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Data Science Training Material

Description Data Science Course Content Introduction to Data Science, importance of Data Science, statistical and analytical methods, deploying Data Science for Business Intelligence, transforming data, machine learning and introduction to Recommender systems. Reasons to Use Data Science – Project Life cycle How Data Science solves real world problems, Data Science Project Life Cycle, principles of Data Science, introduction to various BI and Analytical tools, data collection, introduction to statistical packages, data visualization tools, R Programming, predictive modelling, machine learning, artificial intelligence and statistical analysis. Data Conversion Converting data into useful information, Collecting the data, Understand the data, Finding useful information in the data, Interpreting the data, Visualizing the data Terms of Statistics Descriptive statistics, Let us understand some terms in statistics, Variable Plots Dot Plots, Histogram, Stemplo
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DevOps Training Material

What is DevOps? Permits corporations to create a secure gadget of labor, wherein groups are able to fast and independently broaden, take a look at, and install code and price quick, safely, securely, and reliably to clients. Through including the understanding of Dev, QA, IT Operations and statistics security into shipping teams and automatic self-carrier gear and systems, teams are capable of use that knowledge of their each day paintings without being depending on different teams. Allows corporations to maximize developer productivity, enable organizational studying, create high worker satisfaction, and win inside the marketplace   Pre Requisites to learn DevOps Basic understanding of Linux/Unix system concepts Familiarity with Command Line Interface (CLI) Familiarity with a Text Editor Part 0:DevOps Introduction Understanding Development Development SDLC : WaterFall & Agile Understanding Operations Dev vs Ops DevOps to the rescu
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Tableau Training Material

                                                      Tableau Course Content Introduction to Tableau Overview of Tableau, data visualization and analytics, elements of the Tableau dashboard, understanding the significance of Tableau Desktop and Tableau Server, extensively work with data visualization using line, bar, area, stacked bar, and multi line charts, connecting with Excel data. Deep dive into Tableau Graphs Various data representation techniques, like Tables, Graphs and Maps understanding the basics of Tree Map, Histogram, Filled Map, Symbol Map, Pie Chart, Trend Lines, Normal Tables and Multi measure Tables. Tableau Table Joins Understanding the conditions and methodology for joining Tables, knowledge of Multi Table Joins. Working with Metadata Working with Table, creation of Calculated Fields, duplicating and renaming columns, conversion of data types, default aggregation. Hierarchy & Groups Tableau Hierarchy creation, Static Group creation, dep
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C Programming Training Material

Introduction to ‘C’ language Features of C History Structure of C Program Keyword, Identifiers & Constants Data types Primitive Data Types Aggregated Data Types Operators Binary Operators Unary Operators Ternary Operators Special Operators Order of Evaluation Selections Simple if if..else Nested if if..else ladder Goto Statement Break and Continue Statement Switch..Case statement Iteration While For Do..While Nested loop Statements Arrays Introduction to arrays Need for Arrays Types of arrays One Dimensional Arrays Two Dimensional Arrays Multi Dimensional Arrays String manipulation Declaring String Initializing  String String Functions String Formatted Specifiers Multiple Strings Functions Interdiction to Functions Need for Functions Classification of Functions Function Prototype Defining Function Calling Function Function with Arrays Function with Strings Recursive Functions Storage class specifiers
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Hadoop Training Material

Introduction to BigData, Hadoop :-  Big Data Introduction  Hadoop Introduction  What is Hadoop? Why Hadoop?  Hadoop History?  Different types of Components in Hadoop?  HDFS, MapReduce, PIG, Hive, SQOOP, HBASE, OOZIE, Flume, Zookeeper and so on…  What is the scope of Hadoop? Deep Drive in HDFS (for Storing the Data) :-  Introduction of HDFS  HDFS Design  HDFS role in Hadoop  Features of HDFS  Daemons of Hadoop and its functionality o Name Node o Secondary Name Node o Job Tracker o Data Node o Task Tracker  Anatomy of File Wright  Anatomy of File Read  Network Topology o Nodes o Racks o Data Center  Parallel Copying using DistCp  Basic Configuration for HDFS  Data Organization o Blocks and o Replication  Rack Awareness  Heartbeat Signal  How to Store the Data into HDFS  How to Read the Data from HDFS  Accessing HDFS (Introduction of Basic UNIX commands)  CLI commands MapReduce using Java (Processing the Data):-  The
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