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93% Indian Professionals Benefitting From E-Learning During Lockdown: Linkedin

Posted on by Dexlab

93% Indian Professionals Benefitting From E-Learning During Lockdown: Linkedin

The Covid-19 pandemic has struck India like it has scores of countries across the world. As of May 27, over 1,51,000 Indians have been tested positive for the novel virus and over 4000 people have died due to the contagious disease. India has been under lockdown for over two months now in an attempt at abating the spread of the virus due to movement and contact.


 

With all offices closed and work from home decreed across numerous sectors of the economy, professionals have been forced to adapt to a new mode of work and training. With more time on hand since they are working from home, professionals are upgrading their skills by taking up online training modules and classes. A recent LinkedIn survey throws light on this phenomenon.

LinkedIn’s Work Force Confidence Index

India’s foremost social networking site that helps individuals network with professional peers and find jobs and appointments has conducted a survey called Work Force Confidence Index. As per the survey conducted between April 27 and May 3, “India’s professionals are logging learning hours for not just knowledge acquisition but also to increase productivity. About half of respondents from mid-market firms joined courses that help them manage time better, improve prioritisation or stay organised”.

93% Indian Professionals Benefitting From E-Learning During Lockdown: Linkedin

93% respondents to upskill online in next two weeks

According to LinkedIn News India, 1040 professionals were surveyed by LinkedIn and 93% of them said “their time spent on e-learning will either increase or remain the same over the next two weeks”. Moreover, 60% of the respondents of which 74% were from the engineering domain said e-learning was a conduit to furthering industry knowledge. “Advancing in one’s career was a driver for 57% of all respondents and 3 in 10 active job seekers undertook e-learning to make a career pivot,” said LinkedIn News India.

What respondents learnt

Of the respondents, 45% said they hoped to learn to collaborate with peers through online learning in lockdown. Also, 43% said they wished to learn to manage time and prioritise and stay organised. Moreover, 40% said they hoped to learn something unrelated to work through online platforms. Becoming a leader and managing personal finances were pegged at 37% and 32% respectively by the study as goals and 24% said e-learning could actually lead to a change in career paths for them.

Advantages of e-learning

Travelling to work and back is taxing and time consuming. When you are working from home, you save on energy and time that can be used for something productive like e-learning training modules. They are easy on the pocket, accessible from absolutely anywhere you are and convenient to absorb and retain information and new things learnt. Moreover, there is a large online community to help you out with study material and guidance.

Data Science Machine Learning Certification

There are many popular e-learning courses in India, especially those around data science and artificial intelligence. DexLab Analytics is a premier credit risk modeling training institute that also trains professionals in artificial intelligence, machine learning and data science. This article was brought to you by DexLab Analytics.

 


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The Data Science Life Cycle

Posted on by Dexlab

The Data Science Life Cycle

Data Science has undergone a tremendous change since the 1990s when the term was first coined. With data as its pivotal element, we need to ask valid questions like why we need data and what we can do with the data in hand.

The Data Scientist is supposed to ask these questions to determine how data can be useful in today’s world of change and flux. The steps taken to determine the outcome of processes applied to data is known as Data Science project lifecycle. These steps are enumerated here.

  • Business Understanding

Business Understanding is a key player in the success of any data science project. Despite the prevalence of technology in today’s scenario it can safely be said that the “success of any project depends on the quality of questions asked of the dataset.”One has to properly understand the business model he is working under to be able to effectively work on the obtained data.

  • Data Collection

Data is the raison detre of data science. It is the pivot on which data science functions. Data can be collected from numerous sources – logs from webservers, data from online repositories, data from databases, social media data, data in excel sheet format. Data is everywhere. If the right questions are asked of data in the first step of a project life cycle, then data collection will follow naturally.

  • Data Preparation

The available Data set might not be in the desired format and suitable enough to perform analysis upon readily. So the data set will have to be cleaned or scrubbed so to say before it can be analyzed. It will have to be structured in a format that can be analyzed scientifically. This process is also known as Data cleaning or data wrangling. As the case might be, data can be obtained from various sources but it will need to be combined so it can be analyzed.

For this, data structuring is required. Also, there might me some elements missing in the data set in which case model building becomes a problem. There are various methods to conduct missing value and duplicate value treatment.

“Exploratory Data Analysis (EDA) plays an important role at this stage as summarization of clean data helps in identifying the structure, outliers, anomalies and patterns in the data.

These insights could help in building the model.”

  • Data Modelling

This stage is the most, we can say, magical of all. But ensure you have thoroughly gone through the previous processes before you begin building your model. “Feature selection is one of the first things that you would like to do in this stage. Not all features might be essential for making the predictions. What needs to be done here is to reduce the dimensionality of the dataset. It should be done such that features contributing to the prediction results should be selected.”

“Based on the business problem models could be selected. It is essential to identify what is the task, is it a classification problem, regression or prediction problem, time series forecasting or a clustering problem.” Once problem type is sorted out the model can be implemented.

“After the modelling process, model performance measurement is required. For this precision, recall, F1-score for classification problem could be used. For regression problem R2, MAPE (Moving Average Percentage Error) or RMSE (Root Mean Square Error) could be used.”The model should be a robust one and not an overfitted model that will not be accurate.

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  • Interpreting Data

This is the last and most important step of any Data Science project. Execution of this step should be as good and robust as to produce what a layman can understand in terms of the outcome of the project.“The predictive power of the model lies in its ability to generalise.” 

 


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How Company Leaders and Data Scientists Work Together

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How Company Leaders and Data Scientists Work Together

Business leaders across platforms are hungrily eyeing data-driven decision making for its ability to transform businesses. But what needs to be taken into account is the opinion of data scientists in the core company teams for they are the experts in the field and whatever they have to say regarding data driven decisions should be the final word in these matters.

“The ideal scenario is all parties in complete alignment. This can be envisioned as a perfect rectangle, with business leaders’ expectations at the top, fully supported by a foundation of data science capabilities — for example, when data science and AI can achieve management’s goal of reducing customer retention costs by automating identification and outreach to at-risk customers,”says a report.

The much sought after rectangle, however, is rarely achieved. “A more workable shape is the rhombus, depicting the push-and-pull of expectations and deliverables.”

Using the power of your company’s data.

Business leaders must have patience with developments on the part of data scientists for what they expect is usually not in sync with the deliverables on the ground.

“Over the last few years, an automaker, for example, dove into data science on leadership’s blind faith that analytics could revolutionize the driver experience. After much trial and error, the results fell far short of adding anything meaningful to what drivers found valuable behind the wheel of a car.”

Appreciate Small Improvements

Also, what must be appreciated are small improvements made impactful. For instance, “slight increases in profitability per customer or conversion rates” are things that should be taken into account despite the fact that they might be modest gains in comparison to what business leaders had invested in analytics. “Applied over a large population of customers, however, those small improvements can yield big results. Moreover, these improvements can lead to gains elsewhere, such as eliminating ineffective business initiatives.”

Healthy Competition

However, it is advisable for business leaders to constantly push their data scientists to strive for more deliverables and improve their tally with a framework of healthy competition in place. In fact, big companies form data science centers of excellence, “while also creating a healthy competitive atmosphere that encourages data scientists to push each other to find the best tools, strategies, and techniques for solving problems and implementing solutions.”

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Here are three ways to inspire data scientists

  1. Both sides must work together –Take the example of a data science team with expertise in building models to improve customers’ shopping experiences. “Business leaders might assume that a natural next step is to use AI to enhance all customer service needs.”However, AI and machine learning cannot answer the ‘why’ or ‘how’ of the data insights. Human beings have to delve into those aspects by studying the AI output. And on the other hand, data scientists also must understand why business leaders expect so much from them and how to achieve a middle path with regard to expectations and deliverables.
  2. Gain from past successes and achievements – “There is value in small data projects to build capabilities and understanding and to help foster a data-driven culture.”The best policy for firms to follow is to initially keep modest expectations. After executing and implementing the analytics projects, they should conduct a brutally honest anatomy of the successes and failures, and then build business expectations at the same time as analytics investment.
  3. Let data scientists spell out the delivery of analytics results – “Communication around what is reasonable and deliverable given current capabilities must come from the data scientists — not the frontline marketing person in an agency or the business unit leader.” Before signing any contract or deal with a client, it is advisable to allow the client to have a discussion with the data scientists so that there is no conflict of ideas between what the data science team spells out and what the marketing team has in mind. For this, data scientists will have to work on their soft skills and improve their ability to “speak business” regarding specific projects.


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