Softcore Data Science training course lets you master data analysis, deploying R Statistical computing, machine learning algorithms, K-Means Clustering, NaiveBayes, connecting R with Hadoop framework, work, time series analysis, business analytics and more in this course.
You will get hands-on experience in Data Science by working on various projects in domains of e-commerce, entertainment, banking, healthcare, retail, insurance, finance, airlines, music industry, and unemployment.
Data Science Training Course Content
Introduction to Data Science
- Need for Data Scientists
- Foundation of Data Science
- What is Business Intelligence
- What is Data Analysis, Data Mining, and Machine Learning
- Analytics vs Data Science
- Value Chain
- Types of Analytics
- Lifecycle Probability
- Analytics Project Lifecycle
Data
- Basis of Data Categorization
- Types of Data
- Data Collection Types
- Forms of Data and Sources
- Data Quality, Changes and Data Quality Issues, Quality Story
- What is Data Architecture
- Components of Data Architecture
- OLTP vs OLAP
- How is Data Stored?
Big Data
- What is Big Data?
- 5 Vs of Big Data
- Big Data Architecture, Technologies, Challenge and Big Data Requirements
- Big Data Distributed Computing and Complexity
- Hadoop
- Map Reduce Framework
- Hadoop Ecosystem
Data Science Deep Dive
- What is Data Science?
- Why are Data Scientists in demand?
- What is a Data Product
- The growing need for Data Science
- Large-Scale Analysis Cost vs Storage
- Data Science Skills
- Data Science Use Cases and Data Science Project Life Cycle & Stages
- Map-Reduce Framework
- Hadoop Ecosystem
- Data Acquisition
- Where to source data
- Techniques
- Evaluating input data
- Data formats, Quantity and Data Quality
- Resolution Techniques
- Data Transformation
- File Format Conversions
- Anonymization
Intro to R Programming
- Introduction to R
- Business Analytics
- Analytics concepts
- The importance of R in analytics
- R Language community and eco-system
- Usage of R in industry
- Installing R and other packages
- Perform basic R operations using command line
- Usage of IDE R Studio and various GUI
R Programming Concepts
- The datatypes in R and its uses
- Built-in functions in R
- Subsetting methods
- Summarize data using functions
- Use of functions like head(), tail(), for inspecting data
- Use-cases for problem solving using R
Data Manipulation in R
- Various phases of Data Cleaning
- Functions used in Inspection
- Data Cleaning Techniques
- Uses of functions involved
- Use-cases for Data Cleaning using R
Data Import Techniques in R
- Import data from spreadsheets and text files into R
- Importing data from statistical formats
- Packages installation for database import
- Connecting to RDBMS from R using ODBC and basic SQL queries in R
- Web Scraping
- Other concepts on Data Import Techniques
Exploratory Data Analysis (EDA) using R
- What is EDA?
- Why do we need EDA?
- Goals of EDA
- Types of EDA
- Implementing of EDA
- Boxplots, cor() in R
- EDA functions
- Multiple packages in R for data analysis
- Some fancy plots
- Use-cases for EDA using R
Data Visualization in R
- Storytelling with Data
- Principle tenets
- Elements of Data Visualization
- Infographics vs Data Visualization
- Data Visualization & Graphical functions in R
- Plotting Graphs
- Customizing Graphical Parameters to improvise the plots
- Various GUIs
- Spatial Analysis
- Other Visualization concepts
HADOOP
Big Data and Hadoop Introduction
- What is Big Data and Hadoop?
- Challenges of Big Data
- Traditional approach Vs Hadoop
- Hadoop Architecture
- Distributed Model
- Block structure File System
- Technologies supporting Big Data
- Replication
- Fault Tolerance
- Why Hadoop?
- Hadoop Eco-System
- Use cases of Hadoop
- Fundamental Design Principles of Hadoop
- Comparison of Hadoop Vs RDBMS
Understand Hadoop Cluster Architecture
- Hadoop Cluster and Architecture
- 5 Daemons
- Hands-On Exercise
- Typical Workflow
- Hands-On Exercise
- Writing Files to HDFS
- Hands-On Exercise
- Reading Files from HDFS
- Hands-On Exercise
- Rack Awareness
- Before Map Reduce
Map Reduce Concepts
- Map Reduce Concepts
- What is Map Reduce?
- Why Map Reduce?
- Map Reduce in real world and Map Reduce Flow
- What is Mapper, Reducer, and Shuffling?
- Word Count Problem
- Hands-On Exercise
- Distributed Word Count Flow and Solution
- Log Processing and Map Reduce
- Hands-On Exercise
Advanced Map Reduce Concepts
- What is Combiner?
- Hands-On Exercise
- What is Partitioner?
- Hands-On Exercise
- What is Counter?
- Hands-On Exercise
- InputFormats/Output Formats
- Hands-On Exercise
- Map Join using MR
- Hands-On Exercise
- Reduce Join using MR
- Hands-On Exercise
- MR Distributed Cache
- Hands-On Exercise
- Using sequence files & images with MR
- Hands-On Exercise
- Planning for Cluster & Hadoop 2.0 Yarn
- Configuration of Hadoop
- Choosing Right Hadoop Hardware and Software?
- Hadoop Log Files?
Hadoop 2.0 and YARN
- Hadoop 1.0 Challenges
- NN Scalability, SPOF, and HA
- Job Tracker Challenges
- Hadoop 2.0 New Features
- Hadoop 2.0 Cluster Architecture & Federation
- Hadoop 2.0 HA
- Yarn & Hadoop Ecosystem
- Yarn MR Application Flow
PIG
- Introduction to Pig
- What Is Pig?
- Pig’s Features & Pig Use Cases
- Interacting with Pig
- Basic Data Analysis with Pig
- Hands-On Exercise
- Pig Latin Syntax
- Loading Data
- Hands-On Exercise
- Simple Data Types
- Field Definitions
- Data Output
- Viewing the Schema
- Hands-On Exercise
- Filtering and Sorting Data
- Hands-On Exercise
- Commonly-Used Functions
- Hands-On Exercise: Pig for ETL Processing
- Processing Complex Data with Pig
- Hands-On Exercise
- Storage Formats
- Complex/Nested Data Types
- Hands-On Exercise
- Grouping
- Hands-On Exercise
- Built-in Functions for Complex Data
- Hands-On Exercise
- Iterating Grouped Data
- Hands-On Exercises
- Multi-Dataset Operations with Pig
- Hands-On Exercise
- Techniques for Combining Data Sets
- Joining Data Sets in Pig
- Hands-On Exercise
- Splitting Data Sets
- Hands-On Exercise
HIVE
- Hive Fundamentals and Architecture
- Loading and Querying Data in Hive
- Hands-On Exercise
- Hive Architecture and Installation
- Comparison with Traditional Database
- HiveQL: Data Types, Operators and Functions
- Hands-On Exercise
- Hive Tables, Managed Tables and External Tables
- Hands-On Exercise
- Partitions and Buckets
- Hands-On Exercise
- Storage Formats, Importing Data, Altering Tables, Dropping Tables
- Hands-On Exercise
- Querying Data, Sorting and Aggregating, Map Reduce Scripts
- Hands-On Exercise
Module-9
- Joins & Sub queries, Views
- Hands-On Exercise
- Integration, Data manipulation with Hive
- Hands-On Exercise
- User Defined Functions
- Hands-On Exercise
- Appending Data into existing Hive Table
- Hands-On Exercise
- Static partitioning vs dynamic partitioning
- Hands-On Exercise
HBASE
- CAP Theorem
- HBase Architecture and concepts
- Introduction to HBase
- Client API’s and their features
- HBase tables The ZooKeeper Service
- Data Model, Operations
Module-11
- Programming and Hands on Exercises
SQOOP
- Introduction to Sqoop
- MySQL Client & server
- Connecting to relational data base using Sqoop
- Importing data using Sqoop from Mysql
- Exporting data using Sqoop to MySql
- Incremental append
- Importing data using Sqoop from Mysql to hive
- Exporting data using Sqoop to MySql from hive
- Importing data using Sqoop from Mysql to hbase
- Using queries and sqoop
Flume and Oozie
- What is Flume?
- Why use Flume, Architecture, configurations
- Master, collector, Agent
- Twitter Data Sentimental Analysis project
- Oozie
- What is Oozie, Architecture, configurations?
- Oozie Job Submission
- Oozie properties
- Hands-on exercises
Projects
- Social Media Final Project
- Hadoop Project
- Objective
- Problem Definition
- Solution
- Discuss datasets and specifications of the project
Project in Healthcare Domain
- Hadoop Project in Healthcare
- Objective
- Problem Definition
- Solution
- Discuss datasets and specifications of the project
Project in Finance/Banking Domain
- Hadoop Project in Banking Domain
- Objective
- Problem Definition
- Solution
- Discuss datasets and specifications of the project
Spark
Apache Spark
- Introduction to Apache Spark
- Why Spark
- Batch Vs. Real-Time Big Data Analytics
- Batch Analytics – Hadoop Ecosystem Overview
- Real-Time Analytics Options
- Streaming Data – Storm
- In Memory Data – Spark, What is Spark?
- Spark benefits to Professionals
- Limitations of MR in Hadoop
- Components of Spark
- Spark Execution Architecture
- Benefits of Apache Spark
- Hadoop vs Spark
Introduction to Scala
- Features of Scala
- Basic Data Types of Scala
- Val vs Var
- Type Inference
- REPL
- Objects & Classes in Scala
- Functions as Objects in Scala
- Anonymous Functions in Scala
- Higher Order Functions
- Lists in Scala
- Maps
- Pattern Matching
- Traits in Scala
- Collections in Scala
Spark Core Architecture
- Spark & Distributed Systems
- Spark for Scalable Systems
- Spark Execution Context
- What is RDD
- RDD Deep Dive and Dependencies
- RDD Lineage
- Spark Application In Depth and Spark Deployment
- Parallelism in Spark
- Caching in Spark
Spark Internals
- Spark Transformations, Actions, Cluster and SQL Introduction
- Spark Data Frames
- Spark SQL with CSV, JSON, and Database
Spark Streaming
- Features of Spark Streaming
- Micro Batch
- Dstreams
- Transformations on Dstreams
- Spark Streaming Use Case
Statistics + Machine Learning
Statistics
What is Statistics?
- Descriptive Statistics
- Central Tendency Measures
- The Story of Average
- Dispersion Measures
- Data Distributions
- Central Limit Theorem
- What is Sampling
- Why Sampling
- Sampling Methods
- Inferential Statistics
- What is Hypothesis testing
- Confidence Level
- Degrees of freedom
- what is pValue
- Chi-Square test
- What is ANOVA
- Correlation vs Regression
- Uses of Correlation and Regression
Machine Learning
Machine Learning Introduction
- ML Fundamentals
- ML Common Use Cases
- Understanding Supervised and Unsupervised Learning Techniques
- Clustering
- Similarity Metrics
- Distance Measure Types: Euclidean, Cosine Measures
- Creating predictive models
- Understanding K-Means Clustering
- Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
- Case study
- Implementing Association rule mining
- Case study
- Understanding Process flow of Supervised Learning Techniques
- Decision Tree Classifier
- How to build Decision trees
- Case study
- Random Forest Classifier
- What is Random Forests
- Features of Random Forest
- Out of Box Error Estimate and Variable Importance
- Case study
- Naive Bayes Classifier
- Case study
- Project Discussion
- Problem Statement and Analysis
- Various approaches to solving a Data Science Problem
- Pros and Cons of different approaches and algorithms
- Linear Regression
- Case study
- Logistic Regression
- Case study
- Text Mining
- Case study
- Sentimental Analysis
- Case study
Python
Getting Started with Python
- Python Overview
- About Interpreted Languages
- Advantages/Disadvantages of Python pydoc
- Starting Python
- Interpreter PATH
- Using the Interpreter
- Running a Python Script
- Python Scripts on UNIX/Windows, Editors and IDEs
- Using Variables
- Keywords
- Built-in Functions
- StringsDifferent Literals
- Math Operators and Expressions
- Writing to the Screen
- String Formatting
- Command Line Parameters and Flow Control
Sequences and File Operations
- Lists
- Tuples
- Indexing and Slicing
- Iterating through a Sequence
- Functions for all Sequences
- Using Enumerate()
- Operators and Keywords for Sequences
- The xrange() function
- List Comprehensions
- Generator Expressions
- Dictionaries and Sets
Deep Dive – Functions Sorting Errors and Exception Handling
- Functions
- Function Parameters
- Global Variables
- Variable Scope and Returning Values. Sorting
- Alternate Keys
- Lambda Functions
- Sorting Collections of Collections, Dictionaries and Lists in Place
- Errors and Exception Handling
- Handling Multiple Exceptions
- The Standard Exception Hierarchy
- Using Modules
- The Import Statement
- Module Search Path
- Package Installation Ways
Regular Expressionist’s Packages and Object – Oriented Programming in Python
- The Sys Module
- Interpreter Information
- STDIO
- Launching External Programs
- path directories and Filenames
- Walking Directory Trees
- Math Function
- Random Numbers
- Dates and Times
- Zipped Archives
- Introduction to Python Classes
- Defining Classes
- Initializers
- Instance Methods
- Properties
- Class Methods and Data Static Methods
- Private Methods and Inheritance
- Module Aliases and Regular Expressions
Debugging, Databases and Project Skeletons
- Debugging
- Dealing with Errors
- Using Unit Tests
- Project Skeleton
- Required Packages
- Creating the Skeleton
- Project Directory
- Final Directory Structure
- Testing your Setup
- Using the Skeleton
- Creating a Database with SQLite 3
- CRUD Operations
- Creating a Database Object.
Machine Learning Using Python
- Introduction to Machine Learning
- Areas of Implementation of Machine Learning
- Why Python
- Major Classes of Learning Algorithms
- Supervised vs Unsupervised Learning
- Learning NumPy
- Learning Scipy
- Basic plotting using Matplotlib
- Machine Learning application
Supervised and Unsupervised learning
- Classification Problem
- Classifying with k-Nearest Neighbours (kNN)
Algorithm
- General Approach to kNN
- Building the Classifier from Scratch
- Testing the Classifier
- Measuring the Performance of the Classifier
- Clustering Problem
- What is K-Means Clustering
- Clustering with k-Means in Python and an
Application Example
- Introduction to Pandas
- Creating Data Frames
- GroupingSorting
- Plotting Data
- Creating Functions
- Converting Different Formats
- Combining Data from Various Formats
- Slicing/Dicing Operations.
Scikit and Introduction to Hadoop
- Introduction to Scikit-Learn
- Inbuilt Algorithms for Use
- What is Hadoop and why it is popular
- Distributed Computation and Functional Programming
- Understanding MapReduce Framework Sample MapReduce Job Run
Hadoop and Python
- PIG and HIVE Basics
- Streaming Feature in Hadoop
- Map Reduce Job Run using Python
- Writing a PIG UDF in Python
- Writing a HIVE UDF in Python
- Pydoop and MRjob Basics
Data Science Training Course in Hyderabad