{"id":3959,"date":"2026-10-09T02:00:57","date_gmt":"2026-10-08T20:30:57","guid":{"rendered":"https:\/\/www.skillovilla.com\/blogs\/data-analytics-course-syllabus"},"modified":"2026-10-09T02:01:23","modified_gmt":"2026-10-08T20:31:23","slug":"data-analytics-course-syllabus","status":"publish","type":"post","link":"https:\/\/www.skillovilla.com\/blogs\/data-analytics-course-syllabus","title":{"rendered":"Data Analytics Course Syllabus (2026): Module by Module, With Weeks"},"content":{"rendered":"<p>A data analytics course syllabus worth paying for has six modules: Excel, SQL, statistics, Python, a BI tool such as Power BI, and a project block where you use all five on a real dataset. A full-time-equivalent track covers them in 4 to 5 months, about 20 weeks. Anything shorter is a tools course, and anything that skips the project block is a certificate you will struggle to defend in an interview.<\/p>\n<p>This page sets out what belongs inside each module, roughly how many weeks it needs, what you should have built by the end of it, and how to spot a syllabus that has padded its contents list.<\/p>\n<h2>The syllabus at a glance<\/h2>\n<p>Course pages rarely publish a week-by-week split, so the weeks below are how the 20 weeks of a 4 to 5 month track fall in practice, not a figure lifted off a brochure. Use them to judge whether a syllabus you are shown is balanced.<\/p>\n<table>\n<thead>\n<tr>\n<th>Module<\/th>\n<th>Weeks<\/th>\n<th>What it covers<\/th>\n<th>What you should have at the end<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Excel<\/td>\n<td>3 to 4<\/td>\n<td>Cleaning, lookups, pivot tables, conditional formatting, date handling<\/td>\n<td>A cleaned workbook with a one-sheet summary a manager could read<\/td>\n<\/tr>\n<tr>\n<td>SQL<\/td>\n<td>4 to 5<\/td>\n<td>SELECT and filters, aggregates, joins, subqueries, CTEs, window functions<\/td>\n<td>Forty queries written against a multi-table database<\/td>\n<\/tr>\n<tr>\n<td>Statistics<\/td>\n<td>2 to 3<\/td>\n<td>Descriptive statistics, distributions, correlation versus causation, hypothesis testing<\/td>\n<td>A written conclusion with its limits stated, not just charts<\/td>\n<\/tr>\n<tr>\n<td>Python<\/td>\n<td>3 to 4<\/td>\n<td>Syntax basics, pandas, cleaning, grouping, merging, plotting<\/td>\n<td>A notebook that takes a messy CSV to an answer end to end<\/td>\n<\/tr>\n<tr>\n<td>Power BI<\/td>\n<td>2 to 3<\/td>\n<td>Data model, relationships, DAX measures, visuals, publishing<\/td>\n<td>One dashboard that answers a stated business question<\/td>\n<\/tr>\n<tr>\n<td>Projects and case studies<\/td>\n<td>3 to 4<\/td>\n<td>Framing a business problem, choosing the method, presenting the finding<\/td>\n<td>Three portfolio projects and a two-minute spoken explanation of each<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Two things that table is telling you. SQL gets the largest block because it is the most tested skill in Indian analyst interviews at every level. And the project block is a module, not an afterthought bolted onto the last weekend.<\/p>\n<h2>Module 1: Excel, 3 to 4 weeks<\/h2>\n<p>Excel comes first in every serious syllabus for a reason that has nothing to do with difficulty. It is the tool Indian companies still run their reporting on, so it is where you learn the shape of business data fastest, and the ad hoc request that lands at 5pm arrives as a spreadsheet.<\/p>\n<p>A complete Excel module covers:<\/p>\n<ul>\n<li>Cleaning and deduplication, text to columns, and handling the date formats Indian financial-year reporting throws at you<\/li>\n<li>VLOOKUP, INDEX MATCH and XLOOKUP<\/li>\n<li>Pivot tables and pivot charts, including grouping and calculated fields<\/li>\n<li>Conditional formatting and basic data validation<\/li>\n<li>Named ranges and structured references, so a model does not break when rows are added<\/li>\n<\/ul>\n<p>What it should not include at this stage: VBA macros. They are a separate skill and they eat a week you needed for SQL. Our guide to <a href=\"https:\/\/www.skillovilla.com\/blogs\/excel-for-data-analysis\">Excel for data analysis<\/a> walks the formulas themselves, and <a href=\"https:\/\/www.skillovilla.com\/blogs\/pivot-tables-excel\">pivot tables in Excel<\/a> covers the single feature interviewers test most often.<\/p>\n<h2>Module 2: SQL, 4 to 5 weeks<\/h2>\n<p>This is the heart of the syllabus and the module to judge a course on. Ask to see the SQL section before you pay, because a weak one is easy to spot: it stops at joins.<\/p>\n<p>The order that works, and the order a good syllabus follows:<\/p>\n<ul>\n<li>SELECT, WHERE, ORDER BY, and the basics of how a query is actually evaluated<\/li>\n<li>GROUP BY and aggregate functions, then HAVING<\/li>\n<li>INNER, LEFT, RIGHT and FULL joins, plus what a silently dropped row looks like<\/li>\n<li>Subqueries and common table expressions<\/li>\n<li>Window functions: ROW_NUMBER, RANK, LAG and LEAD, and running totals<\/li>\n<li>Query performance basics, indexes, and why SELECT star is a habit worth losing<\/li>\n<\/ul>\n<p>The measure of the module is not topics covered but queries written. Forty is a reasonable minimum, written rather than copied. If you want the reference while you study, our explainers on <a href=\"https:\/\/www.skillovilla.com\/blogs\/sql-joins\">SQL joins<\/a> and <a href=\"https:\/\/www.skillovilla.com\/blogs\/sql-database-types\">SQL database types<\/a> cover two of the areas beginners most often get wrong, and the <a href=\"https:\/\/www.skillovilla.com\/blogs\/sql-interview-questions\">SQL interview questions<\/a> set is the standard to aim at.<\/p>\n<h2>Module 3: Statistics, 2 to 3 weeks<\/h2>\n<p>Statistics is where a syllabus quietly reveals whether it was written for analysts or copied from a data science curriculum. An analyst needs the statistics that stop you drawing a wrong conclusion, not the statistics that build a model.<\/p>\n<p>What belongs in it:<\/p>\n<ul>\n<li>Mean, median, mode, variance and standard deviation, and when the median is the honest number<\/li>\n<li>Distributions, including the normal distribution and why real business data rarely is one<\/li>\n<li>Correlation against causation, with worked examples where the difference costs money<\/li>\n<li>Sampling, sample size and confidence intervals<\/li>\n<li>Hypothesis testing and p-values at a working level, plus A\/B test reading<\/li>\n<\/ul>\n<p>What does not belong: matrix algebra, calculus, or a derivation of anything. If your syllabus opens statistics with linear algebra, it was written for a machine learning course.<\/p>\n<h2>Module 4: Python, 3 to 4 weeks<\/h2>\n<p>Analyst Python is a narrow slice of the language, and a good syllabus keeps it narrow. You are not being hired as a software developer.<\/p>\n<ul>\n<li>Python basics: variables, types, control flow, functions, and list and dictionary handling<\/li>\n<li>pandas: reading files, indexing, filtering, groupby, merge and concat, and handling missing values<\/li>\n<li>NumPy at the level pandas needs<\/li>\n<li>Plotting with matplotlib or seaborn, enough to explore a dataset before you visualise it properly<\/li>\n<li>Working in Jupyter, and writing a notebook someone else can follow<\/li>\n<\/ul>\n<p>Warning sign: a syllabus that spends its Python weeks on object-oriented programming, file handling and recursion. That is an introduction to programming, not analytics. Our <a href=\"https:\/\/www.skillovilla.com\/blogs\/python-for-data-analysis\">Python for data analysis<\/a> guide covers the analyst-relevant slice, and SkilloVilla sells it as a standalone course, <a href=\"https:\/\/www.skillovilla.com\/courses\/data-analytics-and-statistics-using-python\">Data Analytics and Statistics using Python<\/a> at \u20b938,110, if Python is the only gap you have.<\/p>\n<h2>Module 5: Power BI, 2 to 3 weeks<\/h2>\n<p>Pick one BI tool and go deep. Power BI appears more often than Tableau in Indian job postings, particularly at IT services companies and global capability centres, and the concepts carry across if you switch later.<\/p>\n<ul>\n<li>Connecting to sources and shaping data in Power Query<\/li>\n<li>The data model: relationships, star schema, and why a flat table stops working<\/li>\n<li>DAX: calculated columns against measures, CALCULATE, and time intelligence<\/li>\n<li>Visual choice, and building a report page that answers one question rather than showing twelve charts<\/li>\n<li>Publishing to the service, refresh schedules, and sharing<\/li>\n<\/ul>\n<p>A dashboard module that teaches only chart types has skipped the part interviews test, which is the data model. Our <a href=\"https:\/\/www.skillovilla.com\/blogs\/power-bi-tutorial\">Power BI tutorial<\/a> is the walkthrough, and the <a href=\"https:\/\/www.skillovilla.com\/blogs\/power-bi-interview-questions\">Power BI interview questions<\/a> set shows the depth the module has to reach.<\/p>\n<h2>Module 6: Projects and case studies, 3 to 4 weeks<\/h2>\n<p>This is the module that decides whether the other five convert into a job, and it is the first one weak courses cut.<\/p>\n<p>A real project block gives you a business question, a messy dataset, and no instructions. You frame the problem, choose the method, do the work, and present a finding someone could act on. Three finished projects is the working minimum for a portfolio: one SQL-heavy, one dashboard, one end-to-end in Python.<\/p>\n<p>Structured problem solving deserves its own place here, and it is the piece self-study most often misses. Knowing which question to ask of a dataset is a taught skill, and it is what separates an analyst from someone who can operate the tools. Our guide to a <a href=\"https:\/\/www.skillovilla.com\/blogs\/data-analyst-portfolio\">data analyst portfolio<\/a> sets out what a defensible project looks like.<\/p>\n<h2>How a 4 to 5 month track is laid out<\/h2>\n<p>A live track runs the six modules in sequence rather than in parallel, because each one feeds the next: the workbook you clean in Excel becomes the table you query in SQL, which becomes the model behind your dashboard.<\/p>\n<p>SkilloVilla&#8217;s <a href=\"https:\/\/www.skillovilla.com\/tracks\/data-analytics-python\">Data Analytics with Python track<\/a> lists its modules in that order on its own page: Excel Beginner to Advanced, SQL Beginner to Advanced, Structured Problem Solving and Case Studies, Data Visualization in Power BI, and Python Fundamentals. It runs 4 to 5 months of live classes at \u20b971,999, currently \u20b958,999, with 1:1 mentorship from working analysts, real business projects and <a href=\"https:\/\/www.skillovilla.com\/placement-assistance\">placement support<\/a>. Fees last checked September 2026; confirm current numbers with the provider before enrolling. The <a href=\"https:\/\/www.skillovilla.com\/tracks\/data-analytics-and-ai\">Data Analytics and Generative AI track<\/a> adds a Generative AI module on top of the same five and is listed at \u20b984,999, currently \u20b971,999.<\/p>\n<p>Two of those modules are also sold on their own, which is useful if you have a specific gap rather than a full career switch: <a href=\"https:\/\/www.skillovilla.com\/courses\/sql-beginner-to-advanced\">SQL Beginner to Advanced<\/a> and <a href=\"https:\/\/www.skillovilla.com\/courses\/excel-beginner-to-advanced\">Excel Beginner to Advanced<\/a>, both \u20b933,110, at 8 and 7 weeks respectively. Inside each module the teaching is a mix of live sessions, readings, quizzes and practice problems rather than video alone, which matters more than the topic list: a syllabus with no assessment in it cannot tell you whether you have actually learnt the module.<\/p>\n<p>If you are studying on your own instead, our <a href=\"https:\/\/www.skillovilla.com\/blogs\/data-analyst-roadmap\">data analyst roadmap<\/a> runs the same six blocks across six months at 10 to 12 hours a week, with a checkpoint at the end of each.<\/p>\n<h2>How other providers&#8217; syllabi compare<\/h2>\n<p>Curricula in this market look similar on a contents page and differ in what they weight. ExcelR&#8217;s data analyst course lists Excel, MySQL, Tableau and Power BI as its core, with Python, R, business statistics, SAS and ChatGPT described as value adds, over 150+ hours, as listed in August 2026. The Google Data Analytics Certificate on Coursera is built around spreadsheets, SQL, R and Tableau, self-paced, with most learners finishing inside six months at about ten hours a week, as listed in September 2026.<\/p>\n<p>Two patterns worth noticing. R and SAS appear on many Indian syllabi and in very few Indian analyst job postings, so treat them as optional extras rather than reasons to choose a course. And the number of tools listed is a poor quality signal: a syllabus naming nine tools in six months is teaching none of them to interview depth.<\/p>\n<h2>Six checks before you accept a syllabus<\/h2>\n<ol>\n<li>Does SQL get the largest block? If not, ask why.<\/li>\n<li>How many queries, dashboards and notebooks will you have built, not watched?<\/li>\n<li>Is there a project module with unstructured problems, or only guided exercises?<\/li>\n<li>Are the statistics analyst-level, or lifted from a machine learning course?<\/li>\n<li>Is there any assessment, so you know when a module is actually finished?<\/li>\n<li>Does the tool list match Indian job postings, or pad the page?<\/li>\n<\/ol>\n<p>Our <a href=\"https:\/\/www.skillovilla.com\/blogs\/how-to-choose-data-analytics-course\">ten-point checklist for choosing a data analytics course<\/a> covers the commercial side of the same decision, including placement terms and refunds.<\/p>\n<h2>Frequently asked questions<\/h2>\n<h3>What is the syllabus of a data analytics course?<\/h3>\n<p>Six modules: Excel, SQL, statistics, Python, a BI tool such as Power BI, and a project block that uses all of them on real data. A 4 to 5 month track covers them in sequence, with SQL taking the largest share and the project block taking three to four weeks at the end. Courses that skip statistics or the project block are tool training rather than analyst training.<\/p>\n<h3>How many months does a data analytics syllabus take to complete?<\/h3>\n<p>About 4 to 5 months, or roughly 20 weeks, on a structured live track. Studying alone at 10 to 12 hours a week, the same six modules take about six months. At twenty hours a week you can compress it to three or four months; at five hours a week it will take a year, and it is better to know that at the start than to quit in month three.<\/p>\n<h3>Which subject should I start with in a data analytics course?<\/h3>\n<p>Excel, then SQL. Excel teaches you what business data looks like in a fortnight, and SQL is the skill most likely to decide your interview. Starting with Python is the most common self-study mistake, because it feels like the serious choice and leaves you four months in with no business question answered.<\/p>\n<h3>Does a data analytics syllabus include Python?<\/h3>\n<p>Yes, but only a narrow part of it: pandas, cleaning, grouping, merging and plotting, plus enough basic syntax to write readable code. Object-oriented programming, algorithms and app development belong to a software syllabus, not an analytics one. If a course spends more weeks on Python than on SQL, it is aimed at a different job.<\/p>\n<h3>Is statistics compulsory in a data analytics course?<\/h3>\n<p>For an analyst role, the working level is compulsory and the advanced level is not. You need descriptive statistics, distributions, correlation against causation, sampling and hypothesis testing, because those are what stop you presenting a wrong conclusion. Calculus and linear algebra only become relevant if you move towards data science and machine learning later.<\/p>\n<h3>What is the difference between a data analytics and a data science syllabus?<\/h3>\n<p>An analytics syllabus ends where the data science one begins. Analytics covers Excel, SQL, statistics, Python and BI tools to answer business questions about what happened and why. Data science adds probability and statistics in more depth, machine learning, and often deep learning and text analysis, to build models that predict. The first four modules overlap almost entirely, which is why analysts move across later without starting again.<\/p>\n<h2>See the syllabus against a live track<\/h2>\n<p>Put this module list beside the <a href=\"https:\/\/www.skillovilla.com\/tracks\/data-analytics-python\">SkilloVilla Data Analytics with Python track<\/a>, which teaches Excel, SQL, structured problem solving, Power BI and Python across 4 to 5 months of live classes at \u20b971,999, currently \u20b958,999, with 1:1 mentorship from working analysts, real business projects and placement support, and scholarships based on your profile. Book a counselling call and ask to see the module breakdown before you decide.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The full data analytics course syllabus, module by module: Excel, SQL, statistics, Python, Power BI and projects, and the weeks each one needs.<\/p>\n","protected":false},"author":27,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[245],"tags":[],"class_list":["post-3959","post","type-post","status-publish","format-standard","hentry","category-data-analytics-course"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Data Analytics Course Syllabus (2026): Module by Module, With 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