Data Science Training in New York City

Data can be defined as a collection of facts like numbers, measurements, words, etc. In the technical sense, data can be defined as a collection of quantitative or qualitative variables. A datum is a single unit of a single variable. Everything that we work completely deals with data and the data is everywhere. It is considered as the most important element because it exists everywhere.

Topics Covered in this module are:

•  What is Data?
• Types of Data
• Types of Data Collections
• Different forms of sources and Data
• The architecture of Data and its components
• OLAP vs OLTP
• How to structure large amounts of data and store it

Big Data is a field that mainly deals with large volumes of data. You will find lots of data to analyze, process, and extract. You will find many new ways to organize large amounts of data. It finally leads to the best strategic business moves. Big Data describes both structured and unstructured data.  

Topics Covered in this module are:

• Introduction to Big Data
• Big Data Architecture
• Distributed Computing and Complexity
• Hadoop Introduction
• Hadoop Ecosystem
• Map Reduce Framework

Data Science is a field of study that makes use of scientific processes, methods, systems, and algorithms to extract insights from nonstructural and structural data. This field is mainly related to Big Data and Data Mining. The Data Science field is mainly used to solve analytically complex problems.

Topics Covered in this module are:

• Data Science Life Cycle
• Data Analysis and Business Intelligence
• Types of Analytics
• Foundation of Data Science
• Data Acquisition
• Need for Data Science
• Data Transformation
• Applications of Data Science
• Resolution Techniques
• Big Data and Hadoop
• Introduction to R programming, Deep Learning, and Machine Learning
• Data Science Use Cases

R is one of the most prominently used programming languages in the field of Data Science. It provides a friendly environment for statistical computing. This language is most widely used by data miners and statisticians. This language is frequently used for data analysis. Furthermore, it is used to develop statistical software. 

Topics Covered in this module are:

• R programming Introduction
• Analytics Concepts
• Usage of R in Industry
• Installation of R and other packages
• Performing R operations using Command Line
• R studio
• Importance of R in Analytics

In this module, you will get in-depth knowledge of Data import techniques in R programming. You will also learn how to import data in R to a simple text file. 

Topics Covered in this module are:

• Techniques to import data in R programming
• Data import using Statistical methods
• Installation of Packages required for Database Import
• How to establish an RDBMS connection with R using ODBC
• Web Scraping

Data Exploration is one of the key essentials in Data Science. It is an approach that is quite similar to data analysis. The Data Analysts make use of visual exploration to understand what is present in the dataset and data characteristics. Data Exploration technique helps the users build familiarity with the existing information to find better answers in simpler ways.

Topics Covered in this module are:

• What is Data Exploration
• Importance of Data Exploration
• Data Exploration using R
• Data export from external sources
• Data Frames
• User-defined functions
• Data Import and Export
• Looping Statements 
• Accessing Individual Elements

Data Manipulation in R is defined as a process of modifying data to make it easier to read and more organized. The data is manipulated mainly for visualization and analysis. Data manipulation plays a major role in Data Exploration, it helps in organizing data using available sets of variables. Different data manipulation functions are used to manipulate data in R programming.

Topics Covered in this module are:

• How to manipulate Data
• Various phases of Data Cleaning
• Data Cleaning Techniques
• Functions used in the inspection
• Methods used for Data Manipulation in R

Data Visualization is considered as one of the beautiful arts to turn numbers into useful information or knowledge. R programming provides built-in functions and libraries to build visualizations and present data in a well-organized format. 

Topics Covered in this module are:

• What is Data Visualization
• Key elements used in Data Visualization
• Graphics Introduction
• Bar-plots for categorical variables
• ggvis package
• Visualization using ggvis package
• Building web applications with shinyR
• Data Visualization vs Infographics
• Various GUIs
• Graphical functions in R
• Histogram and various density plots

EDA is abbreviated as Exploratory Data Analysis is a process of visualizing and analyzing data to get better insights from it. Various steps are included in performing EDA in R programming such as import data, clean data, process data, and visualize data. 

Topics Covered in this module are:

• Introduction to EDA
• Different types of EDAs
• Goals of EDA
• The need for EDA in Data Science
• EDA Implementation
• Various EDA functions
• Use-cases for EDA using R
• Multiple packages for data analysis

Hadoop is the collection of open-source software utilities that can be facilitated using a network of a number of computers to solve problems that involve a massive amount of computation and data. This provides a software framework for distributed storage. It is designed and developed by Apache Software Foundation. 

Topics Covered in this module are:

• Hadoop Architecture
• Why Hadoop
• Hadoop vs Traditional Approach
• Distributed Model
• Use-cases of Hadoop
• Hadoop EcoSystem
• Hadoop FUndamental Design Principles
• Hadoop vs RDBMS
• Hadoop Cluster
• Writing and reading files to and from HDFS

MapReduce is a programming model that is mainly designed to process a large volume of data parallelly by dividing the whole work into a set of independent tasks. The user expresses the algorithm in two functions Map and Reduce. The Map function is only applied to the input data and mainly used to produce a list of intermediate pairs.

Topics Covered in this module are:

• Introduction to MapReduce
• The need for MapReduce 
• various concepts of MapReduce
• Log Processing
• Word Count Problem

MapReduce is a programming model that is mainly designed to process a large volume of data parallelly by dividing the whole work into a set of independent tasks. The user expresses the algorithm in two functions Map and Reduce. The Map function is only applied to the input data and mainly used to produce a list of intermediate pairs.

Topics Covered in this module are:

• What is Partitioner?
• What is Combiner?
• Counter
• I/o formats
• Map Join using MR
• Reduce Join using MR
• Distributed MR Cache
• Planning for cluster
• Hadoop Configuration

Statistics is the defined form of mathematical analysis that mainly uses representations, synopses, quantified models for real-life case studies or sets of experimental data. Statistics mainly deals with the study of the methodologies to gather, analyze, review, and derive a conclusion from the given data. There are various statistical measures like Mean, Median, etc.

Topics Covered in this module are:

• The need for Statistics
• Different Categories of Statistics
• Different types of data
• Correlation
• Covariance
• Normalization
• What is Probability?
• Binary Distribution 
• Normal Distribution
• How to build a statistical analysis model
• Sampling
• ANOVA
• What is Regression?

The term Machine Learning is defined as a scientific study of statistical models and algorithms that are mainly used by computer systems to perform different tasks without using explicit instructions. Machine Learning is considered as a subset of Artificial Intelligence.

Topics Covered in this module are:

• Why the trend for Machine Learning is rising
• Clustering
• Machine Learning Fundamentals
• Machine Learning Use Cases
• Clustering
• Decision Tree Classifier
• Naive Bayes Classifier
• K-Means Clustering
• Predictive Models
• Linear Regression
• Random Forests
• Similarity Metrics
• Sentimental Analysis
• Logistic Regression

Python is a general-purpose, interpreted, object-oriented programming language. It is the easiest programming language to learn and code programs. Top technology trends like Artificial Intelligence, Data Science, Deep Learning, Machine Learning, and many more. Python is the most widely used programming language across many organizations. 

Topics Covered in this module are:

• What is Python Programming
• History of Python
• Installation of Python
• Environmental Setup
• Python Syntax
• Key Features of Python
• Key fundamentals of Python

Python is a general-purpose, interpreted, object-oriented programming language. It is the easiest programming language to learn and code programs. Top technology trends like Artificial Intelligence, Data Science, Deep Learning, Machine Learning, and many more. Python is the most widely used programming language across many organizations. Python is the most prominently used language by the data scientist for designing and developing Data Science applications. Most libraries are provided to develop Data Science applications.

Topics Covered in this module are:

• Why Python
• Introduction to Data Science
• List of areas in which Python programming is used in the field of Data Science
• Developing Data Science applications using Python
• Libraries for developing Data Science Applications

The term Machine Learning is defined as a scientific study of statistical models and algorithms that are mainly used by computer systems to perform different tasks without using explicit instructions. Machine Learning is considered as a subset of Artificial Intelligence. It mainly makes use of python programming to focus on the development of new programs that can be changed when exposed to new data.

Topics Covered in this module are:

• Machine Learning Introduction
• Why Python?
• NumPy and Scipy
• Matplotlib and its use
• Machine Learning Applications
• Supervised vs Unsupervised Learning
• Usage of Python Programming in Developing Machine Learning Applications

Supervised Learning is a Machine Learning task. It is a process of learning i.e., the process of mapping the input data based on the given input and output example pair. In this Supervised Learning process, the input data is provided to map it with the desired output. The example will be provided for the user to get a clear idea about mapping elements and they proceed according to it. Here you need to supervise the model using given input and output data.

Topics Covered in this module are:

• Classification Problem
• K-Nearest Neighbouring Problem

Unsupervised Learning is a Machine Learning technique. In this, you need not supervise the given model. In this Learning process, you need to work on your own. It provides a platform for you to learn on your own and discover desired information. It completely deals with labeled data. You will not be provided with any kind of input or output data and also no examples are provided to work. You need to research on your own and perform whatever you want to get the desired output.

Topics Covered in this module are:

• Introduction to Unsupervised Learning
• Clustering and its types
• Clustering Algorithms
• K-means in Scree-plot Dendrogram
• Hierarchical Clustering
• PCA in R
• Implementation of PCA