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Data Science

Data Science Courses Information

This Data Science training course, will introduce you to a collection of powerful, open-source, tools needed to analyze data and to conduct data science.

The demand for skilled data science practitioners is rapidly growing, and this data science course prepares you to tackle real-world data analysis challenges.

 

Curriculum
Module 1 - Data Science Foundation - I (Java Programming)
  • Basics of Java
  • OOPs Concepts
  • Serialization
  • Synchronization
  • Input / Output
  • Network Programming
  • Exception Handling
  • String Handling
  • Nested Classes
  • Multithreading
  • AWT
  • Layout Managers
  • Data Manipulation
  • JDBC
Module 2 - Data Science Foundation - II (Python Programming)
  • Flow Controllers
  • Modules and Packages
  • File Handling
  • Functions
  • String Handling
  • List, Tuples and Dictionaries
  • Database Connectivity
  • Exception Handling
Module 3 - Data Science Foundation - III ( Advanced Python)
  • OOPs Concept
  • Multi Threading
  • Installing modules
  • Regular Expression
  • Working with random and math module
  • Working with CSV, TSV and JSON Files
Module 4 - NumPy
  • Introduction To Numpy
  • Data Types
  • Arrays
  • Matrices
  • Useful functions of NumPy
  • Working with Null values and missing values
Module 5 - Pandas
  • Introduction To Pandas
  • Series Objects
  • Built-In Functions
  • Working with text
  • String methods
  • Groups
  • Working with DataFrames
  • Applying filters
  • Data Wrangling
  • Data Munging
  • Joins
  • Attributes of Series and DataFrames
Module 6 - MySQL
  • Introduction To DBMS
  • DDL and DML Statements
  • Working with Constraints
  • Implementing Views
  • Working with Indexes
  • Implementing Triggers
  • Working with Queries (DQL)
  • Aggregate Functions
  • Joins and Set Operations
  • Implementation of Data integrity
  • Working with Stored Procedures
  • Working With Functions
Module 7 - Big Data & Hadoop - I (HDFS)
  • Big Data Introduction
  • HDFS Design
  • HDFS Architecture
  • HDFS commands
  • Cluster setup
  • Adding New Data Node Dynamically
  • High Availability
  • Read and Write Architecture
Module 8 - Big Data & Hadoop - II (MapReduce)
  • Map Reduce Job Run
  • Shuffling and Sorting
  • Distributed Cache
  • YARN Concepts
  • Legacy Architecture
  • Hands on word count in Map/Reduce
  • Optimization Techniques
  • Map Side Joins
Module 9 - PigLatin
  • Introduction To pig
  • Execution Types
  • Grunt Shell
  • Primitive Data types
  • SPLITS and JOINS
  • Filtering
  • Pig Functions
  • Data Processing , Schema on Read
  • Pig UDF
  • Pig Schema
  • Data Loading , Storing
  • Grouping & Joining
Module 10 - HIVE
  • Hive Introduction
  • Working with Tables
  • Partitions , Bucketing
  • RC File , Indexes
  • Architecture
  • Hive Service , Shell , server
  • External Partitioned tables
  • Joins
Module 11 - SQOOP
  • Import data
  • Incremental Import
  • Export data
  • Eval Function
Module 12 - R Programming
  • Installing R and R Studio
  • Syntax of R
Module 13 - Variables and Data Types in R
  • Data types introduction
  • Introduction to Variable
  • Examples and usage of Variables
Module 14 - Operators in R
  • Arithmetic Operators
  • Relational Operators
  • Logical Operators
  • Assignment Operators
  • Miscellaneous Operators
Module 15 - Decision Making in R
  • if statement
  • if..else statement
  • Switch Statement
  • if..else ladder
  • Examples of branching statements
Module 16 - Loop Control in R
  • for loop
  • while Loop
  • repeat loop
  • Break Statement
  • Next Statement
Module 17 - Matrices in R
  • Matrix Construction
  • Addition & Subtraction
  • Multiplication & Division
Module 18 - Advanced R
  • Importing and exporting data to/from external sources
  • Working with dataframes
  • Accessing individual elements
  • vectors and factors,
  • matrix, list and array
Module 19 - Data Manipulation
  • Need for Data Manipulation
  • Mean, Median Mode
  • Variance, Standard Deviation
  • Covariance
  • Correlation
Module 20 - Visualization
  • Introduction to grammar of graphics & ggplot2 package
  • Building frequency polygons with geom_freqpoly()
  • Making a scatter-plot with geom_pont()
  • Histograms
Module 21 - Analysis Continuous Variables
  • Anova
  • One Sample T-Test
  • Two Independent Samples Tests
  • Wilcoxon Test
  • Kruskal Wallis Test
Module 22 - Hands-on
  • Working on some dataset using R / Python
Module 23 - Statistics
  • Mean, Median, Mode
  • Standard deviation
  • Data Modeling
  • Probability
  • Combination
Module 24 - Tableau
  • Introduction to Tablue
  • Working with Tablue
  • Data Visualization
Module 25 - Machine Learning
  • Linear Regression
  • Recommendation system
  • User Based Collaborative
  • Logistic Regression
  • Association Rules- Market Basket Analysis
  • Item Based collaborative

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