{"id":3704,"date":"2026-10-29T10:39:36","date_gmt":"2026-10-29T07:39:36","guid":{"rendered":"https:\/\/niteducation.com\/resources\/?p=3704"},"modified":"2026-10-29T10:39:39","modified_gmt":"2026-10-29T07:39:39","slug":"data-analyst-projects-for-beginners-kenya","status":"publish","type":"post","link":"https:\/\/niteducation.com\/resources\/data-analyst-projects-for-beginners-kenya\/","title":{"rendered":"7 Data Analyst Projects for Beginners in Kenya (2026)"},"content":{"rendered":"\n<article aria-labelledby=\"nit-article-title\" class=\"nit-article nit-article--cyber nit-article--software nit-article--pathway-guide nit-article--c-plus-degree\" itemscope itemtype=\"https:\/\/schema.org\/Article\">\n<meta itemprop=\"datePublished\" content=\"2026-10-29\">\n<meta itemprop=\"dateModified\" content=\"2026-10-29\">\n<meta itemprop=\"inLanguage\" content=\"en-KE\">\n<meta itemprop=\"author\" content=\"Newton Institute of Technology Editorial Team\">\n<div aria-labelledby=\"nit-article-title\" class=\"nit-pg-hero\">\n  <div>\n    <p class=\"nit-pg-kicker\">Data Science &amp; Analytics \u00b7 Beginner Projects \u00b7 Kenya<\/p>\n    <p class=\"nit-hero__title\" id=\"nit-article-title\" itemprop=\"headline\">Data Analyst Projects for Beginners in Kenya: 7 Step-by-Step Projects<\/p>\n    <p class=\"nit-pg-hero__line\">Ask <i>\u2192<\/i> Check <i>\u2192<\/i> Analyse <i>\u2192<\/i> Explain<\/p>\n    <p class=\"nit-pg-hero__summary\" itemprop=\"description\">Build seven practical Data Analyst projects with Excel, SQL, Power BI, Python and real Kenyan public data. Each project shows what to analyse, what to check and what evidence to finish with.<\/p>\n    <div class=\"nit-pg-hero__actions\"><a class=\"nit-pg-button nit-pg-button--gold\" href=\"#projects\">See the 7 projects<\/a><a class=\"nit-pg-button nit-pg-button--outline\" href=\"#nit-register\">Data Science &amp; Analytics course<\/a><\/div>\n  <\/div>\n  <div class=\"nit-code-window\" aria-label=\"Project reasoning example\"><div class=\"nit-code-window__bar\"><span class=\"nit-code-window__dot\"><\/span><span class=\"nit-code-window__dot\"><\/span><span class=\"nit-code-window__dot\"><\/span><span class=\"nit-code-window__file\">analyst-project.txt<\/span><\/div><div class=\"nit-code-window__body\"><span class=\"nit-code-line\"><span class=\"nit-code-comment\"># question \u2192 evidence<\/span><\/span><span class=\"nit-code-line\"><span class=\"nit-code-keyword\">ask<\/span> = <span class=\"nit-code-string\">&#8220;clear question&#8221;<\/span><\/span><span class=\"nit-code-line\"><span class=\"nit-code-keyword\">check<\/span> = <span class=\"nit-code-string\">&#8220;trust the data?&#8221;<\/span><\/span><span class=\"nit-code-line\"><span class=\"nit-code-keyword\">analyse<\/span> = <span class=\"nit-code-string\">&#8220;right tool&#8221;<\/span><\/span><span class=\"nit-code-line\"><span class=\"nit-code-keyword\">prove<\/span> = <span class=\"nit-code-string\">&#8220;findings + limits&#8221;<\/span><\/span><\/div><\/div>\n  <div aria-label=\"Article summary\" class=\"nit-pg-hero__meta\"><p><span>Projects<\/span>7 step-by-step<\/p><p><span>Tools<\/span>Excel \u00b7 SQL \u00b7 Power BI \u00b7 Python<\/p><p><span>Level<\/span>Beginner \u2192 intermediate<\/p><p><span>Local data<\/span>KNBS \u00b7 procurement<\/p><\/div>\n<\/div>\n<div class=\"nit-article__body\">\n<section class=\"nit-quick-answer\" aria-labelledby=\"quick-answer-title\"><div class=\"nit-quick-answer__head\"><span class=\"nit-quick-answer__mark\">\u2713<\/span><div><p class=\"nit-quick-answer__label\">Quick answer<\/p><h2 id=\"quick-answer-title\">Which Data Analyst projects should a beginner build?<\/h2><\/div><\/div><div class=\"nit-quick-answer__body\"><p>Start with one small project and finish it properly. A strong beginner sequence is <strong>Excel \u2192 SQL \u2192 Power BI \u2192 Python\/data quality \u2192 one end-to-end capstone<\/strong>. The goal is not to collect dashboards; it is to show that you can move from a clear question to a checked, defensible answer.<\/p><ul class=\"nit-quick-answer__signals\"><li><strong>Begin with Excel<\/strong> if this is your first analytical project.<\/li><li><strong>Add SQL and Power BI<\/strong> to show database and reporting skills.<\/li><li><strong>Use Kenyan public data<\/strong> to demonstrate sourcing, interpretation and responsible evidence.<\/li><li><strong>Finish with a capstone<\/strong> that combines several tools without hiding the reasoning.<\/li><\/ul><\/div><\/section>\n<nav class=\"nit-toc\" aria-label=\"Article contents\"><h2>On this page<\/h2><ol><li><a href=\"#projects\">The 7 projects at a glance<\/a><\/li><li><a href=\"#project-1\">1. Excel retail sales<\/a><\/li><li><a href=\"#project-2\">2. SQL customer orders<\/a><\/li><li><a href=\"#project-3\">3. Power BI branch report<\/a><\/li><li><a href=\"#project-4\">4. Kenya inflation analysis<\/a><\/li><li><a href=\"#project-5\">5. Data-quality audit<\/a><\/li><li><a href=\"#project-6\">6. Kenya procurement analysis<\/a><\/li><li><a href=\"#project-7\">7. End-to-end capstone<\/a><\/li><li><a href=\"#portfolio-audit\">Project audit and interview test<\/a><\/li><li><a href=\"#datasets\">Kenyan datasets<\/a><\/li><li><a href=\"#faq\">Frequently asked questions<\/a><\/li><li><a href=\"#nit-register\">Study at NIT<\/a><\/li><\/ol><\/nav>\n<section class=\"nit-section-panel\" id=\"projects\"><h2>The 7 projects at a glance<\/h2><p>You do not need to complete all seven immediately. Choose a project that adds a skill you cannot yet demonstrate, complete it, then move to the next one.<\/p><div class=\"nit-table-wrap\"><table class=\"nit-table\"><thead><tr><th>#<\/th><th>Project<\/th><th>Main tool<\/th><th>Level<\/th><th>Finish with<\/th><\/tr><\/thead><tbody><tr><td>1<\/td><td>Retail sales and gross-profit analysis<\/td><td>Excel<\/td><td>Beginner<\/td><td>Workbook + PivotTables + report<\/td><\/tr><tr><td>2<\/td><td>Customer and order analysis<\/td><td>SQL<\/td><td>Beginner<\/td><td>SQL script + result tables<\/td><\/tr><tr><td>3<\/td><td>Branch-performance report<\/td><td>Power BI<\/td><td>Beginner\u2013Intermediate<\/td><td>Data model + Power BI report<\/td><\/tr><tr><td>4<\/td><td>Kenya inflation analysis<\/td><td>Excel \/ Python \/ Power BI<\/td><td>Beginner\u2013Intermediate<\/td><td>Official-data trend analysis<\/td><\/tr><tr><td>5<\/td><td>Data-quality audit<\/td><td>Python \/ Excel<\/td><td>Intermediate<\/td><td>Audit report + cleaned dataset<\/td><\/tr><tr><td>6<\/td><td>Kenya public-procurement analysis<\/td><td>SQL \/ Excel \/ Power BI<\/td><td>Intermediate<\/td><td>Public-data case study<\/td><\/tr><tr><td>7<\/td><td>End-to-end business capstone<\/td><td>Multiple tools<\/td><td>Intermediate<\/td><td>Complete portfolio case study<\/td><\/tr><\/tbody><\/table><\/div><p class=\"nit-table-note\">On a small screen, swipe the table sideways to view every column.<\/p><\/section>\n<section class=\"nit-section-panel\"><h2>Use one analytical habit in every project<\/h2><p>The software changes, but the reasoning should remain disciplined. Use this sequence whenever you work with a new dataset.<\/p>\n<div class=\"nit-pg-reasons\" aria-label=\"Reusable Data Analyst project workflow\"><div><span>01<\/span><h3>Define the question<\/h3><p>Start with a question a person could act on\u2014not simply \u201canalyse this CSV.\u201d<\/p><\/div><div><span>02<\/span><h3>Understand the grain<\/h3><p>Know what one row represents before you group, join or count anything.<\/p><\/div><div><span>03<\/span><h3>Inspect and clean<\/h3><p>Check missing values, duplicates, dates, categories, types and unusual records before analysis.<\/p><\/div><div><span>04<\/span><h3>Analyse and verify<\/h3><p>Use the simplest suitable method, then independently check important totals or outputs.<\/p><\/div><div><span>05<\/span><h3>Explain the evidence<\/h3><p>Write the finding in ordinary language and distinguish what the data shows from what you infer.<\/p><\/div><div><span>06<\/span><h3>State the limit<\/h3><p>Say what the available data cannot establish and what should be investigated next.<\/p><\/div><\/div>\n<div class=\"nit-callout nit-callout--success\"><p class=\"nit-callout__title\">Why this matters<\/p><p>Current Kenyan entry-level analytical work can involve questions from sales, operations and finance, plus SQL, Python\/pandas, visualisation and careful checking of results before they are shared. Academic or personal projects can also provide evidence of hands-on experience for an early-career applicant.<sup><a href=\"#ref-1\" aria-label=\"Reference 1\">[1]<\/a><\/sup><sup><a href=\"#ref-2\" aria-label=\"Reference 2\">[2]<\/a><\/sup><\/p><\/div>\n<\/section>\n<section class=\"nit-section-panel\" id=\"project-1\"><h2>Project 1: Analyse retail sales in Excel<\/h2><div class=\"nit-action-strip\"><p><strong>Beginner \u00b7 Excel<\/strong> \u2014 Turn transaction rows into revenue, gross-profit and branch insights.<\/p><\/div><p>Imagine a small retailer exports the following fictional records. Your question is: <strong>Which products and branches are contributing most to revenue and gross profit?<\/strong><\/p><div class=\"nit-table-wrap\"><table class=\"nit-table\"><thead><tr><th>Date<\/th><th>Branch<\/th><th>Product<\/th><th>Category<\/th><th>Quantity<\/th><th>Unit price<\/th><th>Unit cost<\/th><\/tr><\/thead><tbody><tr><td>03\/01\/2026<\/td><td>Migori<\/td><td>Product A<\/td><td>Household<\/td><td>3<\/td><td>KSh 1,200<\/td><td>KSh 850<\/td><\/tr><tr><td>03\/01\/2026<\/td><td>Kisii<\/td><td>Product B<\/td><td>Electronics<\/td><td>1<\/td><td>KSh 4,500<\/td><td>KSh 3,600<\/td><\/tr><tr><td>04\/01\/2026<\/td><td>Migori<\/td><td>Product C<\/td><td>Household<\/td><td>5<\/td><td>KSh 650<\/td><td>KSh 400<\/td><\/tr><\/tbody><\/table><\/div><p class=\"nit-table-note\">Training data: fictional example created for this guide.<\/p>\n<div class=\"nit-pg-reasons\" aria-label=\"Excel retail-sales workflow\"><div><span>01<\/span><h3>Inspect the records<\/h3><p>Check dates, numeric quantities, consistent branch names, missing prices, duplicate rows and unusual negative quantities.<\/p><\/div><div><span>02<\/span><h3>Calculate revenue<\/h3><p>Use <strong>Revenue = Quantity \u00d7 Unit Price<\/strong>. Product A: 3 \u00d7 KSh 1,200 = <strong>KSh 3,600<\/strong>.<\/p><\/div><div><span>03<\/span><h3>Calculate total cost<\/h3><p>Use <strong>Total Cost = Quantity \u00d7 Unit Cost<\/strong>. Product A: 3 \u00d7 KSh 850 = <strong>KSh 2,550<\/strong>.<\/p><\/div><div><span>04<\/span><h3>Calculate gross profit<\/h3><p>Use <strong>Gross Profit = Revenue \u2212 Total Cost<\/strong>. Product A: KSh 3,600 \u2212 KSh 2,550 = <strong>KSh 1,050<\/strong>.<\/p><\/div><div><span>05<\/span><h3>Build PivotTables<\/h3><p>Compare branch revenue, branch gross profit, category units, monthly performance and products with strong sales but weaker margins.<\/p><\/div><div><span>06<\/span><h3>Present the result<\/h3><p>Show only the measures and charts needed to answer the original question, then write three findings in plain language.<\/p><\/div><\/div>\n<div class=\"nit-callout nit-callout--info\"><p class=\"nit-callout__title\">Finish with<\/p><p>The original data, a cleaned analysis table, formulas, PivotTables, a small report, three written findings and at least one limitation. Example: \u201cMigori recorded higher revenue in the selected period, while Kisii produced the stronger gross-profit percentage.\u201d<\/p><\/div>\n<\/section>\n<section class=\"nit-section-panel\" id=\"project-2\"><h2>Project 2: Analyse customer orders with SQL<\/h2><div class=\"nit-action-strip\"><p><strong>Beginner \u00b7 SQL<\/strong> \u2014 Learn to retrieve and combine information stored across related tables.<\/p><\/div><p>SQL remains a practical skill in current entry-level analytical work in Kenya.<sup><a href=\"#ref-1\" aria-label=\"Reference 1\">[1]<\/a><\/sup><sup><a href=\"#ref-2\" aria-label=\"Reference 2\">[2]<\/a><\/sup> Build four fictional tables: <code>customers<\/code>, <code>orders<\/code>, <code>order_items<\/code> and <code>products<\/code>. Ask: <strong>Which counties generated the highest recorded sales revenue?<\/strong><\/p>\n<div class=\"nit-callout nit-callout--warning\"><p class=\"nit-callout__title\">Understand the grain before you join<\/p><p>One row in <code>orders<\/code> represents one order. One row in <code>order_items<\/code> represents one product line within an order. If an order contains four products, it can appear once in <code>orders<\/code> but four times in <code>order_items<\/code>. Misunderstanding this can create double-counting.<\/p><\/div>\n<div class=\"nit-code-window\" aria-label=\"Basic SQL query examples\"><div class=\"nit-code-window__bar\"><span class=\"nit-code-window__dot\"><\/span><span class=\"nit-code-window__dot\"><\/span><span class=\"nit-code-window__dot\"><\/span><span class=\"nit-code-window__file\">01-start.sql<\/span><\/div><div class=\"nit-code-window__body\"><span class=\"nit-code-line\"><span class=\"nit-code-keyword\">SELECT<\/span> *<\/span><span class=\"nit-code-line\"><span class=\"nit-code-keyword\">FROM<\/span> orders;<\/span><span class=\"nit-code-line\"><\/span><span class=\"nit-code-line\"><span class=\"nit-code-keyword\">SELECT<\/span> *<\/span><span class=\"nit-code-line\"><span class=\"nit-code-keyword\">FROM<\/span> orders<\/span><span class=\"nit-code-line\"><span class=\"nit-code-keyword\">WHERE<\/span> order_date &gt;= <span class=\"nit-code-string\">&#8216;2026-01-01&#8217;<\/span>;<\/span><\/div><\/div>\n<div class=\"nit-code-window\" aria-label=\"SQL revenue by county example\"><div class=\"nit-code-window__bar\"><span class=\"nit-code-window__dot\"><\/span><span class=\"nit-code-window__dot\"><\/span><span class=\"nit-code-window__dot\"><\/span><span class=\"nit-code-window__file\">02-revenue-by-county.sql<\/span><\/div><div class=\"nit-code-window__body\"><span class=\"nit-code-line\"><span class=\"nit-code-keyword\">SELECT<\/span><\/span><span class=\"nit-code-line\">    c.county,<\/span><span class=\"nit-code-line\">    <span class=\"nit-code-name\">SUM<\/span>(<\/span><span class=\"nit-code-line\">        oi.quantity * oi.unit_price<\/span><span class=\"nit-code-line\">    ) <span class=\"nit-code-keyword\">AS<\/span> revenue<\/span><span class=\"nit-code-line\"><span class=\"nit-code-keyword\">FROM<\/span> customers <span class=\"nit-code-keyword\">AS<\/span> c<\/span><span class=\"nit-code-line\"><span class=\"nit-code-keyword\">JOIN<\/span> orders <span class=\"nit-code-keyword\">AS<\/span> o<\/span><span class=\"nit-code-line\">    <span class=\"nit-code-keyword\">ON<\/span> c.customer_id = o.customer_id<\/span><span class=\"nit-code-line\"><span class=\"nit-code-keyword\">JOIN<\/span> order_items <span class=\"nit-code-keyword\">AS<\/span> oi<\/span><span class=\"nit-code-line\">    <span class=\"nit-code-keyword\">ON<\/span> o.order_id = oi.order_id<\/span><span class=\"nit-code-line\"><span class=\"nit-code-keyword\">GROUP BY<\/span> c.county<\/span><span class=\"nit-code-line\"><span class=\"nit-code-keyword\">ORDER BY<\/span> revenue <span class=\"nit-code-keyword\">DESC<\/span>;<\/span><\/div><\/div>\n<p>PostgreSQL documents querying, joins and aggregate functions as core SQL operations.<sup><a href=\"#ref-3\" aria-label=\"Reference 3\">[3]<\/a><\/sup> A query that runs is not automatically a trustworthy answer: check whether cancelled orders are included, whether unit price means the actual selling price, whether order items are duplicated and how missing counties are handled.<\/p>\n<div class=\"nit-pg-reasons\" aria-label=\"SQL extension challenges\"><div><span>01<\/span><h3>Repeat customers<\/h3><p>Count orders by customer and identify customers with more than one valid order.<\/p><\/div><div><span>02<\/span><h3>Category revenue<\/h3><p>Join products to order items and compare revenue by category.<\/p><\/div><div><span>03<\/span><h3>Monthly movement<\/h3><p>Group orders by month and compare totals over time.<\/p><\/div><div><span>04<\/span><h3>Frequency vs value<\/h3><p>Find products that appear often in orders but contribute relatively little revenue.<\/p><\/div><\/div>\n<div class=\"nit-callout nit-callout--info\"><p class=\"nit-callout__title\">Finish with<\/p><p>A small schema diagram, one SQL file containing the important queries, result tables, the checks you performed and written findings. Do not publish dozens of nearly identical queries simply to make the project look large.<\/p><\/div>\n<\/section>\n<section class=\"nit-section-panel\" id=\"project-3\"><h2>Project 3: Build a Power BI branch-performance report<\/h2><div class=\"nit-action-strip\"><p><strong>Beginner\u2013Intermediate \u00b7 Power BI<\/strong> \u2014 Practise data modelling, measures and decision-focused reporting.<\/p><\/div><p>A technical distinction matters: a multi-page artefact created in Power BI Desktop is a <strong>report<\/strong>. Microsoft describes a Power BI <strong>dashboard<\/strong> as a single-page canvas in the Power BI service, and dashboards are not created in Power BI Desktop.<sup><a href=\"#ref-4\" aria-label=\"Reference 4\">[4]<\/a><\/sup><\/p><p>Create Sales, Branch, Product and Date tables. Ask: <strong>Which branches are performing well, and where should management investigate further?<\/strong><\/p>\n<div class=\"nit-pg-reasons\" aria-label=\"Power BI report workflow\"><div><span>01<\/span><h3>Inspect in Power Query<\/h3><p>Check dates, data types, duplicate sales rows, blank keys and inconsistent product categories.<\/p><\/div><div><span>02<\/span><h3>Model the data<\/h3><p>Treat Sales as the fact table and Branch, Product and Date as dimensions describing those sales events.<\/p><\/div><div><span>03<\/span><h3>Build relationships<\/h3><p>Use clear one-to-many relationships such as Date \u2192 Sales, Product \u2192 Sales and Branch \u2192 Sales.<\/p><\/div><div><span>04<\/span><h3>Create measures<\/h3><p>Build Total Revenue, Total Cost, Gross Profit, Gross Margin %, Sales Target and Target Achievement %.<\/p><\/div><div><span>05<\/span><h3>Build the report<\/h3><p>Answer branch revenue, margin, target, product and time questions with a small number of purposeful visuals.<\/p><\/div><div><span>06<\/span><h3>Interpret carefully<\/h3><p>A branch below target is a finding; it is not automatic proof of poor staff performance. Check stock, opening days, target design, demand and data completeness.<\/p><\/div><\/div>\n<p>Microsoft\u2019s modelling guidance explains that dimension tables support filtering and grouping, fact tables hold observations to summarise, and fact tables should maintain a consistent grain.<sup><a href=\"#ref-5\" aria-label=\"Reference 5\">[5]<\/a><\/sup><\/p>\n<div class=\"nit-callout nit-callout--info\"><p class=\"nit-callout__title\">Finish with<\/p><p>A model-view screenshot, the important measures, report screenshots, two or three findings, one data-quality issue and the limitations of the analysis.<\/p><\/div>\n<\/section>\n<section class=\"nit-section-panel\" id=\"project-4\"><h2>Project 4: Analyse Kenya\u2019s inflation data<\/h2><div class=\"nit-action-strip\"><p><strong>Beginner\u2013Intermediate \u00b7 Official Kenyan data<\/strong> \u2014 Practise time-series analysis with an authoritative source.<\/p><\/div><p>The Kenya National Bureau of Statistics publishes official Consumer Price Index and inflation releases.<sup><a href=\"#ref-6\" aria-label=\"Reference 6\">[6]<\/a><\/sup> Ask: <strong>How has Kenya\u2019s Consumer Price Index changed over the selected period?<\/strong><\/p><p>For September 2026, KNBS reported annual inflation of <strong>6.8%<\/strong>. Use the official release and its underlying series to build a longer time sequence rather than analysing one headline number in isolation.<sup><a href=\"#ref-6\" aria-label=\"Reference 6\">[6]<\/a><\/sup><\/p>\n<div class=\"nit-pg-reasons\" aria-label=\"Kenya CPI project workflow\"><div><span>01<\/span><h3>Collect the series<\/h3><p>Record the month, CPI, monthly inflation and annual inflation, together with source, release date, units and definitions.<\/p><\/div><div><span>02<\/span><h3>Check continuity<\/h3><p>Look for missing or duplicated months, inconsistent formats and any revisions to previously published values.<\/p><\/div><div><span>03<\/span><h3>Plot the trend<\/h3><p>Use a line chart so the time order is visually clear.<\/p><\/div><div><span>04<\/span><h3>Compare movements<\/h3><p>Identify larger changes, stable periods and the difference between monthly and annual inflation measures.<\/p><\/div><div><span>05<\/span><h3>Interpret the release<\/h3><p>Use category-level evidence when explaining movements; do not infer a cause from the headline CPI series alone.<\/p><\/div><\/div>\n<p>KNBS also publishes downloadable statistical tables covering areas such as agriculture, labour, finance, public finance, international trade, energy and education, so the same method can be extended to other Kenyan indicators.<sup><a href=\"#ref-7\" aria-label=\"Reference 7\">[7]<\/a><\/sup><\/p>\n<div class=\"nit-callout nit-callout--info\"><p class=\"nit-callout__title\">Finish with<\/p><p>A source table, cleaned dataset, trend chart, calculations, written interpretation, limitations and source notes.<\/p><\/div>\n<\/section>\n<section class=\"nit-section-panel\" id=\"project-5\"><h2>Project 5: Perform a data-quality audit<\/h2><div class=\"nit-action-strip\"><p><strong>Intermediate \u00b7 Python or Excel<\/strong> \u2014 Find data problems before they contaminate the analysis.<\/p><\/div><p>Create a <strong>synthetic<\/strong> school-attendance dataset containing deliberate problems: duplicated rows, blank statuses, inconsistent class labels and invalid dates. Current early-career analytical work can explicitly require careful checking and reliable data handling before results are shared.<sup><a href=\"#ref-1\" aria-label=\"Reference 1\">[1]<\/a><\/sup><sup><a href=\"#ref-2\" aria-label=\"Reference 2\">[2]<\/a><\/sup><\/p><div class=\"nit-table-wrap\"><table class=\"nit-table\"><thead><tr><th>learner_id<\/th><th>class<\/th><th>date<\/th><th>status<\/th><\/tr><\/thead><tbody><tr><td>L001<\/td><td>F2A<\/td><td>2026-02-01<\/td><td>Present<\/td><\/tr><tr><td>L002<\/td><td>form 2A<\/td><td>2026-02-01<\/td><td>P<\/td><\/tr><tr><td>L002<\/td><td>form 2A<\/td><td>2026-02-01<\/td><td>P<\/td><\/tr><tr><td>L003<\/td><td>F2A<\/td><td>invalid<\/td><td>Absent<\/td><\/tr><\/tbody><\/table><\/div><p class=\"nit-table-note\">Synthetic training data only. No real learner records are used.<\/p>\n<div class=\"nit-code-window\" aria-label=\"Python data-quality starting point\"><div class=\"nit-code-window__bar\"><span class=\"nit-code-window__dot\"><\/span><span class=\"nit-code-window__dot\"><\/span><span class=\"nit-code-window__dot\"><\/span><span class=\"nit-code-window__file\">quality_check.py<\/span><\/div><div class=\"nit-code-window__body\"><span class=\"nit-code-line\"><span class=\"nit-code-keyword\">import<\/span> pandas <span class=\"nit-code-keyword\">as<\/span> pd<\/span><span class=\"nit-code-line\"><\/span><span class=\"nit-code-line\">df = pd.read_csv(<span class=\"nit-code-string\">&#8220;attendance.csv&#8221;<\/span>)<\/span><span class=\"nit-code-line\"><\/span><span class=\"nit-code-line\"><span class=\"nit-code-name\">print<\/span>(df.isna().sum())<\/span><span class=\"nit-code-line\"><span class=\"nit-code-name\">print<\/span>(df.duplicated().sum())<\/span><\/div><\/div>\n<p>pandas provides documented methods for identifying missing data and working with tabular information.<sup><a href=\"#ref-8\" aria-label=\"Reference 8\">[8]<\/a><\/sup> Investigate the meaning of a problem before automatically dropping the affected rows.<\/p>\n<div class=\"nit-table-wrap\"><table class=\"nit-table\"><thead><tr><th>Test<\/th><th>Records affected<\/th><th>Action<\/th><\/tr><\/thead><tbody><tr><td>Exact duplicates<\/td><td>17<\/td><td>Investigated<\/td><\/tr><tr><td>Missing attendance status<\/td><td>31<\/td><td>Flagged<\/td><\/tr><tr><td>Invalid dates<\/td><td>4<\/td><td>Sent for correction<\/td><\/tr><tr><td>Inconsistent class labels<\/td><td>52<\/td><td>Standardised after confirming meaning<\/td><\/tr><\/tbody><\/table><\/div>\n<div class=\"nit-callout nit-callout--warning\"><p class=\"nit-callout__title\">Protect personal data<\/p><p>For a public portfolio, use synthetic data, open public data, authorised data or appropriately anonymised data where lawful and suitable. Do not publish real student, employee, patient or customer records merely to demonstrate a project. Kenya\u2019s data-protection principles include lawful and transparent processing, purpose limitation, data minimisation, accuracy and appropriate safeguards.<sup><a href=\"#ref-9\" aria-label=\"Reference 9\">[9]<\/a><\/sup><\/p><\/div>\n<div class=\"nit-callout nit-callout--info\"><p class=\"nit-callout__title\">Finish with<\/p><p>The raw synthetic dataset, audit script or workbook, a quality summary, cleaning log, cleaned version and a short explanation of your decisions.<\/p><\/div>\n<\/section>\n<section class=\"nit-section-panel\" id=\"project-6\"><h2>Project 6: Analyse Kenya\u2019s public-procurement data<\/h2><div class=\"nit-action-strip\"><p><strong>Intermediate \u00b7 Excel, SQL or Power BI<\/strong> \u2014 Work responsibly with a large public administrative dataset.<\/p><\/div><p>Kenya\u2019s Public Procurement Information Portal publishes public procurement information, including contract records.<sup><a href=\"#ref-10\" aria-label=\"Reference 10\">[10]<\/a><\/sup> Kenyan PPRA data is also available through the Open Contracting Data Registry in reusable formats including CSV, Excel and JSON.<sup><a href=\"#ref-11\" aria-label=\"Reference 11\">[11]<\/a><\/sup><\/p><p>Ask: <strong>How are published contract awards distributed by procuring entity, procurement method or time period?<\/strong><\/p>\n<div class=\"nit-pg-reasons\" aria-label=\"Public procurement analysis workflow\"><div><span>01<\/span><h3>Choose a manageable scope<\/h3><p>Use one financial year, category, county or selected group of procuring entities rather than every record at once.<\/p><\/div><div><span>02<\/span><h3>Inspect the export<\/h3><p>Confirm what fields such as procuring entity, supplier, value, method and date mean in the downloaded data.<\/p><\/div><div><span>03<\/span><h3>Audit quality<\/h3><p>Check missing names, inconsistent text, extreme values and duplicated-looking records before aggregation.<\/p><\/div><div><span>04<\/span><h3>Describe the pattern<\/h3><p>Compare award counts by month, procurement-method frequency, entity totals and the distribution of award values.<\/p><\/div><div><span>05<\/span><h3>Investigate anomalies<\/h3><p>Treat unusually large values or concentrations as prompts for verification, not as proof of misconduct.<\/p><\/div><\/div>\n<p>The Open Contracting registry exposes data-quality information for the Kenyan publication, which makes quality checking part of the project rather than something to hide.<sup><a href=\"#ref-11\" aria-label=\"Reference 11\">[11]<\/a><\/sup><\/p>\n<div class=\"nit-callout nit-callout--warning\"><p class=\"nit-callout__title\">Evidence is not an allegation<\/p><p>A defensible statement is: \u201cSupplier X had the highest aggregate published award value in the selected dataset and period.\u201d A high value by itself does not prove fraud or corruption.<\/p><\/div>\n<div class=\"nit-callout nit-callout--info\"><p class=\"nit-callout__title\">Finish with<\/p><p>The source and period, data-quality notes, cleaned data or queries, descriptive analysis, charts, exact definitions, limitations and no unsupported allegations.<\/p><\/div>\n<\/section>\n<section class=\"nit-section-panel\" id=\"project-7\"><h2>Project 7: Build an end-to-end Data Analyst capstone<\/h2><div class=\"nit-action-strip\"><p><strong>Intermediate \u00b7 Multiple tools<\/strong> \u2014 Bring the entire analytical process together in one case study.<\/p><\/div><p>Imagine a fictional Kenyan retailer provides sales transactions, branch information, product costs, sales targets and customer records. Management asks: <strong>Why did gross profit change between Quarter 2 and Quarter 3, and where should the company investigate first?<\/strong><\/p>\n<div class=\"nit-pg-reasons\" aria-label=\"End-to-end capstone workflow\"><div><span>01<\/span><h3>Define the comparison<\/h3><p>Specify Q3 versus Q2 and identify the branches, categories and measures to examine.<\/p><\/div><div><span>02<\/span><h3>Audit the records<\/h3><p>Check unique IDs, missing keys, duplicates, dates, unexpected negative values and inconsistent categories.<\/p><\/div><div><span>03<\/span><h3>Build the analytical dataset<\/h3><p>Use SQL to join only the tables you need, keeping the grain clear.<\/p><\/div><div><span>04<\/span><h3>Separate the measures<\/h3><p>Calculate Revenue, Cost, Gross Profit and Gross Margin. Revenue can rise while margin deteriorates.<\/p><\/div><div><span>05<\/span><h3>Investigate the drivers<\/h3><p>Test whether volume, selling price, cost, one branch, one category or a quality problem explains the movement.<\/p><\/div><div><span>06<\/span><h3>Build the report<\/h3><p>Show the smallest set of visuals needed to answer the management question.<\/p><\/div><div><span>07<\/span><h3>Write the conclusion<\/h3><p>State the evidence, identify where to investigate next and avoid turning an association into a proven cause.<\/p><\/div><div><span>08<\/span><h3>State what remains unknown<\/h3><p>Name missing information\u2014such as supplier contracts or promotional expenditure\u2014that could change the explanation.<\/p><\/div><\/div>\n<div class=\"nit-callout nit-callout--success\"><p class=\"nit-callout__title\">Example conclusion<\/p><p>\u201cGross profit declined between Quarter 2 and Quarter 3 despite relatively stable revenue. Most deterioration occurred in Category B, where recorded unit costs increased while selling prices changed comparatively little. Branch C contributed a substantial share of the decline and should be investigated first.\u201d<\/p><\/div>\n<\/section>\n<section class=\"nit-section-panel\"><h2>Which project should you build first?<\/h2><div class=\"nit-table-wrap\"><table class=\"nit-table\"><thead><tr><th>Your current skill<\/th><th>Start here<\/th><\/tr><\/thead><tbody><tr><td>New to Data Analysis<\/td><td>Project 1 \u2014 Excel<\/td><\/tr><tr><td>Know Excel and basic SQL<\/td><td>Project 2 \u2014 SQL<\/td><\/tr><tr><td>Learning Power BI<\/td><td>Project 3 \u2014 Power BI<\/td><\/tr><tr><td>Want real Kenyan public data<\/td><td>Project 4 \u2014 Inflation<\/td><\/tr><tr><td>Know basic pandas<\/td><td>Project 5 \u2014 Data quality<\/td><\/tr><tr><td>Comfortable with larger public datasets<\/td><td>Project 6 \u2014 Procurement<\/td><\/tr><tr><td>Comfortable with several tools<\/td><td>Project 7 \u2014 Capstone<\/td><\/tr><\/tbody><\/table><\/div><p class=\"nit-table-note\">The sequence is not compulsory. Choose the project that adds evidence you cannot yet show.<\/p><\/section>\n<section class=\"nit-section-panel\" id=\"portfolio-audit\"><h2>Before publishing a project: use this 10-point audit<\/h2><p>Give yourself one point for each \u201cyes\u201d. The score is not a certification; it simply exposes what is missing.<\/p>\n<div class=\"nit-pg-reasons\" aria-label=\"10-point project audit\"><div><span>01<\/span><h3>Clear question?<\/h3><p>Can you state the problem in one sentence?<\/p><\/div><div><span>02<\/span><h3>Source identified?<\/h3><p>Can another person tell where the data came from and whether it is public, authorised or synthetic?<\/p><\/div><div><span>03<\/span><h3>Grain understood?<\/h3><p>Do you know exactly what one row represents?<\/p><\/div><div><span>04<\/span><h3>Quality inspected?<\/h3><p>Did you check missing values, duplicates, categories, dates and unusual records?<\/p><\/div><div><span>05<\/span><h3>Calculations explainable?<\/h3><p>Can you explain every important formula, measure or query?<\/p><\/div><div><span>06<\/span><h3>Totals checked?<\/h3><p>Did you independently verify important numbers instead of trusting one tool blindly?<\/p><\/div><div><span>07<\/span><h3>Visuals purposeful?<\/h3><p>Does each important chart answer a question?<\/p><\/div><div><span>08<\/span><h3>Findings readable?<\/h3><p>Could a non-technical reader understand the main result?<\/p><\/div><div><span>09<\/span><h3>Evidence separated from recommendation?<\/h3><p>Did you distinguish what the data shows from what someone should do next?<\/p><\/div><div><span>10<\/span><h3>Limitations stated?<\/h3><p>Did you explain what the dataset cannot prove?<\/p><\/div><\/div>\n<h3>Can you defend the project in an interview?<\/h3><p>Close the tutorial and answer these questions without reading your notes:<\/p><ol class=\"nit-choice-list\"><li>What question were you trying to answer?<\/li><li>Why did that question matter?<\/li><li>What does one row represent?<\/li><li>Where did the data come from?<\/li><li>What was wrong with the raw data?<\/li><li>What did you change?<\/li><li>Why did you choose that method?<\/li><li>How did you check the result?<\/li><li>What was the most important finding?<\/li><li>What could change your conclusion?<\/li><\/ol>\n<div class=\"nit-callout nit-callout--info\"><p class=\"nit-callout__title\">If you cannot answer these yet<\/p><p>Keep working on the project before adding another one. A portfolio is stronger when the work is yours and you can defend the reasoning.<\/p><\/div>\n<\/section>\n<section class=\"nit-section-panel\" id=\"datasets\"><h2>Where can you find credible Kenyan datasets?<\/h2><p><strong>Kenya National Bureau of Statistics (KNBS):<\/strong> official economic, demographic and social statistics, including downloadable statistical tables.<sup><a href=\"#ref-6\" aria-label=\"Reference 6\">[6]<\/a><\/sup><sup><a href=\"#ref-7\" aria-label=\"Reference 7\">[7]<\/a><\/sup><\/p><p><strong>Kenya National Data Archive (KeNADA):<\/strong> an official catalogue of Kenyan survey and microdata resources; check each study\u2019s access conditions before use.<sup><a href=\"#ref-12\" aria-label=\"Reference 12\">[12]<\/a><\/sup><\/p><p><strong>Public Procurement Information Portal:<\/strong> published public-procurement information suitable for carefully scoped contract and procurement-method analysis.<sup><a href=\"#ref-10\" aria-label=\"Reference 10\">[10]<\/a><\/sup><\/p><p><strong>World Bank Open Data:<\/strong> downloadable Kenyan indicators across economic, social and development subjects.<sup><a href=\"#ref-13\" aria-label=\"Reference 13\">[13]<\/a><\/sup><\/p><p><strong>Energy and Petroleum Regulatory Authority (EPRA):<\/strong> official energy and petroleum statistics that can support energy-trend projects.<sup><a href=\"#ref-14\" aria-label=\"Reference 14\">[14]<\/a><\/sup><\/p>\n<div class=\"nit-callout nit-callout--success\"><p class=\"nit-callout__title\">Synthetic data is also valid for learning<\/p><p>You can create fictional sales, attendance, customer-order or operations data when a real dataset would expose confidential information. Label it clearly as synthetic and never present invented values as official Kenyan statistics.<\/p><\/div>\n<\/section>\n<section class=\"nit-section-panel\"><h2>Common mistakes that weaken beginner projects<\/h2>\n<div class=\"nit-pg-reasons\" aria-label=\"Common project mistakes\"><div><span>01<\/span><h3>A beautiful report with no question<\/h3><p>Start with the problem, not the colours.<\/p><\/div><div><span>02<\/span><h3>A copied tutorial<\/h3><p>Use tutorials to learn, then make independent choices in your own version.<\/p><\/div><div><span>03<\/span><h3>A result you cannot reproduce<\/h3><p>You should be able to explain how every important number was produced.<\/p><\/div><div><span>04<\/span><h3>Too many tools<\/h3><p>Do not add Python or another tool merely because it sounds advanced.<\/p><\/div><div><span>05<\/span><h3>Machine learning for appearance<\/h3><p>A clear SQL analysis can be more relevant to a Data Analyst role than an unexplained model.<\/p><\/div><div><span>06<\/span><h3>Private information published publicly<\/h3><p>Use public, authorised, anonymised or synthetic information instead.<\/p><\/div><div><span>07<\/span><h3>Unsupported accusations<\/h3><p>An anomaly deserves investigation; it is not automatically evidence of wrongdoing.<\/p><\/div><\/div>\n<\/section>\n<section class=\"nit-section-panel\" id=\"faq\"><h2>Frequently asked questions<\/h2><div class=\"nit-faq\"><details><summary>What is the easiest Data Analyst project for a beginner?<\/summary><p>Start with an Excel sales-analysis project. It lets you practise cleaning, formulas, PivotTables, charting and interpretation without first learning programming.<\/p><\/details><details><summary>What SQL project should a beginner build?<\/summary><p>A customer-and-orders database is useful because it teaches filtering, grouping and joins using a structure similar to many business databases.<\/p><\/details><details><summary>What Power BI project is good for beginners?<\/summary><p>Build a branch or sales-performance report using separate Sales, Product, Branch and Date tables. This lets you practise both reporting and basic data modelling.<\/p><\/details><details><summary>Do I need Python before building Data Analyst projects?<\/summary><p>No. Excel, SQL and Power BI can support strong beginner projects. Python becomes especially useful for repeatable cleaning, automation and programmable analysis.<\/p><\/details><details><summary>Do I need a portfolio to become a Data Analyst?<\/summary><p>Not every employer requires a public portfolio. Projects are useful because they give an employer concrete evidence to inspect when you have limited professional experience. A current Nairobi junior Data Analyst internship explicitly recognises academic projects and personal portfolio work as forms of hands-on experience.<sup><a href=\"#ref-2\" aria-label=\"Reference 2\">[2]<\/a><\/sup><\/p><\/details><details><summary>How many Data Analyst projects do I need?<\/summary><p>There is no official number. Build projects that demonstrate different abilities. Three distinct projects you can explain and defend can be more useful than ten nearly identical dashboards.<\/p><\/details><details><summary>Can I use fictional data?<\/summary><p>Yes. Synthetic data is useful for practising business problems without exposing confidential records. Label it clearly as synthetic.<\/p><\/details><details><summary>Should every project have a dashboard?<\/summary><p>No. A SQL case study, data-quality audit or Python notebook can be a strong project without an interactive dashboard.<\/p><\/details><details><summary>Can I use AI to help with my projects?<\/summary><p>Yes, as an assistant. Use it to explain an error, generate synthetic practice data, suggest test cases or clarify syntax, but verify the output and do not publish work you cannot explain.<\/p><\/details><\/div><\/section>\n<section class=\"nit-section-panel\"><h2>The final test: can someone see how you think?<\/h2><p>The dashboard is not the whole project. The code is not the whole project either.<\/p><div class=\"nit-action-strip\"><p><strong>Question \u2192 trustworthy data \u2192 method \u2192 check \u2192 evidence \u2192 next step \u2192 limitation<\/strong><\/p><\/div><p>A strong beginner project makes that reasoning visible. Build that habit from your first project and every later project becomes easier to defend.<\/p><p>For the wider career path, employer expectations and entry-level roles, continue to NIT\u2019s <a href=\"https:\/\/niteducation.com\/resources\/data-analyst-career-in-kenya\/\">Data Analyst Career in Kenya<\/a> guide.<\/p><\/section>\n<p>Course information below reflects NIT\u2019s current Data Science &amp; Analytics programme page.<sup><a href=\"#ref-15\" aria-label=\"Reference 15\">[15]<\/a><\/sup><\/p><section class=\"nit-pg-enrol\" id=\"nit-register\"><div class=\"nit-pg-enrol__copy\"><p class=\"nit-pg-eyebrow\">Data Science &amp; Analytics at NIT<\/p><h2>Learn the tools, then practise them on complete analytical problems<\/h2><p>Newton Institute of Technology offers practical Data Science and Analytics training covering Excel, statistics, SQL, Python, data cleaning, visualisation and introductory machine learning.<\/p><\/div><div class=\"nit-pg-enrol__details\"><p><strong>Institutional training<\/strong><span>6 months<\/span><\/p><p><strong>Industrial attachment<\/strong><span>Compulsory 3 months<\/span><\/p><p><strong>Study mode<\/strong><span>Physical + online<\/span><\/p><p><strong>School fee<\/strong><span>KSh 60,000<\/span><\/p><p><strong>Admissions<\/strong><span>Continuously open<\/span><\/p><\/div><div class=\"nit-pg-enrol__actions\"><a class=\"nit-pg-button nit-pg-button--gold\" href=\"tel:+254713584858\">Call +254 713 584 858<\/a><a class=\"nit-pg-button nit-pg-button--outline\" href=\"https:\/\/wa.me\/254713584858\" target=\"_blank\" rel=\"noopener\">Ask on WhatsApp<\/a><a class=\"nit-pg-button nit-pg-button--outline\" href=\"mailto:info@niteducation.com\">Email admissions<\/a><a class=\"nit-pg-button nit-pg-button--outline\" href=\"https:\/\/niteducation.com\/resources\/data-science-course-in-kenya\/\">View course<\/a><\/div><p>Newton Institute of Technology \u00b7 Migori Town, Kenya<\/p><\/section>\n<section class=\"nit-sources\" aria-labelledby=\"sources-title\"><h2 id=\"sources-title\">Sources and references<\/h2><ol><li id=\"ref-1\"><a href=\"https:\/\/moko.co.ke\/pages\/careers-page\" rel=\"noopener\" target=\"_blank\">Moko Home + Living \u2014 Careers: Junior Data Analyst Internship<\/a>. Current Kenya-based role context: turning questions from sales, operations and finance into numbers, reports and recommendations.<\/li><li id=\"ref-2\"><a href=\"https:\/\/www.myjobmag.co.ke\/job\/junior-data-analyst-internship-moko-home-living\" rel=\"noopener\" target=\"_blank\">MyJobMag \u2014 Junior Data Analyst Internship, Moko Home + Living (27 October 2026)<\/a>. Current evidence that academic projects or a personal portfolio can count as hands-on analytical experience, with SQL, pandas\/Python and visualisation among requested skills.<\/li><li id=\"ref-3\"><a href=\"https:\/\/www.postgresql.org\/docs\/current\/tutorial-sql.html\" rel=\"noopener\" target=\"_blank\">PostgreSQL Documentation \u2014 SQL Tutorial<\/a>. Primary technical documentation for SQL querying; related PostgreSQL tutorial pages cover joins and aggregate functions.<\/li><li id=\"ref-4\"><a href=\"https:\/\/learn.microsoft.com\/en-us\/power-bi\/create-reports\/service-dashboards\" rel=\"noopener\" target=\"_blank\">Microsoft Learn \u2014 Dashboards for Power BI business users<\/a>. Primary technical source for the distinction between a Power BI report and a single-page dashboard in the Power BI service.<\/li><li id=\"ref-5\"><a href=\"https:\/\/learn.microsoft.com\/en-us\/power-bi\/guidance\/star-schema\" rel=\"noopener\" target=\"_blank\">Microsoft Learn \u2014 Understand star schema and the importance for Power BI<\/a>. Primary technical source for fact tables, dimension tables, relationships and consistent grain.<\/li><li id=\"ref-6\"><a href=\"https:\/\/www.knbs.or.ke\/reports\/consumer-price-indices-and-inflation-rates-september-2026\/\" rel=\"noopener\" target=\"_blank\">Kenya National Bureau of Statistics \u2014 Consumer Price Indices and Inflation Rates, September 2026<\/a>. Official source for September 2026 CPI and inflation information.<\/li><li id=\"ref-7\"><a href=\"https:\/\/www.knbs.or.ke\/reports\/2025-statistical-abstract\/\" rel=\"noopener\" target=\"_blank\">Kenya National Bureau of Statistics \u2014 2025 Statistical Abstract<\/a>. Official source for downloadable Kenyan statistical tables across multiple sectors.<\/li><li id=\"ref-8\"><a href=\"https:\/\/pandas.pydata.org\/docs\/user_guide\/missing_data.html\" rel=\"noopener\" target=\"_blank\">pandas Documentation \u2014 Working with missing data<\/a>. Primary technical documentation for identifying and handling missing tabular data.<\/li><li id=\"ref-9\"><a href=\"https:\/\/www.odpc.go.ke\/rights-of-a-data-subject\/\" rel=\"noopener\" target=\"_blank\">Office of the Data Protection Commissioner \u2014 Rights of a Data Subject \/ Data Protection Principles<\/a>. Official Kenyan guidance supporting lawful, purpose-limited, accurate and appropriately protected handling of personal data.<\/li><li id=\"ref-10\"><a href=\"https:\/\/www.tenders.go.ke\/Contracts\" rel=\"noopener\" target=\"_blank\">Kenya Public Procurement Information Portal \u2014 Contracts<\/a>. Official portal for published Kenyan public-procurement contract information.<\/li><li id=\"ref-11\"><a href=\"https:\/\/data.open-contracting.org\/en\/publication\/147\" rel=\"noopener\" target=\"_blank\">Open Contracting Data Registry \u2014 Kenya Public Procurement Regulatory Authority<\/a>. Registry page providing Kenyan PPRA procurement data in reusable formats and data-quality information.<\/li><li id=\"ref-12\"><a href=\"https:\/\/statistics.knbs.or.ke\/nada\/index.php\/home\" rel=\"noopener\" target=\"_blank\">Kenya National Data Archive (KeNADA)<\/a>. Official KNBS catalogue for Kenyan survey and microdata resources.<\/li><li id=\"ref-13\"><a href=\"https:\/\/data.worldbank.org\/country\/kenya\" rel=\"noopener\" target=\"_blank\">World Bank Open Data \u2014 Kenya<\/a>. Downloadable Kenyan economic, social and development indicators.<\/li><li id=\"ref-14\"><a href=\"https:\/\/epra.go.ke\/statistics-0\" rel=\"noopener\" target=\"_blank\">Energy and Petroleum Regulatory Authority \u2014 Statistics<\/a>. Official source for Kenyan energy and petroleum statistical reports.<\/li><li id=\"ref-15\"><a href=\"https:\/\/niteducation.com\/resources\/data-science-course-in-kenya\/\" rel=\"noopener\">Newton Institute of Technology \u2014 Data Science and Analytics Course in Kenya<\/a>. Current NIT programme information: six months of training, compulsory three-month attachment, physical and online study, KSh 60,000 fee and continuous admission.<\/li><\/ol><\/section>\n<div class=\"nit-article__footer\"><p><strong>Prepared by:<\/strong> Newton Institute of Technology Editorial Team<\/p><p><strong>Last reviewed:<\/strong> 29 October 2026<\/p><\/div><\/div><\/article>\n","protected":false},"excerpt":{"rendered":"<p>Data Science &amp; Analytics \u00b7 Beginner Projects \u00b7 Kenya Data Analyst Projects for Beginners in Kenya: 7 Step-by-Step Projects Ask \u2192 Check \u2192 Analyse \u2192 Explain Build seven practical Data Analyst projects with Excel, SQL, Power BI, Python and real Kenyan public data. Each project shows what to analyse, what to check and what evidence [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[30],"tags":[758,756,757],"class_list":["post-3704","post","type-post","status-publish","format-standard","hentry","category-ict","tag-data-analyst-portfolio-projects","tag-data-analyst-projects-for-beginners","tag-data-analytics-projects-for-beginners"],"_links":{"self":[{"href":"https:\/\/niteducation.com\/resources\/wp-json\/wp\/v2\/posts\/3704","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/niteducation.com\/resources\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/niteducation.com\/resources\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/niteducation.com\/resources\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/niteducation.com\/resources\/wp-json\/wp\/v2\/comments?post=3704"}],"version-history":[{"count":1,"href":"https:\/\/niteducation.com\/resources\/wp-json\/wp\/v2\/posts\/3704\/revisions"}],"predecessor-version":[{"id":3705,"href":"https:\/\/niteducation.com\/resources\/wp-json\/wp\/v2\/posts\/3704\/revisions\/3705"}],"wp:attachment":[{"href":"https:\/\/niteducation.com\/resources\/wp-json\/wp\/v2\/media?parent=3704"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/niteducation.com\/resources\/wp-json\/wp\/v2\/categories?post=3704"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/niteducation.com\/resources\/wp-json\/wp\/v2\/tags?post=3704"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}