4.8

Description

By taking Data science training from Tekslate, you’ll master major data science topics such as data analysis, methods to deploy R statistical computing, important Machine Learning algorithms, and K-Means Clustering. This course also includes time-series analysis, Naïve Bayes, business analytics and  Hadoop framework. Our curriculum is designed by industry experts based on real-time scenarios. You will get hands-on experience in Data Science by working on various real-time applica

Key Features

  • 30 hours of Instructor Led Data Science Training
  • Lifetime Access to Recorded Sessions
  • Practical Approach
  • 24/7 Support
  • Expert & Certified Trainers
  • Real World use cases and Scenarios
Trusted By Companies Worldwide

Course Overview

By the end of Data science training, you will be able to:

  • Understand the business intelligence and business analysis
  • Understand the descriptive statistics of Data analysis
  • Worki on excel with Tableau
  • Understand R and data exploration to R
  • Create decision trees.
  • Gain knowledge on data collection and data mining
  • Know the importance of big data technologies and debugging tools
  • To learn running non-parametric tests
     
  • There are around 5,000+ unique job postings for data scientists every month.
  • There is a huge requirement of around 3,00,000+ Skilled Data Scientists by the End of 2020.
  • The average salary for a professional data scientist is about $120000 USD per annum.
     
  • Big Data professionals 
  • BI and Analyst professionals
  • Big Data statisticians
  • Machine Learning professionals
  • Predictive analysts
  • Information achitects
  • Aspirants willing to build their career in the field of Data science
     

Basic knowledge in Python programming is required to learn Data science. The following job roles will get benefited by taking up this course:

  • Managers
  • Data analysts
  • Business analysts
  • Developers
  • IT professionals
     

The tutor will take care of handling the projects. We will provide two real-time projects with a highly-skilled guide who can assist you throughout the project.
 

Course Curriculum

  • 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
     
  • The 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?
     
  • 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
     
  • 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
     
  • 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 the command line
  • Usage of IDE R Studio and various GUI
     
  • 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
     
  • Various phases of Data Cleaning
  • Functions used in Inspection
  • Data Cleaning Techniques
  • Uses of functions involved
  • Use-cases for Data Cleaning using 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
     
  • 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
     
  • 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
     
  • 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
     
  • 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
  • What is Map Reduce?
  • Why Map Reduce?
  • Map Reduce in the 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
     
  • What is Combiner?
  • Hands-On Exercise
  • What is Partitioner?
  • Hands-On Exercise
  • What is a 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 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
     
  • 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 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
     
  • Joins & Subqueries, 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
     
  • CAP Theorem
  • HBase Architecture and concepts
  • Introduction to HBase
  • Client API’s and their features
  • HBase tables The ZooKeeper Service
  • Data Model, Operations
     
  • Programming and Hands-on Exercises
     
  • Introduction to Sqoop
  • MySQL Client & server
  • Connecting to relational database using Sqoop
  • Importing data using Sqoop from Mysql
  • Exporting data using Sqoop to MySql
  • Incremental append
  • Importing data using Sqoop from Mysql to a hive
  • Exporting data using Sqoop to MySql from hive
  • Importing data using Sqoop from Mysql to h-base
  • Using queries and sqoop
     
  • What is Flume?
  • Why use Flume, Architecture, configurations
  • Master, collector, Agent
  • Twitter Data Sentimental Analysis project
  • Oozie
  • What are Oozie, Architecture, configurations?
  • Oozie Job Submission
  • Oozie properties
     

FAQ's

We have a strong team of professions who are experts in their fields. Our trainers are highly supportive and render a friendly working environment to the students positively stimulating their growth. 
 

We will share you the missed session from our recordings. We at Tekslate maintains a recorded copy of each live course you undergo.
 

Our Trainers will provide the student with the Server Access ensuring practical real-time experience and training with all the utilities required for the in-depth understanding of the course.
 

We provide all the training sessions LIVE using either GoToMeeting or WebEx, thus promoting one-on-one trainer student Interaction.
 

Live training uncovers distinct benefits as they are mighty to reach your desired audience converting your prospects into customers in less time. Pre-recorded videos offer plenty of advantages for entrepreneurs to educate entertain and inspire your audience as long as you want.
 

You can contact our Tekslate support team, or you can send an email to info@tekslate.com for your queries.
 

Yes. We provide the course materials available after course completion.
 

There exist some discounts for weekend batches and group participants if the joiners are more than 2.
 

If you are enrolled in classes and have paid fees but want to cancel the registration for any reason, we will attain you in 48 hours will be processed within 30 days of prior request.
 

Certifications

Certification is a matter of demonstrating your skills and establishing your credibility. Tekslate will provide you with a course completion certificate after you complete the course. After the course completion, you will be in a position to clear the certification exams with ease. We advise you to take up the following certification exams.

  • SDS: Senior Data Scientist 
  • PDS: Principal Data Scientist

Click here to get certified.