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Tag: data analytics career

Understanding the Differences between Linear & Logistic Regression

Posted on by Imarticus Learning

Linear regression is one of the most common types of statistical models used in data prediction. It’s easy to use, and it assumes that a straight line can express a relationship between two variables. Logistic is another type of statistical model that also makes predictions.

Still, it does so using a logarithmic function rather than a linear function. This post will explain how these two methods differ and give you some practical applications for them both.

What is Linear Regression?

The linear discriminant analysis uses when predicting one variable based on another. This model assumes that if the slope of the line changes, so does the value of the predictions. It fits a straight line to your statistics using an ordinary least squares method. If you have more than two variables, consider using multivariate instead.

What is Logistic Regression?

Suppose you have an extensive collection of data that includes three different variables, one of which is the outcome you’re trying to predict (label). Using Linear might not make sense if your result isn’t directly related to the other two variables (independent variables or features).

Logistics can easily handle non-linear relationships between the label and the elements. For example, if you’re trying to predict if an email is a spam or not. The probabilities generated by logistics can work even if your features are unrelated to each other.

The Differences between Linear & Logistic Regression

LINEAR REGRESSION LOGISTIC REGRESSION
It is a simple and robust method based on linear algebra. It is one of the most well-known machine learning algorithms.
It assumes that the relationship between features and the outcome is linear, making it ideal for modeling or analyzing existing relationships in data. It is for binary classification problems, but you can also use it to solve multi-class classification problems by using an approach called “one versus all.”
You can, however, add interactions between variables to create non-linear models such as quadratic functions. You can not add interactions between variables to create non-linear models.
Unlike logistic, linear doesn’t require the outcome to be known beforehand.

 

 Logistic gets considered a supervised algorithm because you must provide the predicted (outcome) label beforehand.
 It is excellent at handling these events because it only cares about the relationship between features and outcomes. Logistic, on the other hand, is designed for discrete data (categorical or binary). It can’t handle these cases effectively because of how the sigmoid function works.

 

The table summarizes some of the critical differences between linear and logistic. Notice that, like all machine learning models, both algorithms are affected by outliers or data points outside the normal range (tails) that generate significant errors.

Explore and learn with Imarticus Learning

Learn the fundamentals of data analytics and machine learning and the most in-demand data science tools and methods to become job-ready. With Tableau, a student can learn Python,  SQL, Data Analytics, Machine Learning, and data visualization. The curriculum includes Capstone Projects, real-world projects, relevant case studies, and mentoring to assist students in becoming a master in their fields.

Some course USP:

  • Data science courses in India aid the students in learning job-relevant skills that prepare them for an exciting data scientist career.
  • Impress employers & showcase skills with a certification endorsed by India’s most prestigious academic collaborations.

World-Class Academic Professors to learn from through live online sessions and discussions. It will help students understand the 360-degree practical learning implementation with assignments.

Contact us through the live chat support system or schedule a visit to Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon training centers.

Different Ways Data Mining Helps Marketers

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What is Data Mining?

Everything that we do leaves data behind. That data is ultimately used to extract useful information. Data mining is a technique of analyzing that large amount of data and making use of it in marketing strategies by determining the correlations and patterns among them. Budding industries tend to use data mining at a great advantage by using the information uncovered to target audiences accordingly.

It is highly advisable to have a data analytics certification through a data mining course to become a professional in this field.

Steps of data mining –

  1.     Extract and transform the information collected
  2.     Digitally store and manage the data
  3.     Analyze the data by using different data mining techniques
  4.     Display the data in a useful way, like in a graph or number chart

Frequently used data mining techniques

Data mining can turn out to be a highly effective technique if done correctly. Here are some basic techniques that are quite common:

  • Observe a pattern – Simple line graphs and bar charts can be used to represent the data collected so it becomes easier to recognize certain patterns and intervals in them.

  • Classification – Classification is a more detailed data analysis technique. It helps in classifying data further into groups or clusters for better analysis.

  • Association – This helps in finding a hidden pattern. This technique helps you unfold links between two different types of variables.

  • Regression – This technique is used to spot the relationship between different variables depending on the presence of another factor. It is a form of planning and modeling.

  • Outer detection – This technique is also known as an outlier technique or analysis that helps in finding variables in a data set or group that do not match the pattern.

  • Prediction – As its name suggests, this technique helps in analyzing past or similar data to predict future patterns or events.

Benefits of data mining

Data mining can add great value to a business that is why marketers interested in growing a business must understand its benefits.

  • Marketing campaigns are more optimized –. Data mining is a great way to analyze marketing campaigns and which methods will help in generating more engagement and leads.

  • Helps in making better business decisions – The information and insights gathered from data mining can help you make better decisions for your business.

  • It will help you detect any kind of fraud – Data mining in business can help in detecting any kind of fraud or potential fraud like fake money transactions.

  • Improves external market performance – It helps to understand and predict employee behavior which in turn helps in improving external market performances.

Data mining is the future

Data analysis and management is a huge source of employment even today. Data mining professionals can work individually or as employees of a company to evaluate important information and discard the ones that are not reliable.

This requires detailed knowledge of understanding data. This knowledge can be acquired through data mining courses in India that are available online. Since every company is going digital now, reliable data analysts will be in a lot of demand so it is better to start now.

Why problem-solving using Data Analytics needs new thinking?

Posted on by Imarticus Learning

Data analytics helps organizations examine all sorts of data, be its real-time, historical, unstructured, structured, or qualitative data to uncover trends and develop insights that may be used to guide and, in some circumstances, automate decisions, thus connecting intelligence and action.

best data analytics certification courses in IndiaThe best solutions now enable the entire analytical process, from data access, preparation, and analysis through analytics operationalization and evaluating results. Hence, there is a rise in data analytics courses in India.

Data analytics enables firms to digitally transform their company and culture, allowing them to make more innovative and forward-thinking decisions. Algorithm-driven firms are the emerging innovators and business leaders, going beyond typical KPI monitoring and reporting to uncover hidden patterns in data.

In the present scenario, many firms have had to adjust their emphasis, review their existing timeframes, and rethink how they function. Companies that prioritized the digitalization of their business, particularly investments in analytics and automation, have fared the best during this crisis. Nonetheless, AI continues to be a source of consternation for many business leaders across industries.

Why is data analytics used to solve business problems?

Businesses sometimes overlook the fact that the most complicated business issues can be solved using analytics by utilizing the most recent data analytics solutions. It is critical to understand that the analyst spends approximately 70% – 80% of his or her time generating analytical files.

Only the remaining 20-30% is spent on developing a solution. Predictive models or simple business rules are used in this approach to solution creation. A corporation can use analytics tools to extract simple structured data and reduce its effort in retrieving unstructured and semi-structured information.

The rising need for an analytical solution has compelled businesses to invest in analytics solutions that will allow business users and employees across the enterprise to get the answers they need. Businesses can use these powerful tools to perform complex analytics without the need for programming assistance from data scientists. As a result, businesses will gain a new competitive advantage and the ability to unearth previously undetected patterns, propelling them to a position of influence.

Today’s problem solving necessitates a new outlook in order to obtain these insights quickly and efficiently, and there is a need for a cohesive technological change to displace the conventional data analytics tools and dissolve the walls between business groups.

Technology behemoths have demonstrated what can be accomplished when data and analytics are at the core of a business model. Needless to mention, the world’s most successful firms are all data-driven, with their focus on leveraging data to understand, market to, and increase income from their customers. This cultural shift of democratization of access to data and analytics across an organization has allowed these organizations to capitalize on the data economy and drive digital transformation.

Analytic Process Automation, or APA, is a new category that could be the key to capturing the best of man and machine at scale. APA automates business processes and provides immediate self-service access to business-critical data insights to even the most inexperienced knowledge workers. In reality, this implies that more employees will be able to use as well as gain from data with hardly any training.

This, in turn, alleviates the long-standing friction between data specialists and business managers, the latter of whom has relied on the former’s access to critical information. APA de-emphasizes data analytics at a level that the business sector has never seen before.

Conclusion

Data Science as we all know is emerging as an extremely important profession in the present scenario and it is important to have a data analytics certification to be an expert in this field. There are good data analytics courses in India that provide data analytics certification and will also increase your career prospects if you wish to pursue it further.

How Do Business Analytics & Data Analytics Differ? What Are Their Applications?

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Both business and data analytics is about processing data and gaining insights from data.

The terms business analytics and data analytics might be used interchangeably in organizations on a small scale. On the other hand, big firms hire both business analysts and data analysts to perform well-defined functions, thus making it very necessary to understand the difference between the two.

With Imarticus’s, you can choose between the best data analytics courses and business analytics courses in India. Read on to have a deep insight into the two important career paths and their respective applications.

best data analytics courses in IndiaBusiness Analytics

Business analytics course focuses on business impact when analyzing data and the actions that should result from the insights. For example, it provides answers to the following questions, “Should a company develop a new product line?” Or, “Should it prioritize one particular project over another?”

Business analytics combines various capabilities, tools, and applications to measure and improve the effectiveness of core business functions such as marketing, customer service, sales, or IT.

Business analysis (also Business Analytics) is the iterative exploration of a company’s data. There is a statistical analysis method used to bring information to light that can help to stimulate innovation and business results. Companies that rely on analysis consider big data to be a valuable asset of the company: data helps to advance business planning and forms the basis for future strategies. Business analysis helps these companies get the maximum value from this gold mine of insights.

Sufficiently large volumes of high-quality data are required for business analysis. Companies that want to achieve the most accurate results possible have to integrate and compare the data from different systems. A decision is then made as to which subsets will be made available to the business.

Data Analytics

A Data analytics career involves combing through massive data sets to identify patterns and trends, create hypotheses, and support business decisions with data-based insights.

For example, data analytics seeks to answer questions such as, “What impact do geographic factors or time of year have on customer preferences?” Or, “What is the likelihood that a customer will defect to a competitor?” In practice, data analytics encompasses many different techniques and approaches. It is also known as data science, data mining, data modeling, and big data analysis.

In data analysis, raw data is collected and examined to conclude it. Every company collects huge amounts of data, such as sales figures, market research, logistics, and transaction data. The real benefit of data analysis is in identifying patterns in a data set that can indicate trends, risks, and opportunities.

Data analysis enables companies to use this knowledge to change their processes to make better decisions. In practice, data analysis can help, for example, to decide about the next product developments, to develop customer loyalty strategies, or to evaluate the effectiveness of new medical treatments.

Applications of Business Analytics and Data Analytics

Since Business Analytics and data analytics are both based on big data tools, they have several applications in various industries across the globe. Some of them are mentioned below:

  1. Digital Advertising
  2. Energy Management
  3. Medical Applications
  4. City planning and mapping
  5. GPS tracking
  6. Transportation
  7. Risk detection and management
  8. Security
  9. Transportation
  10. Traveling
  11. Customer interaction
  12. Internet Browsing
  13. Expenditure Management

Conclusion

Business and Data analytics share the same overarching goal: use technology and data to drive business success. We live in a data-driven world where the amount of information available to businesses is growing exponentially. Both functions in combination can help companies achieve maximum efficiency and gain some useful insights.

How Data Analytics is Reviving Up the Digital Economy

Posted on by Imarticus Learning

Today, data analytics is a booming industry. It has been estimated that the data analytics market will grow to $187 billion by 2025. These numbers are staggering and they just show how much potential this industry holds for those willing to take on it as a career.

Data analysts have an essential role in our digital economy because they dig out insights from raw data to help companies make better decisions. To succeed as a data analyst, one must be adept with programming languages such as Python, R, SAS, or SQL, among others, and know statistical analysis techniques.

Data analytics course is used to make decisions about important marketing campaigns, product development, and customer service. Data analysis techniques include descriptive statistics, inferential statistics, hypothesis testing, cluster analysis, exploratory data analysis (EDA), causal modeling, and regression modeling.

Use of Big Data and Analytics

The definition of “digital economy” has been blurred by its widespread use but in general, let’s understand what it means. The digital economy uses technology to create, capture, store, manage, transmit and exchange information. One example of this includes using a cell phone app to order food online.

Here, the companies have created an online interface for customers to easily find restaurants that deliver in their area and then order by clicking on what they want. This eliminates the need for human intervention because all orders are taken or placed through the device. This means that the entire process has become hassle-free.

Another most common example where big data is used is retail stores. They use it to analyze their customers’ shopping habits, so they know what products to put out on display during a particular time frame throughout the day. This helps them in maximizing profits while also making sure that every customer gets a personalized experience when they visit the store.

In the same way, most companies use big data to analyze and find patterns in the available data. They leverage this data to derive insights that offer a unique experience to their target group. Companies dig deeper into these insights to identify untapped business opportunities that they can explore. Companies can identify a niche and expand into that area to fill the need gap.

They also have many other options for growth, such as partnerships, joint ventures or acquisitions within their target industry verticals; these strategies allow them to survive and thrive in changing markets while often expanding business operations. While exploring new business areas, companies always study the data available to forecast the project’s profitability.

Use Case of Big Data and Data Analytics

Data analytics is an essential tool when it comes to modern marketing & growing the digital economy. Without it, a company’s decisions would be made without any data to back them up, and you could miss out on some excellent growth opportunities!

best data analytics courses in IndiaThe good news is that there are many different types of data analysis techniques available, so you can find one that works best with the kind of information you have at hand.

If this sounds like something worth investing in, you can consider enrolling for several online courses on Data Analytics from Imarticus Learning. You will learn how to analyze data yourself and come up with insights or use a computer program such as Microsoft Excel and several other data science tools to analyze data.

Conclusion

The digital economy is expanding day by day, and it might replace traditional economies in the future. Artificial intelligence (AI) powered tools manage data in this new environment, while manual tasks remain essential for understanding the numbers.

However, businesses rely heavily on consumer insights obtained through machine learning algorithms. These algorithms are decoded with the help of analysts who provide valuable input during the decision-making processes.

Marketing of the Future: How Data Analytics is Changing

Posted on by Imarticus Learning

The corporate industry is ever-evolving, and even more so when it comes to data analytics. From standard out-of-the-box reports to a tool operated by digital mediums, it has come a long way. In the past, it was mostly a way to decipher and keep records of bygone trends, but now it has turned into a tool that helps you predict future trends.

It even helps you understand your target customer better through their online habits. And as daily life gets more and more virtual-oriented, the need for machine learning and AI-oriented data analytics will keep growing. So will grow the number of positions for good data analysts.

Here, you have a chance to learn data analytics with Imarticus Learning’s new PG program to enhance your skills to the max.

Coming back to the evolution of data analytics throughout the years, there are a few major changes that need to be addressed. Such as the shift to digital analytics from standard reports, as well as live AI reports and alerts.

Digital Analytics from standard reports

 The major difference that has gained traction in the last few years would be how virtualized the data analytics industry has become. In the past, most of the analysis process was done through tool-generated out-of-the-box reports which today are almost out of date. Now the main focus has shifted towards reading the targeted customer’s habits and needs and forming strategies to cater directly to it.

It becomes a lot less hassling on both parts as instead of guessing you’re actually finding out what your client needs and providing solutions for it. Now with the change in how data is extracted and processed the job of an analyst has also evolved. This is one of the reasons why data analytics courses in India have been getting more and more popular in recent years.

 Live AI reports and alerts

 Another system that has become immensely popular is live AI reports that alert the clients to any ups and downs in trends. This helps them to develop quick strategies to respond to the changing trends. Not just that, the sources from which data is generated have changed drastically. Not just real-time market trends, data today can be sourced from online retails, social media, intelligence tools as well as pop-ups and advertising platforms.

The bandwidth of data has increased and along with it, the variety, responsibility, and positions of a data analyst job. As this process evolves more the scopes will increase even more in numbers. This is a good time to enhance your skills if you are thinking about career prospects. There are many institutes that offer good data analytics courses in India that might suit your preference. However, there are few things you should keep in mind when it comes to data analytics:

  • Many companies are venturing into exploring open-source software, but the majority is still using business analytics tools and visual analytics.
  • Data analysis has become a part of our daily lives. As a result, new and faster ways to generate data are coming out that need higher levels of skills.
  • The industry is constantly evolving, so it is necessary to always be top of your game as a data analyst.

Conclusion

Data analytics is one of the careers that has a firm base. More and more opportunities are going to open up in the future. This might be a good time to learn data analytics with a proper PG program that will help your career bloom.

Data Analytics, Productivity, and Well-being: Are they interrelated?

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Forms and benefits of data analytics have shifted majorly in the last few years. Major firms have also been using it as a way to decipher the habit patterns of their employees to take care of their well-being. Needless to say, their wellbeing directly relates to their workplace productivity. However, with the evolution of data analytics, data analysts also have to be able to evolve constantly.

And that is only possible when they have the proper training to adapt to situations, and that can only happen if they learn data analytics from a proper institution. Imarticus Learning has come forth with a great opportunity for people who would like to polish their skills with their new PG program with data analytics and machine learning certification.

Coming back to the impact of the well-being of employees on company productivity, studies have found that being comfortable in their workplace can make the workers about 12% more productive.

Data analysis as a tool to ensure happiness

Major firms have been trying to gather and decipher the patterns of their employees’ behavior to provide a personalized experience to them for some time now. There are a lot of ways companies have decided to approach it. Some offered wearables to their employees that will teach how much time they spend, sitting, talking, writing, or moving about. Some have chosen the company intranet to trace their online tracks.

Now it falls to the data analytics team to extract, and decipher these patterns. It is fruitful in the sense that these patterns always give indications of not only their physical health but also their mental ones. This is also a way where companies can provide personalized suggestions for their well-being. And the employees get benefitted from it in a way that impacts their daily life. As a result, they feel more loyalty towards the company.

Hurdles to jump over

There are some evident advantages of exchanging data for the betterment of the employees’ wellbeing, and in turn, increase their productivity. However, it is also a possibility that when it comes to data gathering and analysis, the employees might have some privacy concerns about them.

Which in turn, might make them unwilling to participate actively in the bandwagon. This is why, there are a few things that should be kept in mind when it comes to the interrelation of wellbeing and productivity of the workers, such as:

  • The first and most important thing is to make sure the workers consent to the exchange of data for the company’s use, as many might have privacy concerns relating to that.
  • Companies will need to have experts in the analytics team to properly extract and use the data and make it a priority for both the main wing of the business and the analytics team to prioritize the employees well being.They need to communicate properly to the workers how it benefits them and the company both at once.
  • The key is to recognize what the employees want and be able to cater to it. Otherwise, even after using data analytics to define the workers’ needs, it will be utterly irrelevant.

Conclusion

Using data analytics as a mode of communication between the employer and employee needs time and expert skills. And it can only come if you learn data analytics from a good place. Many institutes in India offer a data analytics certificate course. Imarticus Learning is one of the topmost among them, so do check out their PG program of data analytics and machine learning.

Imarticus Learning: Fuelling India’s Data Analytics Workforce

Posted on by Imarticus Learning

What is Data Analytics?

 Data Analytics involves analyzing raw data and drawing meaningful conclusions and patterns from that data. In data analytics, a lot of processes are automated to eliminate manual intervention. You can take up a data analytics course to understand the intricacies of the subject.

In data analytics, a lot of algorithms are prepared to make the job easy. These days you can take up a data analytics course with placement. A data analytics certification course makes you credible enough for the job.

Understanding Data Analytics

best data analytics certification courses in IndiaData Analytics can be complex when you try to understand it. A data analytics certification course can help you know what the subject entails and how to make the best use of it. The data analytics course will also introduce you to the world of algorithms.

Data Analytics is a broad subject that includes several diverse types of data analysis techniques.

Data Analytics can be used to mine different kinds of data insights. These insights can be used in improving processes and transforming them for the convenience of the data users. You can take up a data analytics course with placement to practically apply these algorithms and techniques of data sorting and data analysis.

Companies like Imarticus Learning are tirelessly working towards making the Indian workforce tech-savvy and well-versed with data analytics and its application. If more and more workforce joins hands with Imarticus to learn data analytics, the workforce will become digitally enabled to deal with a large amount of data. They would know how the data would be put to proper use.

Use Cases of Data Analytics

Data Analytics training can be used to understand several trends that dominate the market. You can apply predictive analysis using the insights from these data points. Several industries are now making use of data analytics to optimize their processes.

For instance, in the manufacturing industry, data analytics is used to store and record runtime, work queue, and downtime of all the machines in the factory. The data can then be utilized to optimize all the processes and to make manufacturing better.

However, data analytics is not limited to spotting bottlenecks in the process. It can do much more. It can make the entire process better and more efficient. You can also use data analytics to speed up the manufacturing process as a whole, as with data analytics, you can reduce the waste to a great extent.

Types of Data Analytics

If the workforce knows how to use Data Analytics, they will be able to use technology better. Some of the types of Data Analytics are:

  1. Descriptive Analytics: This is used to understand what has happened over a while.
  2. Diagnostic Analytics: If something happens, you can analyze what went wrong using diagnostic analytics.
  3. Predictive Analytics: In the case of predictive analytics, the algorithms are used to predict a future trend.
  4. Prescriptive Analytics: These algorithms are used to take a suggestive measure for any action.

Conclusion

Building an analytics workforce is the need of the hour. Therefore, it is essential to train more professionals and prepare them for the analytics world. Digital literacy is very important to automate functions, and data analytics is an integral part of it.

Imarticus is on a spree to enable people to use data analytics to decode patterns and understand data. Imarticus has several courses on data analytics. You can enroll in all of these courses to get an in-depth insight into how data analytics works and make the best use of it. The certifications from Imarticus have a great value in the industry.

How A Security Camera Vendor Exposed User Data While Performing Analytics?

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A company responsible for manufacturing security cameras recently cost a huge number of its users their privacy as well as personal data which was accidentally exposed onto the internet. After admitting and owning up to their mistake, they went on to explain exactly what went wrong.

According to the company data belonging to users like their email address, body metrics, and IDs belonging to their WiFi networks were left exposed while critical information like financial information and the user’s passwords were left unaffected. This took place in December of 2019.

This incident occurred as a result of a misconfiguration in the database of Elasticsearch. This contained data that was generated by millions of the company’s customers, which they blamed on one of their employees. After explaining the issue, the company promised and was determined to keep looking into the database’s lack of proper security standards.

For those wondering what Elasticsearch is, it is a scalable analytics and search engine that is both open-sourced and includes full-text searches. It allows its users to analyze, look for and store large volumes of data efficiently and in real-time.

How exactly did the incident take place?

During an internal project which was meant to determine ways to measure metrics related to businesses in a more efficient manner, the incident took place. The method was to be used to measure activations of various devices, failed connections, and so on. This, however, would require data replications from the main or original production servers and into a database that would be more flexible.

This would make it easier to query. Bigger volumes of queries may possibly impact the customer’s product experience if it is done on the central database as it is very compute-intensive. Processing without there being any system lag required an individual data subset to be moved into a database that was different.

In doing so the user data was exposed in an unencrypted manner while it was being transferred to a novel database for querying. An employee had cleared out all prior made security protocols due to an error that led to the unfortunate incident. The new database only held a data subset and did not include any government-regulated or vital data such as passwords and financial intel.

What was exposed to the web exactly?

Until now, the company has only owned up to exposing the data which has been revealed and denies any evidence of data breaches. The company informed the public about the customer data that had been exposed which included data from their artificial intelligence assistants like Alexa, data pertaining to their health, their email addresses, and IDs to the networks of their Wifi’s.

Users have been asked to be careful against possible phishing ambushes because hackers could possibly have access to their user names and email addresses. These could still potentially be used to thieve credit and financial information.

The company also claimed that there was no evidence of exposure to their tokens required to log in. Users, however, have been logged out of their accounts as a precautionary or safety measure, allowing new tokens to be generated for logging in. The company has been working towards ramping up its security. This may include the addition of a two-factor method for user authentication. For aspirants looking to know more about the world of data science, a data analytics course would be perfect.

best Data Analytics courses in India Not only would allow aspirants to learn more about the field but it would also equip them with all the necessary information and tools required to succeed in a data analytics career.

Data Analytics and Other Skills Which Can Boost Banking Careers

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Data Analytics and Other Skills Which Can Boost Banking Careers

We are living in a fast-paced world that is constantly changing with the advent of new and progressive technology. What’s relevant today might be of no use in the near future, such as the frequency and intensity of change. In the contemporary scenario, the digital world has taken over the real one and the world is moving online.

This totally changes the equation for businesses that were earlier accustomed to traditional methods. Now it possesses another challenge for the employees who were trained as per the traditional methods.

This evolution and challenges also apply to the banking industry. With the advent and proliferation of e-commerce, more people like to purchase goods and services using an online method that requires a digital banking facility to make payments. Also, the digital banking system is an evolved version of traditional banking that tackles the challenges faced by traditional banking methods. It makes it more convenient for people to avail and use banking services from the comfort of their home.

An important observation about the digital space is that it runs on data. Tons and tons of data are generated and recorded from various data points. Most of these data are related to customer transactions that help the business to gain valuable insights about the market and the customers.

Earlier this goldmine was left untapped as businesses didn’t have the tools to explore the benefits of data. This has changed over the past decade and businesses have realized the latent potential of data science, data analytics is indispensable for businesses in almost all industries, especially banking.

Banking and underwriting course can help you easily break into the banking industry. Also, wealth management training online can help you obtain a comprehensive understanding of the industry and give you an edge over the competition in finance and banking-related jobs.

Let’s explore some of the most prominent data analytics skills that can boost the banking career for people aspiring to make a career in this industry.

Skills needed in the contemporary banking industry

  • Analytical Skills

In today’s data-driven world, analytical skills top the list of must-have skills for the banking sector. Data analytics skill plays a key role when it comes to analyzing large chunks of data and deriving valuable insights from it. You should be able to identify trends and patterns hidden in the data sets and derive a conclusion from the same.

You should also develop your technical analytics skills by learning software such as Python, excel, etc. Your decisions should not be based on a hunch and it should be backed by reliable data. Data-driven thinking along with strong analytical skills is the only way forward when it comes to excelling in the banking and related sectors.

  • Commercial Awareness

Commercial awareness is all about being up to date with the latest trends and developments in the business world, especially for businesses in the banking and finance sector. The ability to anticipate a financial trend distinguishes a highly successful banker from a mediocre one.

You have to keep up with the market and be able to predict future movements. It’s vital to understand how organizations function and the primary driver for their growth. You need to know everything that might have a financial impact on a business.

  • Stress Management

Technical skills are important but what’s even more important is your soft skill. Emotional intelligence is an interesting concept that emphasizes the need to manage your emotions in the workplace. When you are working in the banking industry, you will be exposed to a lot of uncertainty that might induce and cause stress.

Stress management is critical to succeeding in the banking world. Burnout and stress from work are far too common and can reduce your productivity drastically. Being able to manage stress is one of the most important skills that you can hone.