Big Data and Hadoop Certification Bundle

Step into the world of big data, data science and data analytics to propel your career forward

Big Data and Hadoop Fundamentals Business Analytics Fundamentals with Excel Data Science using Python

Self-Paced Learning

Price: $799

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365 days of access to high-quality, self-paced learning content designed by industry experts

What is this bundle about?

The ‘Big Data and Hadoop Developer Certification Training’ is an ideal course package for individuals who want to understand the basic concepts of Big Data and Hadoop. On completing this course, learners will be able to interpret what goes behind the processing of huge volumes of data as the industry switches over from excel-based analytics to real-time analytics. The course focuses on the basics of Big Data and Hadoop. It further trains you in Hadoop components like HDFS and MapReduce.

Business Analytics with excel training has been designed to help initiate you to the world of analytics. For this we use the most commonly used analytics tool i.e. Microsoft Excel. The training will equip you with all the concepts and hard skills required to kick start your analytics career. For a newcomer to the analytics field, this course provides the best required foundation.

The Data Science using Python course explores different Python libraries and tools that help you tackle each stage of Data Analytics. Python is a general purpose multi-paradigm programming language for data science that has gained wide popularity-because of its syntax simplicity and operability on different eco-systems. This Python course can help programmers play with data by allowing them to do anything they need with data - data munging, data wrangling, website scraping, web application building, data engineering and more. Python language makes it easy for programmers to write maintainable, large scale robust code.

Who should do this course?

This course is meant for both beginners and experienced professionals who are uninitiated to or have recently stepped into the field of analytics and want to build a career in this area. This course is beneficial for

✔ Software Developers and Architects
✔ Analytics Professionals
✔ Data Management Professionals
✔ Business Intelligence Professionals
✔ Project Managers
✔ Aspiring Data Scientists
✔ Graduates looking to build a career in Big Data
✔ Anyone interested in Big Data

Course preview

Bid Data and Hadoop Developer

1.1 - Data explosion and the need for Big Data

1.2 - Concept of Big Data

1.3 - Basics of Hadoop

1.4 - History and milestones of Hadoop

1.5 - How to use Oracle Virtual Box to open a VM

2.1 - Use of Hadoop in commodity hardware

2.2 - Various configurations and services of Hadoop

2.3 - Difference between a regular and a Hadoop Distributed File System

2.4 - HDFS architecture

3.1 - Steps to install Ubuntu Server 14.04 for Hadoop

3.2 - Steps involved in single and multi-node Hadoop installation on Ubuntu server

3.3 - Steps to perform clustering of the Hadoop environment

4.1 - YARN architecture

4.2 - Different components of YARN

4.3 - Concepts of MapReduce

4.4 - Steps to install Hadoop in Ubuntu machine

4.5 - Roles of user and system

5.1 - Advanced HDFS and related concepts

5.2 - Steps to decommission a DataNode

5.3 - Advanced MapReduce concepts

5.4 - Various joins in MapReduce

Business Analytics with Excel

1.1 - IntroductionPreview02:15

1.2 - What Is in It for Me00:10

1.3 - Types of Analytics02:18

1.4 - Areas of Analytics04:06

1.5 - Quiz

1.6 - Key Takeaways00:52

1.7 - Conclusion00:11

2.1 - Introduction02:12

2.2 - What Is in It for Me00:21

2.3 - Custom Formatting Introduction00:55

2.4 - Custom Formatting Example03:24

2.5 - Conditional Formatting Introduction00:44

2.6 - Conditional Formatting Example01:47

2.7 - Conditional Formatting Example202:43

2.8 - Conditional Formatting Example301:37

2.9 - Logical Functions04:00

2.10 - Lookup and Reference Functions00:28

2.11 - VLOOKUP Function02:14

2.12 - HLOOKUP Function01:19

2.13 - MATCH Function03:13

2.14 - INDEX and OFFSET Function03:50

2.15 - Statistical Function00:24

2.16 - SUMIFS Function01:27

2.17 - COUNTIFS Function01:13

2.18 - PERCENTILE and QUARTILE01:59

2.19 - STDEV, MEDIAN and RANK Function03:02

2.20 - Exercise Intro00:35

2.21 - Exercise

2.22 - Quiz

2.23 - Key Takeaways00:53

2.24 - Conclusion00:09

3.1 - Introduction01:47

3.2 - What Is in It for Me00:22

3.3 - Pivot Table Introduction01:03

3.4 - Concept Video of Creating a Pivot Table02:47

3.5 - Grouping in Pivot Table Introduction00:24

3.6 - Grouping in Pivot Table Example01:42

3.7 - Grouping in Pivot Table Example 201:57

3.8 - Custom Calculation01:14

3.9 - Calculated Field and Calculated Item00:25

3.10 - Calculated Field Example01:22

3.11 - Calculated Item Example02:52

3.12 - Slicer Intro00:35

3.13 - Creating a Slicer01:22

3.14 - Exercise Intro00:58

3.15 - Exercise

3.16 - Quiz

3.17 - Key Takeaways00:35

3.18 - Conclusion00:07

4.1 - Introduction01:18

4.2 - What Is in It for Me00:18

4.3 - What is a Dashboard00:45

4.4 - Principles of Great Dashboard Design00:45

4.5 - How to Create Chart in Excel02:26

4.6 - Chart Formatting01:45

4.7 - Thermometer Chart03:32

4.8 - Pareto Chart02:26

4.9 - Form Controls in Excel01:08

4.10 - Interactive Dashboard with Form Controls04:13

4.11 - Chart with Checkbox05:48

4.12 - Interactive Chart04:37

4.13 - Exercise Intro00:55

4.14 - Exercise1

4.15 - Exercise2

4.16 - Quiz

4.17 - Key Takeaways00:34

4.18 - Conclusion00:06

5.1 - Introduction02:12

5.2 - What Is in It for Me00:24

5.3 - Concept Video Histogram05:18

5.4 - Concept Video Solver Addin05:00

5.5 - Concept Video Goal Seek02:57

5.6 - Concept Video Scenario Manager04:16

5.7 - Concept Video Data Table02:03

5.8 - Concept Video Descriptive Statistics01:58

5.9 - Exercise Intro00:52

5.10 - Exercise

5.11 - Quiz

5.12 - Key Takeaways00:39

5.13 - Conclusion00:09

6.1 - Introduction01:51

6.2 - What Is in It for Me00:21

6.3 - Moving Average02:50

6.4 - Hypothesis Testing04:20

6.5 - ANOVA02:47

6.6 - Covariance01:56

6.7 - Correlation03:38

6.8 - Regression05:15

6.9 - Normal Distribution06:49

6.10 - Exercise1 Intro00:34

6.11 - Exercise 1

6.12 - Exercise2 Intro00:17

6.13 - Exercise 2

6.14 - Exercise3 Intro00:19

6.15 - Exercise 3

6.16 - Quiz

6.17 - Key Takeaways00:52

6.18 - Conclusion00:08

7.1 - Introduction01:17

7.2 - What Is in It for Me00:18

7.3 - Power Pivot04:16

7.4 - Power View02:36

7.5 - Power Query02:45

7.6 - Power Map02:06

7.7 - Quiz

7.8 - Key Takeaways00:32

7.9 - Conclusion00:11

Data Science using Python

0.1 - Course Overview04:34

1.1 - Introduction to Data Science08:42

1.2 - Different Sectors Using Data Science05:59

1.3 - Purpose and Components of Python05:02

1.4 - Quiz

1.5 - Key Takeaways00:44

2.1 - Data Analytics Process07:21

2.2 - Knowledge Check

2.3 - Exploratory Data Analysis(EDA)

2.4 - EDA-Quantitative Technique

2.5 - EDA - Graphical Technique00:57

2.6 - Data Analytics Conclusion or Predictions04:30

2.7 - Data Analytics Communication002:06

2.8 - Data Types for Plotting

2.9 - Data Types and Plotting02:29

2.10 - Knowledge Check

2.11 - Quiz

2.12 - Key Takeaways00:57

3.1 - Introduction to Statistics01:31

3.2 - Statistical and Non-statistical Analysis

3.3 - Major Categories of Statistics01:34

3.4 - Statistical Analysis Considerations

3.5 - Population and Sample02:15

3.6 - Statistical Analysis Process

3.7 - Data Distribution01:48

3.8 - Dispersion

3.9 - Knowledge Check

3.10 - Histogram03:59

3.11 - Knowledge Check

3.12 - Testing08:18

3.13 - Knowledge Check

3.14 - Correlation and Inferential Statistics02:57

3.15 - Quiz

3.16 - Key Takeaways01:31

4.1 - Anaconda02:54

4.2 - Installation of Anaconda Python Distribution (contd.)

4.3 - Data Types with Python13:28

4.4 - Basic Operators and Functions06:26

4.5 - Quiz

4.6 - Key Takeaways01:10

5.1 - Introduction to Numpy05:30

5.2 - Activity-Sequence it Right

5.3 - Creating and Printing an ndarray04:50

5.4 - Knowledge Check

5.5 - Class and Attributes of ndarray

5.6 - Basic Operations07:04

5.7 - Activity-Slice It

5.8 - Copy and Views

5.9 - Mathematical Functions of Numpy05:01

5.10 - Assignment 01

5.11 - Assignment 01 Demo03:55

5.12 - Assignment 02

5.12 - Assignment 02 Demo03:16

5.13 - Quiz

5.13 - Key Takeaways00:55

6.1 - Introduction to SciPy06:57

6.2 - SciPy Sub Package - Integration and Optimization05:51

6.3 - Knowledge Check

6.4 - SciPy sub package

6.5 - Demo - Calculate Eigenvalues and Eigenvector01:36

6.6 - Knowledge Check

6.7 - SciPy Sub Package - Statistics, Weave and IO05:46

6.8 - Assignment 01

6.9 - Assignment 01 Demo01:20

6.10 - Assignment 02

6.11 - Assignment 02 Demo00:55

6.12 - Quiz

6.13 - Key Takeaways01:10

7.1 - Introduction to Pandas12:29

7.2 - Knowledge Check

7.3 - Understanding DataFrame05:31

7.4 - View and Select Data Demo05:34

7.5 - Missing Values03:16

7.6 - Data Operations09:56

7.7 - Knowledge Check

7.8 - File Read and Write Support00:31

7.9 - Knowledge Check-Sequence it Right

7.10 - Pandas Sql Operation02:00

7.11 - Assignment 01

7.12 - Assignment 01 Demo04:09

7.13 - Assignment 02

7.14 - Assignment 02 Demo02:34

7.15 - Quiz

7.16 - Key Takeaways01:34

8.1 - Machine Learning Approach03:57

8.2 - Steps 1 and 201:00

8.3 - Steps 3 and 4

8.4 - How it Works01:24

8.5 - Steps 5 and 601:54

8.6 - Supervised Learning Model Considerations00:30

8.7 - Knowledge Check

8.8 - Scikit-Learn02:10

8.9 - Knowledge Check

8.10 - Supervised Learning Models - Linear Regression11:19

8.11 - Supervised Learning Models - Logistic Regression08:43

8.12 - Unsupervised Learning Models10:40

8.13 - Pipeline02:37

8.14 - Model Persistence and Evaluation05:45

8.15 - Knowledge Check

8.16 - Assignment 01

8.16 - Assignment 02

8.16 - Assignment 0205:14

8.16 - Quiz

8.16 - Key Takeaways01:12

9.1 - NLP Overview10:42

9.2 - NLP Applications

9.3 - Knowledge check

9.4 - NLP Libraries-Scikit12:29

9.5 - Extraction Considerations

9.6 - Scikit Learn-Model Training and Grid Search10:17

9.7 - Assignment 01

9.8 - Demo Assignment 0106:32

9.9 - Assignment 02

9.10 - Demo Assignment 0208:00

9.11 - Quiz

9.12 - Key Takeaway01:03

10.1 - Introduction to Data Visualization08:02

10.2 - Knowledge Check

10.3 - Line Properties

10.4 - (x,y) Plot and Subplots10:01

10.5 - Knowledge Check

10.6 - Types of Plots09:34

10.7 - Assignment 01

10.8 - Assignment 01 Demo02:23

10.9 - Assignment 02

10.10 - Assignment 02 Demo01:47

10.11 - Quiz

10.12 - Key Takeaway00:59

11.1 - Web Scraping and Parsing12:50

11.2 - Knowledge Check

11.3 - Understanding and Searching the Tree12:56

11.4 - Navigating options

11.5 - Demo3 Navigating a Tree04:22

11.6 - Knowledge Check

11.7 - Modifying the Tree05:38

11.8 - Parsing and Printing the Document09:05

11.9 - Assignment 01

11.10 - Assignment 01 Demo01:55

11.11 - Assignment 02 demo04:57

11.12 - Quiz

11.12 - Key takeaways00:44

What are the career benefits in-store for you?

A good understanding of these courses makes it easier to improve your analytic skills, thus increasing your career prospects in the analytics industry. After completing this training you will be in a strong position to embark on your next big data project.
Data Science using Python will help you explore different Python libraries and tools that will help you tackle each stage of Data Analytics.
Business Analytics will help you grasp the fundamentals of excel analytics functions and solve stochastic and deterministic analytical problems using tools like scenario manager, solver and goal seek.
Big Data and Hadoop Certification course is designed to prepare you for your next assignment in the world of Big Data. Hadoop is the market leader among Big Data Technologies and it is an important skill for every professional in this field. This training has been constructed to cater to the current industry requirements and will put you in a strong position to embark on your next big data project.

Faq's

Who provides the certification?

At the end of the training, you will receive a course completion certificate from Certs-School.

What are the modes of training offered for this course?

Online Self-Learning: In this mode, you will receive the lecture videos and you can go through the course as per your convenience

What payment options are available?

Payments can be made using any of the following options. You will be emailed a receipt after the payment is made.
•Visa Credit or Debit card, •MasterCard, •American Express, • Diner’s Club, •PayPal

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