner network, job alerts, and a community of 4,500+ alumni who share referrals and openings. Your job search is not something you do alone.
No advanced math required. You need to be comfortable with percentages, ratios, and basic arithmetic — nothing beyond Class 10 level. Statistics is taught from scratch in the course and focuses on interpretation, not calculation.
Zero coding experience needed. SQL looks nothing like traditional programming — it reads like English sentences. Python is introduced in Week 7 and only covers the 20% of features that analysts actually use daily. Students with no coding background pass it consistently.
Any Windows or Mac laptop from the last 6–7 years works. You need 4 GB RAM minimum, 8 GB preferred. All tools have free versions and we provide step-by-step installation guides. Power BI Desktop is Windows-only; Mac users use Power BI via browser.
We recommend 8–10 hours per week. At that pace you finish in 8 weeks and are job-ready in 90 days. If you can only commit 5 hours per week, plan for 12–14 weeks. The course is self-paced — there are no live attendance requirements.
"I was terrified of coding and thought data analytics was not for me. I enrolled anyway because the price was low enough to just try. By Week 3 I was writing SQL queries and actually enjoying it. Landed my first analyst role 11 weeks after completing the course."
"I had no idea what data analytics even meant when I started. The course explained everything from scratch — why each tool exists, when to use it, and how to present findings to a non-technical manager. Got placed within 3 months of finishing."
"I knew some Excel from my engineering job but nothing else. The SQL and Power BI modules completely changed how I approach problems at work. My manager noticed before I even finished the course and moved me into an analytics role internally."
Every concept assumes you are starting from scratch. We never say "as you already know…". Each tool is introduced with a real business problem first, then the technical concept, then hands-on practice. You always understand the why before learning the how.
Your instructors are working data analysts and analytics leads — not academics. They teach you what actually happens on the job, not the textbook version. Our lead instructor has worked at companies including Flipkart, a Series B SaaS startup, and a top-4 consulting firm.
You get access to a Discord community where other students are at various stages of the same journey. Ask questions at 11 PM before a submission. Share your first dashboard. Get feedback on your resume. You are never learning alone.
We deliberately cover less and cover it well. The curriculum is curated to include only what entry-level analysts actually need — not every advanced feature of every tool. This keeps the course manageable for beginners and ensures you finish instead of burning out.
Each project submission is reviewed by a mentor within 48 hours. You receive specific written feedback on what you did well, what to improve, and how a real analyst would have approached the problem differently. This feedback loop accelerates learning faster than any video course.
No. You do not need any prior math or statistics knowledge to start this course. We teach statistics practically — what a metric means, why it matters, and how to use it — not through formulas or theory. By the time you finish the course, you will understand the statistics that actually come up in analyst interviews and on the job, without needing a math degree.
Absolutely not. This course is specifically designed for people with zero coding background. We start Python from the very first line of code, explain every concept in plain language, and focus only on the Python skills that data analysts actually use. Most students write their first working data analysis script within the first two weeks.
Any laptop or desktop manufactured in the last 6–7 years will work fine — Windows, Mac, or Linux. You need at least 4 GB of RAM (8 GB preferred) and around 5 GB of free disk space for the tools. All software used in the course (Excel, Power BI Desktop, Python via Anaconda) is either free or has a free version. We provide setup guides for every tool.
We recommend 8–10 hours per week for the best results — roughly 1–1.5 hours per day. At this pace, you will complete the full 60-hour curriculum in 8 weeks and be job-ready in about 90 days when you include project work and mock interviews. If you can only commit 5 hours a week, expect 12–14 weeks to finish. The course is self-paced, so you go as fast or slow as you need.
Companies hire based on skills, not certificates. Our placement support focuses on building a real project portfolio (3 end-to-end projects), optimising your LinkedIn profile, and preparing you for the exact SQL and case-study interviews used by companies like Flipkart, Amazon, Swiggy, and analytics firms. 4,500+ students have gone through this course and landed analyst roles — that track record matters far more than a certificate name.
YouTube videos teach tools in isolation. This course teaches you how to think like an analyst — how to take a business problem, figure out what data you need, clean it, analyse it, and present the answer. You also get structured week-by-week learning, real projects with datasets, mentor feedback, a private community, and placement support including resume reviews and mock interviews. Free content cannot replicate that end-to-end learning experience.
SQL, Excel, Power BI, Python, Statistics — structured 8-week program. 4,500+ students. 4.8/5 rating. Zero prerequisites.
Start Learning — ₹1499One-time payment · Lifetime access · Placement support included
Start Learning Data Analytics
2,400+ students · Lifetime access