{"id":283,"date":"2025-11-06T03:59:29","date_gmt":"2025-11-06T03:59:29","guid":{"rendered":"https:\/\/binus.ac.id\/humanitarian-ai\/?p=283"},"modified":"2026-02-04T06:16:03","modified_gmt":"2026-02-04T06:16:03","slug":"python-programming-for-data-science","status":"publish","type":"post","link":"https:\/\/binus.ac.id\/humanitarian-ai\/blog\/2025\/11\/06\/python-programming-for-data-science\/","title":{"rendered":"Python Programming for Data Science"},"content":{"rendered":"<h1 style=\"text-align: center\" data-start=\"326\" data-end=\"342\"><span style=\"font-size: 18pt\">Training Syllabus<\/span><\/h1>\n<h4 data-start=\"1783\" data-end=\"1799\">\ud83d\udd58 Duration: <strong>4\u00a0full days<\/strong> (08:00 AM \u2013 05:00 PM GMT+7)<\/h4>\n<h4 data-start=\"1845\" data-end=\"1873\">\ud83d\udc65 Minimum Participants: <strong data-start=\"1874\" data-end=\"1886\">5 people<\/strong><\/h4>\n<h4 data-start=\"1888\" data-end=\"1899\">\ud83d\udcbc Fee: <strong data-start=\"1900\" data-end=\"1932\">Based on agreement and venue<\/strong><\/h4>\n<hr class=\"\" data-start=\"1934\" data-end=\"1937\" \/>\n<h3 class=\"\" data-start=\"1939\" data-end=\"1967\">\ud83d\udccb <strong data-start=\"1946\" data-end=\"1967\">Training Overview<\/strong><\/h3>\n<pre id=\"tw-target-text\" class=\"tw-data-text tw-text-large tw-ta\" dir=\"ltr\" role=\"text\" data-placeholder=\"Translation\" data-ved=\"2ahUKEwi7u967zdyQAxXvXGwGHVn2CaUQ3ewLegQIChAV\" aria-label=\"Translated text: This Python Programming for Data Science training is designed for participants who want to understand the fundamentals and practical applications of Python in data analysis and data science. Participants will learn everything from basic Python programming concepts, data manipulation using libraries like NumPy and Pandas, data visualization using Matplotlib and Seaborn, and an introduction to machine learning with scikit-learn. This training focuses on hands-on practice and case studies relevant to the business and research worlds, ensuring participants not only understand the theory but also apply Python to data-driven decision-making.\"><span class=\"Y2IQFc\" lang=\"en\">This <strong>Python Programming for Data Science<\/strong> training is designed for participants who want to understand the fundamentals and practical applications of Python in data analysis and data science. Participants will learn everything from basic Python programming concepts, data manipulation using libraries like NumPy and Pandas, data visualization using Matplotlib and Seaborn, and an introduction to machine learning with scikit-learn.\r\nThis training focuses on hands-on practice and case studies relevant to the business and research worlds, ensuring participants not only understand the theory but also apply Python to data-driven decision-making.\u00a0 <a href=\"https:\/\/binus.ac.id\/humanitarian-ai\/wp-content\/uploads\/2025\/11\/Python-Programming-for-Data-Science.pdf\">Download Syllabus<\/a>.<\/span><\/pre>\n<hr class=\"\" data-start=\"2415\" data-end=\"2418\" \/>\n<h3 class=\"\" data-start=\"2420\" data-end=\"2445\">\ud83d\udcda <strong data-start=\"2427\" data-end=\"2445\">Topics Covered<\/strong><\/h3>\n<p><strong>Session 1 \u2013 <strong style=\"font-family: inherit\">Introduction to Python and the Environment<\/strong><\/strong><\/p>\n<ul>\n<li>What is Python and why is it popular in Data Science.<\/li>\n<li>Anaconda \/ Jupyter Notebook Installation.<\/li>\n<li>Basic Python program structure.<\/li>\n<li>Variables, data types, and basic operations.<\/li>\n<li>Input\/output and comments.<\/li>\n<\/ul>\n<p><strong>Session 2 \u2013 Data Structures and Control Flow<\/strong><br \/>\n\u2022 Lists, Tuples, Dictionaries, Sets.<br \/>\n\u2022 Looping (for, while).<br \/>\n\u2022 Conditionals (if, elif, else).<br \/>\n\u2022 Functions (def, return, parameters).<br \/>\n\u2022 Practice: Creating simple functions for data processing.<\/p>\n<p><strong>Session 3 \u2013 NumPy for numeric Computation<\/strong><\/p>\n<ul>\n<li>Array vs List<\/li>\n<li>Mathematical Operation with NumPy<\/li>\n<li>Indexing, slicing, reshaping<\/li>\n<li>Case study.<\/li>\n<\/ul>\n<p><strong>Session 4 \u2013 Pandas untuk Manipulasi Data<\/strong><\/p>\n<ul>\n<li>Series and DataFrame<\/li>\n<li>Reading and writing data (CSV, Excel, JSON).<\/li>\n<li>Data cleaning: menangani missing value, duplikasi, outlier.<\/li>\n<li>Transformation and data aggregation.<\/li>\n<li>Case study.<\/li>\n<\/ul>\n<p><strong>Sesssion 5 \u2013 Data Visualization<\/strong><\/p>\n<ul>\n<li>Introduction to Matplotlib and Seaborn<\/li>\n<li>Membuat grafik batang, garis, scatter, boxplot<\/li>\n<li>Grapchics customization and Laoyout.<\/li>\n<li>Case study.<\/li>\n<\/ul>\n<p><strong>Session 6 \u2013 Data Analysis and Basic Statistics using Python<\/strong><\/p>\n<ul>\n<li>Descriptive Statistics\u00a0 (mean, median, std, correlation).<\/li>\n<li>Grouping and\u00a0 pivot table with Pandas.<\/li>\n<li>Exploratory Data Analysis (EDA).<\/li>\n<li>case study.<\/li>\n<\/ul>\n<p><strong>Sesi 7 \u2013 Introduction to Machine Learning<\/strong><\/p>\n<ul>\n<li>Supervised and unsupervised learning.<\/li>\n<li>Dataset train-test split.<\/li>\n<li>Implementaton of regression model and simple classification model (Linear Regression, KNN, Decision Tree).<\/li>\n<li>Model Evaluation (accuracy, RMSE, confusion matrix).<\/li>\n<\/ul>\n<p><strong>Session 8 \u2013 Mini Project: Data Science Case Study<\/strong><\/p>\n<ul>\n<li>selecting dataset (public\/real).<\/li>\n<li>Data cleaning and EDA.<\/li>\n<li>Visualisasi insight.<\/li>\n<li>creating model and result interpretation.<\/li>\n<li>Result Presentation.<\/li>\n<\/ul>\n<p><strong>Target:<\/strong> After completing this course, participatn will be able to:<\/p>\n<ol>\n<li><strong>Understanding principle of Python programming<\/strong> ( data structure, control flow, function and using libraries).<\/li>\n<li><strong>Implementing basic statistic and analysis <\/strong>with\u00a0Python.<\/li>\n<\/ol>\n<hr class=\"\" data-start=\"2710\" data-end=\"2713\" \/>\n<h3 class=\"\" data-start=\"2715\" data-end=\"2743\">\ud83c\udfaf <strong data-start=\"2722\" data-end=\"2743\">Learning Outcomes<\/strong><\/h3>\n<p class=\"\" data-start=\"2744\" data-end=\"2791\">By the end of this training, participants will:<\/p>\n<p>1. Understand the basics of Python (data structures, control flow, functions, and library usage).<br \/>\n2. Manage and manipulate data using the Pandas and NumPy libraries.<br \/>\n3. Explore and visualize data to discover patterns and initial insights.<br \/>\n4. Apply basic statistical analysis with Python.<br \/>\n5. Understand and implement simple machine learning models using scikit-learn.<br \/>\n6. Use Python for business case studies or real-world data research.<\/p>\n<hr class=\"\" data-start=\"3004\" data-end=\"3007\" \/>\n<h3 class=\"\" data-start=\"3009\" data-end=\"3023\">\ud83d\udcde Contact<\/h3>\n<ul data-start=\"3024\" data-end=\"3182\">\n<li class=\"\" data-start=\"3024\" data-end=\"3089\">\n<p class=\"\" data-start=\"3026\" data-end=\"3089\">\ud83d\udce7 Email: <a class=\"cursor-pointer\" rel=\"noopener\" data-start=\"3036\" data-end=\"3087\">wbudiharto@binus.edu<\/a><\/p>\n<\/li>\n<li class=\"\" data-start=\"3090\" data-end=\"3136\">\n<p class=\"\" data-start=\"3092\" data-end=\"3136\">\ud83d\udcf1 Mr. Widodo (WhatsApp): +62 856 9887 384<\/p>\n<\/li>\n<li class=\"\" data-start=\"3137\" data-end=\"3182\">\n<p class=\"\" data-start=\"3139\" data-end=\"3182\">\ud83d\udcf1 Ms. Emny (WhatsApp): +62 813 8741 3863<\/p>\n<\/li>\n<\/ul>\n<hr \/>\n<h2 class=\"\" style=\"text-align: center\" data-start=\"162\" data-end=\"191\"><strong data-start=\"168\" data-end=\"189\">Silabus Pelatihan<\/strong><\/h2>\n<h3 class=\"\" style=\"text-align: center\" data-start=\"192\" data-end=\"237\">Python Programming for Machine Learning<\/h3>\n<h4 data-start=\"266\" data-end=\"280\">\ud83d\udd58 Durasi: <strong>4\u00a0hari fullday<\/strong> (08:00 &#8211; 17:00 WIB)<\/h4>\n<h4 data-start=\"321\" data-end=\"351\">\ud83d\udc65 Jumlah Peserta Minimum: <strong data-start=\"352\" data-end=\"363\">5 orang<\/strong><\/h4>\n<h4 data-start=\"365\" data-end=\"378\">\ud83d\udcbc Biaya: <strong data-start=\"379\" data-end=\"429\">Sesuai dengan kesepakatan dan lokasi pelatihan<\/strong><\/h4>\n<hr class=\"\" data-start=\"431\" data-end=\"434\" \/>\n<h3 class=\"\" data-start=\"436\" data-end=\"466\">\ud83d\udccb <strong data-start=\"443\" data-end=\"466\">Deskripsi Pelatihan<\/strong><\/h3>\n<p>Pelatihan <strong>Python Programming for Data Science<\/strong> ini dirancang untuk peserta yang ingin memahami dasar hingga penerapan praktis Python dalam analisis data dan sains data. Peserta akan belajar mulai dari konsep dasar pemrograman Python, manipulasi data menggunakan <em>library<\/em> seperti <strong>NumPy<\/strong> dan <strong>Pandas<\/strong>, visualisasi data menggunakan <strong>Matplotlib<\/strong> dan <strong>Seaborn<\/strong>, hingga pengenalan <strong>machine learning<\/strong> dengan <strong>scikit-learn<\/strong>.<\/p>\n<p>Pelatihan ini berfokus pada praktik langsung dan studi kasus yang relevan dengan dunia bisnis dan riset, sehingga peserta tidak hanya memahami teori tetapi juga mampu mengaplikasikan Python untuk pengambilan keputusan berbasis data.<\/p>\n<hr class=\"\" data-start=\"906\" data-end=\"909\" \/>\n<h3 class=\"\" data-start=\"911\" data-end=\"949\">\ud83d\udcda <strong data-start=\"918\" data-end=\"949\">Materi yang Akan Dipelajari<\/strong><\/h3>\n<p><strong>Sesi 1 \u2013 Pengenalan Python dan Environment<\/strong><\/p>\n<ul>\n<li>Apa itu Python dan mengapa populer di Data Science<\/li>\n<li>Instalasi Anaconda \/ Jupyter Notebook<\/li>\n<li>Struktur dasar program Python<\/li>\n<li>Variabel, tipe data, dan operasi dasar<\/li>\n<li>Input\/output dan komentar<\/li>\n<\/ul>\n<p><strong>Sesi 2 \u2013 Struktur Data dan Control Flow<\/strong><\/p>\n<ul>\n<li>List, Tuple, Dictionary, Set<\/li>\n<li>Looping (for, while)<\/li>\n<li>Conditional (if, elif, else)<\/li>\n<li>Fungsi (def, return, parameter)<\/li>\n<li>Praktik: Membuat fungsi sederhana untuk pengolahan data<\/li>\n<\/ul>\n<p><strong>Sesi 3 \u2013 NumPy untuk Komputasi Numerik<\/strong><\/p>\n<ul>\n<li>Array vs List<\/li>\n<li>Operasi matematis dan statistik dengan NumPy<\/li>\n<li>Indexing, slicing, reshaping<\/li>\n<li>Studi kasus<\/li>\n<\/ul>\n<p><strong>Sesi 4 \u2013 Pandas untuk Manipulasi Data<\/strong><\/p>\n<ul>\n<li>Series dan DataFrame<\/li>\n<li>Membaca dan menulis data (CSV, Excel, JSON)<\/li>\n<li>Data cleaning: menangani missing value, duplikasi, outlier<\/li>\n<li>Transformasi dan agregasi data<\/li>\n<li>Studi kasus<\/li>\n<\/ul>\n<p><strong>Sesi 5 \u2013 Visualisasi Data<\/strong><\/p>\n<ul>\n<li>Pengenalan Matplotlib dan Seaborn<\/li>\n<li>Membuat grafik batang, garis, scatter, boxplot<\/li>\n<li>Kustomisasi grafik dan layout<\/li>\n<li>Studi kasus<\/li>\n<\/ul>\n<p><strong>Sesi 6 \u2013 Analisis Data dan Statistik Dasar<\/strong><\/p>\n<ul>\n<li>Statistik deskriptif (mean, median, std, korelasi)<\/li>\n<li>Grouping dan pivot table dengan Pandas<\/li>\n<li>Exploratory Data Analysis (EDA)<\/li>\n<li>Studi kasus<\/li>\n<\/ul>\n<p><strong>Sesi 7 \u2013 Pengenalan Machine Learning<\/strong><\/p>\n<ul>\n<li>Konsep supervised dan unsupervised learning<\/li>\n<li>Dataset train-test split<\/li>\n<li>Implementasi model regresi dan klasifikasi sederhana (Linear Regression, KNN, Decision Tree)<\/li>\n<li>Evaluasi model (accuracy, RMSE, confusion matrix)<\/li>\n<\/ul>\n<p><strong>Sesi 8 \u2013 Mini Project: Data Science Case Study<\/strong><\/p>\n<ul>\n<li>Pemilihan dataset (publik\/real)<\/li>\n<li>Data cleaning dan EDA<\/li>\n<li>Visualisasi insight<\/li>\n<li>Pembuatan model sederhana dan interpretasi hasil<\/li>\n<li>Presentasi hasil analisis<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<hr class=\"\" data-start=\"1207\" data-end=\"1210\" \/>\n<h3 class=\"\" data-start=\"1212\" data-end=\"1239\">\ud83c\udfaf <strong data-start=\"1219\" data-end=\"1239\">Tujuan Pelatihan<\/strong><\/h3>\n<p class=\"\" data-start=\"1240\" data-end=\"1286\">Setelah mengikuti pelatihan ini, peserta akan mampu:<\/p>\n<ol>\n<li><strong>Memahami dasar-dasar Python<\/strong> (struktur data, control flow, fungsi, dan penggunaan library).<\/li>\n<li><strong>Mengelola dan memanipulasi data<\/strong> menggunakan library Pandas dan NumPy.<\/li>\n<li><strong>Melakukan eksplorasi dan visualisasi data<\/strong> untuk menemukan pola dan wawasan awal.<\/li>\n<li><strong>Menerapkan analisis statistik dasar<\/strong> dengan Python.<\/li>\n<li><strong>Mengenal dan mengimplementasikan model machine learning sederhana<\/strong> menggunakan scikit-learn.<\/li>\n<li><strong>Menggunakan Python untuk studi kasus bisnis atau riset data nyata.<\/strong><\/li>\n<\/ol>\n<hr class=\"\" data-start=\"1513\" data-end=\"1516\" \/>\n<h3 class=\"\" data-start=\"1518\" data-end=\"1531\">\ud83d\udcde Kontak<\/h3>\n<ul data-start=\"1532\" data-end=\"1679\">\n<li class=\"\" data-start=\"1532\" data-end=\"1597\">\n<p class=\"\" data-start=\"1534\" data-end=\"1597\">\ud83d\udce7 Email: <a class=\"cursor-pointer\" rel=\"noopener\" data-start=\"1544\" data-end=\"1595\">wbudiharto@binus.edu<\/a><\/p>\n<\/li>\n<li class=\"\" data-start=\"1598\" data-end=\"1639\">\n<p class=\"\" data-start=\"1600\" data-end=\"1639\">\ud83d\udcf1 Bpk. Widodo (WA): +62 856 9887 384<\/p>\n<\/li>\n<li class=\"\" data-start=\"1640\" data-end=\"1679\">\n<p class=\"\" data-start=\"1642\" data-end=\"1679\">\ud83d\udcf1 Ibu Emny (WA): +62 813 8741 3863<\/p>\n<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Training Syllabus \ud83d\udd58 Duration: 4\u00a0full days (08:00 AM \u2013 05:00 PM GMT+7) \ud83d\udc65 Minimum Participants: 5 people \ud83d\udcbc Fee: Based on agreement and venue \ud83d\udccb Training Overview This Python Programming for Data Science training is designed for participants who want to understand the fundamentals and practical applications of Python in data analysis and data science. [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":171,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-283","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-syllabus"],"_links":{"self":[{"href":"https:\/\/binus.ac.id\/humanitarian-ai\/wp-json\/wp\/v2\/posts\/283","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/binus.ac.id\/humanitarian-ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/binus.ac.id\/humanitarian-ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/binus.ac.id\/humanitarian-ai\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/binus.ac.id\/humanitarian-ai\/wp-json\/wp\/v2\/comments?post=283"}],"version-history":[{"count":5,"href":"https:\/\/binus.ac.id\/humanitarian-ai\/wp-json\/wp\/v2\/posts\/283\/revisions"}],"predecessor-version":[{"id":393,"href":"https:\/\/binus.ac.id\/humanitarian-ai\/wp-json\/wp\/v2\/posts\/283\/revisions\/393"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/binus.ac.id\/humanitarian-ai\/wp-json\/wp\/v2\/media\/171"}],"wp:attachment":[{"href":"https:\/\/binus.ac.id\/humanitarian-ai\/wp-json\/wp\/v2\/media?parent=283"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/binus.ac.id\/humanitarian-ai\/wp-json\/wp\/v2\/categories?post=283"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/binus.ac.id\/humanitarian-ai\/wp-json\/wp\/v2\/tags?post=283"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}