Learning materials, Quizzes & Assignment solutions for the entire IBM data science professional certification. Also included, a few resources that I found helpful.
Generally taught about basic of Python for application in statistics, especially in data mining.
Made module of teaching materials for classes that are taught at weekly meetings
Organized exams, practice questions, midterm exam reports and final exam reports, and evaluated the results of the work.
Specifically taught about: Introduction to Python, Statistics Descriptive and Data Preprocessing, Transformation & Dimensionality Reduction, Association Rule.
Generally taught about basic of Python for application in statistics, especially in data analysis.
Made module of teaching materials for classes that are taught at weekly meetings
Organized exams, practice questions, midterm exam reports and final exam reports, and evaluated the results of the work.
Specifically taught about: Introduction to Python, Data Manipulation with Pandas, Join Data, Exploration and Visualization Data with Python, Scrapping Data from Web and API, Supervised Learning and Unsupervised Learning, Introduction to Deep Learning.
Generally taught about basic of R for application in statistics, especially in data analysis.
Organized exams, practice questions, midterm exam reports and final exam reports, and evaluated the results of the work.
Specifically taught about: Introduction to R, Data Manipulation with Tidyverse, Distribution Transformation of Random Variables, Monte Carlo Simulation, and Linear Programming.
Requirement Skill: R Programming Language (Intermediate – Advance), Data Analysis.
Taught, the basics of R for application in statistics, especially in data exploration and visualization
Made module of teaching materials for classes that are taught at weekly meetings
Held exams, midterm exam reports and final exam reports. Include assessed the results of the work
Specifically taught about: Basic introduction to the programming language R, descriptive statistics and numerical summaries, data visualization using R, data transformation (one batch and several batches), simple random sample, student's t distribution and Snedecor F, 1 sample tests (Z and T test), 2 sample tests (paired t and 2 – sample t test), One Way Analysis of Variance (One-Way ANOVA), and Simple Linear Regression
Requirement Skill: R Programming Language (Intermediate – Advance), Data Analysis, Exploratory Data Analysis, Data Visualization
Graded the assignments given to students in official statistics courses.
Provided a final report on the results of the assessment assigned to students.
Prepared answers and explanations of student assignment
Requirement Skill: Official Statistics
Awards / Scholarship :
1. [1st Winner Airlangga Statistics Essay Competition – Airlangga Statistics Event 2022] November 2022, Universitas Airlangga
Research : “eVal : Solusi Monitoring Destinasi Pariwisata Berbasis Natural Language Processing dan Analisis Sentimen”
2. [Finalist of Olympic - Dokter Data Universitas Diponegoro 2022] October 2022, Universitas Diponegoro
Research : “Pengaruh COVID-19 Terhadap Volume Transaksi Saham per Bulan Juni Tahun 2019 - 2022 Menggunakan Analisis Regresi Data Panel”
3. [Finalist of Data Analysis Competition – Informatics Festival (IFest) 2022] October 2022, Universitas Padjadjaran
Research : “Implementasi Berbagai Pendekatan Machine Learning Untuk Prediksi Fakultas dan Lama Studi Mahasiswa”
4. [Finalist of Essay Competition - Olympic of Statistics (OLS) 2022] September 2022, Universitas Negeri Makassar
Research : “Analisis Sentimen terhadap Respons Pengguna Aplikasi PeduliLindungi Menggunakan Algoritma Naïve Bayes, Support Vector Machine, dan Multilayer Perceptron”