ADVANCED BIG DATA SCIENCE TRAINING IN KOCHI

756 Reviews

☆☆☆☆☆ 4.8/5

956 Learners

Duration : 3 Months – Weekends 3 Hours on Saturday and Sundays

Real-Time Projects, Assignments, scenarios are part of this course

Data Sets, installations, Interview Preparations, Repeat the session until 6 months are all attractions of this particular course

This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median mode, etc., and eventually covers all aspects of an analytics (or) data science career from analyzing and preparing raw data to visualize your findings. If you’re a programmer or a fresh graduate looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic to Advance techniques used by real-world industry data scientists.

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INTRO VIDEO

Why Radical Technologies

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Course description

Spark’s core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We’ll cover a variety of datasets and algorithms including PageRank, MapReduce, and Graph datasets.

Course Content

Introduction To Data Science

What is Data Science?
Why Python for data science?
Relevance in industry and need of the hour
How leading companies are harnessing the power of Data Science with Python?
Different phases of a typical Analytics/Data Science projects and role of python
Anaconda vs. Python

Python Essentials (Core)

Overview of Python- Starting with Python
Introduction to the installation of Python
Introduction to Python Editors & IDE’s(Canopy, pycharm, Jupyter, Rodeo, Ipython, etc…)
Understand Jupyter notebook & Customize Settings
Concept of Packages/Libraries – Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
Installing & loading Packages & Name Spaces
Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
List and Dictionary Comprehensions
Variable & Value Labels – Date & Time Values
Basic Operations – Mathematical – string – date
Reading and writing data
Simple plotting
Control flow & conditional statements
Debugging & Code profiling
How to create classes and modules and how to call them?
Scientific distributions used in python for Data Science – Numpy, scify, pandas, scikitlearn, statmodels, nltk etc

Accessing/Importing And Exporting Data Using Python Modules

Importing Data from various sources (CSV, txt, excel, access, etc)
Database Input (Connecting to the database)
Viewing Data objects – subsetting, methods
Exporting Data to various formats
Important python modules: Pandas, beautifulsoup

Data Manipulation – Cleansing – Munging Using Python Modules

Cleansing Data with Python
Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
Python Built-in Functions (Text, numeric, date, utility functions)
Python User Defined Functions
Stripping out extraneous information
Normalizing data
Formatting data
Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime, etc)

Data Analysis – Visualization Using Python

Introduction exploratory data analysis
Descriptive statistics, Frequency Tables and summarization
Univariate Analysis (Distribution of data & Graphical Analysis)
Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc)

Basic Statistics & Implementation Of Stats Methods In Python

Basic Statistics – Measures of Central Tendencies and Variance
Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
Inferential Statistics -Sampling – Concept of Hypothesis Testing
Statistical Methods – Z/t-tests (One sample, independent, paired), Anova, Correlation, and Chi-square
Important modules for statistical methods: Numpy, Scipy, Pandas

Python: Machine Learning -Predictive Modeling – Basics

Introduction to Machine Learning & Predictive Modeling
Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting
Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
Overfitting (Bias-Variance Tradeoff) & Performance Metrics
Feature engineering & dimension reduction
Concept of optimization & cost function
Concept of the gradient descent algorithm
Concept of Cross-validation(Bootstrapping, K-Fold validation, etc)
Model performance metrics (R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics)

Machine Learning Algorithms & Applications – Implementation In Python

Linear & Logistic Regression
Segmentation – Cluster Analysis (K-Means)
Decision Trees (CART/CD 5.0)
Ensemble Learning (Random Forest, Bagging & boosting)
Artificial Neural Networks(ANN)
Support Vector Machines(SVM)
Other Techniques (KNN, Naïve Bayes, PCA)
Introduction to Text Mining using NLTK
Introduction to Time Series Forecasting (Decomposition & ARIMA)
Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk, etc)
Fine-tuning the models using Hyperparameters, grid search, piping, etc.

Project – Consolidate Learnings

Applying different algorithms to solve the business problems and bench mark the results

Introduction To Big Data

Introduction and Relevance
Uses of Big Data analytics in various industries like Telecom, E-commerce, Finance, and Insurance, etc.
Problems with Traditional Large-Scale Systems

Hadoop(Big Data) Eco-System

Motivation for Hadoop
Different types of projects by Apache
Role of projects in the Hadoop Ecosystem
Key technology foundations required for Big Data
Limitations and Solutions of existing Data Analytics Architecture
Comparison of traditional data management systems with Big Data management systems
Evaluate key framework requirements for Big Data analytics
Hadoop Ecosystem & Hadoop 2.x core components
Explain the relevance of real-time data
Explain how to use Big Data and real-time data as a Business planning tool

Hadoop Cluster-Architecture-Configuration Files

Hadoop Master-Slave Architecture
The Hadoop Distributed File System – Concept of data storage
Explain different types of cluster setups(Fully distributed/Pseudo etc)
Hadoop cluster set up – Installation
Hadoop 2.x Cluster Architecture
A Typical enterprise cluster – Hadoop Cluster Modes
Understanding cluster management tools like Cloudera manager/Apache ambari

Hadoop-HDFS & MapReduce (YARN)

HDFS Overview & Data storage in HDFS
Get the data into Hadoop from local machine(Data Loading Techniques) – vice versa
Map Reduce Overview (Traditional way Vs. MapReduce way)
Concept of Mapper & Reducer
Understanding MapReduce program Framework
Develop a MapReduce Program using Java (Basic)
Develop a MapReduce program with streaming API) (Basic)

Data Integration Using Sqoop & Flume

Integrating Hadoop into an Existing Enterprise
Loading Data from an RDBMS into HDFS by Using Sqoop
Managing Real-Time Data Using Flume
Accessing HDFS from Legacy Systems

Data Analysis Using Pig

Introduction to Data Analysis Tools
Apache PIG – MapReduce Vs Pig, Pig Use Cases
PIG’s Data Model
PIG Streaming
Pig Latin Program & Execution
Pig Latin: Relational Operators, File Loaders, Group Operator, COGROUP Operator, Joins and COGROUP, Union, Diagnostic Operators, Pig UDF
Writing JAVA UDF’s
Embedded PIG in JAVA
PIG Macros
Parameter Substitution
Use Pig to automate the design and implementation of MapReduce applications
Use Pig to apply structure to unstructured Big Data

Data Analysis Using Hive

Apache Hive – Hive Vs. PIG – Hive Use Cases
Discuss the Hive data storage principle
Explain the File formats and Records formats supported by the Hive environment
Perform operations with data in Hive
Hive QL: Joining Tables, Dynamic Partitioning, Custom Map/Reduce Scripts
Hive Script, Hive UDF
Hive Persistence formats
Loading data in Hive – Methods
Serialization & Deserialization
Handling Text data using Hive
Integrating external BI tools with Hadoop Hive

Data Analysis Using Impala

Impala & Architecture
How Impala executes Queries and their importance
Hive vs. PIG vs. Impala
Extending Impala with User Defined functions

Introduction To Other Ecosystem Tools

NoSQL database – Hbase
Introduction Oozie

Spark: Introduction

Spark: Spark In Practice

Invoking Spark Shell
Creating the Spark Context
Loading a File in Shell
Performing Some Basic Operations on Files in Spark Shell
Caching Overview
Distributed Persistence
Spark Streaming Overview(Example: Streaming Word Count)

Spark: Spark Meets Hive

Analyze Hive and Spark SQL Architecture
Analyze Spark SQL
The context in Spark SQL
Implement a sample example for Spark SQL
Integrating hive and Spark SQL
Support for JSON and Parquet File Formats Implement Data Visualization in Spark
Loading of Data
Hive Queries through Spark
Performance Tuning Tips in Spark
Shared Variables: Broadcast Variables & Accumulators

Spark Streaming

Extract and analyze the data from Twitter using Spark streaming
Comparison of Spark and Storm – Overview

Spark GraphX

Overview of GraphX module in spark
Creating graphs with GraphX

Introduction To Machine Learning Using Spark

Understand Machine learning framework
Implement some of the ML algorithms using Spark MLLib

Project

Consolidate all the learnings
Working on Big Data Project by integrating various key components

Training Options

Live Online Training

Highly practical oriented training
Installation of Software On your System
24/7 Email and Phone Support
100% Placement Assistance until you get placed
Global Certification Preparation
Trainer Student Interactive Portal
Assignments and Projects Guided by Mentors
And Many More Features

Course completion certificate and Global Certifications are part of our all Master Program

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Live Classroom Training

Weekend / Weekdays / Morning / Evening Batches
80:20 Practical and Theory Ratio
Real-life Case Studies
Easy Coverup if you missed any sessions
PSI | Kryterion | Redhat Test Centers
Life Time Video Classroom Access ( coming soon )
Resume Preparations and Mock Interviews
And Many More Features

Course completion certificate and Global Certifications are part of our all Master Program

Get Started

I had a wonderful experience in Radical technologies where i did training in Hadoop development under the guidance of Shanit Sir. He started from the very basic and covered and shared everything he knew in this field. He was brilliant and had a lot of experience in this field. We did hands on for every topic we covered, and that’s the most important thing because honestly theoretical knowledge cannot land you a job.

Rohit Agrawal Hadoop

I have recently completed Linux course under Anand Sir and can assuredly say that it is definitely the best Linux course in Pune. Since most of the Linux courses from other sources are strictly focused on clearing the certification, they will not provide an insight into real-world server administration, but that is not the case with Anand Sir’s course. Anand Sir being an experienced IT infrastructure professional has an excellent understanding of how a data center works and all these information is seamlessly integrated into his classes.

Manu Sunil Linux

I had undergone oracle DBA course under Chetan sir’s Guidance an it was a very good learning experience overall since they not only provide us with theoretical knowledge but also conduct lot of practical sessions which are really fruitful and also the way of teaching is very fine clear and crisp which is easier to understand , overall I had a great time for around 2 months , they really train you well.also make it a point to clear all your doubts and provide you with clear and in-depth concepts hence hope to join sometime again

Reema banerjee Oracle DBA

I have completed Oracle DBA 11g from Radical technology pune. Excellent trainer (chetna gupta ). The trainer kept the energy level up and kept us interested throughout. Very practical, hands on experience. Gave us real-time examples, excellent tips and hints. It was a great experience with Radical technologies.

Mrudul Bhokare Oracle DBA

Linux learning with Anand sir is truly different experience… I don’t have any idea about Linux and system but Anand sir taught with scratch…He has a great knowledge and the best trainer…he can solve all your queries related to Linux in very simple way and giving nice examples… 100 🌟 to Anand Sir.

Harsh Singh Parihar Linux

Radical Technologies is truly progressing and offer best possible services. And recognition towards Radical Technologies is increasing steeply as the demand is growing rapidly.

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