Digital Bootcamp - Intensive Class

Fullstack Data Science

Q: Apakah kursus ini dilakukan secara langsung (live) atau mandiri?

Kursus ini menggunakan metode blended learning , yaitu kombinasi antara: Belajar mandiri melalui platform e-learning, di mana peserta dapat mengakses materi, video, dan tugas kapan saja. Virtual meet via Zoom (live session) bersama mentor, dijadwalkan secara rutin untuk diskusi, tanya jawab, atau membahas topik penting secara interaktif. Metode ini memberikan fleksibilitas belajar sekaligus pengalaman interaktif dengan pendampingan mentor.

Q: Apakah ada dukungan jika saya mengalami kesulitan saat belajar?

Ya. Kami menyediakan forum diskusi , sesi tanya jawab bersama mentor, serta dukungan teknis untuk membantu Anda selama proses belajar.

Q: Apa spesifikasi minimal Laptop atau Komputer yang dibutuhkan?

Untuk kursus secara umum (selain Mobile Development ), perangkat minimal yang disarankan adalah: Prosesor: Minimal Dual-core, seperti Intel Core i3 generasi ke-6 atau AMD Ryzen 3 2200U RAM: Minimal 4GB (disarankan 8GB) Sistem Operasi: Windows 10, macOS 10.13 atau versi lebih baru Koneksi Internet: Stabil, minimal 10 Mbps Untuk kursus Mobile Development dan Game Development , disarankan: Prosesor: Quad-core, seperti Intel Core i5 generasi ke-8 atau AMD Ryzen 5 3500U RAM: Minimal 8GB (disarankan 12GB atau lebih) Penyimpanan: SSD minimal 256GB

Q: Apakah sertifikat kursus ini dapat meningkatkan CV & portofolio saya?

Ya. Kursus ini bekerja sama dengan CCIT FT Universitas Indonesia , sehingga sertifikat yang diterbitkan memiliki kredibilitas tinggi dan dapat menjadi nilai tambah pada CV Anda.

"Master the Science of Data – Power Up Your Career in AI & Analytics"

📚 Kenapa harus belajar Fullstack Data Science?

Keahlian yang Relevan dengan Transformasi Digital. Seiring dengan pesatnya transformasi digital di Indonesia, berbagai sektor industri membutuhkan profesional yang mampu mengolah data untuk membuat keputusan yang lebih baik. Data Science tidak hanya tentang memproses data, tetapi juga tentang menemukan pola, meramalkan tren, dan memberikan wawasan yang dapat meningkatkan strategi bisnis. Menguasai data science memberi Anda keahlian yang sangat relevan dengan kebutuhan pasar.
Peluang Karir yang Luas dan Menjanjikan. Data Science adalah bidang yang berkembang pesat, dengan permintaan yang terus meningkat, baik dari perusahaan teknologi besar, startup, maupun sektor-sektor lain seperti fintech, e-commerce, dan pemerintahan. Seiring semakin banyaknya data yang dihasilkan, kebutuhan akan ahli data science yang mampu menganalisis dan mengubah data menjadi wawasan berharga semakin besar.
Gaji yang Tinggi dan Keamanan Karir. Profesi di bidang data science, seperti Data Analyst, Data Scientist, dan Machine Learning Engineer, termasuk dalam kategori pekerjaan dengan gaji yang sangat kompetitif. Sebagai profesional yang menguasai keterampilan ini, Anda dapat mengakses peluang karir dengan gaji yang lebih tinggi dan kestabilan pekerjaan yang lebih baik, mengingat tingginya permintaan terhadap keahlian ini.
Pengaruh Besar dalam Pengambilan Keputusan Bisnis. Data Science membantu perusahaan membuat keputusan berbasis data, yang lebih akurat dan terukur. Keahlian dalam analisis data memberikan kemampuan untuk mengubah data mentah menjadi informasi yang dapat digunakan untuk meningkatkan performa bisnis, merancang produk, dan merumuskan strategi pemasaran yang lebih efektif.
Peluang untuk Bekerja di Perusahaan Terkemuka. Banyak perusahaan besar di Indonesia, seperti Gojek, Tokopedia, Bukalapak, dan startup teknologi lainnya, sangat mengandalkan data untuk mengembangkan produk dan layanan mereka. Dengan menguasai Data Science, Anda akan siap berkarir di perusahaan-perusahaan terkemuka ini yang memiliki dampak besar di pasar digital Indonesia.
Meningkatkan Kemampuan Problem-Solving dan Analitik. Belajar Data Science bukan hanya tentang menguasai teknologi, tetapi juga tentang mengasah kemampuan problem-solving dan analitik. Anda akan mempelajari teknik-teknik untuk menganalisis data kompleks, membuat prediksi yang akurat, dan memberikan solusi berbasis data yang dapat mengubah cara perusahaan beroperasi.
Fleksibilitas dalam Karir dan Pekerjaan Freelance. Data Science memberikan fleksibilitas untuk bekerja di berbagai sektor industri, serta peluang untuk bekerja sebagai freelancer atau konsultan data. Anda bisa memilih untuk mengerjakan proyek-proyek dari berbagai perusahaan atau membangun bisnis sendiri dengan menawarkan solusi berbasis data.

👨‍🏫 Trainer yang akan mengajar 👩‍🏫

🎯 Target dan Sasaran kelas bootcamp ini

Menguasai skill data science secara holistik dengan mempelajari keterampilan teknis dan analitis, mulai dari pengolahan data hingga implementasi model AI, untuk menjadi data scientist yang siap kerja.
Meningkatkan kesiapan karir di bidang data dengan mengembangkan kemampuan dalam pemrograman, analisis data, machine learning, dan data engineering yang dibutuhkan oleh perusahaan di semua sektor industri.
Penguasaan alat dan teknik data science terbaru sesuai standar dan best practice industri.
Membangun portofolio proyek nyata yang dapat digunakan untuk menunjukkan keahlian dalam analisis data, storytelling, dan model prediktif di dunia profesional

💻 Topik yang akan dipelajari

Overview of Data Science & Its Role in Industry
Key Differences: Data Scientist vs. Data Analyst vs. Data Engineer
Data Science Workflow: From Data Collection to Deployment
Ethical Considerations in Data Science
Fundamentals of Artificial Intelligence & Machine Learning
Supervised vs. Unsupervised Learning
Bias & Fairness in AI Models
AI Model Deployment & MLOps Overview
Descriptive & Inferential Statistics
Probability Distributions & Hypothesis Testing
Correlation vs. Causation
Data Normalization & Outlier Detection
Python Basics for ML (NumPy, Pandas, Scikit-learn)
Feature Engineering & Data Preprocessing
Training & Evaluating ML Models
Model Optimization Techniques
Data Extraction from APIs, Databases, and Web Scraping
ETL (Extract, Transform, Load) Process
Data Cleaning & Preprocessing
Big Data Technologies Overview (Hadoop, Spark)
Data Modeling Techniques & Feature Selection
Using Looker, Flourish, and Power BI/Tableau
Effective Data Storytelling
Exploratory Data Analysis (EDA)
Data Wrangling Techniques
Time-Series Analysis
SQL for Data Science (Joins, CTE, Subqueries)
Query Optimization & Indexing
Combining SQL with Python for Analysis
Regression & Classification Techniques
Time-Series Forecasting
Evaluating Model Accuracy
Hands-on: Building a predictive model in Python
Automating Data Workflows
Building Scalable Data Pipelines with Apache Airflow
Cloud Data Engineering (AWS/GCP/Azure)
Hands-on: Implementing a real-time data pipeline
Tokenization, Lemmatization, and Sentiment Analysis
Named Entity Recognition (NER)
Word Embeddings & Transformer Models
Hands-on: Sentiment analysis on social media data
Understanding Retrieval-Augmented Generation (RAG)
Implementing LLMs for Data Science (ChatGPT, GPT-4, BERT)
Hands-on: Using OpenAI API for automated data insights
Projects
Assesment / Uji Kompetensi

🎁 Benefit yang didapat

⚙️ Tools yang akan digunakan

📝 Proyek yang akan dikerjakan

Solusi Aplikasi Media Monitoring berbasis AI
Solusi Aplikasi Fraud Detection berbasis AI.
Solusi Aplikasi Text Recoginition berbasis AI
Solusi Aplikasi Executive Dashboard

🏢 Prospek karir

Data Scientist
Machine Learning Engineer
Data Analyst (Level Lanjutan)
AI/ML Researcher (Entry Level)
Data Engineer
Product/Data Consultant
Freelance Data Professional

📢 Untuk siapa kelas ini?

Mahasiswa (UI & Non UI) dan Umum yang ingin belajar dan memperkaya portofolio di bidang Data, Artificial Intellegent(AI) dan Machine Learning(ML)
Fresh Graduate Ingin memperkuat CV dengan skill praktis yang dicari industri
Programmer yang ingin Upskilling bidang Data Scientist
Freelance yang sedang Reskilling / Upskilling
Startup Enthusiast untuk jadi Data Scientist, ML Engineer, atau AI Specialist
Manajer IT atau tim manajerial yang ingin memiliki insight mendalam tentang Data, AI dan ML

🕣 Jadwal

Live Zoom setiap Selasa dan Kamis pukul 18.30 – 21.30 (Malam)
Durasi kelas 2,5 – 3 jam per sesi selama 3 bulan.
Kelas dimulai tanggal 12 Agustus 2025.

🗂️ Teknis Pelaksanaan

Peserta yang melakukan pendaftaran, wajib join di group Whatsapp yang diberikan.
Setiap sesi live akan dilaksanakan secara online menggunakan Zoom selama 24x pertemuan dilanjutkan dengan project portofolio dan bimbingan softskill untuk siap kerja dan pengembangan karir.
Peserta wajib aktif di platform LMS (Learning Management System) baik dalam pembelajaran maupun forum kolaborasi.
Peserta dapat mendownload ataupun mengakses materi belajar termasuk sampel source code (khusus kelas programming) di LMS.
Peserta dapat bertanya dan berdiskusi dengan mentor dan peserta lain terkait materi, tugas dan konsultasi di LMS.

Premium

Rp.7.000.000

Umum

Rp.5.500.000

Mahasiswa

Rp.3.500.000

Module 1: Introduction Data Science

Topic: Overview of Data Science & Its Role in Industry

What is Data Science? Key Concepts & Applications
Evolution of Data Science & Industry Adoption
Emerging Trends: AI, ML, and Big Data in Business
Hand-on: Exploring real-world case studies of data science-driven decision-making

Topic: Key Differences: Data Scientist vs. Data Analyst vs. Data Engineer

Role Comparison: Responsibilities, Skills, and Tools Used
Career Paths & Specializations in Data Science
When to Hire a Data Scientist vs. Data Engineer vs. Data Analyst
How These Roles Work Together in a Data Team
Hand-on: Identifying real-world job descriptions & role alignment

Module 2: AI/ML Concept

Topic: Fundamentals of Artificial Intelligence & Machine Learning

What is AI? Evolution & Key Concepts
Machine Learning vs. Deep Learning vs. Traditional AI
Real-World Applications of AI/ML in Various Industries
Key ML Components: Data, Features, Algorithms, and Models
Hand-on: Exploring AI use cases & identifying business applications

Topic: Supervised vs. Unsupervised Learning

Supervised Learning:
Regression & Classification Techniques
Common Algorithms (Linear Regression, Decision Trees, SVM, etc.)
Model Evaluation & Performance Metrics (RMSE, Accuracy, Precision, Recall)
Unsupervised Learning:
Clustering & Dimensionality Reduction Techniques
K-Means, Hierarchical Clustering, PCA
Applications in Anomaly Detection & Customer
Segmentation Hand-on:
Implementing a Supervised ML Model using Python (Scikit-learn)
Performing Clustering Analysis on real-world data

Topic: Bias & Fairness in AI Models

Understanding Bias in Data & AI Models
Types of Bias: Selection Bias, Confirmation Bias, Algorithmic Bias
Fairness Metrics in AI: Demographic Parity, Equalized Odds
Techniques to Mitigate Bias in Machine Learning Models
Hand-on: Analyzing bias in an ML model and applying mitigation techniques

Topic: AI Model Deployment & MLOps Overview

Introduction to MLOps & AI Model Lifecycle
Model Deployment Strategies (On-Premise, Cloud, Edge AI)
CI/CD for Machine Learning Models
Monitoring AI Models in Production & Performance Optimization
Hand-on: Deploying an ML Model on a Cloud Platform (AWS, Azure, or Google Cloud)

Module 4: Machine Learning Programming with Phyton

Topic: Python Basics for ML (NumPy, Pandas, Scikit-learn)

Overview of Python for Data Science & Machine Learning
Introduction to NumPy: Arrays, Vectorized Operations, Indexing
Data Manipulation with Pandas: DataFrames, Cleaning, Aggregation
Machine Learning Basics with Scikit-learn
Exploratory Data Analysis (EDA) Techniques
Hand-on:
– Working with real-world datasets using NumPy & Pandas
– Implementing basic data exploration and visualization

Topic: Python Basics for ML (NumPy, Pandas, Scikit-learn)

Importance of Feature Engineering in ML
Handling Missing Data: Imputation Strategies
Feature Scaling & Normalization (Min-Max, Standardization)
Encoding Categorical Variables (One-Hot, Label Encoding)
Feature Selection Techniques (PCA, Mutual Information)
Hand-on: Preprocessing a dataset for ML using Scikit-learn pipelines

Topic: Python Basics for ML (NumPy, Pandas, Scikit-learn)

Understanding Train-Test Splitting & Cross-Validation
Common ML Algorithms:
– Linear Regression, Logistic Regression
– Decision Trees, Random Forest, Gradient Boosting
– k-Nearest Neighbors, Support Vector Machines (SVM)
Evaluating Model Performance:
– Metrics for Regression (RMSE, R²)
– Metrics for Classification (Accuracy, Precision, Recall, F1-score, AUC-ROC)
Hand-on: Training & Evaluating Multiple ML Models on a Business Dataset

Module 5: Data Engineering: from collecting to transformation

Topic: Data Cleaning & Preprocessing

Handling Missing & Duplicate Data
– Imputation Techniques (Mean, Median, Mode)
– Dropping vs. Filling Missing Values
Data Standardization & Normalization
– Min-Max Scaling, Z-score Standardization
– Handling Categorical Data (One-Hot Encoding, Label Encoding)
Detecting & Handling Outliers
– Z-score, IQR Method, Winsorization
Ensuring Data Integrity
– Data Consistency Checks & Schema Validation
Hand-on:
– Cleaning a messy dataset and preparing it for machine learning

Topic: Big Data Technologies Overview (Hadoop, Spark)

Introduction to Big Data & Distributed Computing
– What is Big Data? 3Vs (Volume, Velocity, Variety)
– Traditional Databases vs. Distributed Systems
Hadoop Ecosystem Overview
– HDFS, MapReduce, Apache Hive, Apache Pig
Apache Spark for Data Processing
– Spark vs. Hadoop: Key Differences
– Spark DataFrames & RDDs
– Parallel Computing & Performance Optimization
Cloud-based Big Data Solutions
– AWS EMR, Google BigQuery, Databricks
Hand-on:
– Running data transformations using PySpark

Module 6: Data Modelling & Visualization

Topic: Using Looker, Flourish, and Power BI/Tableau

Introduction to Data Visualization
– Importance of Visual Analytics in Decision-Making
– Choosing the Right Chart Type for Different Data
Hands-on with Looker & Flourish
– Looker: Building Dashboards & Real-time Analytics
– Flourish: Creating Interactive & Storytelling-based Visuals
Power BI & Tableau for Enterprise Reporting
– Connecting to Data Sources & Data Cleaning
– Designing Interactive Dashboards & Reports
– Advanced Features (DAX for Power BI, Calculated Fields for Tableau)
Hand-on:
– Creating visual report & interactive dashboards in Power BI/Tableau
– Designing an engaging data story with Flourish

Topic: Effective Data Storytelling

Principles of Data Storytelling
– Structuring a Data Narrative (Hook, Data, Insights, Action)
– Balancing Data & Context for Decision-Makers
– Using Visual Cues & Cognitive Load Reduction Techniques
Techniques for Communicating Insights
– Simplifying Complex Data for Non-Technical Audiences
– Storyboarding & Slide Design for Presentations
– Creating Engaging Reports with Infographics & Dynamic Charts
Hand-on:
– Presenting a data-driven business case using storytelling techniques

Module 7: Data Analysis with Phyton

Topic: Data Wrangling Techniques

Understanding Data Wrangling
– Difference between Data Cleaning, Transformation, and Feature Engineering
– Common Challenges in Raw Data
Handling Missing & Inconsistent Data
– Imputation Techniques (Mean, Median, Mode, Forward Fill, KNN)
– Dealing with Duplicates & Anomalies
Transforming & Reshaping Data
– Pivot Tables, Melting, Grouping, and Aggregation
– Encoding Categorical Variables (One-Hot, Label Encoding)
– Feature Scaling & Normalization (Min-Max Scaling, Standardization)
Automating Data Wrangling Workflows
– Using Python Libraries: Pandas, NumPy, Dask
– Writing Efficient Data Pipelines
Hand-on: Cleaning and transforming messy datasets using Python (Pandas, NumPy, Scikit-learn)

Module 8: Applied Data Science with SQL

Topic: SQL for Data Science (Joins, CTE, Subqueries)

Introduction to SQL for Data Science
– Role of SQL in Data Science Workflows
– SQL vs. NoSQL: When to Use Each
Data Querying with SQL
– Basic Queries: SELECT, WHERE, GROUP BY, ORDER BY
– Filtering & Aggregating Data
Advanced SQL Queries
– Using Joins (INNER, LEFT, RIGHT, FULL OUTER) for Data Merging
– Using Common Table Expressions (CTEs) for Readability & Optimization
– Using Subqueries for Complex Data Analysis
– Window Functions (RANK, DENSE_RANK, LAG, LEAD)
Hands-on:
– Writing advanced SQL queries on real-world datasets using PostgreSQL/MySQL
– Using CTEs and subqueries for multi-step analysis

Topic: Query Optimization & Indexing

Understanding SQL Query Performance
– How SQL Queries are Executed (Query Execution Plan)
– Common Performance Bottlenecks
Optimizing SQL Queries
– Indexing Strategies (Clustered vs. Non-Clustered Indexes)
– Query Rewriting Techniques (Avoiding SELECT *)
– Using Partitioning for Large Datasets
Database Optimization Techniques
– Caching Strategies & Materialized Views
– Using EXPLAIN ANALYZE for Query Debugging
Hands-on:
– Optimizing slow queries using EXPLAIN ANALYZE
– Implementing indexes & partitioning in PostgreSQL/MySQL

Module 9: Predictive Analysis

Topic: Time-Series Forecasting

Introduction to Time-Series Forecasting
– Time-Series Data vs. Traditional Datasets
– Components of Time-Series (Trend, Seasonality, Noise)
Exploratory Data Analysis for Time-Series
– Visualizing Trends & Seasonality (Line Plots, Decomposition)
– Stationarity Testing (Augmented Dickey-Fuller Test)
Time-Series Forecasting Techniques
– Moving Averages & Exponential Smoothing
– ARIMA & SARIMA Models
– Machine Learning for Forecasting (XGBoost, LSTMs)
Hands-on:
– Forecasting sales, stock prices, or demand using ARIMA & LSTMs
– Evaluating time-series models with Mean Absolute Error (MAE) & RMSE

Module 10: Data Pipeline

Topic: Automating Data Workflows

Introduction to Data Workflow Automation
– The Importance of Automation in Data Science
– Batch vs. Real-time Data Processing
– Common Tools for Workflow Automation (Airflow, Prefect, Luigi)
Scheduling & Orchestrating Automated Tasks
– Creating Task Pipelines for Data Extraction & Transformation
– Implementing Monitoring & Logging for Data Workflows
Hands-on: Automating ETL (Extract, Transform, Load) Pipelines
– Building a simple automated ETL pipeline
– Automating Data Cleaning & Transformation with Python

Topic: Building Scalable Data Pipelines with Apache Airflow

Introduction to Apache Airflow
– Why Airflow? Benefits for Data Pipeline Management
– DAGs (Directed Acyclic Graphs) & Task Dependencies
Designing Scalable Data Pipelines
– Creating & Scheduling DAGs for Data Processing
– Managing Task Failures & Retries
– Parallel Processing & Performance Optimization
Hands-on: Deploying Airflow Pipelines
– Writing Python-based DAGs for Workflow Orchestration
– Integrating Airflow with SQL, APIs, and Cloud Storage

Topic: Cloud Data Engineering (AWS/GCP/Azure)

Overview of Cloud Data Engineering
– Cloud Data Services: AWS, GCP, Azure
– Serverless vs. Managed Data Services
Building Cloud-based Data Pipelines
– AWS: Using AWS Glue, Lambda, S3, Redshift
– GCP: Using BigQuery, Cloud Functions, Dataflow
– Azure: Using Azure Data Factory, Synapse Analytics
Security & Cost Optimization in Cloud Data Engineering
– Role-based Access Control (RBAC)
– Cost-efficient Storage & Compute Strategies
Hands-on: Deploying a Cloud-based Data Pipeline
– Setting up an end-to-end pipeline using AWS/GCP/Azure
– Automating data ingestion & transformation in the cloud

Topic: Hands-on: Implementing a real-time data pipeline

Module 12: RAG & LLM

Topic: Understanding Retrieval-Augmented Generation (RAG)

Introduction to Retrieval-Augmented Generation (RAG)
– How RAG Combines Retrieval & Generation for AI-powered Applications
– Benefits of RAG over Traditional LLMs
– Real-world Use Cases (Search Engines, Knowledge Assistants, Q&A Systems)
RAG Pipeline Architecture
– Understanding the Role of Vector Databases (FAISS, Pinecone, Weaviate)
– How LLMs Retrieve and Generate Contextually Accurate Responses
Hands-on: Implementing a Basic RAG System
– Setting up a Simple RAG Model with OpenAI API & FAISS

Topic: Implementing LLMs for Data Science (ChatGPT, GPT-4, BERT)

Overview of Large Language Models (LLMs)
– Understanding ChatGPT, GPT-4, BERT, and T5
– Differences Between Fine-tuned vs. Zero-shot Models
LLM Applications in Data Science
– Automating Data Cleaning, Feature Engineering, and Insights Generation
– Using LLMs for Code Generation & Debugging
Fine-tuning & Prompt Engineering for LLMs
– How to Customize LLMs for Domain-specific Tasks
– Optimizing Prompting Techniques for Improved Response Accuracy
Hands-on: Deploying an LLM-powered Data Science Assistant
– Using OpenAI API for Text Summarization, Classification, and Question Answering
– Integrating LLMs with Pandas, SQL, and Visualization Tools

Topic: Hands-on: Using OpenAI API for automated data insights

Module 1: Introduction Data Science

Topic: Data Science Workflow: From Data Collection to Deployment

“End-to-End Data Science Process: – Data Collection & Sourcing Strategies – Data Cleaning & Preprocessing – Exploratory Data Analysis (EDA) – Feature Engineering & Model Building – Model Evaluation & Optimization – Deployment & Monitoring (MLOps)”
Tools & Technologies Used in the Workflow (Python, SQL, Cloud Platforms, etc.)
Hand-on: Mapping the Data Science workflow for a given business scenario

Topic: Ethical Considerations in Data Science

Importance of Ethical AI & Responsible Data Usage
Data Privacy Regulations (GDPR, CCPA, and Local Laws)
Bias & Fairness in AI/ML Models
Transparency & Explainability in Data-Driven Decision-Making
Hand-on: Case study on biased AI models and how to mitigate bias

Module 3: Basic Statistics for Data Science

Topic: Descriptive & Inferential Statistics

Descriptive Statistics:
Measures of Central Tendency (Mean, Median, Mode) Measures of Dispersion (Variance, Standard Deviation, Range, IQR)
Data Visualization (Histograms, Box Plots, Scatter Plots)
Inferential Statistics:
Sampling Methods & Central Limit Theorem
Confidence Intervals & Margin of Error
Parametric vs. Non-Parametric Tests
Hand-on: Calculating descriptive statistics & visualizing distributions using Python (Pandas, Matplotlib, Seaborn)

Topic: Probability Distributions & Hypothesis Testing

Probability Distributions:
Normal, Binomial, Poisson, and Exponential Distributions
Skewness & Kurtosis in Data Distribution
Probability Density & Cumulative Distribution Functions
Hypothesis Testing:
Null vs. Alternative Hypothesis
One-Tailed vs. Two-Tailed Tests
p-values, Significance Levels, and Type I/II Errors
t-Tests, ANOVA, Chi-Square Test
Hand-on: Conducting hypothesis testing on real-world datasets using Python (Scipy, Statsmodels)

Module 3: Basic Statistics for Data Science

Topic: Correlation vs. Causation

Correlation Analysis:
– Pearson, Spearman, and Kendall Correlation Coefficients
– Interpreting Correlation Matrices
– Pitfalls of Spurious Correlations
Causation & Causal Inference:
– Observational vs. Experimental Studies
– A/B Testing & Randomized Control Trials
– Simpson’s Paradox & Confounding Variables
Hand-on: Performing correlation analysis on business data & visualizing relationships

Topic: Correlation vs. Causation

Data Normalization:
– Standardization (Z-score) vs. Min-Max Scaling
– When to Normalize Data & Impact on Machine Learning Models
Outlier Detection:
– Box Plot & Z-Score Method
– Mahalanobis Distance & Isolation Forest
– Handling Outliers: Removal vs. Transformation
Hand-on: Detecting & handling outliers in a dataset using Python (Scikit-learn)

Module 4: Machine Learning Programming with Phyton

Topic: Model Optimization Techniques

Hyperparameter Tuning: Grid Search vs. Random Search
Regularization Techniques (L1, L2, Elastic Net)
Ensemble Learning: Bagging vs. Boosting (XGBoost, LightGBM)
Bias-Variance Tradeoff & Overfitting Prevention
Hand-on: Implementing hyperparameter tuning using Scikit-learn & Optuna

Module 5: Data Engineering: from collecting to transformation

Topic: Data Extraction from APIs, Databases, and Web Scraping

Data Extraction from APIs
– Understanding RESTful APIs
– Authentication & API Tokens (OAuth, API Keys)
– Extracting Data using Python (Requests, JSON, Pandas)
Database Extraction (SQL & NoSQL)
– Connecting to Databases (PostgreSQL, MySQL, MongoDB)
– Querying & Extracting Large Datasets
Web Scraping Fundamentals
– HTML & XPath Basics
– Scraping with BeautifulSoup & Selenium
– Ethical Considerations & Legal Aspects
Hand-on:
– Extracting real-time data from a public API (e.g., Twitter, OpenWeather)
– Scraping e-commerce data using BeautifulSoup

Topic: ETL (Extract, Transform, Load) Process

Understanding the ETL Pipeline
– Extracting Data from Multiple Sources
– Data Transformation Techniques (Aggregation, Normalization)
– Data Loading Strategies (Batch vs. Streaming)
ETL Tools & Frameworks
– Apache Airflow for Workflow Automation
– Data Pipelines with Python (Pandas, PySpark)
– Cloud-based ETL (AWS Glue, Google Dataflow)
Hand-on:
– Building an ETL pipeline to extract, clean, and load data into a database

Module 6: Data Modelling & Visualization

Topic: Data Modeling Techniques & Feature Selection

Understanding Data Modeling
– Conceptual, Logical, and Physical Data Models
– Data Relationships & Entity-Relationship Diagrams (ERD)
Feature Engineering & Selection
– Importance of Feature Engineering in ML Models
– Feature Transformation Techniques (Polynomial Features, Log Transform)
– Dimensionality Reduction (PCA, t-SNE, Autoencoders)
– Feature Importance Techniques (SHAP, LIME, Mutual Information)
Hand-on:
– Applying feature selection techniques on a real-world dataset
– Implementing PCA for dimensionality reduction

Module 7: Data Analysis with Phyton

Topic: Exploratory Data Analysis (EDA)

Introduction to EDA
– Importance of EDA in Data Science
– Understanding the Data Lifecycle
Summary Statistics & Data Distribution
– Measures of Central Tendency & Dispersion (Mean, Median, Variance)
– Data Distribution & Normality Tests
– Identifying Outliers using Boxplots, Z-score, and IQR
Data Visualization for EDA
– Univariate Analysis (Histograms, KDE, Boxplots)
– Bivariate & Multivariate Analysis (Scatter Plots, Heatmaps, Pair Plots)
– Correlation Analysis & Feature Relationships

Module 7: Data Analysis with Phyton

Topic: Time-Series Analysis

Fundamentals of Time-Series Data
– Time-Series vs. Traditional Datasets
– Components of Time-Series Data (Trend, Seasonality, Residual)
Time-Series Data Preprocessing
– Handling Missing Values in Time-Series
– Resampling & Aggregating Time-Series Data
– Rolling Statistics & Moving Averages
Exploratory Analysis for Time-Series
– Visualizing Time-Series Trends (Line Plots, Decomposition)
– Autocorrelation & Partial Autocorrelation Functions (ACF & PACF)
Introduction to Time-Series Forecasting
– Classical Models: ARIMA, SARIMA, Exponential Smoothing
– Machine Learning for Time-Series (XGBoost, LSTMs)
Hand-on:
– Analyzing stock market, weather, or sales data using Statsmodels & Prophet
– Building an ARIMA-based forecasting model
Cleaning and transforming messy datasets using Python (Pandas, NumPy, Scikit-learn)

Module 8: Applied Data Science with SQL

Topic: Combining SQL with Python for Analysis

Integrating SQL with Python
– Using Pandas & SQLAlchemy for Database Queries
– Connecting to Databases via PostgreSQL, MySQL, SQLite
Automating Data Analysis Workflows
– Writing & Executing Queries with Python
– Storing & Processing Large Datasets using Pandas & SQL
Building a Data Pipeline with SQL & Python
– Extracting Data with SQL Queries
– Transforming Data with Pandas
– Exporting Processed Data for Machine Learning
Hands-on:
– Writing Python scripts to fetch & process SQL data using SQLAlchemy & Pandas
– Automating ETL workflows with SQL & Python

Module 9: Predictive Analysis

Topic: Regression & Classification Techniques

Introduction to Regression & Classification
– Understanding Supervised Learning
– Key Differences: Regression vs. Classification
– Common Use Cases in Business & Industry
Regression Techniques
– Simple & Multiple Linear Regression
– Polynomial Regression
– Regularization Methods (Ridge, Lasso, ElasticNet)
Classification Techniques
– Logistic Regression
– Decision Trees & Random Forests
– Support Vector Machines (SVM)
– Neural Networks for Classification (Intro to Deep Learning)
Hands-on:
– Building Regression & Classification Models using Scikit-learn
– Evaluating Model Performance with RMSE, R², Precision-Recall, F1-score

Module 9: Predictive Analysis

Topic: Evaluating Model Accuracy

Understanding Model Performance Metrics
– Regression Metrics: MSE, RMSE, R² Score
– Classification Metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC
– Forecasting Metrics: MAE, RMSE, MAPE
Bias-Variance Tradeoff
– Overfitting vs. Underfitting
– Techniques to Improve Generalization
Hyperparameter Tuning & Model Optimization
– Grid Search vs. Random Search
– Cross-Validation Strategies
Hands-on:
– Comparing multiple models using Hyperparameter Tuning & Cross-Validation
– Visualizing confusion matrices & ROC curves

Topic: Hands-on: Building a predictive model in Python

Module 11: Text Analytic NLP

Topic: Tokenization, Lemmatization, and Sentiment Analysis

Introduction to NLP & Text Processing
– Understanding Unstructured Data
– NLP Pipeline & Preprocessing Steps
Tokenization & Text Normalization
– Word vs. Subword Tokenization
– Sentence Segmentation & Stopword Removal
– Hands-on with NLTK & SpaCy
Lemmatization & Stemming
– Differences & Use Cases
– Implementing Lemmatization with Python
Sentiment Analysis Techniques
– Rule-based vs. Machine Learning Approaches
– Implementing Sentiment Analysis with VADER & TextBlob
– Hands-on: Building a Simple Sentiment Classifier

Topic: Named Entity Recognition (NER)

Understanding Named Entity Recognition (NER)
– Types of Named Entities (People, Organizations, Locations, etc.)
– Applications in Industry (Chatbots, Search Engines, Finance)
Building an NER Model with SpaCy & Hugging Face
– Rule-based vs. Machine Learning-based NER
– Pre-trained Models vs. Custom Training
Hands-on: Implementing NER
– Extracting entities from real-world datasets
– Fine-tuning an NER model using transformer-based approaches

Topic: Word Embeddings & Transformer Models

Understanding Word Embeddings
– Word2Vec, GloVe, FastText
– Contextual vs. Non-contextual Embeddings
Transformer-based Language Models
– Introduction to Transformer Architecture
– Understanding BERT, GPT, and T5 Models
Fine-tuning Transformer Models for NLP Tasks
– Sentiment Analysis, NER, Text Classification
– Hands-on: Fine-tuning BERT for Custom NLP Tasks

Topic: Hands-on: Sentiment analysis on social media data

Module 13: Project Portofolio

Topic: Projects

Topic: Assesment / Uji Kompetensi

Frequently Asked Question

Apakah saya perlu memiliki latar belakang IT untuk mengikuti kursus ini?

Tidak. Kursus ini dirancang dan disesuaikan untuk pemula , mahasiswa, umum dan profesional tanpa latar belakang IT. Materi disusun secara bertahap, mulai dari dasar hingga tingkat lanjut, sehingga dapat diikuti oleh siapa saja.

Apakah saya akan mendapatkan sertifikat setelah menyelesaikan kelas di Digiskill Hub?

Ya. Setelah menyelesaikan seluruh materi dan tugas yang diberikan, Anda akan menerima sertifikat resmi dikeluarkan oleh CCIT FT Universitas Indonesia (UI) yang dapat digunakan untuk melamar pekerjaan atau menambah portofolio profesional.

Apakah kursus ini dilakukan secara langsung (live) atau mandiri?

Kursus ini menggunakan metode blended learning, yaitu kombinasi antara:

Belajar mandiri melalui platform e-learning, di mana peserta dapat mengakses materi, video, dan tugas kapan saja.
Virtual meet via Zoom (live session) bersama mentor, dijadwalkan secara rutin untuk diskusi, tanya jawab, atau membahas topik penting secara interaktif.
Metode ini memberikan fleksibilitas belajar sekaligus pengalaman interaktif dengan pendampingan mentor.

Apakah ada dukungan jika saya mengalami kesulitan saat belajar?

Ya. Kami menyediakan forum diskusi, sesi tanya jawab bersama mentor, serta dukungan teknis untuk membantu Anda selama proses belajar.

Apa spesifikasi minimal Laptop atau Komputer yang dibutuhkan?

Untuk kursus secara umum (selain Mobile Development), perangkat minimal yang disarankan adalah:

Prosesor: Minimal Dual-core, seperti Intel Core i3 generasi ke-6 atau AMD Ryzen 3 2200U
RAM: Minimal 4GB (disarankan 8GB)
Sistem Operasi: Windows 10, macOS 10.13 atau versi lebih baru
Koneksi Internet: Stabil, minimal 10 Mbps

Untuk kursus Mobile Development dan Game Development, disarankan:

Prosesor: Quad-core, seperti Intel Core i5 generasi ke-8 atau AMD Ryzen 5 3500U
RAM: Minimal 8GB (disarankan 12GB atau lebih)
Penyimpanan: SSD minimal 256GB

Apakah sertifikat kursus ini dapat meningkatkan CV & portofolio saya?

Ya. Kursus ini bekerja sama dengan CCIT FT Universitas Indonesia, sehingga sertifikat yang diterbitkan memiliki kredibilitas tinggi dan dapat menjadi nilai tambah pada CV Anda.

Berapa lama durasi kelas Intensive Bootcamp?

Durasi kelas intensive bootcamp adalah 3 bulan, dengan sesi live melalui Zoom 2 kali dalam seminggu, masing-masing berdurasi 3 jam. Jadwal berlangsung pada hari kerja (weekdays) pukul 19.00 – 22.00 WIB atau hari libur (weekend) pukul 09.00 - 12.00

Berapa lama durasi kelas Fast Track?

Durasi kelas fast track adalah 5 hari, dengan sesi live melalui Zoom 5 kali dalam seminggu, masing-masing berdurasi 3 jam. Jadwal berlangsung pada hari kerja (weekdays) pukul 19.00 – 22.00 WIB atau hari libur (weekend) pukul 09.00 - 12.00

Apakah saya bisa belajar di luar jam virtual meet?

Ya. Materi kursus dapat diakses kapan saja melalui platform LMS atau LXP, sehingga Anda bisa belajar secara fleksibel di luar jadwal live session.

Apakah ada tugas atau proyek yang harus diselesaikan?

Ya. Tugas diberikan di setiap akhir pertemuan. Selain itu, peserta akan mengerjakan proyek nyata (real project) sebagai bagian dari proses belajar dan portofolio.

Apakah kursus ini berbayar?

Ya. Kursus ini berbayar, namun Anda akan mendapatkan akses seumur hidup ke seluruh materi pembelajaran, termasuk video, modul, dan forum diskusi.

Apakah ada pendampingan langsung dari mentor?

Ya. Peserta akan mendapatkan bimbingan langsung dari mentor profesional, serta akses ke group chat khusus untuk berdiskusi dan berkonsultasi.

Apakah ada tes masuk untuk bisa mengikuti program ini?

Tidak ada tes masuk untuk dapat mengikuti program di Digiskill Hub, semua orang dengan latar belakang apapun dapat mengikuti program ini