Big Data Analytics 5/5 (3)

Review (3)


Our Big data Analytics Program is designed by the industry experts. These skills fulfil the requirements of jobs related to Big data Analytics. Now a days there are huge requirements of Big data professionals in the industry.

20 Sessions
160 Hours

Skills you will master

Big Data
HDFS Architecture
Apache Spark
Classification Algorithms
Machine Learning
Decision Tree
Random Forest


Mob : +91-9810600764
Address : M8, Lower ground Floor, Sector 14 OLD DLF Gurgaon 122001
Email : info@palin.co.in


About The Program

Big Data Analytics Training Program

Our Data Science on Hadoop Program is designed by the industry experts. These skills fulfill the requirements of jobs related to Data Science on Hadoop. Now a days there are huge requirements of data science professionals in the industry. This program gives you high quality content to analyse and making predictions for future references from past data using various modelling techniques and machine learning. Training includes Industry specific data as well as assignments & project.

During the training participants will be going through


After the completion of the course you will be able to crack the interviews with most of the renowned organizations like Airtel, Amazon, IBM, Flipkart and many more.

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Career Advisor
Vinit Kumar, Consultant
Palin Delivers real world relevance with activities n assignments that helps students build critical thinking and analytic skills that will transfer to other courses n professional life.
Program Description
Big data Analytics program is designed as per the needs of Analytics experts in the industry who are good at programming as well as analytical background to build models to bring out inferences and predictions from large amount of historical data over big data platform. It includes analytics using python and R language along with statistical modelling and machine learning algorithms to bring inferences for decision support in any business.
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   Session 1 - Introduction to Big Data

Big Data & Hadoop Introduction: Understand what big data is, Limitation of existing systems, Hadoop ecosystem, Understanding Hadoop 2.x component, Performing read and write operations, RAC awareness, Installation of Hadoop in virtual machine.

   Session 2 - Hadoop Distributed File System (HDFS)

Hadoop Architecture, Horizontal scaling, Movement of only code and not data over network, High availability, Scalability: Multiple Name Node, HDFS Commands, Hadoop configuration files, sword less SSH

   Session 3 - Hadoop Mapreduce Framework

How MapReduce is different from traditional way, Hadoop 2.x MapReduce architecture and component, Understand processing part i.e. YARN, MapReduce concept, Run the basic MapReducer program, Understanding Input Splits, MapReduce job submission flow, Performance improvement using combiners, Partitioners & MapReduce.

   Session 4 - MapReduce Advanced

Understanding counters, Map Side Join, Reduce side Join, MR units, Custom input formats, Sequence file format

   Session 5 - PIG

Pig how PIG came into picture, Where PIG is a good fit, Where PIG should not be used, Conceptual data flow, Different versions of PIG execution, Data models in PIG, PIG relational operators, UDF in PIG: Customized function in Java, Describe, explain and illustrate, Demo

   Session 6 - HIVE

Why and how HIVE came into picture, How is this different from PIG, Hive architecture and component, Where and where not HIVE to be used, Data type is HIVE, Perform basic HIVE operations, Joins in Hive, Create UDF for Hive, Dynamic Partitioning, performance Tuning

   Session 7 - HBase

Understand NoSQL Database, Understand CAP theorem, Comparison of RDBMS and HBASE, HBASE Architecture,How updated is implement on top of HDFS, Data model and physical storage in HBASE, Execute basic HBASE command, Data loading techniques in HBASE, Understanding Zookeeper

   Session 8 - Flume, Sqoop & OOZIE

Implement Flume & Sqoop, Understand Oozie, Schedule job in Oozie, Oozie workflow.

   Session 9 - Introduction to Apache Spark

Spark Introduction, Implement Spark operations on Spark Shell, Understand Spark and its Ecosystem, Spark Common operations

   Session 10 - Spark Streaming & Spark SQL

Playing with RDD: Learn how to work in RDD in Spark, Understand the role of Spark RDD Spark Streaming & Spark SQL: Understand Spark SQL Architecture, Learn Spark Streaming API

   Session 11 - Introduction to Python

Introduction to Python, Python Basics: Data Types & Variables(Integers, Floats, Strings,Lists, Dictionaries, Sets and Tuples), Conditional Statements (If, If- else, Nested if-else), Iterations/ Looping (For While, Nested loops), Functions (Writing reusable code), Exception Handling in Python

   Session 12 - Working with Files and Databases

Reading and Writing common file formats, Extracting and Storing data from Databases

   Session 13 - Programming with Spark

Programming with Spark: Spark Transformation, Spark Action, Python Spark Programming Examples Spark SQL: Spark SQL Overview, Python – Spark SQL Examples Spark Streaming: Streaming with Apache Spark, Python Spark Streaming examples

   Session 14 - Machine Learning Prerequisites

Numpy: Lists vs. Arrays, Vectors and Matrices, Dot Products and Matrix Products, Universal Array Functions Pandas: Series and DataFrames, Columns and Indexes, Summary Statistics, The apply() function Matplotlib: Box plots, Line Chart, Scatterplot, Histograms

   Session 15 - Introduction to ML & Data Preprocessing

Introduction to Machine Learning: What is Machine Learning, Applications of Machine Learning, Setting up the working environment Data Preprocessing: Importing the Dataset, Handling missing data, Handling Categorical data

   Session 16 - Regression Modelling

Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Support Vector Regression

   Session 17 - Classification

Logistic Regression, K-Nearest Neighbors (K-NN), Support Vector Machines, Kernel SVM, Naive Bayes Classifier, Decision Tree Classification, Random Forest Classification

   Session 18 - Unsupervised Learning

Clustering – Intuition, K-Means Clustering, Hierarchical Clustering

   Session 19 - Dimensionality Reduction & Model Selection

Dimensionality Reduction: Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel PCA Model Selection & Boosting: Introduction, What is Overfitting, Bias Variance Tradeoff, K-Fold Cross Validation, Grid Search


Use Cases & Project

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Program Highlights
Live interactive and classroom training will include the practical approach and assessments on regular basis.
Live Case Study
Domain Specific Live interactive case study using industry specific data, problem statements, solution architecture.
Assignments which helps in conceptual understanding, Prepared for interview training & techniques.
Lifetime Access
Class recording, Study material ppt’s, pdf, assignments, datasets, case studies can be accessed through out the lifetime.
24 X 7 Support
We will support If any concern is raised related to the training, assignments, projects, case studies & interview questions.
After successful completion of the training and case study Palin will certify you as Palin certified Big Data Analyst.
Career Counselor
Avail career guidance and Professional guidance for resume building, unlimited opportunities and interviews.

Highly motivated with leadership skills having Master’s Degree in Statistics. Holds good working experience in Big Data Profiles in different domains like Telecom, Retail, Banking, Healthcare FMCG, Marketing analytics using Spark R, Spark Python, Spark Scala
   Who can go for Big Data Analytics?
Any Graduate having a programming background and database handling skills can opt for big data analytics program to make his career into big data developer & analytics profiles.
   What is Big Data Analytics?
Big Data Analytics is a process of examining large data sets.Hadoop is a platform used to store and process very large amount of data which cannot be possible in conventional databases using distributed storage and then this data is integrated and analysed to be the solution for all big data problems.
   What is future of Big Data Analytics?
With advance analytical solutions you can efficiently and effectively capture, process, analyze, and store large & varified of data of all types. Big data is the new hot pick in industry and as the amount of data is increasing every organisation is short of big data professionals. Making Big data as your next job helps you make a secure and creative career.
   Pre-requisite for Big Data Analytics?
A basic knowledge of programming and logical approach is must to look forward to learning big data development & Analytics. Any technical graduate can opt for this course having a degree from a renowned institute.
   Is this a classroom training or online?
In both of the modes Palin arrange classes, can go in either of that classroom as well as online.
   Should I go for Online or Classroom?
Both the training modes are good to get trained. But online training is more better than the classroom training because online trainings are live interactive where you can raise your concerns at any point during the session. Additionally recording of the same session will be provided which you can access anytime anywhere.
   Which one is better classroom or online?
Everyone have their own opinion towards classroom or online training. In my point of view online classes are far better than classroom session. Online trainings have more much benefits comparative to classroom like we can easily record the session we can access that session whenever we want. In online session we just need a laptop and internet connection can easily access the classroom.
   After the course for which companies I can apply for?
Most of the organizations are working on Big Data platform Like TCS, Mckinsey, Delloitte, Airtel, Facebook, Twitter, PayTM, Accenture, Cognizant, IBM, Amex, RBS, UHG and many more..
   What are pay packages for fresher in Big Data Analytics?
An Entry-Level Big Data Professional earns an average salary of Rs 579,714 per year. The highest paying skills associated with this job is python.
   What excatly we cover in Big Data Analytics?
Introduction to Big data, HDFS Architecture, Map-Reduce, PIG, HIVE, Sqoop, Flume, oozie, yarn, Apache Spark, Python Basics, Programming with Spark, Machine Learning, Data Preprocessing, Regression Modelling, Classification, Unsupervised Learning, Dimensionality Reduction & Model Selection


    Precisely the best trainers they have for big data training.

    Best place to get trained on Big Data in Delhi/NCR.

    they provide best trainers for big data training with a good placement assistance.

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