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PMP® Exam Is Changing from January 02, 2021

PMP® Exam Is Changing from January 02, 2021

1. The PMP exam has been postponed to January 02, 2021, by PMI. 

2. The new PMP exam will focus on the following three new domains:

  1. People – emphasizing the skills and activities associated with effectively leading a project team
  2. Process – reinforcing the technical aspects of managing a project
  3. Business Environment – highlighting the connection between projects and organization strategy

3. The new PMP Exam content will cover 50% Predictive Approach and 50% Agile / Hybrid Approach across all Three Exam Domains

 

The PMP exam based on the new course outline that was scheduled to come into effect from July 1, 2020, has now been postponed to January 02, 2021.

PMI has made this move after the impact of the pandemic that has paralyzed the world and disrupted lives.

Shifting the date to months ahead is a move aimed to make time and space for project management professionals wanting to get PMP certified.

The present climate is not conducive given the grave health concerns that have grappled countries with lockdowns and self-isolations. PMI has stated that the safety and well-being of the community are paramount and precedes everything. The task force set up in PMI is monitoring the situations on the ground on a daily basis, and assessing the turn of events decided to extend the present PMP exam till December 2020.

The postponement of the exam will help the candidates time to get over the challenges faced with health care and provide time and training resources to prepare for the PMP exam.

These are compelling reasons for the PMP exam to be launched on January 02, 2021.

 

Will the Course Outline change?

PMI has made no reference to any change in the course outline so far. Only the Exam date has been rescheduled to January 02, 2021.  Other things, until an announcement is made by PMI, remain the same. The new course outline of PMP has been covered at length in our blog: PMP Exam is Changing

 

The new PMP Exam course outline was released by PMI on 30 June 2019. PMI will update the syllabus when the new PMP exam comes into effect by January 2021.

The links for the New PMP Content outline and the existing content outline are presented below.

It is to be noted that the PMP exam is not based on A Guide to The Project Management Body of Knowledge (PMBOK® Guide). PMBOK® Guide only serves as reference material. Candidates need to access other learning aids and study guides to enhance their understanding and improve their chances of clearing the PMP exam.

What are the New PMP Exam Changes?

1. The New PMP Exam will focus on the following Three New Domains:

  1. People – emphasizing the skills and activities associated with effectively leading a project team
  2. Process – reinforcing the technical aspects of managing a project
  3. Business Environment – highlighting the connection between projects and organization strategy

Download the New PMP Exam Content Outline (PDF)

 

The three domain areas will focus on predictive, agile, and hybrid approaches. The table below will help to understand the framework of the new PMP exam and the focus on each domain.

 

New PMP Exam Outline

DOMAIN

% WEIGHT

Domain I. People

42%

Domain II. Process

50%

Domain III. Business Environment

8%

TOTAL

100%

There is a new term introduced in the new PMP exam content outline called “enablers”.

But what are “Domain”, “Tasks” and “Enablers” according to the new PMP exam changes.?

Domain – is the knowledge area necessary for the project management practice.

Tasks –  These are the tasks to be executed by the project manager in the domain areas for the successful execution and completion of a project.

 Enablers – These are Descriptive illustrations of the work associated with the task.

 

New PMP Exam

Domains

3

Tasks

35

Enablers

Yes

 

Looking at the table above, one might get the impression that new PMP exam has shrunk as the objectives are fewer. It is not so, and if anything the new PMP exam will be more complex and harder as compared to the existing PMP exam. This is the main driver to prepare and appear for the PMP exam before December 2020.

While PMBOK-version 6 as the reference material has not changed for the new PMP course outline, how PMI will assess the the PMP certification exam has changed.

 

2. The new PMP Exam content will cover 50% Predictive Approach and 50% Agile / Hybrid Approach across all Three Exam Domains

3. Project Management Domains Aligned To Real Life Practices

 

Will PMBOK change?

NO. There will be no change as PMBOK® Guide Sixth Edition will continue as the reference material. You can use the PMBOK® Guide Sixth Edition till December 2020. The seventh edition of the PMBOK® Guide is expected in the second quarter of 2021.

I am preparing for the PMP Exam. How will the changes affect me?

It is strongly recommended to take the exam when you have time till December 2020 since the existing PMP exam and its patterns are well known. Post-January 2, 2021, the new PMP exam will come into effect and its not clear how the exam will be structured.

In case, you plan to take the PMP exam on or before 31 December 2020

  •  Follow the existing PMP course outline. There are no changes to the existing syllabus
  • Continue with your studies and earn the PDUs through training or other means as prescribed by PMI
  • Use the PMBOK® Guide Sixth Edition as the reference material. In addition, spend time on other learning aids and resources
  • Check carefully about the safety measures and other health-related concerns before scheduling your exam. Aim to take the exam by end of October 2020 to avoid the scramble in securing a slot as there would be a jam in November and December by candidates to sit for the exam before the changeover. The 2 months of November and December are also a window to retake the exam in case you are unable to clear in the first attempt.

In case, you plan to take the PMP exam on or after 2 January 2021

  • Follow the new PMP Exam Content Outline (June 2019) and become familiar with the topics on which you will be tested in the new exam.
  • Follow the PMBOK® Guide Sixth Edition for your studies. The PMBOK® Guide Sixth Edition holds good for the new PMP exam.
  • In addition, you need to Study The Agile Practice Guide and agile approaches.
  • Check with your training provider and confirm the updated course outline and required study material aligned with the new course outline
  • Check for additional study materials, learning aids and other resources
  • Make a schedule any day on or after 2 January 2021 for the new PMP exam.

What happens if I have already scheduled my exam?

If you have scheduled your exam and unable to appear due to restrictions arising out of the COVID-19 or other health concerns,  you can reschedule your exam at a date convenient. PMI has assured to make an arrangement with regard to exam reschedule and also extend your eligibility till 18 January 2021

Can I reschedule at this time?

Yes, you can reschedule your exam. You can check about the specific test center in your location using this  PearsonVUE’s Coronavirus Update Page.  You can reschedule your exam at available time slots using your online Pearson VUE account.

Can I get a refund of the fees paid?

The fees paid will be transmitted by PMI. You will also be able to reschedule your exam without any penalty charges.

How will I get to know if the exam gets canceled?

In case you have chosen Pearson VUE and paid your fees, there will be an email cancellation notice and also the refund. In case your appointment remains unchanged then expect the exam to be conducted as scheduled. Ensure you have all your safety measures in place and take all the necessary precautions.

 

 

Benchmark your current level of preparation for the current version of the PMP Exam. 
Download Free PMP® Exam Practice Test with 200 PMP® Questions.

You may also be interested in Sample PMP® Exam Prep Questions before you download the Free PMP® Practice Test.

Download our Free PMP Brochure for more information. Do visit our Corporate Training to know more about core offerings for enterprises in empowering their workforce.

iCert Global conducts Project Management, Quality Management, Business Analysis, Agile, Scrum, and DevOps Certification courses across various locations in the United States. 

Visit us at https://www.icertglobal.com/ for more information about our professional certification training courses or
Call Now! on +1-713-287-1213 / +1-713-287-1214 or e-mail us at info {at} icertglobal {dot} com.

You may also be interested in Sample PMP® Exam Prep Questions before you download the Free PMP® Practice Test.

Download our Free PMP Brochure for more information.

iCert Global conducts Project Management, Quality Management, Business Analysis, Agile, Scrum, and DevOps Certification courses across various locations in the United States.

Visit us at https://www.icertglobal.com/ for more information about our professional certification training courses or Call Now! on +1-713-287-1213 / +1-713-287-1214 or e-mail us at info {at} icertglobal {dot} com.

The company conducts both Instructor-led Classroom training workshops and Instructor-led Live Online Training sessions for learners from across the United States and around the world.

Please Contact Us for more information about our professional certification training courses to accelerate your career in the new year. Wish you all the best for your learning initiatives in the new year.

Are you ready to take the first step towards getting a PMP Certification? The last day to take the current PMP Exam is December 31, 2020, and the PMP Exam is changing from January 0201, 2021.
 

By when are you planning to achieve the PMP Certification? Have you set a target to get PMP Certified before December 2020? Let us know your thoughts in the 'Comments' section below. Thank you.

Connect with iCert Global on: 

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Wish you all the best for your PMP Certification Exam Prep Training. iCert Global would be privileged to play a part in your journey towards achieving a globally recognized PMP Certification from The Project Management Institute, Inc. 

Read more about PMP Exam Changes on PMI.org.

 

Professional Certification Training Courses from iCert Global

- PMP Certification Training

- CAPM Certification Training

 

Quality Management Training by iCert Global:

- Lean Six Sigma Yellow Belt (LSSYB) Certification Training Courses

- Lean Six Sigma Green Belt (LSSGB) Certification Training Courses

- Lean Six Sigma Black Belt (LSSBB) Certification Training Courses

 

Scrum Training by iCert Global:

- CSM (Certified ScrumMaster) Certification Training Courses

 

Agile Training by iCert Global:

- PMI-ACP (Agile Certified Professional) Certification Training Courses

 

DevOps Training by iCert Global:

- DevOps Certification Training Courses

 

Business Analysis Training by iCert Global:

- ECBA (Entry Certificate in Business Analysis) Certification Training Courses

- CCBA (Certificate of Capability in Business Analysis) Certification Training Courses

- CBAP (Certified Business Analysis Professional) Certification Training Courses

 

The company conducts both Instructor-led Classroom training workshops and Instructor-led Live Online Training sessions for learners from across the United States and around the world.

Please Contact Us for more information about our professional certification training courses to accelerate your career in the new year. Wish you all the best for your learning initiatives in the new year.

Let us know your thoughts in the 'Comments' section below. Thank you.

 

 

 


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Data Science Masters Program

Data Science Masters Program
You will learn the following courses in our Data Science Masters Program:

 

1. Python Statistics for Data Science Course

2. R Statistics for Data Science Course

3. Data Science Certification Course using R

4. Python Certification Training for Data Science

5. AI & Deep Learning with TensorFlow

6. Tableau Training & Certification

7. Data Science Master Program Capstone Project

8. SQL Essentials Training & Certification

9. R Programming Certification Training

10. Python Programming Certification Training

11. Scala Essentials

12. MongoDB® Training And Certification

 

Overview
iCert Global is offering the Data Science Masters Program which is a series of 12 courses that are designed based on data-driven strategies of the organizations. With the Data Scientist Master Program, the aspirants can gain hands-on experience on python Statistics, Python Programming, R Programming, R Statistics, AI & Deep Learning with TensorFlow, Tableau, Capstone Project, SQL Essentials, Scala Essentials, and MongoDB®. 


Program Advantage 

  • Get certified in most in-demand Data Science field
  • Enhance your analytical and statistical skills 
  • Expertise in the latest trend of Data Science
  • Get hands-on visualization tools
  • Lucrative career path

Program Benefits 

  • Gain a foundational understanding of Data Science
  • Enhance your understanding of the data structure used in different tools and languages.
  • Understand and use the various graphics through data visualization
  • Gain a basic understanding of various statistical concepts
  • Understand and use hypothesis testing method to drive business decisions
  • Understand and use linear, non-linear regression models, and classification techniques for data analysis

Target Audience

The top-notch data science program will be best suited for:

  • IT Professionals
  • Analytics Managers
  • Business Analysts
  • Banking and Finance Professionals
  • Marketing Managers
  • Network Managers
  • Graduate in Bachelors or Master’s Degre

Prerequisites for Data Science Masters program

  • There are no such prerequisites for taking the Data Science Masters Program
  • Basic knowledge of mathematics, statistics and programming will be an added advantage.

Industry Demand

Data Scientist is the most promising job in the U.S according to LinkedIn. 

  • Median Base Salary: $130,000
  • Job Openings (YoY Growth): 4,000+ (56%)
  • Career Advancement Score (out of 10): 9

Also, the demand for Data Scientists is growing exponentially in all the industries. Out of all the openings, 19% of data science professionals jobs are secured by the Finance Industry.

Demand for Data Science as per industry

According to Glassdoor.com,

Average annual salary for Data Scientists

Python Statistics for Data Science

Python Statistics for Data Science

Python statistics is one of the most important python built-in libraries developed for descriptive statistics. 

Python statistics is all about the ability to describe, summarize, and represent data visually through comprehensive python statistics libraries. The curriculum of the course is mainly focused on the overall concept of python statistics that deals with the collection, analysis, interpretation, and presentation of masses of numerical data.

Prerequisites:

  • Basic knowledge of mathematics
  • Fundamental knowledge of statistics
  • Basic knowledge of Python language

Target Audience:

Statisticians who want to use python for data manipulation, data exploration or statistical analysis.

Industry Demand

There is a huge demand for statisticians with python skills.

According to Payscale.com

The average annual salary to a statistician with python skill can reach up to $109k/year

Average annual salary for Statistician with Python skill

R Statistics for Data Science Course 

R statistics

With R statistics, enhance your knowledge on statistical inference to understand and compute p-values and confidence intervals, all while analyzing data with R. 

Prerequisites 

There are no such prerequisites required to take up R-Statistics 

  • Basic programming knowledge
  • Basic mathematics knowledge

Target Audience:

Statisticians who want to use python for data manipulation, data exploration or statistical analysis.

Industry demand 

According to Payscale.com,

Average annual salary for Statiscian with R skill

The annual salary of a statistician with R skills can earn up to $100k/year.

 

Data Science Certification Course using R

Data Science Certification using R

R is a comprehensive programming language and considered as a primary language for Data Science and it provides support for object-oriented programming with generic functions for developing web applications.

R program holds the capability of transforming any data into structured data. R language helps in complex operations with vectors, arrays, data frames as well as other data objects that have varying sizes.

Prerequisites

  • Basic knowledge of R language
  • Basic knowledge of statistics

Target Audience

  • IT professionals interested in data science and analytics
  • Software developers wanting to pursue a career in data science and analytics
  • Business analytics Professionals
  • Fresh graduates wanting to build in career in the Analytics space
  • Experienced professionals who would like to expand their expertise in data analytics

Industry demand 

There is a huge demand for data scientists with R programming skills. The annual salary of the data scientists with R skills can earn up to $126k/year

According to Payscale.com,

Average Data Scientists with R skill salary

Python Certification Training for Data Science

Python Certification Training for Data Science

Python is in a leading position in the language used by data science professionals. It is the de facto language for data science. Python has many libraries, especially for data manipulation and data analysis. 

It is used to develop highly efficient and cost-effective applications. It is widely used by data scientists for data mining, web development, scientific computing, and more.

Prerequisites:

  • Basics of Python language
  • Basic knowledge of mathematics

Target Audience:

  • Data analytics professionals working with python 
  • software and IT professionals

Industry Demand

As per Payscale,

The annual salary of data scientists with python skills can earn up to $130k/year.

Average annual salary for Data Scientists with python skill

 

AI & Deep Learning with TensorFlow 

AI and Deep learning with TensorFlow

TensorFlow is an open-source library used to build complicated AI and Deep learning models and to manipulate the data by creating a DataFlow graph or a Computational graph. It is an end-to-end platform to deploy machine learning models.

AI and deep learning with the TensorFlow program include the concept of SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM) and work with libraries like Keras & TFLearn.

With this course, the learner will be able to use TensorFlow to implement deep learning functions as tools to build scalable AI-powered algorithms.

Prerequisites

There are no prerequisites to take this course. The following knowledge will be an added advantage.

  • Basic programming knowledge 
  • Concepts about Machine Learning
  • The basic concept of Deep Learning

Course benefits:

At the end of the course, you will be able to:

  • Build complicated models using best practices of TensorFlow
  • Build image recognition algorithms with deep neural networks and convolutional neural networks
  • In-depth knowledge of Convolutional Neural Network, Recurrent Neural Network, Autoencoders
  • Hands-on real-life industry-based projects
  • Understanding of Machine Learning Paradigms
  • Get certification


Target Audience

  • Object-Oriented application developers aspiring to become ‘Data scientists’

  • Analytics Managers 
  • Business Analysts interested to learn Deep Learning (ML) Techniques
  • Information Architects
  • Data Analysts
  • Software professionals who already have Big Data background 

Industry Demand

TensorFlow is the most popular and in-demand tech skill. According to Forbes, TensorFlow was the top tech skill in the past three years.

TensorFlow demand

The average annual salary of a TensorFlow according to Payscale.com,

Average annual salary for TensorFlow

Tableau Training & Certification

Tableau training and certification

Tableau is a data visualization tool that transforms unstructured raw data into structured and understandable formats. Tableau enhances the speed of data analysis by visualization through dashboards and worksheets. It allows code and customizes the reports. It is easily understandable and is popular in all sectors of the industry. The best features of Tableau are:

  • Data Blending
  • Real-time analysis
  • Collaboration of data

Tableau is integrated with over 250 applications and can extract data from simple to complicated databases like excel, pdf, oracle, Amazon Web services, Microsoft Azure SQL database, Google Cloud SQL.

The Tableau product suite consists of:

  • Tableau Desktop
  • Tableau Public
  • Tableau Online
  • Tableau Server
  • Tableau Reader

Prerequisites

Tableau certification does not require any prerequisites, but preferably the following knowledge will be an added advantage:

  • Basic knowledge of SQL 
  • Basic knowledge of DW (Data Warehousing) concepts 
  • Basic understanding of Microsoft Excel
  • Basic knowledge of mathematics, statistics, microeconomics, and marketing

Course Benefits:

At the end of the course, you will be able to,

  • Understand the transformation of unstructured data into dashboards and worksheets
  • Ability to create interactive plots for business analysis
  • Build dashboards for complicated data
  • Work in different industries
  • Higher pay

Target Audience:

Tableau users vary from beginner to intimidate level

  • Model Developer
  • Data Analyst
  • Data Strategist
  • Data Quality Operator
  • Change Manager
  • Data Visualization Analyst
  • Workflow Integrator
  • Business Intelligence Manager
  • Business Intelligence Developer

Industry Demand:

There is a soaring demand for Tableau professionals in all the sectors.

According to Gartner,

For the 8th consecutive year, Tableau is positioned as a leader.

Demand for Tableau

The annual salary of data analytics with Tableau skill can reach up to $89k/year.

Avwerage annual salary for Tableau

 

Data Science Master Program Capstone Project

Data science master program capstone project

The capstone project is all about implementing data science skills in the real world. This course validates the knowledge you gained in our Data Scientist Master Program. The capstone project ensures you become a job-ready data scientist and enhances the ability to solve complex industry-aligned problems. The course involves an end to end process: preparing the data, organizing data, transforming data, constructing a model, and evaluating results.

The Data Science Master Program Capstone Project includes,

  • Data Processing
  • Data Wrangling
  • Data Visualization
  • Model building
  • Model finalization
  • Dashboarding the results 

Prerequisites: 

  • The candidate must complete the iCert Global’s Data Scientist Master Program.
  • In-depth knowledge of programming languages such as python and R
  • Basic knowledge of data visualization tools like Tableau.

Target Audience

  • Anyone who is interested in solving the industry based data science problem.

SQL Essentials Training & Certification

SQL essentials training and certification

Structured Query Language (SQL) is one of the most sought after languages for data wrangling. Data wrangling helps the user to easily scale the process of a large volume of data. It is used to retrieve answers from stored business data.

SQL is designed to interact with data and to manipulate data in the Relational Database Management Systems (RDBMS). SQL Essential and training will enable you to code and manage database-driven applications and relational databases. SQL includes MySQL, Oracle, SQL Server, PostgreSQL, and SQLite with the same application programming interface (API). 

The key features of SQL essentials include,

  • Creating tables 
  • Defining relationships 
  • Manipulating strings, numbers, and dates.
  • Using triggers to automate actions  
  • Using subselects and views.

Course benefits:

After completion of the course, you will be able to,

  • Create and manage tables using Data definition language (DDL) statements
  • Manipulate data using Data Manipulation Language (DML)
  • Retrieve Data using the SQL select statement
  • Restrict and sort data

Prerequisites 

There are no prerequisites for this course.

  • Basic knowledge about DBMS (Database Management System) will be an additional advantage.
  • Basic knowledge of Microsoft excel

Target Audience

  • Software Developer
  • Student interested to become Data Analysts
  • Research Professional
  • Information Technology Consultant
  • Information Technology Support Specialist

Industry Demand 

SQL is used by the top companies. Uber, Netflix, Airbnb, Facebook, Google, and Amazon use SQL to create their own database systems. 

Especially for the job posting for Data Analysts, the most in-demand listed skill in the U.S is SQL. According to the research report,

Demand for SQL

 

According to Payscale, the annual salary of data analysts with SQL can go up to $88k/year.

Average annual salary for SQL

 

R Programming Certification Training

R programming Certification Training

R Programming is an open-source programming language and analytical tool used by Data Scientists, Data Miners, Software Programmers, Statistics to facilitate the performance of statistical operations. R is most popular for its visualization libraries. 

R produces portable and machine-independent code that facilitates easy debugging of errors in the code.

Prerequisites

There is no such prerequisite to learn R programming.

  • Basic knowledge of mathematics and statistics will be an added advantage.

Target Audience

  • Software developers 
  • Statisticians
  • Data miners

Industry Demand

In the recent ranking on the popularity of programming languages for March 2020 by TIOBE, R is one of the top programming languages in terms of popularity. 

According to Payscale.com  average annual salary is drawn by a certified R programmer is:

salary for R skill

 

Python Programming Certification Training 

Python programming certification training

Python is a strongly-typed procedural language, interpreted, object-oriented, high-level programming language with dynamic semantics. It's high-level built-in data structures, combined with dynamic typing and dynamic binding.

According to Stack Overflow’s annual developer survey

“Python, the fastest-growing major programming language, has risen in the ranks of programming languages in our survey yet again, edging out Java this year and standing as the second most loved language (behind Rust).”

Python is an open-source platform designed to run on Windows and Linux environments. There are python libraries developed for,

  • Data manipulation
  • Data Visualization
  • Statistics
  • Mathematics
  • Machine Learning
  • Natural Language Processing

Prerequisites

There is no such prerequisite to learn R programming.

  • Basic knowledge of mathematics and statistics will be an added advantage.

Target Audience

  • Software developers 
  • Statisticians
  • Data miners

Industry demand

Python is considered as the top skill required for data science

demand for python

average salary for python

Scala Essentials 

Scala essentials

The name Scala is a combination of the words “Scalable” and “Language,” Scala is a combination language of object-oriented design with functional programming. Scala uses an object-oriented design with functional programming. Scala enables data scientists to custom functions, parallel processing, and programming Spark with Scala. 

Prerequisites

  • Basic knowledge and experience in object-oriented languages such as Java, C#
  • Basic knowledge and  experience in a functional programming language (e.g. Haskell, Lisp) 
  • Basic understanding of database and any query language

Target Audience

  • Software Developer
  • Research professional
  • Information Technology Engineer

Industry Demand

Scala is a highly flexible and functional language. Top companies like LinkedIn, Twitter, Netflix, Tumblr, Sony, Apple, Foursquare use scala. Scala is also used to build  Android Applications and Desktop Applications.

According to Payscale,

Average annual salary for scala

MongoDB® Training And Certification

MongoDB Training and Certification

MongoDB is an open-source document-oriented database program. MongoDB is based on C++. It is a leading NoSQL database which means the data is not in a rational format. The data is in documents. MongoDB is used for high volume data storage. 

The main feature of MongoDB is that it enables ad hoc queries and helps in searching by field, range queries, and regular expression searches.

MongoDB provides indexing, replication, and load balancing. MongoDB will help to deploy a highly scalable and performance-oriented database.

Prerequisites 

  • Basic understanding of database, text editor and execution of programs.
  • Basic concept of RDBMS
  • Basic knowledge of JavaScript programming 

 Target Audience

  • Database Admin
  • Database Server Engineer
  • Database Specialist
  • Data Analytics
  • Visualization Engineer 
  • Senior Developer
  • Team Lead
  • Web Developer
  • Applications Engineer

Industry Demand

According to ITJobsWatch,

Demand for MongoDB

There is an increasing demand for Certified MongoDB professionals.

According to Payscale.com

The average annual salary of Data scientist wit MongoDB skill is:

Average annual salary for MongoDB


 

For more information on how iCert Global can help you to achieve your Data Science Certification goals, please visit our Data Science Certification Training Courses on our website.

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iCert Global conducts Project Management, Quality Management, Business Analysis, Agile, Scrum, and DevOps Certification courses across various locations in the United States.

Visit us at https://www.icertglobal.com/ for more information about our professional certification training courses or Call Now! on +1-713-287-1213 / +1-713-287-1214 or e-mail us at info {at} icertglobal {dot} com.

 


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Introduction to R Programming for Data Science

R Programming is an open-source programming language and analytical tool used by Data Scientists, Data Miners, Software Programmers, Statistics to facilitate the performance of statistical operations. R programming is one of the most popular languages used for Data Analytics.

In the recent ranking on the popularity of programming languages for March 2020 by TIOBE, R is one of the top programming languages in terms of popularity. R is used in many sectors of the industries such as, 

  • Finance
  • Banking
  • Healthcare
  • Healthcare
  • Social Media
  • E-Commerce
  • Manufacturing

 

As mentioned before, R is an open-source programming language and can be obtained for free from the website: www.r-project.org and the packages available for free are called CRAN.

Command lines are to be followed and executed by the user in a prompt. 

The R project was developed by Ross Ihaka and Robert Gentleman and released in 1992, its first version in 1995 and a stable beta version in the year 2000.

The final release of the recent version of R-Programming, R version 4.0.0 (Arbor Day) prerelease versions is  expected on 04.24.2020

 

What are the features of R-Programming in Data Science? 

The features of R-Programming are:

  • R is a comprehensive programming language and considered as a primary language for Data Science and it provides support for object-oriented programming with generic functions for developing web applications.
  • In order to facilitate programming with functions, there are more than 10,000 packages in the repository of R programming to help in different fields that deal with data. 
  • R is an interpreter based language and makes the development of code easier. 
  • R produces portable and machine-independent code that facilitates easy debugging of errors in the code.
  • R language helps in complex operations with vectors, arrays, data frames as well as other data objects that have varying sizes. 
  • R can be integrated with other technologies such as Hadoop and programming languages like C, C++, Python, Java, FORTRAN, and JavaScript.
  • R packages can be installed and used on any OS in any software environment.

How to perform data analysis through R programming?

The following steps are to be followed to perform data analysis through R programming: 

  • Import
  • Transform
  • Visualize
  • Model
  • Communicate 

Import: The first step is to import data stored in files, databases, HTML tables to the R environment. To perform data analysis it is required to convert or import all the stored data into R data. 

Transform: The data collected is transformed into tabular form. The columns of the table are made variable by keeping rows for an observation. 

Visualize: In this step, the graphical representation of the data is used to make the data more understandable. The graphical representation of the data helps in recognizing the pattern and allows to convey the information quickly.

Model: Models are created as a complementary tool for visualization. These models are used to answer the question related to the observations. The models are computational tools. 

Communicate: In this step, the results obtained from visualization and models are communicated to others. It enables the user to produce well-designed print- quality plots for sharing.

What are the advantages of R programming? 

The various advantages of the R programming language  are:

Advantages of R- Programming

Open-source

R is an open-source programming language. The user can even customize the packages, resolve the issues or generate a new package

Support for Data Wrangling

R program holds the capability of transforming any data into structured data. The packages like dplyr, readr can support Data Wrangling. Data wrangling helps the user to easily scale the process of large volume of data.

Array of Packages

There are more than 10,000 packages in the CRAN repository and it is constantly growing. The packages are available for all the industries. 

Quality Plotting and Graphing

R facilitates quality plotting and graphing. The popular libraries like ggplot2 and plotly provides visually appealing graphs

Highly compatible 

R is highly compatible and can be integrated with many other programming languages like C, C++, Java, and Python. It can also be integrated with technologies like Hadoop and various other database management systems.

Platform-Independent language

R is a platform-independent language. R is a cross-platform programming language and can be used on Windows, Linux, and Mac.

High-quality reports

The reports can be created embedded with data, plots and R scripts easily using packages life shiny and markdown. Interactive web apps can be created to customize the report according to the requirement. 

Machine Learning Operations

R programming enables machine learning operations like classification, regression and also provides features for developing artificial neural networks.

Statistics

R is known as the lingua franca of statistics. It is mainly used to create statistical tools.

Continuously evolving language

R is a constantly evolving programming language. R has a strong user base and will continue to grow in the future. 

What are the job opportunities for R Programmers? 

R is a constantly evolving language and there is a huge demand for certified R programmers all over the world. It is one of the most popular programming languages used by Data Scientists. 

According to KDnuggets

There are about 50,000 R programmers in the world. The US has over 25% of all R programmers. The second position is India, with about 4000 to 6000 R Programmers (the US has twice this number or a bit more). Canada has over 1000 R Programmers.

R programming is not only used in the IT sector. There are many industries using R programming to transform the problems into solutions. The industries in which the R programming is in demand are:

Demand for R industry wise

R programming is one of the most used languages among data scientists.  It is used for  statistical inference, data analysis, and Machine Learning. 

According to a survey conducted by  KCnuggets,

Almost 60 % of 7955 respondents prefer R Programming language. 

Demand for R

According to Payscale.com  average annual salary is drawn by a certified R programmer is:

Average annual salary

For more information on how iCert Global can help you to achieve your Data Science Certification goals, please visit our Data Science Certification Training Courses on our website.

The Data Scientist certification validates data scientist’s knowledge on SAS, R, Hadoop, Python and Spark and how to use data concepts such as data exploration, visualization hypothesis testing, and predictive analytics. There is a huge demand for Data Scientists in industries like Aerospace industry, IT industry, e-commerce industry, and healthcare industry.

Know more about our Professional Certification Training Courses for preparing for the above certifications.

 

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Visit us at https://www.icertglobal.com/ for more information about our professional certification training courses or Call Now! on +1-713-287-1213 / +1-713-287-1214 or e-mail us at info {at} icertglobal {dot} com.

 


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What will be the job scenario for data science in 2020?

:

Data Scientist is one of the most in-demand professions. Data is generated at a rapid pace in recent times. Data scientists are a true asset to the organizations nowadays to extract, understand, analyze, process, visualize and communicate the huge amount of data generated every day. The top companies like Google, Amazon, and Visa are using Data Science in order to optimize themselves and to address their rapidly expanding data.

According to Raconteur,

By 2025, it is predicted that 463 exabytes of data will be created each day globally — that’s the equivalent of 212,765,957 DVDs per day.

Every day,

  • 500 million tweets are sent
  • 294 billion emails are sent
  • 4 petabytes of data are created on Facebook
  • 4 terabytes of data are created from each connected car
  • 65 billion messages are sent on WhatsApp
  • 5 billion searches are made

Due to the huge data created, there is a huge demand for data science professionals to manage the data. 

The data science job vacancy has experienced rapid growth in recent years as shown below.

Data Science Vacancy trends

(Image Source: IT Jobs Watch)

According to IBM, an increment of 3,64,000 to 2,720,000 openings will be generated in 2020. The demand will only grow further to astonishing 7,00,000 openings.

The role of data scientists and business analysts is more sought after in the U.S.A.

Data science is a broad spectrum. The roles in the data science field in demand are: 

  • Data Engineer
  • Data Administrator
  • Machine Learning Engineer
  • Statistician
  • Data and Analytics Manager

Which sector of the industry has a high demand for Data Science Professionals in 2020?

The demand for Data Scientists is growing exponentially in all the industries. Out of all the openings, 19% of data science professionals job is secured by the Finance Industry. 

Demand for data science

  • Finance and Banking industry: Data Science is used in risk analytics, real-time analytics, fraud detection, algorithmic trading, customer analytics, customer data management, Algorithmic Trading. There is a demand for data scientists due to the huge amount of data created.
  • Healthcare: Data Science is used for predictive diagnosis, medical imaging, drug discovery, genetics.
  • Airline and Aviation Industry: companies use data science to improve their services like customer experience, fix flight schedules, safety data, route optimization, preventive maintenance, etc.
  •  Manufacturing Industry: Data Science is used for optimizing production, reducing cost, and boosting profits. 
  • Transportation Industry: Data Science is used for an extensive analysis of fuel consumption patterns, driver behavior, and active vehicle monitoring.
  • E-commerce and Retail industries: Data Science is used for identifying potential customer base, optimizing pricing structure, predictive analysis for forecasting and knowing the recent trends. 

Which job roles in Data Science are in demand for 2020? 

1. Data Architect — The data architect develops architecture effectively to organize, integrate, centralize and maintain data. There is a tremendous demand for data architects. 

Skills required: Pattern recognition, clustering for handling data, text mining JavaScript frameworks like HTML5, RESTful services, Spark, Python, Hive, Kafka, and CSS 

According to Glassdoor.com

Data science architect average annual salary

The average annual salary of a Data Architect is $115K/year.:

 

2. Data Engineer — Develops, tests and maintains data architectures to keep data ready for analysis. They mainly focus on development, deployment, management, optimization of data pipelines and infrastructure to transform and transfer data to data scientists for further analysis

Skills required: Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop)

According to Glassdoor.com,

Data engineer average annual salary

 

3. Data Analyst — The data analysts process and interpret the data to understand and analyze the insights from structured and unstructured data. 

Data analysts are responsible for translating technical analysis to qualitative action items.

Skills required: Programming skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualization

According to Glassdoor.com,

Data analysts

4. Data Scientist — Once the analysis is done, the technical aspects required are taken care of by data scientists. Data scientists are used to source, manage, and analyze large amounts of unstructured data. 

Skills Required: Statistical and Mathematical skills, Programming skills (SAS, R, Python), Storytelling and Data Visualization, Hadoop, SQL, Machine Learning

According to Glassdoor.com

Data scientists average annual salary

According to  Data Science Salary Report 2020 Europe by Big Cloud,

 

The salaries of data science professionals are highest in Switzerland for different Data Science Professionals.

Data scientist professional salary in singapore

U.K Data scientist professional salary

Netherland data scientists salary

Italy

Germany

France

 

For more information on how iCert Global can help you to achieve your Data Science Certification goals, please visit our Data Science Certification Training Courses on our website.

The Data Scientist certification validates data scientist’s knowledge on SAS, R, Hadoop, Python and Spark and how to use data concepts such as data exploration, visualization hypothesis testing, and predictive analytics. There is a huge demand for Data Scientists in industries like Aerospace industry, IT industry, e-commerce industry, and healthcare industry.

Know more about our Professional Certification Training Courses for preparing for the above certifications.

 

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Data Science Certification Training Courses

 

CRISC Certification Training Courses

 

CISM Certification Training Courses

 

PMP Certification Training Courses

Free Download: PMP Practice Test with 200 Questions

CEH Certification Training Courses

 

CSM Certification Training Courses

 

We provide instructor-led classroom and instructor-led live online training across the globe. We also provide Corporate Training for enterprise workforce development.

 

Connect with us:

 

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iCert Global conducts Project Management, Quality Management, Business Analysis, Agile, Scrum, and DevOps Certification courses across various locations in the United States.

 

Visit us at https://www.icertglobal.com/ for more information about our professional certification training courses or Call Now! on +1-713-287-1213 / +1-713-287-1214 or e-mail us at info {at} icertglobal {dot} com.

 

 


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What Are The Top 5 Data Science Trends in 2020?

:

The Top 5 Data Science Trends in 2020 are: 

  • AI in Data Science
  • Deep Learning in Data Science 
  • ML in Data Science
  • Python, the fastest-growing major programming language
  • Demand for Data Security Professionals

In today’s exponentially growing data-driven world, organizations are going through major transformations. Organizations rely on digital technologies. Data science has become an integral part of the organizations now. The decisions are taken based on the real data-driven facts. The main aim of data science is to integrate the data into the business process.

According to Google Search Trends,

The interest in Data Science has increased significantly in the past 5 years

Google search trends -Data Science

The Top 5 Data Science Trends in 2020 are: 

  • AI in Data Science
  • Machine Learning in Data Science
  • Deep Learning in Data Science 
  • Python, the fastest-growing major programming language
  • Demand for Data Security Professionals

AI in Data Science 

Artificial intelligence in Data Science is used to acquire insights from data through intelligence.  AI is a tool that helps data science get results and solutions for specific problems. Data storing, cleaning, exploring and modeling the data takes a lot of time. Artificial intelligence helps organizations in handling the data and improve the overall business process.  In the future, advanced AI will be applied in all fields. Artificial Intelligence makes the use of algorithms to perform autonomous actions. 

According to McKinsey,

Amazon has achieved impressive results from its $775 million acquisition of Kiva, a robotics company that automates picking and packing.

Netflix has also achieved impressive results from the algorithm it uses to personalize recommendations to its 100 million subscribers worldwide.

Machine Learning in Data Science 

Machine Learning automates the data analytical model building by turning the information to knowledge. With the explosion of data in recent times, it is difficult to bring new predictive models. Machine learning is used to analyze a large number of data. Machine learning develops fast algorithms and data-driven models for real-time processing data.  

The adoption of Machine learning is reaching new heights. 

According to Statista.com,

  • 1/3 of IT leaders are planning to use ML for business analytics. 
  • 25% of IT leaders plan to use ML for security purposes 
  • 16% of IT leaders want to use ML in sales and marketing

Deep Learning in Data Science 

 

Deep learning is a technique of machine learning. ML uses deep learning as the powerhouse between Artificial Intelligence and Data Science. It strengthens the process of AI. The process of deep learning requires a lot of learning and implementation. Google uses deep learning to deliver solutions. The various applications google use deep learning is 

Google Deepmind’s AlphaGo

DeepMind’s WaveNet

Google Translate 

Google PlaNet

According to teks.co.in,

  • The estimated value of the US deep learning software market in 2025 is $935 million
  • The estimated compound annual growth rate of the US deep learning market in 2025 is 42% 

Python, the fastest-growing major programming language 

Python is in a leading position in the language used by data science professionals. It is the de facto language for data science. 

According to Stack Overflow’s annual developer survey

“Python, the fastest-growing major programming language, has risen in the ranks of programming languages in our survey yet again, edging out Java this year and standing as the second most loved language (behind Rust).” 

 

Python is an open-source platform designed to run on Windows and Linux environments. There are python libraries developed for,

  • Data manipulation
  • Data Visualization
  • Statistics
  • Mathematics
  • Machine Learning
  • Natural Language Processing 

Python is considered as the top skill required for data science:

 

Demand for Data Security Professionals 

In any organization, data privacy and data security is very important. The more the data, the more concerned about data theft. Data generation is very high due to IoT.  In order to control data thefts and its impact, the GDPR – General Data Protection Regulation, was passed by states of the European Union in May 2018. It has also been reported that such regulation for data protection shall again be passed by California in 2020.

Due to this, there is a huge demand for Data Security Professionals. 

According to ZDNet,



 

For more information on how iCert Global can help you to achieve your Data Science Certification goals, please visit our Data Science Certification Training Courses on our website.

The Data Scientist certification validates data scientist’s knowledge on SAS, R, Hadoop, Python and Spark and how to use data concepts such as data exploration, visualization hypothesis testing, and predictive analytics. There is a huge demand for Data Scientists in industries like Aerospace industry, IT industry, e-commerce industry, and healthcare industry.

Know more about our Professional Certification Training Courses for preparing for the above certifications.

 

AWS Certified Solutions Architect Certification Training Courses

 

Big Data Certification Training Courses

 

Data Science Certification Training Courses

 

CRISC Certification Training Courses

 

CISM Certification Training Courses

 

PMP Certification Training Courses

Free Download: PMP Practice Test with 200 Questions

CEH Certification Training Courses

 

CSM Certification Training Courses

 

We provide instructor-led classroom and instructor-led live online training across the globe. We also provide Corporate Training for enterprise workforce development.

 

Connect with us:

 

- Follow us on Linkedin

 

- Like us on Facebook

 

- Follow us on Instagram 

 

- Follow us on Twitter  

 

- Follow us on Pinterest

 

- Subscribe to our YouTube Channel

 

iCert Global conducts Project Management, Quality Management, Business Analysis, Agile, Scrum, and DevOps Certification courses across various locations in the United States.

 

Visit us at https://www.icertglobal.com/ for more information about our professional certification training courses or Call Now! on +1-713-287-1213 / +1-713-287-1214 or e-mail us at info {at} icertglobal {dot} com.

 

 


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What is the Future of Data Science in 2020?

:

The future of data science in 2020 is listed below:

  1. Extended data-driven strategies
  2. Data Privacy Regulations
  3. Clearly Defined Roles
  4. Artificial Intelligence for Data
  5. Minimized Codes using ML
  6. Application Programming Interface (APIs)

:

Data science is all about developing methods to record, store and analyze the data effectively. The main aim of data science is to extract the data and obtain insights and knowledge from both structured and unstructured data.  Data science is a concept that covers the entire scope of data collection and processing.

 

Data science involves various tools, statistics, algorithms, and machine learning principles in order to obtain and understand the data from complex and large data sets through the context of mathematics, statistics, computer science, and information science.

 

Data Science

 

In the current scenario, every day 2.5 quintillion bytes of data are generated around the world. Data generation has rapidly increased in recent times due to the Internet of Things (IoT).

 

According to the Paris 21 report 2019,

 

“90 percent of the world’s data has been generated in only the last two years.”

 

The top companies like Google, Amazon, and Visa are using Data Science in order to optimize themselves and to address their rapidly expanding data.

 

There is a huge demand for certified Data Scientists. According to Forbes,

 

“By 2020, the number of jobs for all US data professionals will increase by 364,000 openings to 2,720,000 according to IBM.”

 

Data Science salary

(Source: Forbes)

 

  • According to CIO.com, Data science helped Zoomcar (Self Driven Service) capture 75% of the Indian market.
  • According to GCN.com, Data science could help Californians battle future wildfires.

The future of data science in 2020 is listed below:

 

  1. Extended data-driven strategies
  2. Data Privacy Regulations
  3. Clearly defined roles
  4. Artificial Intelligence for data
  5. Minimized Codes using ML
  6. Application Programming Interface (APIs)

 

1. Extended data-driven strategies

In many organizations, the decisions are taken based on authoritarian advice or general consensus due to a lack of data processing power. Data scientists are building the system for an organization that can anticipate, predict, and even speaks.

Organizations’ inability to handle the data to analyze can harm productivity and might slow down the project progress. Data science is a quantitative approach. The adoption of data science can increase productivity.

2. Data Privacy Regulations

Data is important for any organization. The management has become more cautious while sharing any data in business. In order to control data thefts and its impact, the GDPR – General Data Protection Regulation, was passed by states of the European Union in May 2018. It has also been reported that such regulation for data protection shall again be passed by California in 2020. With the revised data privacy regulations the future of data science is very bright.

3. Clearly defined roles

Data science is a very broad stream. The roles in an organization aligned with data create a lot of confusion. Data science typically separate the data roles into 4 distinct but overlapping positions:

  • Data Architect — The data architect develops architecture effectively to organize, integrate, centralize and maintain data.
  • Data Engineer — Develops, tests and maintains data architectures to keep data ready for analysis.
  • Data Analyst — The data analysts process and interpret the data to understand and analyze the insights from structured and unstructured data.
  • Data Scientist — Once the analysis is done, the technical aspects required are taken care of by data scientists.

4. Artificial Intelligence for data

The more the data, the more difficult to manage.

 According to  Raconteur:

  • 500 million tweets are sent
  • 294 billion emails are sent
  • 4 petabytes of data are created on Facebook
  • 4 terabytes of data are created from each connected car
  • 65 billion messages are sent on WhatsApp
  • 5 billion searches are made

By 2025, it is predicted that 463 exabytes of data will be created each day globally — that’s the equivalent of 212,765,957 DVDs per day.

Managing such huge data is very difficult. Automated tools can help data scientists with Routine tasks listed below:

  • Exploratory data analysis
  • Data cleaning
  • Statistical modeling
  • Building machine learning model 

 

5. Minimized Codes using ML

In the current state, a lot of codes are written. This doesn’t mean tools like R, Python, and Spark will not be used. Machine learning plays an important role in reducing the effort in writing complex programs. When the data is fed to machine learning systems, they will collect, clean, manipulate, label, analyze and visualize the data. This generates neural networks.

In data science, the software engineer’s role will be “data curator”.

 

6. Application Programming Interface (APIs)

Using the Application Programming Interface (APIs) is very useful in data science. Data scientists will be able to rapidly construct their model, build and test multiple algorithms in one go, and can visually validate results with the entire team.

In the coming future, the softwares will be crafted by visually tapping and leveraging whatever service required through API.

 

 

 

For more information on how iCert Global can help you to achieve your Data Science Certification goals, please visit our Data Science Certification Training Courses on our website.

The Data Scientist certification validates data scientist’s knowledge on SAS, R, Hadoop, Python and Spark and how to use data concepts such as data exploration, visualization hypothesis testing, and predictive analytics. There is a huge demand for Data Scientists in industries like Aerospace industry, IT industry, e-commerce industry, and healthcare industry.

Know more about our Professional Certification Training Courses for preparing for the above certifications.

 

AWS Certified Solutions Architect Certification Training Courses

 

Big Data Certification Training Courses

 

Data Science Certification Training Courses

 

CRISC Certification Training Courses

 

CISM Certification Training Courses

 

PMP Certification Training Courses

Free Download: PMP Practice Test with 200 Questions

CEH Certification Training Courses

 

CSM Certification Training Courses

 

We provide instructor-led classroom and instructor-led live online training across the globe. We also provide Corporate Training for enterprise workforce development.

 

Connect with us:

 

- Follow us on Linkedin

 

- Like us on Facebook

 

- Follow us on Instagram 

 

- Follow us on Twitter  

 

- Follow us on Pinterest

 

- Subscribe to our YouTube Channel

 

iCert Global conducts Project Management, Quality Management, Business Analysis, Agile, Scrum, and DevOps Certification courses across various locations in the United States.

 

Visit us at https://www.icertglobal.com/ for more information about our professional certification training courses or Call Now! on +1-713-287-1213 / +1-713-287-1214 or e-mail us at info {at} icertglobal {dot} com.


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What are the different types of Project Management Leadership Styles?

:

There are 10 types of leadership styles a project manager should know to play an effective role in all the situations. 

  1. Autocratic Leadership Style
  2. Democratic Leadership Style
  3. Coercive Leadership Style
  4. Strategic Leadership
  5. Laissez-faire Leadership style
  6. Affiliative Leadership Style
  7. Pacesetting Leadership Style
  8. Coaching Leadership Style
  9. Transformational Leadership Style
  10. Transactional Leadership style


 

Leadership style is a way of approaching, managing and supporting a team. Leadership style is different for every individual based on their personality. Leadership is a default trait in project managers to lead and manage a team. Each leader owns a unique style of leading and managing a team. 

“One-size-fits-all” solutions do not hold good for all the projects. A multi-dimensional framework is introduced based on the fundamental practices of project management.

Leadership style is all about creating systematic work culture within an organization which contributes to the successful completion of a project. The main aspect of leadership is to apply the correct leadership style in the correct situation. 

Project managers can possess effective leadership quality by the following ways that can improve the success rate of the project:

  • Setting Measurable goals: The common goals are to be set by project managers. The timeframe required to reach the objective is set to accomplish the common goals.
  • Social- Influence: The performance of the team can be influenced by conformity, socialization, obedience and peer pressure. The productivity of the team can be improved by improvement in work culture. The opinions, emotions, and behavior of team members are affected by the project managers.
  • Clarity: Clear vision towards goal. Without a clear vision of the consequences, the team cannot be led by the project manager effectively.
  • Inspire team members: Project managers should make things happen. The inspiring team is making team members work willingly.
  • Motivate team: The motivated team always gives the best result. The team can be motivated by appreciation, opportunities, and rewards.
  • Guidance: Project managers guide the team by providing all the required information clearly.

 

There are 10 types of leadership styles a project manager should know to play an effective role in all the situations. 

  1. Autocratic Leadership Style
  2. Democratic Leadership Style
  3. Coercive Leadership Style
  4. Strategic Leadership
  5. Laissez-faire Leadership style
  6. Affiliative Leadership Style
  7. Pacesetting Leadership Style
  8. Coaching Leadership Style
  9. Transformational Leadership Style
  10. Transactional Leadership style

1. Autocratic Leadership Style 

An autocratic leadership style is an authoritative style of leadership where a leader takes all the decisions without considering the team’s input. This style of leadership involves authoritarian control over the team members. 

In this leadership style, the work structure is highly structured and rigid. The rules are very clearly communicated. 

The main benefit of the autocratic leadership style is the decisions are made quickly with a clear chain of command.

The autocratic style of leadership can be effective when the leader is knowledgeable and skilled.

2. Democratic Leadership Style 

Democratic leadership style is the inverse of the autocratic leadership style. This style involves all the team members in decision making by considering all the inputs given by them. The primary aim of the democratic leadership style is to build commitment and generate new ideas. 

The democratic approach is aligned with emotional intelligence traits such as collaboration, teamwork, influence and conflict management. This style is very effective as it allows all the team members to be confident and open. 

3. Coercive Leadership Style 

Coercive leadership style is a highly commanding style and an authoritarian approach to team members. The leader or project manager demands immediate compliance with their orders.

This style of leadership can be described as:

“Do what I tell you.”

The main advantage of coercive leadership style is 

  • The leaders know to get the work done quickly
  • Increases productivity
  • Eliminates disobedience
  • Enables team members to follow the rules and regulations of the organization 

4. Strategic Leadership Style 

A strategic leadership style is adopted when the leader is able to express the strategic vision to the team members and motivate them to reach the goal. The key traits if this style of leadership is the leaders are:

  • Visionary
  • Open
  • Focused
  • Courageous
  • Prudent

There are 4 types of strategic leadership style:

  • High Control Innovator (HCI)
  • Participative Innovator(PI)
  • Status Quo Guardian (SQG)
  • Process Manager (PM)

 

A strategic leadership style focuses more on the macro-level of work. The main aim of strategic leadership is productivity and develop a positive environment in which employees or team members can focus more on the organization’s and stakeholder’s requirements. 

5. Laissez-faire Leadership style 

Laissez-faire Leadership style is a self-rule style empowering individuals or teams to take decisions on their own. According to Merriam-Webster, Laissez-faire is "A philosophy or practice characterized by a usually deliberate abstention from direction or interference, especially with individual freedom of choice and action."

The project management with this style has minimal communication with their team. The delegation of authority is done in this leadership style. 

6. Affiliative Leadership Style 

An affiliative leadership style is a very commonly used style of leadership. The leader or project manager creates an emotional bond with team members to encourage team members, promote cooperation and teamwork. It builds trust among the team. The main advantage of adopting this leadership style is that 

  • The leaders resolve conflicts quickly
  • The top priority will always be the employees
  • Reduces stress
  • Positive feedbacks are given

The key characteristic of the affiliative leadership style is that the leaders are ‘Honest to a fault’ and strong communication skills.

The leader motivates the team with nurturing and praising. They make employees feel valued and recognized. 

7. Pacesetting Leadership Style 

Pacesetting leadership style in which the leader sets the high standard for team members’ performance. In this style, the leader is obsessed with getting the work done faster and better. This style is deployed when the team members are highly competent and motivated and well versed in their work. Pacesetting leadership style can be effective when a leader follows the below points:

  • A leader should maintain a high professional level
  • Should possess high knowledge
  • Evaluate the performance of the team
  • Know the consequences

8. Coaching Leadership Style

 

Coaching Leadership style is in which the leader encourages the team members to enhance their technical and personal skills and create a positive impact on the project team. The leaders or coaches guide the team members on a day to day basis and allow them to identify their strengths and weaknesses. In this style of leadership, mistakes are considered as learning opportun