Data Science Learning Path

Machine Learning

Chapter 01 - Pengenalan

1. Apa itu machine learning, artificial intelligence, dan data science
2. Apa saja masalah-masalah yang dapat diselesaikan menggunakan machine learning?
3. Bidang-bidang yang terkait dengan machine learning
4. Apa yang perlu dikuasai untuk menjadi seorang machine learning?

Chapter 02 - Regresi

1. Pengenalan tentang regresi (termasuk evaluation metrics, e.g: MSE dan MAE)
2. Regresi linear sederhana
3. Regresi polinomial
4. Regresi dengan regularisasi
5. Suport vector regression
6. Generalized linear model

Chapter 03 - Klasifikasi

1. Pengenalan tentang klasifikasi dan confusion matrix
2. Logistic regression (regresi logistik)
3. LDA (Linear Discriminant Analysis)
4. k-NN (k-Nearest Neighbors)
5. Naive bayes
6. Decision tree
7. Support vector machine
8. Neural networks

Chapter 04 - Klastering

1. Pengenalan tentang klastering
2. k-means klastering
3. EM (Expectation-Maximization) klastering
4. Klastering hirarkis

Chapter 05 - Metode Kernel

1. Pengenalan tentang metode kernel
2. Kernel k-means
3. Kernel SVM
4. Kernel regresi

Chapter 06 - Data Preprocessing

1. Feature engineering
2. Transformasi data
3. Data cleaning
4. Pengurangan dimensi (PCA, LDA)
5. Seleksi variabel

Deep Learning

Chapter 01 - Pengenalan

1. Pengenalan tentang deep learning dan tools

Chapter 02 - Deep Learning Model

1. CNN (Convolutional Neural Networks): case untuk klasifikasi digit MNIST
2. RNN (Recurrent Neural Networks)
3. Generative Model: GAN (Generative Adversarial Networks) dan Autoencoder

Chapter 03 - State of the Art Model

1. Deep learning Object Detection: SSD, Yolo, Mask RCNN
2. Deep learning Image Segmentation: FCN, SegNet, Mask RCNN

Text Mining dan Natural Language Processing

Chapter 01 - Pengantar Text Mining dan NLP

1. Overview Text Mining dan NLP
2. Corpus
3. Dictionary

Chapter 02 - Feature Extraction

1. Feature extraction
2. Bag of words
3. Term Document matrix
4. Term frequency and Weight
5. TF-IDF

Chapter 03 - Grammar

1. POS Tagging
2. Named Entity Recognition

Chapter 04 - Text Classification

1. Overview Text Classification
2. Binary Classification
3. Multiclass Classification
4. Multilabel Classification

Chapter 05 - Deep NLP

1. Information Retrieval
2. Text Clustering
3. Document Similarity
4. topic modeling
5. Word2Vec
6. Skip.Gram
7. CBOW
8. Language Modeling
9. Natural Language Understanding
10. Natural Language Generation

Computer Vision

Chapter 01 - Pengenalan

1. Pengenalan tentang computer vision dan tools
2. Representasi image dan video di dalam komputer

Chapter 02 - Image Thresholding

1. Binary thresholding
2. Otsu thresholding

Chapter 03 - Spatial Filtering

1. Pengenalan tentang spatial filtering
2. Smoothing (averaging filter)
3. Sharpening
4. Median filter
5. Sobel filter

Chapter 04 - Morphological Processing

1. Erosion
2. Dilation
3. Morphological opening & closing

Chapter 05 - Image Analysis

1. Connected component analysis
2. Image segmentation
3. Object detection: case face detection

Automatic Speech Recognition

Chapter 01 - Introduction

1. Overview Speech Recognition

Chapter 02 - Signal Processing

1. MFCC
2. LPC
3. Noise Reduction

Advance Speech Recognition

1. Speech Recognition for Low Resource
2. Large Vocabulary Continuous Speech Recognition
3. Speaker Indentification
4. Speech Enhancement
5. Speech separation

Data Visualization

Chapter 01 - Pengenalan

1. Overview Data Visualization
2. Principles of Data Visualization
3. Overview Chart

Chapter 02 - Charts

1. Pie Chart
2. Line Chart
3. Bar Chart
4. Stacked Bar Chart
5. Heat Map
6. Bubble Chart
7. Area Charts
8. Box Plot
9. Whisker plot
10. Scatter Plot
11. GeoSpatial
12. Real Time Data Visualization

Toolbox

Chapter 01 - Toolbox

1. MS Excel with Analysis toolpack
2. Java, Python
3. R, Rstudio, Rattle
4. Weka, Knime, RapidMiner
5. Hadoop dist of choice
6. Spark, Storm
7. Flume, Scibe, Chukwa
8. Nutch, Talend, Scraperwiki
9. Webscraper, Flume, Sqoop
10. tm, RWeka, NLTK
11. RHIPE
12. D3.js, ggplot2, Shiny
13. IBM Languageware
14. Microsoft Azure, AWS, Google Cloud
15. Cassandra, MongoDB
16. Microsoft Cognitive API
17. Tensorflow
18. Git

Database

1. Pengenalan Basis Data
2. Basic SQL
3. Intermediate SQL
4. Advance SQL

Building Blocks

Chapter 01 - Fundamentals

1. Matrices, Vector & Algebra fundamentals
2. Hash function, binary tree, O(n)
3. Relational algebra, DB basics (with SQL)
4. Inner, Outer, Cross, theta-join
5. CAP theorem
6. Tabular data
7. Entropy
8. Data frames & series
9. Sharding
10. OLAP
11. Multidimensional Data model
12. ETL
13. Reporting vs BI vs Analytics
14. JSON and XML
15. NoSQL
16. Regex

Chapter 02 - Statistics

1. Pick a dataset
2. Descriptive statistics
3. Exploratory data analysis
4. Histograms
5. Percentiles & outliers
6. Probability theory
7. Bayes theorem
8. Random variables
9. Cumul Dist Fn (CDF)
10. Continuous distributions
11. Skewness
12. ANOVA
13. Prob Den Fn (PDF)
14. Central Limit theorem
15. Monte Carlo method
16. Hypothesis Testing
17. p-Value
18. Chi2 test
19. Estimation
20. Confid Int (CI)
21. MLE
22. Kernel Density estimate
23. Regression
24. Covariance
25. Correlation
26. Pearson coeff
27. Causation
28. Least2-fit
29. Euclidian Distance
30. Measures of centralizing Data
31. Measures of spread Data

Chapter 03 - Programming

1. Python Basics
2. Working in excel
3. R setup / R studio
4. R basics
5. Expressions
6. Variables
7. IBM SPSS
8. Rapid Miner
9. Vectors
10. Matrices
11. Arrays
12. Factors
13. Lists
14. Data frames
15. Reading CSV data
16. Reading raw data
17. Subsetting data
18. Manipulate data frames
19. Functions
20. Factor analysis
21. Install PKGS
22. Code versioning
23. Data Table

Chapter 04 - Big Data

1. Map Reduce fundamentals
2. Hadoop Components
3. HDFS
4. Data replications Principles
5. Setup Hadoop
6. Name & data nodes
7. Job & task tracker
8. M/R programming
9. Sqop: Loading data in HDFS
10. Flume, Scribe
11. SQL with Pig
12. DWH with Hive
13. Scribe, Chukwa for Weblog
14. Using Mahout
15. Zookeeper Avro
16. Storm: Hadoop Realtime
17. Rhadoop, RHIPE
18. RMR
19. Cassandra
20. MongoDB, Neo4j

Chapter 05 - Data Munging

1. Summary of data formats
2. Data discovery
3. Data sources & Acquisition
4. Data integration
5. Data fusion
6. Transformation & enrichment
7. Data survey
8. Google OpenRefine
9. How much data ?
10. Using ETL
11. Dim. and num. reduction
12. Normalization
13. Data scrubbing
14. Handling missing Values
15. Unbiased estimators
16. Binning Sparse Values
17. Feature extraction
18. Denoising
19. Sampling
20. Stratified sampling
21. PCA

Chapter 06 - Python

1. Intro Python
2. Set Up Environment
3. Data Structure
4. Iteration & Conditional
5. Intro Libraries
6. Function
7. OOP
8. Package
9. Numpy