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I T R i d g e

Data Science Training

➤ Beginner & Advanced level Classes.

➤ Data Science With Experiential Learning.

➤ Delivered by 10+ Years of Data Science Certified Expert.

➤ Access to Trendy Projects and Advanced Research Resources.

➤ 14652+ Students Have Been Trained, and 350+ Clients Are Currently Employed.

➤ Upcoming Data Science Training Batch to Begin This Week –  Enroll Your Name Now!

This is How I.T. Ridge Students Prepare for Better Jobs
PLACED IMAGE I.T. Ridge
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.
  • ➤ EThe 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
26-Feb-2024
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

(Class 1Hr - 1:30Hrs) / Per Session

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21-Feb-2024
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

(Class 1Hr - 1:30Hrs) / Per Session

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24-Feb-2024
Sat,Sun

Weekend Regular

(10:00 AM - 01:30 PM)

(Class 3hr - 3:30Hrs) / Per Session

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24-Feb-2024
Sat,Sun

Weekend Fasttrack

(09:00 AM - 02:00 PM)

(Class 4:30Hr - 5:00Hrs) / Per Session

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Key Features

  • ➤ 40 Hours Course Duration
  • ➤ Industry Expert Faculties
  • ➤ Completed 500+ Batches
  • ➤ 100% Job Oriented Training

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  • ➤ Free Demo Class Available
  • ➤ Have a portfolio of various data analysis projects.
  • ➤ Certification Guidance

Overview of Data Science Course

In this I.T. Ridge Data Science Training program, you will learn components of the Data Science lifecycle such as Big data and Hadoop, Machine Learning and Deep Learning, R programming. The instructor will teach you how to adopt a blend of mathematics, business acumen, tools, algorithms and machine learning techniques. You'll also learn how to handle the large amount of data information and implement it according to the organizations business strategy. Join Now with our I.T. Ridge Class Room & Online Training Course for Data Science Training Program.

Our Best Hiring Placement Partners

I.T. Ridge offers arrangement openings as extra to each understudy/proficient who finished our study hall or internet preparing. A portion of our understudies are working in these organizations recorded underneath.

  • ➤ I.T. Ridge has confined with various determination workplaces and affiliations additionally I.T. Ridge is a power extra of a noticeable occupation doorway in India.
  • ➤ OWe have a submitted procedure support pack wing that help understudies in getting circumstance according to their necessities
  • ➤ We will setup Mock tests and Mock parties to refresh the contender data and cutoff points.
  • ➤ We offer Data science-training improvement accreditation with 100% standard occupation coordinated expecting live endeavors..
  • ➤ After fulfillment of 60% Data science-training informative course content, we will gather the party calls to understudies and set them up to f2f alliance.
  • ➤ We are related with top affiliations like HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM, etc it make us capable to place our understudies in top MNCs across the globe.

Get Certified in Data Science with Industry Recognized I.T. Ridge Certificate

Worldwide Organizations and around the globe recognize the I.T. Ridge Data Science Training Certificate. After the conclusion of the full program, the participants and the professors will be taken twice. Your cloud skills will improve as a result of this training. Trainocate offers more discounts on Data Science certification and training courses. Industry Experts Teach This Class. Take your profession to the next level by enrolling in a training program

FAQ's

Call now: +1469-990-1010 and know the exciting offers available for you!

I.T. Ridge is the Legend in offering placement to the students. Please visit our Placed Students List on our website

We have strong relationship with over 700+ Top MNCs like SAP, Oracle, Amazon, HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM etc.

More than 3500+ students placed in last year in India & Globally

I.T. Ridge conducts development sessions including mock interviews, presentation skills to prepare students to face a challenging interview situation with ease.

85% percent placement record

Our Placement Cell support you till you get placed in better MNC

I.T. Ridge is the unique Authorized Oracle Partner, Authorized Microsoft Partner, Authorized Pearson Vue Exam Center, Authorized PSI Exam Center, Authorized Partner Of AWS and National Institute of Education (NIE) Singapore

No worries. I.T. Ridge assure that no one misses single lectures topics. We will reschedule the classes as per your convenience within the stipulated course duration with all such possibilities. If required you can even attend that topic with any other batches.

All the instructors at I.T. Ridge are practitioners from the Industry with minimum 9-12 yrs of relevant IT experience. They are subject matter experts and are trained by I.T. Ridge for providing an awesome learning experience.

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