Trending February 2024 # Frm Level 1 Prep Course, Preparation Material And Mock Tests # Suggested March 2024 # Top 3 Popular

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About FRM Level 1 Prep Course

Course Name Online FRM Level 1 Prep Course

Deal You get access to all 9 courses, Projects bundle. You do not need to purchase each course separately.

Hours 86+ Video Hours

Core Coverage Foundations of Risk Management, Quantitative Analysis, Financial Markets and Products, Investors and risk management, Risk management failures, Enterprise risk management, code of conduct.

Course Validity Lifetime Access

Eligibility Anyone serious about learning Financial Risk Management

Pre-Requisites Basic knowledge about Finance and Maths

Type of Training Video Course – Self Paced Learning

Software Required None

System Requirement 1 GB RAM or higher

Other Requirement Speaker / Headphone

FRM Level 1 Prep Course Curriculum

The Curriculum of the Finacial Risk Manager Course involves primarily all the areas required to be understood in the FRM level I. The course adequately covers all the aspects suggested by the GARP and incudes various important topics such Need for the risk management, Investors and risk management, Risk management failures, Enterprise risk management, code of conduct. The course has been structured in such a way that it included 8 online courses, 58+ hours, online mock tests, and solutions. These courses or videos are available online and could be availed on an online basis, for an unlimited time.

Goals

Objectives

Course Highlights

Goals

The goal of the Financial Risk Manager Level 1 preparation course is to prepare the candidate in such a way that he or she is ready to appear and clear the FRM Level 1 course. The course covers requisite areas of the FRM syllabus set up by the GARP and other relevant skills needed to be understood by the student of FRM level 1.

Objectives

By the end of the course, the candidate would be able to understand various topics of the FRM level I such as Risk management, investors, and its relation with risk management, Risk management failures, Code of conduct, etc. Besides, relevant technical knowledge would also be provided. The knowledge and skill gained in this assignment gained in the course would help the student to clear the FRM Level I exam and apply the acquired knowledge and techniques in the job environment.

Course Highlights

The course is prepared in such a way that it provides the candidate with the flexibility to understand and go through the course at his/her own pace. Also, the course is free for the lifetime, once purchased, the candidate would be comfortable to repeat the concepts consistently until the understanding is complete.

The course is full of relevant concepts, tricks, skills, and guidance to understand and clear the FRM Level I. These skills are wide and provides adequate help to the candidate without a requirement from an additional source.

An additional requirement to clear the level I exam is clear concepts, adequate practice, a well-disciplined approach, and thorough knowledge of the material.

As the exam would require extensive calculations, the skill needed to efficiently use the scientific calculator must be also needed to be practiced.

Qualification in the level I promote the student to appear for Level II of the Final exam of FRM.

What is the FRM Level 1 Prep Course?

The two primary levels to clear the course is level I and Level II. The first level to prepare and clear is FRM Level i. In this level, the preparation material provided by the GARP needs to be completed and the same is required to be submitted to pass the level I exam. The first level course has 4 books to be read and understood and upon which the exam will take place. The topics covered by these tutorials are related to Valuation and Risk models, Financial markets & products, Quantitative Analysis, and foundation of the risk management. These chapters contain various practical and theory related questions that would appear in the exam and the same needs to be thoroughly prepared to crack the exam. So, additional topics and skills are also required to be understood and taken care of. The course FRM Level I prep provide adequate knowledge and relevant skill to complete the course and exam for Level I.

What skills will you learn in this Course?

The course of FRM Level I prep would provide the important subject matter knowledge and the requisite skill to ace exams and learn the relevant skills. The relevant skills expected to be learned under the course FRM Level I Prep course are mentioned as below:

The definition of risk management, its types and its constituents are being taught in this course. Various topics related to it and other items in a similar periphery is also discussed.

Risk management techniques are considered as an important part of the Financial Risk Management course and the same is taught in the course.

The course also makes you aware of various types of risks such as liquidity ratio, business risks, settlement risks, etc. These are the core syllabus of FRM Level i.

Quantitative Aptitude is also being taught in this course. The primary areas covered under this section are Marginal distribution on Deviation, Quantitative Measures VaR, Stress Testing, Risk-Reward Tradeoff, etc. It involves practical and theory part of above -mentioned topics and helps in getting a complete understanding of the subject matter.

The course also provides knowledge of various topics related to debt issuance and management. These topics include Government bond, Corporate Bond yield, Credit risk, evaluating and preparation of various yields such as yield to maturity, current yield, etc, to enhance the knowledge of the student and provide an overall knowledge.

Pre-requisites

Every course needs to have some basic knowledge of the related subject-matter to get the candidate more familiar and used to the operations of the course. So, similarly, a person with a knowledge of finance, or accounts would be very comfortable in learning the concept and practical knowledge imparted in the course. Again, besides student, if anybody has the requisite knowledge in the area of accounts or finance, would help understand the course. Even if the candidate is not from the accounts or finance background, h/she can enroll and gain knowledge by putting additional efforts.

Target Audience

The target audience for the course is mixed of current students, professional, and finance enthusiasts. These are mentioned as below:

Students: Students who are pursuing the commerce or finance field and want to gain knowledge or make a career in the risk management could take the course and learn the requisite knowledge to clear the FRM level I. it will help them to understand the concepts required in the course.

Professionals: The working professionals in the field of finance could enroll themselves in the course to gain an understanding of Financial risk management or to make a career in the same.

Others: Besides students and the professional, anybody, if wants to hone and get the knowledge in the field of risk management, could pursue this course for acquiring knowledge of Risk management and related concepts and workings.

FAQ’s- General Questions How much should I expect to spend weekly for this Course?

The course is conducted on online mode and the duration is around 80 hours. As most of the candidates pursuing the course would be working or studying in their primary time, it depends upon person to person and their respective schedule to finish the course as required.

The course is full of required knowledge and material and helps in getting the candidate aware of the concepts, knowledge bases, and technical skills to master the course. Further, with the help of MCQs, the candidate would be well versed with relevant concepts and skills required for clearing the Level I exam.

I am working in the banking field and planning to take up certification then how is this Training going to help me?

Due to the wide span of the digitalization, and expansion of the banking industry, risk management has turned out to be a primary issue and the same has made Financial Risk Management a lucrative career option. So, a shift could be made from any field to risk management given the liking of the candidate.

Sample Preview

Career Benefits

The Finacial Risk Management Level I prep helps the candidate in understanding the requisite concepts and skills required to excel exam and professional career as an FRM. So, The benefits of the course also help the candidate to develop various aspects of the knowledge to maintain and grow skills to become job-ready. Also, the thorough understanding of the course helps the candidate to perform more confidently in the interview and the same would help the performing day to day activity consistently.

Reviews

Great course!

Great course! Highly recommend as it provides a great overview of risk management tools and techniques, as well as a comprehensive summary of risk management. The most important part for me was the enterprise risk management topic as that”s what I am focusing on right now. This course is a great addition to my education.

Linked

Rostyslav Haleliuk

In-Depth training!

The Amount of Detail involved is Breathtaking, the tutelage is heartwarming on the financial systems and also a lot on Governance the is a very in-depth course and adds a lot of value to an individual looking to have a career in the financial risk and financial modeling area, the course material goes above and beyond expectation. The course offers a really good outlook on all the systems used. This is a great learning tool and has outdone many.

Linked

JOSHUA M WANJOHI

You're reading Frm Level 1 Prep Course, Preparation Material And Mock Tests

Rrb Group D Result 2023(Out): Rrc Level 1 Cut Off Marks, Merit List

The Railway Recruitment Boards across India under Ministry of Railways have compiled the Group D Recruitment Examination for 1,03,769 vacancies. The Written Exam for RRC 01/2024 Notification was compiled in Multiple Phases from August to October 2023 and lakhs of candidates attempted their best performance. Moreover, answer key is also released on 14th October 2023 at official websites of each zone.

Now the RRB Group D Result 2023 is released on 23 December 2023. All of you should note that Railway Group D Result 2023 is published, you will get a direct link are activated over here for every zone. After the declaration of result, RRB Group D Merit List 2023 Zone Wise is published in which Rank Wise Names of all the qualified candidates are mentioned. In this list, you will see your name only if you get scores above RRB Group D Cut Off Marks 2023.

RRB Group D Result 2023

The Indian Railway Group D Result 2023 is declared on 23/12/2024 and all of you should check it on the respective portal of your zone. If you are unable to find the Official Website of your Zone then you can check the table below in which Zone Wise Link are mentioned. As per information given to us by Railway Officials, the RRB Group D Result 2023 Zone Wise can be released on 23 December 2023 approximately.

All the aspirants who pass the examination will get a chance to appear in Tier 2 Examinations. Make sure you get more than 40% Marks in the Railway Level 1 Group D Result to qualify the exam and then only you will get a chance to appear in the level 2 Exam. After passing these 2 Stages of this recruitment, you will be called for the Documentation Stage and on proving your documents, you will get a joining letter by the Railway Recruitment Board.

As we know, RRB Group D Result is already released and now candidates are preparing for the next exam. According to schedule, next exam will be conducted in March 2023.

RRB Group D Result 2023 Zone Wise

Railway RRC Group D Result Date 2023

Bharti NameRailway Group D Recruitment 2023RecruiterRailway Recruitment Board and Indian RailwaysTotal Posts1 lakh 3 thousand 769 vacanciesType of postsALM, Assistant Loco Shed, Washerman and othersRailway Group D Level 1 Exam DateAugust to October 2023Total Phases5 PhasesExam ModeOnlineRailway Group D Result Date 202323 December 2023Passing Marks40% MarksRRB Group D Cut Off Marks 202375-80 Marks (Out of 100 Marks)RRB Group D Merit ListTo be ReleasedArticle CategoryResultOfficial Websiterrbcdg.gov.in

Previous RRB Group D Cut Off (2024)

Applicant CategoryPrevious RRB Group D Cut Off (2024)General (Unreserved)73.73 MarksOther Backward Class70.10 MarksOBC (NCL)69.52 MarksEconomically Weaker Section68.55 MarksScheduled Caste63.37 MarksScheduled Tribes60.62 Marks

RRB Group D Cut Off 2023 Zone Wise (UR, OBC, SC, ST, EWS)

Region NameGeneralOBCSC-STEWSRRB Ajmer Group D Cut Off Marks 202375-80 Marks70-75 Marks60-65 Marks70-75 MarksRRB Allahabad Group D Cut Off 202375-80 Marks70-75 Marks60-65 Marks70-75 MarksRRC Ahmedabad Group D Cut Off Marks 202370-75 Marks65-70 Marks55-60 Marks65-70 MarksRRB Bengaluru Group D Cut Off 202375-80 Marks70-75 Marks60-65 Marks70-75 MarksRRC Bhopal Group D Cut Off Marks 202368-73 Marks63-68 Marks52-57 Marks60-65 MarksRRB Bilaspur Group D Cut Off 202370-75 Marks65-70 Marks55-60 Marks65-70 MarksRRC Bhubaneswar Group D Cut Off 202368-73 Marks63-68 Marks52-57 Marks60-65 MarksRRB Chandigarh Group D Cut Off 202370-75 Marks65-70 Marks55-60 Marks65-70 MarksRRC Chennai Group D Cut Off 202375-80 Marks70-75 Marks60-65 Marks70-75 MarksRRB Gorakhpur Group D Cut Off 202370-75 Marks65-70 Marks55-60 Marks65-70 MarksRRC Guwahati Group D Cut Off 202375-80 Marks70-75 Marks60-65 Marks70-75 MarksRRB Kolkata Group D Cut Off Marks 202378-83 Marks72-77 Marks60-65 Marks70-75  MarksRRC Mumbai Group D Cut Off 202368-73 Marks63-68 Marks52-57 Marks60-65 MarksRRB Patna Group D Cut Off Marks 202378-83 Marks72-77 Marks60-65 Marks70-75  MarksRRC Ranchi Group D Cut Off 202368-73 Marks63-68 Marks52-57 Marks60-65 MarksRRB Secunderabad Group D Cut Off Marks 202375-80 Marks70-75 Marks60-65 Marks70-75 Marks

Applicants who are worried about RRB Group D Result Date 2023 should see this section in which it is clearly mentioned. As per sources revealed to us, Railway Group D Result 2023 is 23 December 2023. Scores can be checked in a very easy manner as all of you have to visit the respective portal and then login with your Candidate number. After that, you can see the scores obtained by you in Group D Tier 1 Exam.

Rrbcdg.gov.in Group D Level 1 Result 2023

The Railway Group D Tier 1 Examination was conducted in 5 Phases from August to October 2023.

Official Answer Key was released by the Railway Recruitment Board on 14th October 2023 at different portals.

Objections were invited from Candidates and now the final answer key is expected soon.

After the declaration of Final Answer Key, rrbcdg.gov.in Group D Result 2023 is published now.

You have to visit the portal of your Zone such as chúng tôi (Chandigarh Region) to check your scores in an easy manner.

RRB Group D Merit List 2023 Zone Wise

As we know, the Railway Group D Result is published on 23-12-2024 which means that the Merit List will also be released.

You should know that RRB Group D Merit List 2023 Zone Wise will be released on the official website of your zone on which you can visit through the link below.

The Merit List is prepared after considering many factors such as Vacancies, Exam Difficulty Level, total Candidates and more.

Apart from this, Tie Breaking Formula is also used while preparing Railway Group D Merit List 2023 in which marks of specific subjects and date of birth are considered.

All the candidates who get their name in the Merit List are allowed to sit in the Tier 2 Examination.

Guidelines to Check RRB Group D Result 2023 @ rrbcdg.gov.in

Go to chúng tôi from your browser or choose your Zone Link from the Table below.

Secondly, you should tap on the RRC 01/2024 Notification Button and then further select the Result Link.

Enter your Login Details such as Registration Number and use your Date of Birth as password.

Now you will see the Scores obtained in Tier 1 Exam being displayed on screen.

Note down your marks and then also download the Railway Group D Result 2023 Scorecard.

Using these guidelines, all of you can check RRB Group D Result 2023 from the official website of your Railway Zone.

FAQs on Railway Group D Level 1 Result 2023

What are the Qualifying Marks in RRB group D Level 1 Exam?

You need to cross the 40% Marks in order to Qualify the RRB Group D Exam 2023.

How to check Previous RRB Cut Off Marks?

Pay attention to the table above in order to see the RRB Group D Cut Off 2023 Zone Wise.

What is the Expected RRB Group D Cut Off Marks 2023 this year?

This year RRB group D cut off 2023 will be around 70-75 Marks out of 100 Marks.

Ai With Python – Data Preparation

AI with Python – Data Preparation

We have already studied supervised as well as unsupervised machine learning algorithms. These algorithms require formatted data to start the training process. We must prepare or format data in a certain way so that it can be supplied as an input to ML algorithms.

This chapter focuses on data preparation for machine learning algorithms.

Preprocessing the Data

In our daily life, we deal with lots of data but this data is in raw form. To provide the data as the input of machine learning algorithms, we need to convert it into a meaningful data. That is where data preprocessing comes into picture. In other simple words, we can say that before providing the data to the machine learning algorithms we need to preprocess the data.

Data preprocessing steps

Follow these steps to preprocess the data in Python −

Step 1 − Importing the useful packages − If we are using Python then this would be the first step for converting the data into a certain format, i.e., preprocessing. It can be done as follows −

import numpy as np import sklearn.preprocessing

Here we have used the following two packages −

NumPy − Basically NumPy is a general purpose array-processing package designed to efficiently manipulate large multi-dimensional arrays of arbitrary records without sacrificing too much speed for small multi-dimensional arrays.

Sklearn.preprocessing − This package provides many common utility functions and transformer classes to change raw feature vectors into a representation that is more suitable for machine learning algorithms.

Step 2 − Defining sample data − After importing the packages, we need to define some sample data so that we can apply preprocessing techniques on that data. We will now define the following sample data −

input_data = np.array([2.1, -1.9, 5.5], [-1.5, 2.4, 3.5], [0.5, -7.9, 5.6], [5.9, 2.3, -5.8])

Step3 − Applying preprocessing technique − In this step, we need to apply any of the preprocessing techniques.

The following section describes the data preprocessing techniques.

Techniques for Data Preprocessing

The techniques for data preprocessing are described below −

Binarization

This is the preprocessing technique which is used when we need to convert our numerical values into Boolean values. We can use an inbuilt method to binarize the input data say by using 0.5 as the threshold value in the following way −

data_binarized = preprocessing.Binarizer(threshold = 0.5).transform(input_data) print("nBinarized data:n", data_binarized)

Now, after running the above code we will get the following output, all the values above 0.5(threshold value) would be converted to 1 and all the values below 0.5 would be converted to 0.

Binarized data

[[ 1. 0. 1.] [ 0. 1. 1.] [ 0. 0. 1.] [ 1. 1. 0.]] Mean Removal

It is another very common preprocessing technique that is used in machine learning. Basically it is used to eliminate the mean from feature vector so that every feature is centered on zero. We can also remove the bias from the features in the feature vector. For applying mean removal preprocessing technique on the sample data, we can write the Python code shown below. The code will display the Mean and Standard deviation of the input data −

print("Mean = ", input_data.mean(axis = 0)) print("Std deviation = ", input_data.std(axis = 0))

We will get the following output after running the above lines of code −

Mean = [ 1.75 -1.275 2.2] Std deviation = [ 2.71431391 4.20022321 4.69414529]

Now, the code below will remove the Mean and Standard deviation of the input data −

data_scaled = preprocessing.scale(input_data) print("Mean =", data_scaled.mean(axis=0)) print("Std deviation =", data_scaled.std(axis = 0))

We will get the following output after running the above lines of code −

Mean = [ 1.11022302e-16 0.00000000e+00 0.00000000e+00] Std deviation = [ 1. 1. 1.] Scaling

It is another data preprocessing technique that is used to scale the feature vectors. Scaling of feature vectors is needed because the values of every feature can vary between many random values. In other words we can say that scaling is important because we do not want any feature to be synthetically large or small. With the help of the following Python code, we can do the scaling of our input data, i.e., feature vector −

# Min max scaling

data_scaler_minmax = preprocessing.MinMaxScaler(feature_range=(0,1)) data_scaled_minmax = data_scaler_minmax.fit_transform(input_data) print ("nMin max scaled data:n", data_scaled_minmax)

We will get the following output after running the above lines of code −

Min max scaled data

[ [ 0.48648649 0.58252427 0.99122807] [ 0. 1. 0.81578947] [ 0.27027027 0. 1. ] [ 1. 0. 99029126 0. ]] Normalization

It is another data preprocessing technique that is used to modify the feature vectors. Such kind of modification is necessary to measure the feature vectors on a common scale. Followings are two types of normalization which can be used in machine learning −

L1 Normalization

It is also referred to as Least Absolute Deviations. This kind of normalization modifies the values so that the sum of the absolute values is always up to 1 in each row. It can be implemented on the input data with the help of the following Python code −

# Normalize data data_normalized_l1 = preprocessing.normalize(input_data, norm = 'l1') print("nL1 normalized data:n", data_normalized_l1)

The above line of code generates the following output &miuns;

L1 normalized data: [[ 0.22105263 -0.2 0.57894737] [ -0.2027027 0.32432432 0.47297297] [ 0.03571429 -0.56428571 0.4 ] [ 0.42142857 0.16428571 -0.41428571]]

L2 Normalization

It is also referred to as least squares. This kind of normalization modifies the values so that the sum of the squares is always up to 1 in each row. It can be implemented on the input data with the help of the following Python code −

# Normalize data data_normalized_l2 = preprocessing.normalize(input_data, norm = 'l2') print("nL2 normalized data:n", data_normalized_l2)

The above line of code will generate the following output −

L2 normalized data: [[ 0.33946114 -0.30713151 0.88906489] [ -0.33325106 0.53320249 0.7775858 ] [ 0.05156558 -0.81473612 0.57753446] [ 0.68706914 0.26784051 -0.6754239 ]] Labeling the Data

We already know that data in a certain format is necessary for machine learning algorithms. Another important requirement is that the data must be labelled properly before sending it as the input of machine learning algorithms. For example, if we talk about classification, there are lot of labels on the data. Those labels are in the form of words, numbers, etc. Functions related to machine learning in sklearn expect that the data must have number labels. Hence, if the data is in other form then it must be converted to numbers. This process of transforming the word labels into numerical form is called label encoding.

Label encoding steps

Follow these steps for encoding the data labels in Python −

Step1 − Importing the useful packages

If we are using Python then this would be first step for converting the data into certain format, i.e., preprocessing. It can be done as follows −

import numpy as np from sklearn import preprocessing

Step 2 − Defining sample labels

After importing the packages, we need to define some sample labels so that we can create and train the label encoder. We will now define the following sample labels −

# Sample input labels input_labels = ['red','black','red','green','black','yellow','white']

Step 3 − Creating & training of label encoder object

In this step, we need to create the label encoder and train it. The following Python code will help in doing this −

# Creating the label encoder encoder = preprocessing.LabelEncoder() encoder.fit(input_labels)

Following would be the output after running the above Python code −

LabelEncoder()

Step4 − Checking the performance by encoding random ordered list

This step can be used to check the performance by encoding the random ordered list. Following Python code can be written to do the same −

# encoding a set of labels test_labels = ['green','red','black'] encoded_values = encoder.transform(test_labels) print("nLabels =", test_labels)

The labels would get printed as follows −

Labels = ['green', 'red', 'black']

Now, we can get the list of encoded values i.e. word labels converted to numbers as follows −

print("Encoded values =", list(encoded_values))

The encoded values would get printed as follows −

Encoded values = [1, 2, 0]

Step 5 − Checking the performance by decoding a random set of numbers −

This step can be used to check the performance by decoding the random set of numbers. Following Python code can be written to do the same −

# decoding a set of values encoded_values = [3,0,4,1] decoded_list = encoder.inverse_transform(encoded_values) print("nEncoded values =", encoded_values)

Now, Encoded values would get printed as follows −

Encoded values = [3, 0, 4, 1] print("nDecoded labels =", list(decoded_list))

Now, decoded values would get printed as follows −

Decoded labels = ['white', 'black', 'yellow', 'green'] Labeled v/s Unlabeled Data

Unlabeled data mainly consists of the samples of natural or human-created object that can easily be obtained from the world. They include, audio, video, photos, news articles, etc.

On the other hand, labeled data takes a set of unlabeled data and augments each piece of that unlabeled data with some tag or label or class that is meaningful. For example, if we have a photo then the label can be put based on the content of the photo, i.e., it is photo of a boy or girl or animal or anything else. Labeling the data needs human expertise or judgment about a given piece of unlabeled data.

There are many scenarios where unlabeled data is plentiful and easily obtained but labeled data often requires a human/expert to annotate. Semi-supervised learning attempts to combine labeled and unlabeled data to build better models.

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Email Marketing And Automation Online Training Course

Email Marketing and Automation Learning Path Improve your email communications and marketing automation using a strategic, data-driven approach and best practices How will this Learning Path help me and my business?

This structured e-learning activity will help you or your team learn how a strategic approach to email marketing communications and targeting can boost audience engagement and sales. You will also learn practical tips and view examples that will help you to optimize your emails to boost response.

What is a Learning Path?

Smart Insight’s Learning Paths are our unique interactive online training courses which explain concepts, give examples and test understanding.

Unlike many online e-learning courses, each module is self-contained, so you can quickly access guidance to help improve your marketing activities.

Common modules are shared between Learning Paths to avoid duplication of learning material. You can also complete the full Learning Path to earn a CPDSO certification.

We appreciate finding time for skills development is a challenge. Our Learning Paths enable training to be bite-sized, engaging and – crucially – results orientated. When combined with our suite of templates, you’ll soon be taking your marketing activities to the next level.

Accredited learning activities with the Continuing Professional Development Standards Office (CPDSO)

Each Smart Insights Learning Path has been independently assessed and accredited by the CPD Standards Office, so you can be confident that the quality of the learning and assessment experience has been audited and recognized for its quality.

Development Objective

Members who successfully complete this Learning Path have the ability to review the current contribution of email marketing and automation to their organization and then create a plan to improve subscriber engagement and value with activities to manage and optimize email sequences as part of the customer journey.

Once you have completed a Learning Path, send an email to [email protected] to request your CPD certificate.

Learning Objectives

Make a case for investment in email marketing and automation by reviewing opportunities and understanding marketing automation options.

Forecast email campaign response and programme improvement by defining goals and metrics as well as auditing current effectiveness against benchmark performance.

Review techniques to grow subscribers, increase subscriber engagement and improve email list quality.

Improve lead nurture, reactivation emails and integration of SMS marketing.

Review lifecycle automation options and the use of segmentation, targeting and creative optimization to improve the response of different email and newsletter formats.

Create and agree an email contact strategy and policy and improve pre-broadcast processes and checklists based on best times and frequency for broadcast.

How is the Learning Path structured?

The Learning Path is separated into these topics and modules:

Topic 1 – Discover email marketing and automation opportunities

Review opportunities for using email for acquisition and retention

Understand marketing automation opportunities

Audit email effectiveness

Topic 2 – Setting targets for email marketing

Goal setting for email

Review techniques to grow and improve email subscription lists

Benchmarking email performance

Topic 3 – Improving your use of email and SMS marketing

Review your use of different email types

Essential email design elements

Improve email copywriting

Create an effective e-newsletter

Test and optimize subject line effectiveness

Define data capture and profiling

Review and improve mobile email effectiveness

Integrated SMS marketing

Topic 4 – Segmentation and targeting for email

Segmentation and targeting

RFM analysis

Understand the principles of machine learning and AI

Topic 5 – Email frequency and contact strategy

Review email lifecycle automation options

Create an email contact strategy

Lead scoring and grading

Topic 6 – Improve email governance

Privacy law requirements for digital communications

Select an email supplier

Auditing and improving email deliverability

Roles who will find this Learning Path useful

Company owners and directors working for smaller businesses

Digital marketing managers, executives and specialists responsible for email marketing

Consultants or agency account managers

Twitter Tests More Visible Alt Text

A visible “ALT” badge, and exposed image descriptions, are among the features Twitter is testing to improve image accessibility on mobile and desktop.

In an announcement, Twitter states it’s testing the features with 3% of users across iOS, Android, and web browsers.

Twitter is aiming to launch these features globally in the beginning of April, following at least a month of testing.

Here’s more about the ALT badge, image descriptions, and how to add descriptive text to an image on Twitter.

ALT Badge On Twitter Images

When a description, also referred to as alt text, is added to an image a rectangular “ALT” badge will be shown in the bottom corner.

This signals to other users there’s descriptive text accompanying the image.

“Adding image descriptions allows people who are blind, have low vision, use assistive tech, live in low-bandwidth areas, or have a cognitive disability, to fully contribute on Twitter.

We know these features have been a long time coming, and we’re grateful for your patience. We’re also working on the image description reminder. We’ll share more on that soon.“

Here’s how to add an image description to a tweet. Soon, Twitter may start reminding users to add image descriptions, but for now it has to be done manually.

How To Add An Image Description On Twitter

To add an image description, follow these steps:

Upload an image

Select “Add description” under the image

Write a description

Select “Save”

Send tweet

Your tweet will be sent with the “ALT” badge on the image.

The description can be anyone from one to one thousand characters in length.

If you add multiple images to a tweet you can add unique descriptions to each of them.

This feature is still in testing, so you may not have access to it right now. A full launch is expected this spring.

Source: Twitter Accessibility

Featured Image: A9 STUDIO/Shutterstock

Conductor Material Required In Overhead Dc Transmission System

Overhead DC Transmission System

The overhead transmission system is the one in which the conductors are hanged with the help of pole supports. When the transmission lines carry direct current, then the system is called the DC transmission system.

There are three types of overhead DC transmission systems viz. −

Two wire DC system with one conductor earthed

Two wire DC system with mid-point earthed

Three wire DC system

Conductor Material Required in Two-Wire DC System with One Conductor Earthed

Consider a two wire DC system with one conductor earthed as shown in Figure-1. Here, one is the positive wire and the other is the negative wire and (say K) is given by,

$$mathrm{mathit{Kmathrm{, =, }mathrm{2}times a_{mathrm{1}}times lmathrm{, =, }mathrm{2}times left ( frac{mathrm{2}P^{mathrm{2}}rho l}{WV_{m}} right )times l}}$$

$$mathrm{mathit{therefore Kmathrm{, =, }frac{mathrm{4}P^{mathrm{2}}rho l^{mathrm{2}}}{WV_{m}} }; ; ; …left ( 1 right )}$$

Conductor Material Required in Two-Wire DC System with 𝑉𝑚, therefore the maximum voltage between two conductors is 2𝑉𝑚. Therefore, the load current is given by,

$$mathrm{mathit{I_{Lmathrm{2}}mathrm{, =, }frac{P}{mathrm{2}V_{m}}}}$$

Let a2 is the area of cross section of the conductor and R2 be the resistance of each line conductor, then

$$mathrm{mathit{R_{mathrm{2}}mathrm{, =, }frac{rho l}{a_{mathrm{2}}}}}$$

Therefore, the total lines losses are given by,

$$mathrm{mathit{Wmathrm{, =, }mathrm{2},I_{Lmathrm{2}}^{mathrm{2}},R_{mathrm{2}}mathrm{, =, }mathrm{2}times left ( frac{P}{mathrm{2}V_{m}} right )^{mathrm{2}}times frac{rho l}{a_{mathrm{2}}}mathrm{, =, }frac{P^{mathrm{2}}rho l}{mathrm{2}V_{m}^{mathrm{2}}a_{mathrm{2}}}}}$$

⇒ Area of cross section,

$$mathrm{mathit{a_{mathrm{2}}mathrm{, =, }frac{P^{mathrm{2}}rho l}{mathrm{2}V_{m}^{mathrm{2}}W}}}$$

Hence, the volume of conductor material required in the two wire DC system with mid-point earthed (let 𝐾1) is given by,

$$mathrm{mathit{K_{mathrm{1}}mathrm{, =, }mathrm{2}times a_{mathrm{2}}times lmathrm{, =, }frac{P^{mathrm{2}}rho l^{mathrm{2}}}{V_{m}^{mathrm{2}}W}; ; : cdot cdot cdot left ( mathrm{2} right )}}$$

Now, comparing equation (1) & (2), we get,

$$mathrm{mathit{frac{K_{mathrm{1}}}{K}mathrm{, =, }frac{left ( mathrm{mathit{frac{P^{mathrm{2}}rho l^{mathrm{2}}}{V_{m}^{mathrm{2}}W}}} right )}{left ( mathrm{mathit{frac{mathrm{4}P^{mathrm{2}}rho l^{mathrm{2}}}{WV_{m}}}} right )}mathrm{, =, }frac{mathrm{1}}{mathrm{4}}}} $$

$$mathrm{mathit{therefore K_{mathrm{1}}mathrm{, =, }frac{K}{mathrm{4}} }; ; ; cdot cdot cdot left ( 3 right )}$$

Thus, the volume of conductor material required in a two-wire DC system with mid-point earthed is one-fourth of that required in a two-wire DC system with one conductor earthed.

Conductor Material Required in Three-Wire DC System

Consider a three-wire DC system in which two outers and a neutral wire be wire. Then, for the balanced load, the load current is given by,

$$mathrm{mathit{I_{Lmathrm{3}}mathrm{, =, }frac{P}{mathrm{2}V_{m}}}}$$

If a3 is the area of cross section of each outer wire, then the resistance R3 of each outer line conductor is given by,

$$mathrm{mathit{R_{mathrm{3}}mathrm{, =, }frac{rho l}{a_{mathrm{3}}}}}$$

Thus, the total line losses are given by,

$$mathrm{mathit{Wmathrm{, =, }mathrm{2},I_{mathrm{3}}^{mathrm{2}},R_{mathrm{3}}mathrm{, =, }mathrm{2}times left ( frac{P}{mathrm{2}V_{m}} right )^{mathrm{2}}times frac{rho l}{a_{mathrm{3}}}mathrm{, =, }frac{P^{mathrm{2}}rho l}{mathrm{2}V_{m}^{mathrm{2}}a_{mathrm{3}}}}}$$

⇒ Area of cross section, $mathrm{mathit{a_{mathrm{3}}mathrm{, =, }frac{P^{mathrm{2}}rho l}{mathrm{2}V_{m}^{mathrm{2}}W}}}$

Also, assuming the area of cross-section of the neutral wire to be half that of the outer line wires. Then, the volume of the conductor material required in the three-wire DC system (say K3) is given by,

$$mathrm{mathit{K_{mathrm{3}}mathrm{, =, }mathrm{2.5}times a_{mathrm{3}}times lmathrm{, =, }mathrm{2.5}times left ( frac{P^{mathrm{2}}rho l}{mathrm{2}V_{m}^{mathrm{2}}W } right )times l}}$$

$$mathrm{mathit{therefore K_{mathrm{3}}mathrm{, =, }frac{mathrm{1.25}P^{mathrm{2}}rho l^{mathrm{2}}}{V_{m}^{mathrm{2}}W }; ; ; cdot cdot cdot left ( mathrm{4} right ) }}$$

Comparing equations (1) & (4), we get,

$$mathrm{mathit{ frac{K_{mathrm{3}}}{K}mathrm{, =, }frac{left ( frac{mathrm{1.25}P^{mathrm{2}}rho l^{mathrm{2}}}{V_{m}^{mathrm{2}}W } right )}{left ( mathrm{mathit{frac{mathrm{4}P^{mathrm{2}}rho l^{mathrm{2}}}{WV_{m}}}} right )}}mathrm{, =, }frac{5}{16}}$$

$$mathrm{mathit{therefore K_{mathrm{3}}mathrm{, =, }}frac{5}{16}times mathit{K}; ; ; cdot cdot cdot left ( 5 right )}$$

Hence, from the equation (5) it is clear that the volume of conductor material required in the three-wire DC system is (5/16)th of that required in a two-wire DC system with one conductor earthed.

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