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Bangalore Training Academy - Best BIG DATA ANALYTICS training institute in bangalore course highlights:
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Learn about course details, fee, faculty and class timings.
Module 1-Introduction to course
Introduction to corporate training
Software life cycle
Methodologies
Soft skills
Professional Ethics
Make you strength
Where I Stands?
Module 2-Data science introduction
What is data science
Need for data science?
Data science vs Business Intelligence
Prerequisite for learning data science
What does a data scientist do?
Data science life cycle with example
Demand for data science
Module 3-Installations
• Installations required for data science
Module 4 – R Programming
R Basics, background
Comprehensive R Archive Network
Demo of Installing R On windows from CRAN Website
Installing R Studios on Windows OS
Setting Up R Workspace.
Getting Help for R-How to use help system
Installing Packages – Loading And Unloading Packages
Getting familiar with basics
Operators in R – Arithmetic,Relational,Logical and
Assignment Operators
Variables,Types Of Variables,Using variables Conditional
statements,ifelse(),switch
Loops: For Loops,While Loops,Using Break statement,Switch
The R Programming Language- Data Types
Creating data objects from the keyword.
How to make different type of data objects.
Types of data structures in R
Arrays And Lists- Create Access the elements
Vectors – Create Vectors,Vectorized Operations,Power of
Vectorized Operations Matrices-
Building the first matrices,Matrix
Operations,Subsetting,visualising subset
Data Frames- create and filter data frames,Building And
Merging data frames.
Functions And Importing data into R
Function Overview – Naming Guidelines
Arguments Matching,Function with Multiple Arguments
Additional Arguments using Ellipsis,Lazy Evaluation Multiple
Return Values Function as
Objects,Anonymous Functions
Importing and exporting Data into R- importing from files
like excel,csv and minitab.
Import from URL and excel Files
Import from database.
Data Descriptive
Statistics, Tabulation, Distribution
Summary Statistics for Matrix Objects. apply() Command.
Converting an Object into a Table
Histograms, Stem and Leaf Plot, Density
Normal Distribution
Graphics in R – Types of graphics
Bar Chart, Pie Chart, Histograms- Create and edit.
Box Plots- Basics of Boxplots- Create and Edit Visualisation
in R using ggplot2.
More About Graphs: Adding Legends to Graphs,Adding Text to
Graphs, Orienting the AxisLabel.
Module 5 -Python programming
Introduction to python
Operators
Data Types
Control Statements
Functions
Data Structures – Lists, Sets, Tuples, Strings,
Dictionaries,
OOPS Concept
Module 6 -Libraries
• Numpy
• Pandas
• Scipy
• Scikit-Learn • Keras
• Matplotlib • Seaborn
• Cufflinks • NLTK
Note: Installations and work end to end. As per requirements
going to work with different libraries.
Module 7 – Data Exploration
Collecting data from different sources
Analyzing data
Data preprocessing
Data munging
Data mining
Data manipulation
Data visualization
Feature Selection
Feature Scaling
Dimensionality reduction
Module 8 -Statistics
Basics of Statistics
Descriptive Statistics
Inferential Statistics
Qualitative vs. Quantitative Analysis
Hypothesis Testing
Data Distribution
Module 9 – Other Mathematics Concepts
Probability
Calculus
Linear algebra
Module 10 -Machine Learning
Introducing Machine Learning models
Supervised learning
Regression and Classification models
Unsupervised learning
Clustering and Aggregation models
Semi supervised learning
Over fitting and under fitting(Linear, Logistic, Navi Bayes,
K-Nearest Neighbors, Support Vector Machine, Decision Trees, Random Forest
…..)Note: Deals with Mathematics and Programming part
Module 11 -Deep Learning and other algorithms
Introduction to Deep learning
OpenCV
Artificial Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Tensor flow
Over fitting and under fitting
Module 12 -Natural Language Processing
Introduction to NLP
Text Preprocessing
Lemmatization
Stemming
Text to features
Important Tasks in NLP
4V ( Volume, Velocity, Variety and Veracity) characteristics
Structured and
Unstructured Data
Application and
use cases of Big Data
Limitations of traditional large Scale systems
How a distributed way of computing is superior (cost and scale)
Opportunities and challenges with Big Data
Introduction to Linux and Big Data Virtual Machine (VM)
Introduction to Linux - Why Linux? -
Windows and the Linux equivalents
Different
flavors of Linux
Unity Shell
(Ubuntu UI)
Basic Linux
Commands
(enough to get started with Hadoop)
HDFS Overview and Architecture
Deployment
Architecture
Name Node, Data
Node and Checkpoint Node ( aka Secondary Name Node)
Safe mode
Configuration
files
HDFS Data Flows
( Read v/s Write)
Load Balancer
Dist Cp
HDFS Federation
HDFS High
Availability
Hadoop Archives
CRC Checksum
Data
replication
Rack awareness
and Block placement policy
Small files
problem
Command Line Interface
File System
Web Interface
Legacy MR v/s Next Generation MapReduce ( aka YARN/ MRv2)
Slots v/s
Containers
Schedulers
Shuffling,
Sorting
Hadoop Data
Types
Input and Output
Formats
Input Splits -
Partitioning ( Hash Partitioner v/s Customer Partitioner)
Speculative execution
Combiners
JVM Reuse
Compression
Word Count
Term Frequency
Inverse
Document Frequency
Log Data
Analysis
Different ways
of joining data
Purchases Data
Analysis
Max Temperatur
PageRank
Inverted Index
Introduction and Architecture
Different Modes
of executing Pig constructs
Data Types
Dynamic
invokers Pig streaming Macros
Pig Latin
language Constructs (LOAD, STORE, DUMP, SPLIT, etc)
User Defined
Functions
Use Cases
Introduction and Architecture
Different Modes
of executing Hive queries
Metastore
Implementations
HiveQL (DDL
& DML Operations)
External v/s
Managed Tables Views
Partitions
& Buckets
Joins, Group
by, Order by
User Defined
Functions
Review of RDBMS
Need for NoSQL
Brewers CAP
Theorem
ACID v/s BASE
Schema on Read
vs. Schema on Write
Different
levels of consistency
Key Value
Columnar
Document
Graph
HBase Architecture
Master and
Region Server
Catalog Tables
(Root and Meta)
HBase Data
Modeling
Loading data in
HBase
Introduction to RDD
Installation
and Configuration of Spark
Spark
Architecture
Different
interfaces to Spark
Data frames and
Datasets
Querying
massive data using SparkSql
Sample Python
programs in Spark
Data
Visualization using Apache Zeppelin
Cloudera Hadoop cluster on the Amazon Using
EMR (Elastic Map Reduce)
Using EC2
(Elastic Compute Cloud)
Importing/ exporting data across RDBMS and HDFS using Sqoop
Getting real-
time events into HDFS using Flume
Creating
workflows in Oozie
Introduction to
Graph processing
Graph
processing with Neo4J
Processing data
in real time using Storm
Interactive
Adhoc querying with Impala
No Interview Questions Found..
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Course fees
Working Professionals as trainers
Trainers Experience
Student Web Portal
Class Room Infrastructure
Reference Pay
Instalment
Lab Infrastructure
Who are our trainers?
Student’s Ratings
Trust & Credibility
Fees Negotiable?
Very competitive and affordable.
Yes
Min 7+ Years experience
We have a dedicated students portal
All classrooms are Ventilated with power backups.
We pay Rs 1000 for every student you refer.
Yes its very flexible, you can pay the fees in instalment
We Have Excellent Lab Facility and Provide server access
IT consultants,Solutions Architects, Technical Leads
5 ***** ratings from more than 4000 students
Very High
Definitely yes we understand the financial situation of each student
Really less fees but compromise with the quality
Very Few
Trainers have less exposure to real time work.
None
Very few institutes
None
Very few institutes
None
hire full time trainers with very little experience
Mixed
Moderate.
Very few