EFFECTIVE COMMUNICATION IN THE WORKPLACE

The ability to communicate is an essential life skill and one that can be continually developed. Even if you are a naturally good communicator, there are always opportunities and ways to enhance your communication skills.

Effective Communication

Communication is a complex two-way process that can involve several iterations before mutual understanding is achieved. Communication takes place in many ways. You can communicate using words, symbols, pictures, graphics, voice, tone, facial expressions, clothing, and body language.

Most communication is a combination of these elements. By understanding how to use these elements effectively you can improve the way you communicate and achieve the best outcome for any situation. Whether speaking formally or informally, addressing a meeting or writing a report, the basic principles of communication are as follows.

Know your audience

Communication should always be packaged to suit the listener’s level of understanding.

Know your purpose and topic

Make it clear whether you are delivering specific information, requesting information, or being social. Be aware of all the facts and details.

Anticipate objections and present a complete picture

Objections often arise due to misunderstandings. Communicate the benefits for both parties. Support your statements with evidence (e.g. statistics or testimonials).

Communicate a little at a time, then check the listener understands

Pause, ask questions and give the listener an opportunity to ask questions.

Present information in several ways

What worked for one listener/reader may not work for another.

Develop practical, useful ways to get feedback

Feedback is the best way to evaluate the effectiveness of your communication.

In all areas of your life, you will play many different roles in the communications process. At the highest level within this process, you will either be the person instigating the exchange or the recipient of it.

Whichever one of these roles you play you will have a variety of options that will shape your communications and the level of preparation you are able to perform.

Those who instigate a communication will generally be performing one the following roles that will match the reason why the communication is taking place.

Communicator – you have a need to inform an individual about some aspect of their work or you require them to take on a task you need to delegate. You might need to persuade someone or a group to accept the topic of your communication and its implications or repercussions. The communication may, or may not, have been expected and you will be able to establish this by listening and observing the recipients’ responses.

Investigator – the reason for your communication is to find out some information or data that you need to make a decision. It can also be that you need to find out the latest status of a project or task.

Assessor – you have to assess how well someone, or a group, is performing their role or task. In this role you may be responsible for gathering and collating the data you collect as part of this communication process.

If you are the individual receiving the communication, then the role you will adopt during the exchange will fall into three broad categories:

Recipient – you need the information or data contained in the communication to ensure that you can complete your task or perform your job. Much of the communication in this role is of a general nature and its purpose it to keep you informed of organizational changes and progress.

Participator – this is the role you will often find yourself in. Your knowledge and skills are required in a discussion or decision-making process that has a mutual benefit to those involved in the communication.

Antagonist – your viewpoint may be contrary to that of other person or people in the exchange and you want to ensure that those involved in the communication are fully informed of all issues in order to ensure that a well-reasoned decision or judgment is the end result.

With each of these broad roles, you will alter your behaviors and method of communicating to suit the environment and circumstance. But whichever high-level role you find yourself performing you will also need to actively listen to the whole of the two-way exchange.

Communication and Active Listening

An important skill that all good communicators possess is their ability to actively listen. Developing the skill of active listening during conversations enables you to avoid misunderstandings, confusion, and misinterpretations. It also ensures that you are always looking at the ‘big picture’ when communicating.

The basic principles of communication

  • Know  your audience
  • Know your purpose and topic
  • Anticipate objections and present a complete picture
  • Communicate a little at a time, then check the listener understands
  • Present information in several ways
  • Develop practical, useful ways to get feedback

Individuals who are good communicators find it easy to develop empathy and trust with others. These people adapt their style of communication to suit the audience and situation they are presented with.

From these visualizations, we can derive various information of the data which can be used Data pre-processing and Feature Engineering.

For Measuring Central tendency of the data, we can use mean, median or mode.

  • Mean provide us with average value of the data, which is a widely used metrics for measuring the central tendency. How ever if the outliers are present, it can be skewed towards a direction based on the value of the outlier
  • Median is a robust central tendency metrics where the middle element is identified as median. It is not impacted by outliers.

Data can be segregated based on the type of the distribution such as uniform distribution, normal distribution, binomial distribution and more. Data science with strong links with statistics can helps us to identify these intrinsic properties of the data and help us to perform corrective actions.

Data Pre-Processing

As part of Data Pre-Processing, we will be involved in identifying the issues in the data and performing the corrective actions.

  • MISSING VALUE IMPUTATIONS

Missing values is one of the important issues in the dataset and it needs to be imputed. There are various statistical methods to perform missing value imputation such as using Mean, median and Mode, using algorithms based on linear trends and, we can apply machine learning based algorithms to perform this action.

  • FEATURE SCALING & REMOVING OUTLINERS

Presence of outliers can influence the model predictions. It can skew the predictions in certain directions based on the value of outliers. Therefore, it is necessary to remove the outliers or perform corrective actions.

Feature Engineering

Feature engineering steps involves feature selection or elimination based on the relevance of the model prediction and feature importance in predictions. As part of this step, a base machine learning model is setup to identify relevant features for the predictions. This stage involves following steps

  • Recursive Feature Elimination
  • Correlation Identification
  • Multi Collinearity check using Variance Inflation Factor

Using Recursive feature elimination, important features are identified for the model based on brute force feature selection and elimination by validating all feature combinations.

Model Selection

As part of model selection method, various models are identified and evaluated as a base model. This step is to identify the best prediction algorithms for this project. Base Models are trained on the training dataset and used to predict for the validation dataset. Based on the output prediction accuracy, top performing models are selected.

Machine learning models are classified into different categories or families based on the characteristics and predicting properties.

data science stages

Supervised learning is based on the dataset which has a label or target to guide the model to train itself. For Example, let us say we are building a Deep learning-based classification models for classifying an Image as one with Dogs or Cats, the dataset which is used for training this model will have prior information of whether the image has Dog or Cat. In this way the dataset will help the model to learn the patterns in the images for performing classification. As part of supervised learning there are many families of algorithms such as tree-based, gradient boosting based, linear models, Support Vector Machines and more.

In a scenario where there is no luxury of training a machine learning model using a dataset with targets or label, we may need to opt for unsupervised learning models. These models use the intrinsic properties of the data points to cluster them into various clusters. In these clusters you can identify data points which are closer to each other in terms of their characteristics. There are various Clustering algorithms such as K-Mean Clustering, Hierarchical Clustering and more.

Reinforcement Learning is an interesting field, where the model is trained based on the rewards and penalties based on agent actions. Agents are ML based bots which can perform actions in the predefined environment. Based on their actions and objectives, rewards are generated.

Deep Learning Models

Deep Learning models evolved due to the advancement in the computation capacity of processing units and innovations in the Algorithms. Deep learning models can handle and analyse huge volumes of data and understand the underlying patterns in it. Deep learning models are widely used in scenarios where Big data, Image based data, time series data are involved.

ERP College courses are designed in such a way it covers the core concepts of the algorithms and how they can be implemented technically. Various Machine learning and Deep learning models are covered in the courses with extensively reviewed topics and use cases.

Model Building, Validation and Fine Tuning

After selecting the best models for a scenario, the next step is to perform Model building and fine tuning the model. This is an iterative step where we would need to identify the base model accuracy and based on it we will fine tune the models on Hyperparameters. Different Machine Learning models have different hyperparameters and we can change these values in a specified range to fine tune the models.>

Fine tuned ML model is trained, and the predictions are validated using evaluation metrics based on the type of it such as regression or classification. This validation score is used to check the performance of the model in terms of accuracy and fine tuned again if required. This an iterative process.

Data Science Model Evaluation

Next steps are to validate the model using actual test data. This step gives us more visibility on the performance of the model in real time scenario. As part of evaluation metrics, there are specific metrics for specific machine learning problem

Model Interpretation and Test Data

After we have built the machine learning and deep learning models, the next steps are to interpret the model predictions and to provide the business context to the stakeholder. Data science provides us with various tools to perform this step such as how the weights are configured and what is the feature importance.

As we can see, Data science is an extensive space where various concepts are brought in to derive insights and predict the outcomes.

ERP College’s Data Science Course

ERP College provides an interactive online data science course,  This course is not only an introduction to data science, but it’s also a course that dives deep into it, it teaches prospect students how to analyze, visualize and design machine learning models and search for answers to queries in a collection of enormous databases which are too complex to analyze using conventional methods. Graduates of this program can analyze data written in different programming languages, from various sites, or in different formats. Participants of this program could qualify for the following positions: Machine Learning Engineer, Data Engineer, or Data Scientist

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