This is How I.T. Ridge Students Prepare for Better Jobs

Gain Our Intelligent Practical Data Science Course

Our Data Science course empowers students with essential skills in data management, insights derivation, and data-informed decision-making. It encompasses a wide range of subjects, including statistics, programming, data analysis, and machine learning.
Our curriculum delves into data collection, visualization, statistical analysis, machine learning, and big data technologies. Students get hands-on experience with programming languages like Python and R.
We provide flexibility with various course formats, including in-person classrooms, online courses, and bootcamps, enabling students to select the one that aligns best with their schedule and learning preferences.
The Data Science course at I.T. Ridge prioritizes practical learning, involving real-world tasks and projects where students work with datasets to solve problems and acquire valuable skills.
Our instructors are often highly experienced professionals in the data science field. They deliver lectures, lead hands-on sessions, and offer guidance to students.
We offer placement assistance and collaborate with corporate partners to facilitate potential job placements for our data science students. This includes resume-building workshops, interview preparation, and career advice.
Our courses provide networking opportunities with instructors, fellow students, and industry experts. Networking can be advantageous for both finding employment opportunities and staying informed about industry trends.

Syllabus of Data Science Course

Module 1: Data Science Basics

Introduction to Data Science
Significance of Data Science
R Programming basics

Module 2: Python Fundamentals

Python Introduction
Indentations in Python
Python data types and operators
Python Functions

Module 3: Data Structures and Data Manipulation

Data Structures Overview
Identifying the Data Structures
Allocating values to the Data Structures
Data Manipulation Significance
Dplyr Package and performing different data manipulation operations

Module 4: Data visualization

Introduction to Data Visualisation
Various kinds of graphs, Graphics grammar
Ggplot2 package
Multivariant analysis by using geom_boxplot
Univariant analysis
Histogram, barplot, multivariate distribution, and density plot
Bar plots for the categorical variables through geop_bar() and the theme() layer

Module 5: Statistics

Statistics Importance
Statistics classification, Statistical terminology
Data types, Probability types, measures of speed, and central tendency
Covariance and Correlation, Binary and Normal distribution
Data Sampling, Confidence, and Significance levels
Hypothesis Test and Parametric testing

Module 6: Introduction to Machine Learning

Machine Learning Fundamentals
Supervised Learning, Classification in Supervised Learning
Linear Regression and mathematical concepts related to linear regression
Classification Algorithms, Ensemble Learning techniques

Module 7: Logistic Regression

Logistic Regression Introduction
Logistic vs Linear Regression, Poisson Regression
Bivariate Logistic Regression, math related to logistic regression
Multivariate Logistic Regression
Building Logistic Models
False and true positive rate
Real-time applications of Logistic Regression

Module 8: Random Forest and Decision Trees

Classification Techniques
Decision Tree Induction Algorithm
Implementation of Random Forest in R
Differences between classification tree and regression tree
Naive Bayes, SVM
Entropy, Gini Index, Information Gain

Module 9: Unsupervised learning

Clustering, K-means clustering, Canopy Clustering, and Hierarchical Clustering
Unsupervised learning, Clustering algorithm, K-means clustering algorithm
K-means theoretical concepts, k-means process flow, and K-means implementation
Implementing Historical Clustering in R
PCA(Principal Component Analysis) Implementation in R

Module 10: Denial-of-Service

DoS/DDoS Concepts
Botnets
DoS/DDoS Attack Techniques
DDoS Case Study
DoS/DDoS Countermeasures

Module 11: Natural Language Processing

Natural language processing and Text mining basics
Significance and use-cases of text mining
NPL working with text mining, Language Toolkit(NLTK)
Text Mining: pre-processing, text-classification and cleaning

Module 12: Mathematics for Data Science

Numpy Basics
Numpy Mathematical Functions
Probability Basics and Notation
Correlation and Regression
Joint Probabilities
Bayes Theorem
Conditional Probability, sum rule, and product rule

Module 13: Scientific Computing through Scipy

Scipy Introduction and characteristics
Integrate, Cluster, Signal, Fftpack, and Bayes Theorem

Module 14: Python Integration with Spark

Pyspark basics
Uses and Need of pyspark
Pyspark installation
Advantages of pyspark over MapReduce
Pyspark applications

Module 15: Deep Learning and Artificial Intelligence

Machine Learning effect on Artificial Intelligence
Deep Learning Basics, Working of Deep Learning
Regression and Classification in the Supervised Learning
Association and Clustering in unsupervised learning
Basics of Artificial Intelligence and Neural Networks
Supervised Learning in Neural Networks, multi-layer network
Deep Neural Networks, Convolutional Neural Networks
Reinforcement Learning
Recurrent Neural Networks, Deep learning graphics processing unit
Deep Learning Applications, Time series modeling

Module 16: Keras and TensorFlow API

Tensorflow Basics and Tensorflow open-source libraries
Deep Learning Models and Tensor Processing Unit(TPU)
Graph Visualisation, keras
Keras neural-network
Define and Composing multi-complex output models through Keras
Batch normalization, Functional and Sequential composition
Implementing Keras with tensorboard
Implementing neural networks through TensorFlow API

Module 17: Restricted Boltzmann Machine and Autoencoders

Basics of Autoencoders and rbm
Implementing RBM for the deep neural networks
Autoencoders features and applications

Module 18: Big Data Hadoop and Spark

Big Data and Hadoop Basics
Hadoop Architecture, HDFS
MapReduce Framework and Pig
Hive and HBase
Basics of Scala and Functional Programming
Kafka basics, Kafka Architecture
Kafka cluster and Integrating Kafka with Flume
Introduction to Spark
Spark RDD Operations, writing spark programs
Spark Transformations, Spark streaming introduction
Spark streaming Architecture, Spark Streaming Features
Structured streaming Architecture, Dstreams, and Spark Graphx

Module 19: Tableau

Data Visualisation Basics
Data Visualisation Applications
Tableau Installation and Interface
Tableau Data Types, Data Preparation
Tableau Architecture
Getting Started with Tableau
Creating sets, Metadata and Data Blending
Arranging visual and data analytics
Mapping, Expressions, and Calculations
Parameters and Tableau prep
Stories, Dashboards, and Filters
Graphs, charts
Integrating Tableau with Hadoop and R

Module 20: MongoDB

MongoDB and NoSQL Basics
MongoDB Installation
Significance of NoSQL
CRUD Operations
Data Modeling and Management
Data Indexing and Administration
Data Aggregation Schema
MongoDB Security
Collaborating with Unstructured Data

Module 21: SAS Basics

SAS Enterprise Guide
SAS functions and Operators
SAS Data Sets compilation and creation
SAS Procedures
SAS Graphs
SAS Macros
PROC SQL
Advance SAS

Module 22: MS Excel

Entering Data
Logical Functions
Conditional Formatting
Validation, Excel formulas
Data sorting, Data Filtering, Pivot Tables
Creating charts, Charting techniques
File and Data security in excel
VBA macros, VBA IF condition, and VBA loops
VBA IF condition, For loop
VBA Debugging and Messaging

Syllabus of Apache Spark Training

Module 1: Introduction to Spark

Introduction to Spark
Spark overcomes the drawbacks of working on MapReduce
Understanding in-memory MapReduce
Interactive operations on MapReduce
Spark stack, fine vs. coarse-grained update, Spark Hadoop YARN, HDFS Revision, and YARN Revision
The overview of Spark and how it is better than Hadoop
Deploying Spark without Hadoop
Spark history server and Cloudera distribution

Module 2: Spark Basics

Spark installation guide
Spark configuration
Memory management
Executor memory vs. driver memory
Working with Spark Shell
The concept of resilient distributed datasets (RDD)
Learning to do functional programming in Spark
The architecture of Spark

Module 3: Working with RDDs in Spark

Spark RDD
Creating RDDs
RDD partitioning
Operations and transformation in RDD
Deep dive into Spark RDDs
The RDD general operations
Read-only partitioned collection of records
Using the concept of RDD for faster and efficient data processing
RDD action for the collect, count, collects map, save-as-text-files, and pair RDD functions

Module 4: Aggregating Data with Pair RDDs

Understanding the concept of key-value pair in RDDs
Learning how Spark makes MapReduce operations faster
Various operations of RDD
MapReduce interactive operations
Fine and coarse-grained update
Spark stack

Module 5: Writing and Deploying Spark Applications

Comparing the Spark applications with Spark Shell
Creating a Spark application using Scala or Java
Deploying a Spark application
Scala built application
Creation of the mutable list, set and set operations, list, tuple, and concatenating list
Creating an application using SBT
Deploying an application using Maven
The web user interface of Spark application
A real-world example of Spark
Configuring of Spark

Module 6: Parallel Processing

Learning about Spark parallel processing
Deploying on a cluster
Introduction to Spark partitions
File-based partitioning of RDDs
Understanding of HDFS and data locality
Mastering the technique of parallel operations
Comparing repartition and coalesce
RDD actions

Module 7: Spark RDD Persistence

The execution flow in Spark
Understanding the RDD persistence overview
Spark execution flow, and Spark terminology
Distribution shared memory vs RDD
RDD limitations
Spark shell arguments
Distributed persistence
RDD lineage
Key-value pair for sorting implicit conversions like CountByKey, ReduceByKey, SortByKey, and AggregateByKey

Module 8: Spark MLlib

Introduction to Machine Learning
Types of Machine Learning
Introduction to MLlib
Various ML algorithms supported by MLlib
Linear regression, logistic regression, decision tree, random forest, and K-means clustering techniques

Module 9: Integrating Apache Flume and Apache Kafka

Why Kafka and what is Kafka?
Kafka architecture
Kafka workflow
Configuring Kafka cluster
Operations
Kafka monitoring tools
Integrating Apache Flume and Apache Kafka

Module 10: Spark Streaming

Introduction to Spark Streaming
Features of Spark Streaming
Spark Streaming workflow
Initializing StreamingContext, discretized Streams (DStreams), input DStreams and Receivers
Transformations on DStreams, output operations on DStreams, windowed operators and why it is useful
Important windowed operators and stateful operators

Module 11: Improving Spark Performance

Introduction to various variables in Spark like shared variables and broadcast variables
Learning about accumulators
The common performance issues
Troubleshooting the performance problems

Module 12: Spark SQL and Data Frames

Learning about Spark SQL
The context of SQL in Spark for providing structured data processing
JSON support in Spark SQL
Working with XML data
Parquet files
Creating Hive context
Writing data frame to Hive
Reading JDBC files
Understanding the data frames in Spark
Creating Data Frames
Manual inferring of schema
Working with CSV files
Reading JDBC tables
Data frame to JDBC
User-defined functions in Spark SQL
Shared variables and accumulators
Learning to query and transform data in data frames
Data frame provides the benefit of both Spark RDD and Spark SQL
Deploying Hive on Spark as the execution engine

Module 13: Scheduling/Partitioning

Learning about the scheduling and partitioning in Spark
Hash partition
Range partition
Scheduling within and around applications
Static partitioning, dynamic sharing, and fair scheduling
Map partition with index, the Zip, and GroupByKey
Spark master high availability, standby masters with ZooKeeper
Single-node recovery with the local file system and high order functions

Upcoming Batches

02-Dec-2024

Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches