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• Overview of Big Data
  • What is Big Data?
  • History of Big Data
  • The Vs’ of Big Data (3Vs’, 4Vs’, 5Vs’)
  • Batch processing vs Stream processing
  • Introduction to Apache Spark
  • Apache Spark Components: Spark RDD API, Spark SQL, Spark MLlib, Spark GraphX, Spark Streaming
• Overview of PySpark
  • Introduction to PySpark: Spark with Python (Python API)
  • Why PySpark?
  • Installing and configuration PySpark
  • Spark context, Spark Session
• PySpark RDDs
  • Introduction to PySpark RDDs (Resilient Distributed Dataset)
  • RDDs operations
    1. Transformation
    2. Action
  • Working with PySpark RDDs
    1. Create RDD: parallelize(), textFile()
    2. RDD Transformations: map(), filter(), flatMap(), RDD1.union(RDD2)
    3. RDD Actions: collect(), take(), count(), first(), reduce(), saveAsTextFile(),…
    4. Pair RDDs:
       1. Create Pair RDDs from key-value tuple/ regular RDD
       2. Transformations: reduceByKey(), groupByKey(), sortByKey(), join()
       3. Actions: countByKey(), collectAsMap()
• PySpark DataFrame
  • Introduction to PySpark DataFrame
  • Features and Advantages
  • Working with PySpark DataFrame
    1. Create DataFrame: createDataFrame(), spark.read.csv(), spark.read.json()
    2. printSchema(), show()
    3. count()
    4. describe()
    5. crosstab()
    6. groupby()
    7. select(), select() và agg, count, max, mean, min, sum..., select().distinct(),
    8. orderby().asc()/desc()
    9. withColumn(), withColumnRenamed()
    10. drop(), dropDuplicates(), dropna()
    11. filter(), where()
    12. Column string transformation
    13. Conditional clauses: .when(<if condition>, <then x>), .otherwise()
    14. User defined functions (UDF)
  • Data Visualization in PySpark using DataFrames
    1. hist(), distplot()
    2. pandas_histogram()
• PySpark SQL
  • Introduction to PySpark SQL
  • Running SQL Queries Programmatically
    1. select()
    2. when()
    3. like()
    4. startswith(), endswith()
    5. substr(), between()
  • Manipulating data
    1. Group by
    2. Filtering
    3. Sorting
    4. Missing and replacing value
    5. Joining Data
    6. Repartitioning
    7. Registering DataFrames as Views
• Data Preprocessing & Analysis
  • Wrangling with Spark Functions
    1. Dropping, Filtering, Joining
    2. Working with missing data
    3. Using lazy processing
    4. Parquet
    5. Removing, Splitting rows/columns
    6. Data validation
  • Feature Engineering
    1. Feature Generation
    2. Differences, Ratios
    3. Deeper Features, Time Features
    4. Time Components, Joining On Time Components
    5. Date Math
    6. Extracting Features/ Text to New Features
    7. Splitting & Exploding
    8. Scaling data
    9. Pivoting & Joining
    10. Binarizing, Bucketing & Encoding
  • Data Analysis
    1. Exploratory Data Analysis (EDA), Corr
    2. Visualization: distplot, implot…
• Overview of PySpark MLlib
  • Introduction to PySpark MLlib
  • PySpark MLlib algorithms
  • Building a Model
  • Estimator and evaluator
  • Cross-validation, Grid Search
  • Interpreting Results
• Machine Learning with PySpark MLlib
  • Supervised Learning (Classification & Regression)
    1. Linear Regression (pyspark.ml.regression)
    2. Logistic Regression (pyspark.mllib.classification)
    3. Decision Tree (pyspark.mllib.classification)
    4. Random forest (pyspark.mllib.classification)
    5. Gradient-Boosted Tree
  • Pipeline
    1. Introduction to Pipeline
    2. Working with Pipeline (pyspark.ml import Pipeline)
  • Unsupervised Learning (Clustering & Recommender System)
    1. Clustering with KMeans
    2. Recommender System - ALS
    3. Association rules – FPGrowth (pyspark.ml.fpm.FPGrowth)
• PySpark Streaming
  • Introduction to PySpark Streaming
  • Why PySpark Streaming?
  • Features and Advantages
  • Streaming Context
  • DStream
  • Streaming Transformation Operations
  • Streaming Checkpoint
• Natural Language Processing - NLP
  • Tools for NLP
    1. Tokenizer
    2. StopWordsRemover
    3. NGram
    4. CountVectorizer
    5. TF-IDF
• Apache Spark standalone cluster
  • Running Master Server
  • Connecting from Slave computers to Master Server
  • Deployment project in Mater – Slave computers system
• GraphX
  • Introduction to GraphX
  • Working with GraphX
    1. Creating graph
    2. Vertex and edge
    3. Visualization Graph
    4. Filtering
    5. Connecting
    6. Motif finding
    7. Triangle count
    8. Page rank

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BẰNG CẤP - CHỨNG CHỈ

Học viên thi đạt kết quả cuối môn học sẽ được cấp chứng nhận "Big Data in Machine Learning" do Trung Tâm Tin Học - Trường ĐH Khoa Học Tự Nhiên cấp.