▷ Data Science Training & Certification

Key Highlights

Preview

Course Video

30 Hrs Instructor Led Training

Self-paced Videos

20 Hrs Project & Exercises

Certification

Job Assistance

Flexible Schedule

Lifetime Free Upgrade

Mentor Support

Curriculum

A complete index of
job-ready skills curated
to meet the industrial need.
Explore.

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

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?

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 the 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 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

Advanced Map Reduce Concepts

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 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 & 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

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 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

Flume and Oozie

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

We have made a tailored curriculum covering the latest industry-ready concepts to serve every individual’s learning desires.

Project Details

We bring you the best learning experience by delivering all our training sessions practical. Following are the few use cases we shall discuss during the training.

Data Science Training Objectives

1. What are the Data science training course objectives?

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

2. Why should you learn a Data science course?

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.

3. Who should attend Data science training?

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

4. What are the prerequisites for learning Data science training?

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

5. What projects are included in this Data science training course?

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.

+1 930 200 4823

( Toll Free )

Training Options

Different individuals. Different upgrade goals. Different modes of learning.

We got solutions for everyone looking for an AWS Architect course. Opt in for your convenient upgrade option, and we will guide you through.

Duration

One-on-one Session

Support

Resources

Time

Fee

Live Online.

30 Hours

Yes

24x7

Additional tips from the trainer

30 July 2024, 07:00 AM

03 August 2024, 08:00 AM

Self-Paced

30 Hours

Weekdays & Working Hours

Accessible through LMS

At your convenience

Data Science Training Upcoming Batches

Weekday

30 July 2024 to 30 August 2024

07:00 AM

Weekend

03 August 2024 to 03 September 2024

08:00 AM

Weekday

06 August 2024 to 06 September 2024

07:00 AM

Weekend

10 August 2024 to 10 September 2024

07:30 AM

Schedules Doesn't Suit You ?

Our Team can set up a batch at your convinient time.

Corporates Training

Experience and witness the express transformation of your workforce from the world-class tech upgrade platform.

Customized training options

Tailored curriculum to fit your project needs.

Practical exposure is assured.

We have got everything covered for any IT upgrade for your organization. We are one click away.

Success Stories from Future Digital Leaders

One of the best places to learn Data Science training. The live sessions were extremely useful.

Pratibha

Great course for the people who are looking for a better career.

Vaishali

Tekslate provided training on all the latest modules in Data Science and the online classes are worth.Got a chance of solving real-world problem.

Sravan

Data Science Training FAQ'S

Have questions? We’ve got the answers. Get the details on how you can grow in this course.

1. Who are the trainers?

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.

2. What if I miss a class?

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

3. How will I execute the practical?

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.