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When:

Sat Mar 16 2019, 9:30am–5:00pm
Sun Mar 17 2019, 9:30am–5:00pm

Where: Orchid Country Club, 1 Orchid Club Road, Yishun, Singapore

Restrictions: All ages

Ticket Information:

  • Candidates: $199.00
  • Additional fees may apply

Listed by: shruthi

Descriptive and Inferential Statistics:

- Samples and Populations (Day 1)
- Sample Statistics
- Estimations of Population Parameters
- Random and Non-random Sampling
- Sampling Distributions
- The Central limit Theorem
- Degree of Freedom
- Percentiles and Quartiles
- Measures of Central Tendency (Day 2)
- Mean
- Median
- Mode
- Measures of Variability/Dispersions
- Range
- IQR
- Variance
- Standard Deviation
- Probability Distributions (Day 3)
- Events, Sample Space and Probabilities
- Conditional Probabilities
- Independence of Events
- Bayes’ Theorem
- Random Variable
- The Normal Distributions (Day 4)
- The Comparison of Two Populations
- Analysis of Variance
- ANOVA Computations
- Two-way ANOVA

Data Wrangling:

- What is Data Wrangling?
- Acquiring Data
- Common Data Formats
- What are Relational Databases?
- Introduction to Databases Schemas
- API’s
- Data in JSON Format
- How to Access an API efficiently
- Missing Values
- Easy Imputation
- Impute using Linear Regression
- Tip of the Imputation Iceberg

Text Mining:

- Sentiment Analysis
- User Behavior Analysis
- Topic Categorization
- Topic Ranking

Data Exploration and Dimension Reduction

- Data Summaries
- Covariance, Correlation, and Distances
- Missing Values Handling
- Outliers Handling
- Principal Component Analysis
- Exploratory Factor Analysis

Machine Learning: Introduction and Concepts

- Differentiating algorithmic and model based frameworks
- Regression
- Ordinary Least Squares
- Ridge Regression
- Lasso Regression
- K Nearest Neighbors Regression & Classification

Supervised Learning with Regression and Classification

- Bias-Variance Dichotomy
- Model Validation Approaches
- Training Set
- Validation Set
- Test Set
- Cross-Validation
- Logistic Regression
- Linear Discriminant Analysis
- Quadratic Discriminant Analysis
- Forecasting (Time-Series Modelling )
- Trend and Seasonal Analysis
- Different Smoothing Techniques
- RIMA Modelling
- ETS Modelling

Unsupervised Learning

- Clustering
- Hierarchical (Agglomerative) Clustering
- Non-Hierarchical Clustering: The k-Means Algorithm
- Associative Rule Mining
- Apriori Algorithms
- Frequent Item-sets
- Support
- Confidence
- Lift Ratio
- Discovering Association Rules

Machine Learning Techniques Using R Part-1

- Machine Learning Overview
- Machine Learning Common Use Cases
- Clustering, Similarity Metrics
- Distance Measure Types: Euclidean, Cosine Measures, Creating predictive models.

Machine Learning Techniques Using R Part-2

- Understanding K-Means Clustering
- Understanding TF-IDF and Cosine Similarity and their application to Vector Space Model
- Implementing Association rule mining in R.

Machine Learning Techniques Using R Part-3

- Understanding Process flow of Supervised Learning Techniques
- Decision Tree Classifier
- How to build Decision trees
- Random Forest Classifier
- What is Random Forests
- Features of Random Forest
- Out of Box Error Estimate and Variable Importance
- Naive Bayes Classifier.

Python Introduction

- What is Python?
- Why Python now?
- How is the Job Market for Python Developers
- Installation and documentation

Python Basics

- Keywords and Identifier
- Statement, Indentation and Comments
- Variables and Datatypes
- Native Datatypes
- Input, Output and Import
- Relational/Logical Operators

Python Functions

- Running Python as a calculator
- Python Functions
- String Functions
- Date Functions
- Numeric Functions
- Loading commands from the library
- User Defined functions

Python commands

- Numbers and other data type function
- Strings
- Lists
- Length of a list; empty list
- Sub lists (slicing)
- Joining two lists
- List methods
- Range function
- Boolean values
- Expressions
- Variables and assignment
- Decisions
- Loops
- For loop
- While loop
- Else in loops
- Break, continue, and pass

Data structures

- More on lists
- The del statement
- Tuples and sequences
- Sets
- Dictionaries
- Modules
- Standard modules
- The dir() Function
- Packages

Files and Input and Output Operations on Files

- Fancier Output Formatting
- Reading and Writing Files
- File Operations
- Appending
- Sorting
- Merging
- Python Directory and Files Management

Exceptions and Error Handling

- Built-in Exceptions
- Exception Handling - Try, Except and Finally
- User-Defined Exception
- Error Handling scenarios

Python Object & Class

- Namespace and Scope
- Inheritance
- Multiple Inheritance
- Operator Overloading

Additional Topics

- Iterators
- Generators
- Closures
- Decorators

Numpy

- Introduction
- Fundamental package for scientific computing with Python
- N-dimensional array object
- Linear algebra, Fourier transform, random number capabilities
- Numerical Operations on Numpy Arrays
- Concatenating, Flattening and Adding Dimensions
- Python, Numpy and Probability
- Synthetical Test Data With Python
- Numpy: Boolean Indexing
- Matrix Arithmetics under Numpy and Python

Introduction into Pandas

- Defining Data Frames
- Operations using Data Frames
- Adding/deleting columns - Index operations
- Stack/Unstack/Transpose functions
- GroupBy function
- Converting between different kinds of formats

Web Scraping with Python

- Reading data from public websites
- Identifying websites DOM structures
- Using BeautifulSoup
- Writing web data to local files
- Handling errors from web data reads

Project outline

In this project you will learn how to read data from any public website and then store data into CSV file format. We will create a programmatic flow to ensure that the data appended to CSV file and then we create various Data Frames to generate analytical results. In this entire process we will use all the python features including classes, modules and libraries including (CSV, OS, GLOB, BS4, BeautifulSoup, Pandas, Numpy and many other modules). We will implement several filter conditions and data cleansing functions to ensure the quality. We will implement several String, Date functions.