Data Science Training Massachusetts

Data Science Training Massachusettsis a "concept to unify statistics, data analysis, machine learning & their related methods" in order to "understand & analyze actual phenomena" with data.Data Science Training MassachusettsIt employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, data mining, databases, and visualization.
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What you will Learn

data science training Massachusettswill help you master skills and tools like Statistics, Hypothesis testing, Clustering, Decision trees, Linear and Logistic regression, Data Science Training Moscow, R Studio, Data Visualization, Regression models, Hadoop, Spark, PROC SQL, SAS Macros, Statistical procedures,Advanced analytics, Matplotlib, Excel analytics functions, Hypothesis testing, Zookeeper, Kafka interfaces data science course Moscow.
data science course MassachusettsYou will master TensorFlow, Machine Learning, and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems. data science course MassachusettsThese skills will help you prepare for the role of a Data Scientist.
Data science course Massachusettsthe program provides access to high-quality eLearning content, simulation exams, a community moderated by experts, Data Science Training Massachusettsand other resources that ensure you follow the optimal path to your dream role of data scientist.

PREREQUISITES

Basic knowledge of statistics
Basic understanding of any programming language

CURRICULUM

Learning Objectives:

Introduction

Analytics Overview

Introduction
Introduction to Business Analytics
Types of Analytics
Areas of Analytics
Analytical Tools
Analytical Techniques

Introduction to SAS

Introduction
What is SAS
Navigating in the SAS Console
SAS Language Input Files
DATA Step
PROC Step and DATA Step
DATA Step Processing
SAS Libraries
Importing Data
Exporting Data

Combining and Modifying Datasets

Introduction
Why Combine or Modify Data
Concatenating Datasets
Interleaving Method
One – to – one Reading
One – to – one Merging
Data Manipulation
Modifying Variable Attributes

PROC SQL

Introduction
What is PROC SQL
Retrieving Data from a Table
Selecting Columns in a Table
Retrieving Data from Multiple Tables
Selecting Data from Multiple Tables
Concatenating Query Results
Activity

SAS Macros

Introduction
Need for SAS Macros
Macro Functions
Macro Functions Examples
SQL Clauses for Macros
The % Macro Statement
The Conditional Statement

Basics of Statistics

Introduction to Statistics
Statistical Terms
Procedures in SAS for Descriptive Statistics
Descriptive Statistics
Hypothesis Testing
Variable Types
Hypothesis Testing
Process
Parametric and Non – parametric Tests
Parametric Tests
Non – parametric Tests
Parametric Tests – Advantages and Disadvantages

Statistical Procedures

Introduction o Statistical Procedures
PROC Means
PROC FREQ
PROC UNIVARIATE
PROC CORR
PROC CORR Options
PROC REG
PROC REG Options
PROC ANOVA

Data Exploration

Introduction
Data Preparation
General Comments and Observations on Data Cleaning
Data Type Conversion
Character Functions
SCAN Function
Date/Time Functions
Missing Value Treatment
Various Functions to Handle Missing Value
Data Summarization

Advanced Statistics

Introduction to Cluster
Clustering Methodologies
K Means Clustering
Decision Tree
Regression
Logistic Regression

Working with Time Series Data

Introduction
Need for Time Series Analysis
Time Series Analysis — Options
Reading Date and Datetime Values
White Noise Process
Stationarity of a Time Series
Stages of ARIMA Modelling
Transform Transpose and Interpolating Time Series Data

Designing Optimization Models

Introduction
Need for Optimization
Optimization Problems
PROC OPTMODEL

Learning Objectives:

Introduction to Business Analytics

Introduction
Objectives
Need of Business Analytics
Business Decisions
Introduction to Business Analytics
Features of Business Analytics
Types of Business Analytics
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
Supply Chain Analytics
Health Care Analytics
Marketing Analytics
Human Resource Analytics
Web Analytics
Application of Business Analytics
Business Decisions
Business Intelligence (BI)
Data Science
Importance of Data Science
Data Science as a Strategic Asset
Big Data
Analytical Tools

Introduction to R

Introduction
Objectives
An Introduction to R
Comprehensive R Archive Network (CRAN)
Cons of R
Companies Using R
Understanding R
Installing R on Various Operating Systems
Installing R on Windows from CRAN Website
Install R
IDEs for R
Installing RStudio on Various Operating Systems
Install R-Studio
Steps in R Initiation
Benefits of R Workspace
Setting the Workplace
Functions and Help in R
Access the Help Document
R Packages o Installing an R Package
Install and Load a Package

R Data Structure

Introduction
Objectives
Types of Data Structures in R
Vectors
Create a Vector
Scalars
Colon Operator
Accessing Vector Elements
Matrices
Accessing Matrix Elements
Create a Matrix
Arrays
Accessing Array Elements
Create an Array
Data Frames
Elements of Data Frames
Create a Data Frame
Factors
Create a Factor
Lists
Create a List
Importing Files in R
Importing an Excel File
Importing a Minitab File
Importing a Table File
Importing a CSV File
Read Data from a File
Read Data from a File
Exporting Files from R

Apply Functions

Introduction
Objectives
Types of Apply Functions
Apply() Function
Lapply() Function
Sapply() Function
Tapply() Function
Vapply() Function
Mapply() Function
Dplyr Package
Installing the Dplyr Package
Functions of the Dplyr Package
Functions of the Dplyr Package – Select()
Use the Select() Function
Functions of Dplyr-Package – Filter()
Use the Filter() Function
Use Select Function
Functions of Dplyr Package – Arrange()
Use Arrange Function
Functions of Dplyr Package – Mutate()
Functions of Dply Package – Summarise()
Use Summarise Function

Data Visualization

Introduction
Objectives
Graphics in R
Types of Graphics
Bar Charts
Creating Simple Bar Charts
Editing a Simple Bar Chart
Create a Stacked Bar Plot and Grouped Bar Plot
Pie Charts
Editing a Pie Chart
Create a Pie Chart
Histograms
Creating a Histogra
Kernel Density Plots
Creating a Kernel Density Plot
Create Histograms and a Density Plot
Line Charts
Creating a Line Chart
Box Plots
Creating a Box Plot
Create Line Graphs and a Box Plot
Heat Maps o Creating a Heat Map
Create a Heatmap
Word Clouds
Creating a Word Cloud
File Formats for Graphic Outputs
Saving a Graphic Output as a File
Save Graphics to a File
Exporting Graphs in RStudio
Exporting Graphs as PDFs in RStudio
Save Graphics Using RStudio

Introduction to Statistics

Introduction
Objectives
Basics of Statistics
Types of Data
Qualitative vs. Quantitative Analysis
Types of Measurements in Order
Nominal Measurement
Ordinal Measurement
Interval Measurement
Ratio Measurement
Statistical Investigation
Normal Distribution
Example of Normal Distribution
Importance of Normal Distribution in Statistics
Use of the Symmetry Property of Normal Distribution
Standard Normal Distribution
Use Probability Distribution Functions
Distance Measures
Distance Measures – A Comparison
Euclidean Distance
Example of Euclidean Distance
Manhattan Distance
Minkowski Distance
Mahalanobis Distance
Cosine Similarity
Correlation
Correlation Measures Explained
Pearson Product Moment Correlation (PPMC)
Pearson Correlation
Dist() Function in R
Perform the Distance Matrix Computations

Hypothesis Testing I

Introduction
Objectives
Hypothesis
Need of Hypothesis Testing in Businesses
Null Hypothesis
Alternate Hypothesis
Null vs. Alternate Hypothesis
Chances of Errors in Sampling
Types of Errors
Contingency Table
Decision Making
Critical Region
Level of Significance
Confidence Coefficient
Bita Risk
Power of Test
Factors Affecting the Power of Test
Types of Statistical Hypothesis Tests
Upper Tail Test
Test Statistic
Factors Affecting Test Statistic
Critical Value Using Normal Probability Table

Hypothesis Testing II

Introduction
Objectives
Parametric Tests
Z-Test
Z-Test in R
T-Test
T-Test in R
Use Normal and Student Probability Distribution Functions
Testing Null Hypothesis
Objectives of Null Hypothesis Test
Three Types of Hypothesis Tests
Hypothesis Tests About Population Means
Decision Rules
Hypothesis Tests About Population Means
Hypothesis Tests About Population Proportions
Chi-Square Test
Steps of Chi-Square Test
Degree of Freedom
Chi-Square Test for Independence
Chi-Square Test for Goodness of Fit
Chi-Square Test for Independence
Chi-Square Test in R
Use Chi-Squared Test Statistics
Introduction to ANOVA Test
One-Way ANOVA Test
The F-Distribution and F-Ratio
F-Ratio Test
F-Ratio Test in R
One-Way ANOVA Test
One-Way ANOVA Test in R
Perform ANOVA

Regression Analysis

Introduction
Objectives
Introduction to Regression Analysis
Use of Regression Analysis
Types Regression Analysis
Simple Regression Analysis
Multiple Regression Models
Simple Linear Regression Model
Perform Simple Linear Regression
Correlation
Correlation Between X and Y
Find Correlation
Method of Least Squares Regression Model
Coefficient of Multiple Determination Regression Model
Standard Error of the Estimate Regression Model
Dummy Variable Regression Model
Interaction Regression Model
Non-Linear Regression
Non-Linear Regression Models
Perform Regression Analysis with Multiple Variables
Non-Linear Models to Linear Models
Algorithms for Complex Non-Linear Models

Classification

Objectives
Introduction to Classification
Examples of Classification
Classification vs. Prediction
Classification System
Classification Process
Classification Process – Model Construction
Classification Process – Model Usage in Prediction
Issues Regarding Classification and Prediction
Data Preparation Issues
Evaluating Classification Methods Issues
Decision Tree
Decision Tree – Dataset
Classification Rules of Trees
Overfitting in Classification
Tips to Find the Final Tree Size
Basic Algorithm for a Decision Tree
Statistical Measure – Information Gain
Calculating Information Gain for Continuous-Value Attributes
Enhancing a Basic Tree
Decision Trees in Data Mining
Model a Decision Tree
Naive Bayes Classifier Model
Features of Naive Bayes Classifier Model
Bayesian Theorem
Naive Bayes Classifier
Applying Naive Bayes Classifier
Naive Bayes Classifier – Advantages and Disadvantages
Perform Classification Using the Naive Bayes Method
Nearest Neighbor Classifiers
Computing Distance and Determining Class
Choosing the Value of K
Scaling Issues in Nearest Neighbor Classification
Support Vector Machines
Advantages of Support Vector Machines
Geometric Margin in SVMs
Linear SVMs
Non-Linear SVMs
Support a Vector Machine

Clustering

Introduction
Objectives
Introduction to Clustering
Clustering vs. Classification
Use Cases of Clustering
Clustering Models
K-means Clustering
K-means Clustering Algorithm
Pseudo Code of K-means
K-means Clustering Using R
K-means Clustering
Perform Clustering Using K-means
Hierarchical Clustering
Hierarchical Clustering Algorithms
Requirements of Hierarchical Clustering Algorithms
Agglomerative Clustering Process
Perform Hierarchical Clustering
DBSCAN Clustering
Concepts of DBSCAN
DBSCAN Clustering Algorithm
DBSCAN in R
DBSCAN Clustering

Association

Introduction
Objectives
Association Rule Mining
Application Areas of Association Rule Mining
Parameters of Interesting Relationships
Association Rules
Association Rule Strength Measures
Limitations of Support and Confidence
Apriori Algorithm
Applying Aprior Algorithm
Step 1 – Mine All Frequent Item Sets
Algorithm to Find Frequent Item Set
Ordering Items
Candidate Generation
Step 2 – Generate Rules from Frequent Item Sets
Perform Association Using the Apriori Algorithm
Perform Visualization on Associated Rules
Problems with Association Mining

Learning Objectives:

Course Introduction

Introduction to Big data and Hadoop Ecosystem

Introduction

Overview to Big Data and Hadoop

Hadoop Ecosystem

HDFS and YARN

Introduction

HDFS Architecture and Components

Block Replication Architecture

YARN Introduction

MapReduce and Sqoop

Introduction

Why Mapreduce

Small Data and Big Data

Data Types in Hadoop

Joins in MapReduce

What is Sqoop

Basics of Hive and Impala

Introduction

Interacting with Hive and Impala

Working with Hive and Impala

Data Types in Hive

Validation of Data

What is Hcatalog and Its Uses

Types of Data Formats

Introduction

Types of File Format

Data Serialization

Importing MySql and Creating hivetb

Parquet With Sqoop

Advanced Hive Concept and Data File Partitioning

Introduction

Overview of the Hive Query Language

Apache Flume and HBase

Introduction

Introduction to HBase

Pig

Introduction

Getting Datasets for Pig Development

Basics of Apache Spark

Introduction

Spark – Architecture, Execution, and Related Concepts

RDD Operations

Functional Programming in Spark

RDDs in Spark

Introduction

RDD Data Types and RDD Creation

Operations in RDDs

Implementation of Spark Applications

Introduction

Running Spark on YARN

Running a Spark Application

Dynamic Resource Allocation

Configuring Your Spark Application

Spark Parallel Processing

Introduction

Parallel Operations on Partitions

Spark RDD Optimization Techniques

Introduction

RDD Persistence

Spark RDD Optimization Techniques

Spark Algorithm

Introduction

Spark: An Iterative Algorithm

Introduction To Graph Parallel System

Introduction To Machine Learning

Introduction To Three C’s

Spark SQL

Introduction

Interoperating with RDDs

Apache Kafka

Core Java

Learning Objectives:

Course Overview

Data Science

Introduction to Data Science
Different Sectors Using Data Science
Purpose and Components of Python

Data Analytics

Data Analytics Process
Exploratory Data Analysis(EDA)
EDA-Quantitative Technique
EDA – Graphical Technique
Data Analytics Conclusion or Predictions
Data Analytics Communication
Data Types for Plotting
Data Types and Plotting

Statistical Analysis and Business Applications

Introduction to Statistics
Statistical and Non-statistical Analysis
Major Categories of Statistics
Statistical Analysis Considerations
Population and Sample
Statistical Analysis Process
Data Distribution
Dispersion o Histogram
Testing

Python Environment Setup and Essentials

Anaconda
Installation of Anaconda Python Distribution (contd.)
Data Types with Python
Basic Operators and Functions

Mathematical Computing with Python (NumPy)

Introduction to Numpy
Activity-Sequence it Right
Creating and Printing an ndarray
Class and Attributes of ndarray
Basic Operations
Activity-Slice It
Copy and Views
Mathematical Functions of Numpy

Scientific computing with Python (Scipy)

Introduction to SciPy
SciPy Sub Package – Integration and Optimization
SciPy sub package
Calculate Eigenvalues and Eigenvector
SciPy Sub Package – Statistics, Weave and IO

Data Manipulation with Pandas

Introduction to Pandas
Understanding DataFrame
View and Select Data
Missing Values
Data Operations
File Read and Write Support
Pandas Sql Operation

Machine Learning with Scikit–Learn

Machine Learning Approach
How it Works
Supervised Learning Model Considerations
Scikit-Learn
Supervised Learning Models – Linear Regression
Supervised Learning Models – Logistic Regression
Unsupervised Learning Models
Pipeline
Model Persistence and Evaluation

Natural Language Processing with Scikit Learn

NLP Overview
NLP Applications
NLP Libraries-Scikit
Extraction Considerations
Scikit Learn-Model Training and Grid Search

Data Visualization in Python using matplotlib

Introduction to Data Visualization
Line Properties
(x,y) Plot and Subplots
Types of Plots

Web Scraping with BeautifulSoup

Web Scraping and Parsing
Understanding and Searching the Tree
Navigating options
Navigating a Tree
Modifying the Tree
Parsing and Printing the Document

Python integration with Hadoop MapReduce and Spark

Why Big Data Solutions are Provided for Python
Hadoop Core Components
Python Integration with HDFS using Hadoop Streaming
Using Hadoop Streaming for Calculating Word Count
Python Integration with Spark using PySpark
Using PySpark to Determine Word Count

Python Basics

Learning Objectives:

Course Introduction

Introduction to Business Analytics

Introduction
What Is in It for Me
Types of Analytics
Areas of Analytics

Formatting Conditional Formatting and Important Functions

Introduction
What Is in It for Me
Custom Formatting Introduction
Conditional Formatting Introduction
Logical Functions
Lookup and Reference Functions
VLOOKUP Function
HLOOKUP Function
MATCH Function
INDEX and OFFSET Function
Statistical Function
SUMIFS Function
COUNTIFS Function
PERCENTILE and QUARTILE
STDEV, MEDIAN and RANK Function

Analyzing Data with Pivot Tables

Introduction
What Is in It for Me
Pivot Table Introduction
Concept Video of Creating a Pivot Table
Grouping in Pivot Table Introduction
Custom Calculation
Calculated Field and Calculated Item
Slicer Intro
Creating a Slicer

Dashboarding

Introduction
What Is in It for Me
What is a Dashboard
Principles of Great Dashboard Design
How to Create Chart in Excel
Chart Formatting
Thermometer Chart
Pareto Chart
Form Controls in Excel
Interactive Dashboard with Form Controls
Chart with Checkbox
Interactive Chart

Business Analytics With Excel

Introduction
What Is in It for Me
Concept Video Histogram
Concept Video Solver Addin
Concept Video Goal Seek
Concept Video Scenario Manager
Concept Video Data Table
Concept Video Descriptive Statistics

Data Analysis Using Statistics
Introduction
Moving Average
Hypothesis Testing
ANOVA
Covariance
Correlation
Regression
Normal Distribution

Power BI

Introduction
Power Pivot
Power View
Power Query
Power Map

Microsoft Power BI Desktop

Microsoft Power BI Recipes

Learning Objectives:

Machine Learning Introduction

Techniques of Machine Learning
Supervised Learning
Unsupervised Learning
Semi-supervised Learning and Reinforcement Learning
Some Important Considerations in Machine Learning

Data Preprocessing

Data Preparation
Feature engineering
Feature scaling
Datasets
Dimensionality reduction

Math Refresher

Eigenvalues, Eigenvectors, and Eigendecomposition
Concepts of Linear Algebra
Introduction to Calculus
Probability and Statistics

Regression

Regression and Its Types
Linear Regression: Equations and Algorithms

Classification

Logistic regression
K-nearest neighbours
Support Vector Machines
Kernel SVM
Naive Bayes
Decision tree classifier
Random forest classifier

Unsupervised learning – Clustering

K-Means Clustering
Clustering Algorithms

Introduction to Deep Learning

Meaning and importance of deep learning
Artificial Neural networks
TensorFlow

Introduction to Artificial Intelligence and Machine Learning
Artificial Intelligence
Machine Learning o Machine Learning algorithms o Applications of Machine Learning

Python Programming for Beginners

Python Django From Scratch

Learning Objectives:

Introduction to Deep Learning with TensorFlow

Introduction to TensorFlow
Intro to TensorFlow
Computational Graph
Key highlights
Creating a Graph
Regression example
Gradient Descent
TensorBoard
Modularity
Sharing Variables
Keras

Perceptrons

What is a Perceptron
XOR Gate

Activation Functions

Sigmoid
ReLU
Hyperbolic Fns
Softmax
Artificial Neural Networks
Introduction
Perceptron Training Rule
Gradient Descent Rule

Gradient Descent and Backpropagation
Gradient Descent
Stochastic Gradient Descent
Backpropagation
Some problems in ANN

Optimization and Regularization
Overfitting and Capacity
Cross Validation
Feature Selection
Regularization
Hyperparameters

Intro to Convolutional Neural Networks
Intro to CNNs
Kernel filter
Principles behind CNNs
Multiple Filters
CNN applications

Intro to Recurrent Neural Networks
Intro to RNNs
Unfolded RNNs
Seq2Seq RNNs
LSTM
RNN

Deep Learning applications
Image Processing
Natural Language Processing
Speech Recognition
Video Analytics

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