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According to a report, India's online food delivery sector is anticipated to reach $4 billion. Before 2010, ordering takeout for delivery at home required calling the establishment.

This growth is expected due to rising smartphone usage, an expanding e-commerce market, a growing number of young people entering the workforce, and rising internet penetration.

But how does zomato manage all its operations to guarantee customer satisfaction? 

Data science has become the key! 

In 2015, Zomato began using data science techniques to improve its services. They created algorithms that could recommend places based on user preferences and past actions taken by users on the site. This allowed them to create more accurate search results than before.

The ability to interpret, analyze and interpret the insights gained from data is one of the most important concerning business growth in the current era. The ability to understand where change is happening in your industry and how it varies between different areas will help you reach new customers and improve your conversion rates. 

In this blog post, I would like to share some insights into how Zomato uses Data Science for its business.

Data Science

Data science is collecting and analyzing data to develop a computer model or algorithm for making decisions about the future. It can also be used to predict outcomes based on past data. Data scientists use a variety of techniques, including statistics and machine learning.

About Zomato 

Zomato is a restaurant search service that allows users to find restaurants and other types of businesses. Zomato was founded in 2010 by Deepinder Goyal and Pankaj Chaddah. It began as a website that allowed people to search for restaurants and other types of businesses using keywords or tags. The company's mission is to help people find the best food, drink, and entertainment near them.

Zomato now uses machine learning algorithms that are trained on billions of data points about what customers want when searching for restaurants. These algorithms aim not only to let people find restaurants but also to provide them with information about what kinds of foods they might like at those places, so they can make better decisions when going into different eateries.

The Zomato ML team emphasizes image processing and NLP-based review extraction to improve product optimization, create recommendation engines, and implement features that enhance the operational elements of the sector. Predictions are generated using this interface once the machine learning model has been deployed, which produces a server to do so using APIs. For more information on NLP and other ML techniques, visit the IBM data science course in Chennai, and learn the concepts in detail. 

How data science assists zomato? 

Initially, image processing and NLP evaluations were used to moderate user-generated content. This gradually expanded to cover things like product optimization, recommendation engines, and feature upgrades and is now extended to business and operational parts of the organization.

Today, to provide customers with personalized experiences, we need to be aware of their preferences in real-time. Zomato is investigating machine learning methods to handle issues like food delivery, allocating the best partners for delivery, food preparation time, etc. This raises revenue, boosts the company, and promotes consumer satisfaction.

To improve customer service through predictive analytics, zomato uses machine learning models to predict when customers will be unsatisfied with their meals and provide customer support at that time. 

The specific models used depend on the type of restaurant being reviewed, but all models are trained using historical data from other customers' experiences with each restaurant type (i.e., they are predicting how likely someone is to have an unsatisfactory experience based on previous data).

Use of Big data in Zomato

Enhancing the menu using big data 

With Big Data solutions at their disposal, their meal delivery software may collect client input regarding the items on the menu of many registered eateries.

The restaurants can then receive recommendations from the system regarding menu items that will help them generate more business. Restaurants can increase their operational efficiency by changing menu items. By doing this, they can increase the promotions they run on popular goods and increase sales.

2. Faster Deliveries

Fast delivery times are the most prominent of the many elements contributing to food technology firms' success. Compared to its rivals, a meal delivery service will do better if it provides users with faster delivery times. Though it might seem straightforward, numerous challenges must be overcome to transport freshly prepared food from restaurants and deliver it to the customers. With Big Data Analytics systems, they can keep an eye on various factors, including traffic, weather, and roadblocks along the route, and provide the shortest route to ensure that the food is delivered as quickly as possible.

3. Customer Sentiment analysis

You cannot afford to ignore customer feedback on social media in the modern world, where social media is a key factor in determining an app's success or failure. Companies that have Big Data technologies can determine how customers feel about their brand.

Questions such as: 

1. Do your delivery boys bring the food on time? 

2. Are your customers satisfied with the performance as a whole? 

3 Are your efforts having an impact? 

After looking at every business mentioned on every social media platform, including Facebook, Twitter, Instagram, and Linked In, you can find the answers to all of these questions. The food-tech sector is currently using this data to inform commercial decisions.

Conclusion

A large amount of data is available in Zomato, and this big data is not just a source of value but also could prove harmful if its vulnerabilities are not addressed on time. The Data Science team at zomato ensures that the security and privacy of user data are controlled and hacking attempts are prevented. Through various operational checks, they optimize the data flow while deriving insights and derive performance reports that help the research and product teams take business decisions quickly. Furthermore, this group also monitors anomalies in traffic which helps prevent hackers from entering via compromised accounts.

Data scientists are constantly breaking new ground, pushing the boundaries of their field, and contributing to our understanding of how the world works. Successful data scientists have spent their careers studying and applying their knowledge, specializing in a particular area driven by real-world problems or industry trends.

In this article, we'll discuss how to future-proof your career with data science and what skills you should strive to develop within your particular time horizon.

The first step is to understand what exactly data science is.

What is Data Science? 

Data science is the practice of using data to solve problems. It involves working with large sets of information and turning them into conclusions that can be used to improve business processes or make decisions. 

There are many different types of data scientists, including those who work with artificial intelligence and ML models and those who work with traditional statistics and mathematics. 

While this sounds like great news, it can also be overwhelming — especially if you're getting your foot in the door. With thousands of open positions for data scientists and countless companies looking to fill them, how can you stand out from the crowd? 

Here are some tips:

Learn to code and master the fundamentals 

Programming is an essential tool to get started as a data scientist. Python and R programming are the primary languages you should be proficient in, excelling at data analysis. You'll need to know how to analyze and visualize your data to make accurate decisions about which problems best suit your skillset.

2. Invest in training and education 

With new technologies developing every day, It's important to stay up to date with them. In other words, regardless of your position, you should create new skills and implement them. Get certified in R, Python, or other statistical languages like SAS or SPSS. You should also learn machine learning algorithms like decision trees and artificial neural networks to apply them more effectively in your day-to-day work tasks.

By taking India’s top data science course in Chennai, you can jumpstart your career as a data scientist and then continue your journey through certifications. 

3. Pick an area of specialization within your career path

Choosing your preferred domain to work as a data scientist is highly beneficial. This will enable you to become more valuable as an employee/consultant/etc. While also giving yourself an edge over other job applicants who may not have taken these same steps yet! 

4. Connect with other people in your industry who are working on similar projects as you

Expanding your network of data scientists will improve your chances of breaking into the profession. The best career possibilities are frequently not advertised on job sites. You can develop a portfolio and a personal brand by solving complex real-world problems and then apply for jobs based on those. So start networking using a platform like LinkedIn, Kaggle, contests, conferences, etc., to build a strong network. 

Data Science — Why is it important?

Data science is a fast-expanding and transforming field. In its infancy, data scientists are creating their own tools and ways to organize and analyze data more effectively. They are typically innovative when it comes to manipulating data and bringing to light what they notice amid all the noise. This is why data scientists are highly sought after by businesses of all sizes. Not everyone is cut out for this position, necessitating analytical solid and observational abilities.

Data science also includes a wide range of career options. This role is becoming increasingly important in every business, large or small, as the amount of data being collected grows. A career in data science can lead to many work opportunities. For example, you may work in the field of data science as a database manager or as a junior data scientist. You could also work in the field of data engineering as a database administrator or as an ETL engineer.

So many doors open to ensure you will never have a boring workday and allow you to shift gears from time to time. You will constantly be engaged and challenged in your work if you keep this in mind.

Data Science – Important Skills

In order to future-proof your career and be open to emerging opportunities, it is crucial to combine a strong foundation in the fundamentals of computer science with a general education in math and engineering. And perhaps most importantly, students should spend much more time building a "data mindset" so they are prepared to take advantage of the other areas where their skills will translate. Here are some of the popular skills for a data scientist: 

Basic knowledge of coding: Java/ Python is an added advantage

1. Data analysis and modeling

2. Intellectual curiosity

3. Critical thinking

4. Math and statistics

5. Problem-solving

6. Market understanding

7. Business acumen

8. Communication and Collaboration

9. Data Visualization and presentation skills

Data Science — Scope and Salary

Data scientist salaries are on the higher end of the spectrum, with a mean salary of about ₹10.3 Lakhs. With experience and constant upgrading the skills, the pay can go up to ₹ 25.8 Lakhs. 

However, this totally depends upon the type of industry and organization. The pay scale can be lower if the business is just getting started or is new, but it will eventually rise as the company develops. A data scientist would be more valuable to the organization because, similarly, experience grows with skills.

Data science-related employment is expected to grow, and it has been dubbed the "sexiest career of the 21st century" by analysts. Consistent growth in demand can be seen. Nearly every company aspires to have a machine learning division, and data scientists are going to be highly important.

Get Ready to Future-Proof your career

The future of data is bright. From businesses to academia, more and more industry experts realize the importance of data in solving challenges, making decisions, and envisioning what skills they need to hire. If you're considering a career in data science, equipping yourself with the proper knowledge and skills can be the difference between another average programmer and that sought-after unicorn who gives you an edge when you enter the job market. 

We all know that data is messy and difficult to manage. Data cleaning techniques are part of a data-driven approach to improve value for our customers, reduce costs and even increase revenue.  Cleaning up and managing the data in your business is a daily activity that can help boost performance, increase accuracy, and improve results. But how do you know whether your cleaning practices are effective? Where should you start, and which techniques should you use in your particular situation?

In this article, we will discuss some effective data-cleaning techniques that can be used to improve the quality of your business’s performance.

What is Data Cleaning?

Data cleansing is a process to improve the quality of data before it gets to your application and business. In other words, data cleansing is the process of cleaning dirty data. Data cleanup can be applied manually as well as automatically, depending on your purpose.

Importance of data cleaning

You can complete your analysis much more quickly if you start with clean data that are free of erroneous and inconsistent values. By completing this task ahead of time, you would save a lot of time. You could stop many errors by cleaning your data before using it. Your results won't be accurate if you use data with false values. Data purification and cleaning take up a lot more time for a data scientist than data analysis. 

Efficiency

Identifying data Quality

Accuracy

 Error Margin 

Consistency

Uniformity 

Effective Data Cleaning Techniques

The first step to cleaning your data is to understand what types of data you have. Once you know this, you can determine what types of tools are best suited for the job. 

You can also select specific values from within each row or column and then export them as text files to create a list of all the data elements that need cleaning up. This is useful when working with large amounts of information because it allows you to see exactly what needs fixing before moving forward with any further steps in order

1. Remove Duplicate Data

Duplicate data is a problem that can be solved easily. In fact, it can be done manually or automatically and the results are almost the same. The first step in removing duplicate data is to identify all duplicate records in your database. Next, you need to merge these records into one record. There are two different methods that you can use for merging records: 

Merge by Right-Clicking/Ctrl+Click

Merge by Using an Excel Add-In

A detailed explanation of data cleaning techniques can be found via the best data science course in Chennai, designed in collaboration with IBM.

 2. Remove irrelevant data

Any analysis you attempt to conduct will be complicated and slowed down by irrelevant data. Deciding what information is relevant and what is not is therefore necessary before you start your data cleaning. You don't have to include their email addresses, for example, if you are analyzing the age range of your customers.

You should also eliminate the following components because they don't add anything to your data:

URLs

HTML tags

Tracking codes

Personal Identifiable (PI)

Blank space between text

3. Remove Nulls

Nulls should be eliminated as well, as they can cause problems when they are used in arithmetic operations or comparisons. You can do this by using a unique index on the column containing the null values and using a WHERE clause to remove them from your data set.

4. Convert data types

The most typical type of data that needs to be converted when cleaning your data is a number. However, they must appear as numerals to be processed. Numbers are frequently imputed as text.

They are classified as a string if they appear as text, which prevents your analysis algorithms from solving mathematical equations on them.

Likewise, dates that are saved as text are accurate. Make them all numerals, please. For instance, you must change entries to read 09/24/2022, if they currently say September 24th, 2022.

5. Clear Formatting

Your information cannot be processed by machine learning models if it is heavily formatted. Different document formats are probably present if you are using data from a variety of sources. Your data may become muddled and inaccurate as a result.

To start over, you should eliminate any formatting that has been applied to your documents. This is typically not a challenging process; for instance, standardization functions are available in both Google Sheets and Excel.

6. Handle missing values

There would always be some information lacking. It's unavoidable. In order to keep your data accurate and clean, you should be aware of how to handle them. Your dataset might contain too many missing values in one particular column.

In that case, since there isn't enough information to work with, it would be prudent to remove the entire column.

Thus, you should never ignore missing values. 

If the missing value is completely removed, your data can now no longer contain insightful information. After all, there was a reason why you initially wanted to gather this information.

Hence, it might be preferable to fill in the missing data by doing the necessary research. You could use the word missing in its place if you have no idea what it is. You can enter a zero in the blank field if it is numerical.

However, you should remove the entire section if there are so many missing values that there isn't enough data to use.

7 Fix the errors 

You should obviously take care to rectify any errors in your data before using it. You might miss out on important data findings if you make mistakes as simple as typos. With something as simple as a quick spell check, some of these can be avoided.

You might miss out on communicating with your customers because of misspellings or extra punctuation in data like an email address. It might also cause you to send unsolicited emails to recipients who have not requested them.

Inconsistency in formatting is another type of error. To maintain a consistent standard currency, for instance, if you have a column of US dollar amounts, you must convert any other currency type into US dollars.

8. Language Translation 

You need everything to be in the same language to have reliable data.

Software used to analyze data typically uses monolingual Natural Language Processing (NLP) models, which are unable to process multiple languages. Therefore, you must translate everything into a single language.

Summary

To sum up, the best way to go about cleaning data is always dependent on the problem you are trying to solve. The time required for data cleaning will always depend on the data itself, and if any anomalies need to be resolved.

Our current technological world is powered by data. Consequently, data science, a developing field, is progressively raising its profile worldwide. Its influence spans many sectors, including big data, machine learning, and data processing.

It is a multi-sectoral field of study that depends on the comprehension and application of several academic, scientific, and analytical themes. It involves using numerous systems and methods, algorithms, and scientific approaches. Their viewpoints will be complemented by it primarily through the usage of data and the extraction of insights from both structured and unstructured data types.

Businesses employ it in a range of tasks. This application strives to integrate various subjects' theoretical and practical aspects, including informatics and technology, domain knowledge, mathematics, statistics, and computers. These domains include statistics, informatics, analysis, and related techniques and practices. It facilitates the analysis and comprehension of the concrete and graphic elements of the obtained data.

The use of data science and its impact across industries

In the simplest words, data science makes complex content more understandable and straightforward. Big data is now easily accessible and interpreted due to the multidisciplinary coalescence of the fundamental sciences. Because of this, people might work really hard to get the best outcomes. Big data's close connection to the rest of the world today makes it possible to replace older sectors' and companies' outdated business models with more modern ones. You should search for some of the top online data science institutes that can assist you in comprehending it if you are serious about pursuing a career in the field.

The intelligent application of data science applications benefits several industries, including marketing, finance and banking, retail or e-commerce, transportation, agriculture, healthcare and medicine, government agencies, and so on. Data-driven businesses are therefore given the capacity to make billions of dollars.  For further information, visit the data science course in Chennai, developed by industry experts along with 15+ real-world projects. 

Some of the most notable effects of data science in numerous disciplines include the following:

1. Process automation: It helps automate a variety of time-consuming, repetitive, and redundant processes and duties. By minimizing the human effort required for these processes, businesses may reroute their workers to work on more crucial tasks.

2. Performance Analysis: Through its uses, organizations may better understand the preferences, comments, and responses of their clients. Using risk modeling and in-the-moment analysis, they will spot areas for improvement and, consequently, the underlying problems impeding overall performance.

3. Quality Improvement: By analyzing, processing, and computing it, it has made it possible to increase the quality of company data. It makes it possible to develop higher-level planning, which leads to the production of better commodities. Consequently, all kinds of jobs have become more effective and pleasant over time due to the development of data-driven and human-driven behavior machines.

4. An exciting future for data science: All of the next digital revolutions are intimately related to the career path of data science. Since it affects our everyday lives, it is easy to see how swiftly it is evolving and how it will stay up with the rapidly changing technological landscape. We can see that the current data science situation is connected to several other significant factors. Think about the increase in data production brought on by the Internet of Things (IoT) development or the everyday data generated on social networking sites.

Final words!

Insurance companies have recently realized the impact of data science and analytics on their bottom line. They are now investing in technology that will help them reduce costs, increase revenue and improve customer satisfaction.In the insurance industry, data science and analytics play an important role in providing better customer service. These two terms provide information about a person or group of people to make decisions on what they need. Data science helps in understanding customer needs by collecting data from different sources and analyzing it using computer algorithms. Analytics helps determine what customers want most by analyzing their needs and behaviors. 5 Ways Data Science Is Changing The Insurance Sector

Insurance companies have recently realized the impact of data science and analytics on their bottom line. They are now investing in technology that will help them reduce costs, increase revenue and improve customer satisfaction.

In the insurance industry, data science and analytics play an important role in providing better customer service. These two terms provide information about a person or group of people to make decisions on what they need. Data science helps in understanding customer needs by collecting data from different sources and analyzing it using computer algorithms. Analytics helps determine what customers want most by analyzing their needs and behaviors.

Data science and analytics in Insurance 

The use of data science and analytics in Insurance can be divided into three categories: predictive modeling, descriptive, and prescriptive modeling. 

1. Insurers use predictive modeling to identify potential risks on actuarial grounds that may lead to loss or damage for their clients through statistical techniques such as regression analysis or neural networks. 

2. Descriptive modeling involves using historical data to describe past events (such as claims rates) or trends (such as claims patterns). 

3. Prescriptive modeling involves using predictive models to make recommendations about managing risks based on what happened in the past. For detailed understanding of these concepts, refer to the data science course in Chennai, accredited by IBM. 

4. Insurance is a complicated business, and it would be effective to use data science to improve the efficiency and profitability of the insurance industry. The following are some of the applications of data science applied in the insurance industry.

1. Automated Insurance Fraud Detection

Detecting fraud is not only necessary for avoiding financial losses but also a valuable contribution to society in general. Nonetheless, some insurers continue to depend only on the claims adjuster's gut instinct for fraud detection, resulting in unreasonably high expenses. With the emergence of big data and increasing access to third-party data to supplement investigations, it is time to shift away from the claims adjuster's gut feeling and toward a more data-driven approach, not to mention the enormous time savings and process optimization advantages.

Fraud detection tools identify fraudulent activities, suspicious relationships, and subtle behavioral patterns using various techniques such as text mining and image screening.

A constant stream of data must be provided to the algorithms in order for them to detect fraud automatically. This information is either from internal records of prior occurrences of fraudulent conduct or from external fraud pools that have been analyzed using sampling methods.

Predictive modeling methods are also used in this case for fraud analysis and filtering. Identifying linkages between suspicious actions aid in the detection of previously undiscovered fraud schemes. In the end, insurers who use these platforms will significantly reduce the number of false claims, resulting in huge savings

2. Healthcare Insurance 

The practice of health insurance is commonplace worldwide. The costs associated with a disease, an accident, a disability, or death are typically covered. Governments in many nations have been vocal supporters of healthcare insurance policies.

The influence of data analytics applications is unavoidable in the era of rapid digital information flows in this area. The market for healthcare analytics is expanding rapidly on a global scale. The insurance industry is under constant pressure to deliver better services while lowering costs.

For the healthcare insurance industry, various data are gathered, organized, processed, and transformed into insightful knowledge. These data include insurance claims data, membership and provider data, benefits and medical records, consumer and case data, internet data, etc. As a result, factors like cost savings, healthcare quality, fraud detection and prevention, and consumer engagement may all significantly improve.

1. Customer Lifetime Value (CLV)

Customers' lifetime value (CLV) is a complicated concept that expresses how valuable a customer is to a business as the difference between revenues earned and expenses incurred throughout the entire future relationship with the customer.

Customer behavior data is typically used to estimate the CLV and forecast the customer's profitability for the insurer. The behavior-based models are thus frequently used to predict cross-selling and retention.

Recency, the financial value of a customer to a business, and frequency are considered crucial variables when estimating future earnings. In order to create the prediction, the algorithms gather and process all the data.

This makes it possible to predict whether customers will maintain their policies or cancel them based on their behavior and attitudes. The CLV prediction may also help develop marketing strategies because it puts customer insights at your disposal.

2. Automated Risk Assessment

An automated risk assessment tool enables you to get to know your customer (KYC), do thorough customer due diligence, and evaluate the risk a customer needs to cover. Adopting risk assessment techniques in the insurance sector ensures risk prediction and reduces it to the bare minimum to reduce losses.

Proper risk analysis takes time for insurers as you have to consider what to cover and who you are insuring.

An automated risk analysis tool allows you to gain real-time access to internal and external data. The risk assessment tool assists you in making quick decisions based on your appetite for risk and data. This accelerates your digitalization and improves the customer experience because the consumer can determine whether or not the insurance policy has already been authorized in seconds.

3. Claims Automation

Claims automation systems offer one of the biggest options for insurers to position themselves from the market and engage their customers for a lifetime.

Claims automation technologies can assist insurers in optimizing claim workflows from the initial report of loss through settlement, saving time and building stronger relationships with policyholders. 

P&C insurers place a high priority on determining damage estimates for a specific claim. The evaluation is frequently based on information gleaned from various sources, including emails and other paper-based or PDF formats, such as damage predictions or police reports. In such instances, an AI algorithm may be trained to extract the relevant data from each document reliably. 

Once the claim information is structured, it can be analyzed using robotic process automation (RPA) and artificial intelligence (AI). It decides whether or not the insured has enough coverage. If a claim is evaluated and determined to be low risk, it may be allowed for instant payout or triaged to an adjuster if it is more complex. If a high-risk claim is found, it is forwarded to a special investigative unit for additional review.

Data Analytics in Insurance – The Future Implication

The insurance industry is adopting data-driven models and machine learning techniques to better manage risk and to create products by analyzing massive amounts of data. Overall, the insurance industry is one of the best industries for using predictive analytics and data science as there is plenty of information available about insurance history. This will help improve their profits, reduce wastage in each operation, and increase their client's success rate by getting predictions about the right customers using analytical tools like logistic regressions and decision trees.

Data science has gained popularity in making organizations flourish and deliver value. 

It has been in the limelight over the past few years and has risen to prominence as a key technology within the HR industry. Among all the sectors, HR analytics can find consistent application in solving problems faced by companies across the globe. This is based on the understanding that many reasons for employee attrition affect companies every year. The basic idea behind data collection and analysis centers around identifying the best practices for HR analytics to combat this challenge.

Data analytics can offer opportunities to improve workforce management by creating personalized and effective training strategies, leveraging onboarding programs to optimize employee recruiting efforts and better managing employee retention metrics. Data science can also be used to provide more precise information about current and past employee engagements, including when and why an employee leaves the company for specialties or areas of expertise that can be addressed in future training initiatives or recruitment decisions.

Overview of Data Science 

Data science is a set of techniques and tools that are used to collect, analyze, and interpret data. This data can be used to gain insight into problems or opportunities to answer questions or make decisions. Data science is also used to predict future outcomes of certain events based on past events.

HR analytics uses data science to help companies with their HR practices. These practices include hiring, training, performance management, compensation and benefits, etc. Data scientists use their skills in programming languages like Python, R and SQL and machine learning techniques such as neural networks and decision trees which can be learnt from India’s best Data Science course in Chennai, developed in partnership with IBM.

What Is HR Analytics?

HR analytics uses data to improve your company's operations and increase its bottom line. It gives you insights into everything from employee satisfaction to turnover rates to productivity trends, allowing you to make informed decisions about how best to run your business.

Application of HR analytics

Employee training is another topic where data science can help improve existing evaluations. For example, data analysis can determine which courses have previously been shown to be most beneficial to employees in later performance reviews. This crucial workflow can also be made better through the use of technology and analytics. Recruitment is another field that makes use of data science to empower hiring managers to define their ideal candidate through applicant tracking systems, social networking sites, market analysis, and applicant review assessments. This will result in a more appreciable recruitment process for both the hiring manager and applicants.

People analytics, workforce analytics, and talent analytics are all covered in HR analytics. These analytics components serve different human resource activities by automating and making them more cost-effective over time.

1. Attendance

2. Employee surveys

3. Salary and remuneration

4. Appraisal and Promotion 

5. Work history of the employee

6. Past database of employees

This information is gathered and simplified for improved strategic decision-making and human resource planning. Data may also aid in greater alignment and coordination among the organization's various departments. Furthermore, the HR software may be upgraded to deal with employee and manager issues.

The top applications of data science in HR are as follows;

1. Workforce analytics  

Data science, by thoroughly analyzing the corporate workforce, enables HR management professionals to grasp the major demands of their firm better and properly monitor critical parameters. HR professionals might locate and hire suitable professionals quicker and directly influence a company's overall performance by properly knowing which candidate's traits are the most beneficial to the company's objectives.

2. Talent analytics 

According to Deloitte's 2017 Global Human Capital Trends Report, 90% of HR professionals desire to overhaul their whole organizational paradigm. This comprises leadership, diverse management methods, and increasing possibilities for applicants to establish successful careers and jobs. 

That's where data science can be beneficial. It facilitates the smart structuring of convenient talents, improving current training programs, evaluating attrition, and perfecting recruitment methods to ensure a high level of staff retention. Data science can drastically revolutionize the whole HR sector by eliminating outdated methods of assessing HR metrics and providing firms with insights they would never have obtained from traditional surveys or candidate interviews.

3. Employee Performance  

Analyzing and measuring employee performance is critical for obtaining a more accurate employee assessment report. Greater analytics may help organizations retain talented and experienced personnel while also providing better employee growth. Analytics may assist in identifying the organization's best and underperforming performers, determining the average length of employment, motivating elements for employees, and so on. This will improve career advancement decisions, enhance employee happiness, identify leadership skills, and motivate them to improve overall performance. As a result, analyzing employee performance will enable the firm to enhance its overall ROI and identify prospective leaders.

4. Training and development 

Many organizations confront the challenge of a skills mismatch. Thus, most employees lack the necessary skills to perform various tasks. In-house training is also in high demand because there is always a shortage of adequate skills in entry-level professions. HR analytics can assist in more efficiently bridging that gap. It can aid in collecting data about employees and their level of expertise to determine how they can be taught. Analytics may also assist in directing resources to the appropriate locations for staff training and in reviewing the overall development process. This will help companies in making their personnel more qualified and competent, which will not only improve corporate performance but also provide a competitive advantage.

5. Employee Retention 

One significant benefit of adopting HR Analytics and having a data scientist on the HR team is the potential to identify why people leave and remain. HR can essentially forecast (and hence avoid) employee attrition by evaluating data from techniques like employee satisfaction surveys, team evaluations, social media, and leave and stay interviews, among others.

Data science specialists could also assist the HR team in identifying issues that contribute to low employee engagement and chances to increase engagement, resulting in a more successful workforce. 

For example, suppose an organization has been experiencing high turnover rates among salespeople. In that case, they could use predictive analytics tools like machine learning algorithms to find out why this is happening—and design strategies to prevent it from happening again.

Summary 

As you can see, the world is rapidly moving towards digitalization, which has revolutionized several industries in many ways. HR Analytics is one such industry that has undergone a lot of changes, especially with the wide range of advanced data science techniques present to help businesses take important, data-driven decisions regarding their staff requirements.

Google Colaboratory and Jupyter Notebook are powerful tools for collaboration when it comes to data science. They are user-friendly, with graphical platforms that allow you to create and run code without needing expert programming skills or installing software libraries on your computer. 

The differences in environment between Google Colab and Jupyter Notebook are also what make them unique from one another. While both can be used to help host research, it's essential to understand the various specifications of each tool before choosing which is better for your purposes. Much of that decision will likely come from the format and content you'll be working with.

In this article, I will tell you which is better for data science by comparing some of its key features and functionality. So let's get started! 

What is Google Colab?

Google Colab is totally free and open source; you can use it without any charges on your cloud account. It is best used for smaller computational tasks for which you won't need the full power of your local machine. Using the Google Cloud Platform, we can access a wide array of Google-related services which we can use to solve problems through computing.

Features:

1. Google Colab is a collaborative software development platform that offers an easy way to develop applications using a variety of coding languages, including python. Google Colab helps users collaborate on code and content management via the Google Cloud Platform (GCP). 

2. It has a built-in debugger, which allows you to see what code is running in the background without slowing down your program. This can be useful if you've written some code and it's not working correctly or if you need to debug something before actually running the program in production mode.

3. It has its own version control system called GitLab CEHQ, which lets you store all of your work on GitHub and keep track of changes made over time, as well as changes made by other users. You can even use this tool for collaboration with others who may not be familiar with GitHub CEHQ itself!

Visit the data science course in Chennai to learn more about how to use Google Colab and practice your codes here! 

What is a Jupyter Notebook?Features:

Jupyter notebook is an interactive computing platform for the web that is free, open-source, and was developed as a spinoff of IPython. Jupyter Notebook is a web application that enables users to create and share computation documents with one another.

1. Jupyter Notebooks are generally written in python, which makes them easy to read and edit by non-techies. 

2. They can also be run on an individual's laptop or mobile device, making them ideal for data scientists working on various machines.

A Quick Comparison – Google Colab or Jupyter Notebook 

If you're not sure about which tool to choose for your projects, here is a detailed comparison between Google Colab vs Jupyter Notebook:

1. Google Colab and Jupyter Notebook are similar tools that offer ways to use the Python programming language. However, in Colab, you can not install any module; you should have python installed on your Google machine. 

2. Jupyter is a web-based interface that allows editing, sharing, and executing documents with code cells. 

3. Colab and Jupyter notebooks provide a nice user interface to write code, plot things or insert interactive plots, share online and collaborate with others. 

4. The Jupyter notebook was implemented in JavaScript, and the code is available on GitHub.

5. A Colab notebook can only run for 12 hours at a time. Idle sessions end far earlier than that.

6. Colab does not save any information about the environment (e.g. installed packages, files, etc.) Every new session necessitates a fresh set-up of the environment.

7. Colab has built-in version control and commenting features. While with Jupyter Notebooks, we must use GitHub/Bitbucket and ReviewNB to accomplish the same.

8. Even with premium Colab Pro/Pro+ subscriptions, there is no resource guarantee

Reasons why Jupyter is Better – (My Opinion) Final Words!

What I like most about Colab is its simplicity and ease of use. The best part is, you don't even have to install or configure anything. Here, you are spending most of your time on documentation, so if the tool itself adds complexities, it defeats the purpose. On the other hand, Jupyter Notebook comes with exciting features like LaTeX rendering and Markdown editing support.

1. First, the Jupyter Notebook has an easier learning curve. It allows you to write code directly in the browser and then integrates it with other tools and services like MATLAB or Python. This makes it easy to start using a new language or tool without learning all the ins and outs of that language.

2. Jupyter Notebook allows users to share their work with others more easily than Google Colab does. If you're working on a project with someone else, you can click "Share" and send them your notebook where they can see what you've done so far. They can also comment if they want or edit themselves if they're so inclined

3. Google Colab has many additional features that make it more powerful than the Jupyter notebook (like live coding). But, they are also more challenging to learn because they require additional software (like MATLAB).

We are now living in a time of automotive innovation as technology is evolving faster than we could have ever imagined. Even though the pandemic has resulted in complications for many companies, especially in the automotive industry, new technologies such as DS, AI, and ML are still in high demand and continue to advance. The automotive sector has seen a significant transformation over the last decade. Nonetheless, you can still consider pursuing your career in this field and get a high-paying job. 

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This blog will help you how you can secure your DS career in the Automotive sector. But first, let's dive briefly into How DS impacts this industry.  

Data Science has always been at the core of transport product and production innovation. For instance, from being a mechanical machine that took us from A to B has now evolved into an intelligent machine that moves on its own! Interesting right?! Thanks to advanced technologies such as AI, ML, and Big data, that provide them with their own brain!

McKinsey estimates that a connected car generates about 25 GB of information every hour. Implementing this information can potentially change the auto industry's outlook. With that being said, DS, ML, and AI help in improving efficiencies at every level of the auto manufacturing processes, from research to developing new innovative products to better-serving customers and marketing processes.

What is the role of an automotive data scientist?

 Data scientists ensure to make high-quality vehicles. They closely examine the entire process from testing parts, suppliers, and data. Their primary tasks include

1. Analyzing the suppliers' financial performance

2. Estimating their ability to deliver on time on performance-based 

3. Verifying the economic conditions of suppliers' locations using econometrics with regression.

For further information on the use of advanced data science and AI in the automotive industry, check out the Artificial intelligence course in Chennai. 

How is data science used in the automotive industry?

With the rise in massive amounts of information, Data science impacts various stages in the automotive lifecycle. Let's dive deeper into how DS is applied in everyday automotive processes: 

Manufacturing:

Manufacturing is the primary core step in developing auto machines. It is the production of goods with the help of equipment, machines, and tools. When it comes to manufacturing, AI-based techniques help automakers generate and manage schedules more accurately, enhance safety testing, and also detect issues in produced components. Additionally, predictive maintenance leads to a cost-effective and efficient manufacturing process.

Supply Chain Management: 

 Global supply chains are powering the auto sector. Supply Chain Management implies implementing software systems and management tactics to secure every step of the manufacturing process, starting from customer orders to ending with product delivery. 

By using a machine learning-driven approach, it is possible to analyze large data sets to rank suppliers, credit ratings, and evaluations. This enables manufacturers to gain greater control over their supply chain, including logistics and management.

How to manage the supply chain in the automotive industry in simple steps:

Using technologically capable software platforms to collect and organize information and analyze trends 

Maintaining the highest possible standards in all stages

Ensuring all of your variables and fixed costs are justified to maintain and improve your margins

Research and Development :

In the coming days, AI will play a significant role in R&D productivity, preventing expensive R&D initiatives from being completely realized. 

The sensor gathers enormous amounts of data from users, saving vast time and energy and allowing them to focus on projects with incredible promise. This extracted data can bring insight into the vehicle's usage, environmental consumption, and vehicular emission. Consequently, this will help in utilizing it for the regulatory and benefitting marketing trends. 

Marketing and Finance: 

In addition to benefiting the vast areas of the core business, Data Science and data analytics can also be used in other lines of business, including Marketing, Sales, and Finance, to introduce efficiencies that significantly impact automation in the bottom line. As in marketing, DS predicts customer movements and churns. It also improves the customer's post-purchase experience and improves the best quality. Some of the other use cases include Product Development, Sustainability, and city solutions. 

How does big data benefit the industry?

As per the reports, the automotive industry has a vast amount of big data and is on the verge of rising. What actually is big data?

Big data analytics frames the premise of several applications as vast amounts of data are being collected through remote sensors. These are then probed and used to change the auto business, support mechanization, and boost automation. It is said that Data Analysis will most likely be the main impetus behind vehicle technology and progress in the near future.

The most crucial benefit is that the utilization of big data leads to a huge expense reduction for automakers by helping them investigate new strategies and using materials that give remarkable benefits.

What are the data science jobs you can do in the automobile industry?

From AI to big data, Independent self-driving cars to applications to sensors, this field is experiencing endless opportunities for unexpectedly crossing information. Apart from Data Scientist, you can apply for other job roles: 

Data Analyst

Data Engineer

Data Architect 

ML Engineer

Business Intelligence Developer

Business Intelligence Analyst

There are more than 14000 auto manufacturing companies in India where you can find multiple job opportunities. Some of them are  Ford, Maruti Suzuki, Hyundai Motors, Tata Motors, and many others. 

Where can you apply for data science jobs?

As the amount of data is expanding day-to-day, several automotive companies are in shortage of skilled data scientists. These are some of the companies hiring data scientists, along with their average pay scale:

1. Ford Motors (2-9 yrs experience): 13.9 LPA

2. Mahindra (2-7 yrs experience): 9.4 LPA

3. Mercedes Benz (2-7 yrs experience): 13.3LPA

4. Panasonic offers an average pay of 32 LPA

5. Maruti Suzuki (4-6 yrs experience): 13.8 LPA

Data science salary in the automotive industry :

Now let us look at the salary range of data scientists on different scales.

Generally, the salary of a data scientist depends on experience, skills, and industry employment. As per the report, the automobile sector is considered the second top-paying industry for data scientists. 

The annual salary of a data scientist in the auto sector in India ranges from Rs. 5 Lakhs to Rs.32 Lakhs, with an average annual salary of INR 13 Lakhs, as mentioned by the Ambition box. However, It varies according to your experience level and skills. 

Hot data science project ideas to optimize your portfolio

It is essential to showcase your skills and talents in your portfolio to qualify for the position you're applying for. Presenting your projects is the most powerful way to accomplish this. I'll list down some of the Automotive domain-based projects for you to gain proficiency in the field.

The revenue forecast for Vehicle Financing 

Modeling and valuation of leasing contracts of Vehicles

Interactive analysis for used car warranties

Final words

A big component of gaining a career in the data science profession is learning new languages and data science technologies. Python is a popular tool and programming language in the data science field. It could be helpful to become familiar with the many Python packages available while working on data science projects.

In the field of data science, the Matplotlib program is frequently used to produce unique graphics and visualizations that highlight the findings of a data research project. These are just a handful of the numerous benefits of adding this Python module to your collection of data science tools.

In this essay, we shall learn about Matplotlib and its significance in data science. Let's get going.

Matplotlib: A Quick Overview

Python's Matplotlib library is commonly used to create two-dimensional graphs and other types of data visualizations and models. The NumPy library, also used in Python to carry out mathematical calculations, was supplemented by Matplotlib in 2003.

You may quickly produce visually appealing results from your statistical procedures and investigations with Matplotlib. Like many other open-source data science tools, the Matplotlib package benefits from the work of a thriving community of Python developers and users.

On the blog with the same name, both professionals and students in the field of data science may discover articles and examples illustrating how they can utilize Matplotlib in their work.

Matplotlib's Adoption by the Data Science Community

Many of Matplotlib's useful data science functions are related to data visualization or modeling, as it is a frequently used tool for plotting and charting graphs. The list below includes a few of this versatile Python package's numerous reporting and data storytelling uses.

Making Charts and Graphs

Before attempting to create data visualizations using the Matplotlib software, one must be familiar with the process of plotting charts and graphs. Making a graph or arranging data points or variables along an x-y axis to show their association is known as plotting in the field of data science.

Matplotlib's Plot function is required to create any graphs or visualizations specified by the library. Data scientists who want to perform a visual analysis of their data benefit from plotting since it allows them to study correlations that emerge within the dataset and make deductions based on factors like slope and clustering.

Each data visualization style has a related method or function you can access after importing Matplotlib into your favorite Python environment. To chart data, Matplotlib offers a variety of helpful ways, such as "plt.hst()" for creating histograms, "plt.bar()" for creating bar charts, and "plt.pie()" for creating pie charts.

The syntax of Matplotlib will be familiar and easy to understand for data scientists proficient in statistical analysis and common mathematical operations. For further information, refer to the data science course in Chennai, designed to meet the demands of industry. 

Thanks to these particular features, data scientists may experiment with their data using a range of graphs and visualizations to see which best communicates their results. 

Data Presentation in Visual Form

You may quickly create a variety of graphs, charts, and other visual representations of data with Matplotlib. The Matplotlib toolkit includes tools for making bar charts, pie charts, and other diagrams to help data scientists graphically describe their results.

Draw a set of points along an x-y axis and link them with a line to make a line plot.

Scatter plots, which are similar to line plots but lack the connecting line, display the placement of several data points on a graph (which is more common among a dataset that has more variability).

In a histogram, bars of differing heights are stacked on top of one another to show the data distribution.

In addition to traditional visualizations, Matplotlib offers a broad range of innovative graphing alternatives. Pie charts and box plots are two forms of graphs that might make you stand out to your viewers.

The common pie chart is beneficial for communicating the outcomes of a data analysis effort focused on comparing portions.

Graphics, animations, and images

The Matplotlib package has tools for altering the colors, creating animations, and labeling the axes of your graphs, among other graph customization options.

You may choose the best color scheme for presenting your data or artwork using the colormaps included in the library. After you've made changes to your graphs and pictures, Matplotlib allows you to add motion.

These animations may be used to create two-dimensional and three-dimensional graphics, enhance the interactivity of any data visualization and even show modifications or updates to an ongoing data analysis project.

Because of its adaptability as a visualization and model-building tool, Matplotlib may also be used to produce other common visuals and images in the data science industry.

By creating graphics like heat maps, data scientists may visualize data on particular demography, healthcare trends, or even the weather and natural disasters.

Given that its output can be incorporated into a broad range of platforms and applications, Matplotlib is a helpful tool for creating, sharing, and displaying visual representations of data.

Last Words

Big Data has become a new field of research and application in the last few years. As Data volume, velocity, variety, and value keep growing exponentially; we must ensure we can handle them all effectively. This article will provide you with an overview of big data architecture.

What is Big Data Architecture? 

Big data architecture is the process of designing, developing, and deploying solutions that utilize a wide range of data sources. It's essential to understand how to create a big data architecture to use your data in the most efficient way possible. 

But why is it so important? 

Suppose you're analyzing something that has nothing to do with statistics or machine learning, like how many people buy one specific product at a particular retailer each year. Well, you'll need an entirely different type of architecture than if you were analyzing something like clickstreams worldwide!

Learn more about big data techniques in a data analytics course in Chennai. 

1. Data sources — These are the data sources that your business uses to generate insights and make decisions. For example, if you own a car repair shop, you may access vehicle information such as mileage, when it was purchased or serviced, and its maintenance history. This information would be considered a "source" of information because it's used by the business itself to make important decisions about how people should be treated when they visit your store for repairs.

2. Data stores — These are physical locations where your organization stores its data. Today's most common database organizations use relational databases such as MySQL (MySQL) or PostgreSQL (PostgreSQL). Others include NoSQL databases such as MongoDB (MongoDB), Cassandra (Cassandra), Couchbase (Couchbase), Redis (Redis), etc.

3. Data processing pipelines: This refers specifically to how you use these different types of

There are three major categories of big data architecture:

Distributed Storage 

This type of architecture uses multiple servers to store the data. It provides fault tolerance and high availability because it distributes the load across multiple servers, which can be located in different locations. However, this approach can be costly due to the additional hardware costs involved with establishing other servers.

2. Hierarchical Storage

This type of architecture uses one or more central servers to store the information and then distributes queries through these servers in a round-robin fashion until they reach their destination server. This approach is often used when extensive datasets must be searched through without having access to each individual record (for example, when searching for a specific piece of information within a database).3. Columnar Storage

This type of architecture stores each record as a single flat file rather than using relational databases like MySQL or Oracle's In Big data architecture is a framework for designing an organization's data-centric information architecture. Big data architecture aims to establish a system that can efficiently handle the processing and storage of large amounts of data while also improving the overall business value.

Benefits of big data architecture

High-Performance parallel computing 

Big data architectures use parallel computing, wherein multiprocessor servers conduct several calculations simultaneously to accelerate the process. By parallelizing large data sets on multiprocessor computers, large data sets can be processed quickly. Part of the job can be completed concurrently.

Elastic scalability 

Big Data architectures allow for horizontal scaling, which enables the environment to be adapted to the magnitude of the workloads.

Big data solutions are typically run in the cloud, where you only pay for the processing and storage power you use.

Freedom of Choice

Big Data architectures can use various commercially available platforms and products, including Apache technologies, MongoDB Atlas, and Azure-managed services. You can choose the best combination of solutions for your unique workloads, installed systems, and IT expertise levels to get the best outcome.

The ability to interoperate with other systems 

To build integrated platforms for various workloads, you can leverage Big Data architecture components for IoT processing, BI, and analytics workflows.

Applications of big data architectures

Different big data architecture layers

The four logical layers that perform the four fundamental activities make up most of the big data analytics architecture components. The layers serve only as an analytic organization tool for the architecture's parts.

1. Big Data source layer – The sources and formats of the data that can be analyzed will differ. The format could be structured, unstructured, or semi-structured; the speed of data arrival and delivery will vary depending on the source; the method of data collection may be direct or through data providers; batch mode or real-time; and the location of the data source may be internal or external to the organization.

2. Data massaging and storage layer — This layer gathers information from the data sources, transforms it, and stores it in a format that data analytics programs can use. Governance policies and compliance standards generally determine the most appropriate storage format for various data types.

3. Analysis layer – To gain insights from the data, it collects the data from the data massaging and storage layer (or straight from the data source).

4. Consumption layer – This layer accepts the output from the analysis layer and presents it to the appropriate output layer. The output's consumers could be people, business processes, visualization software, or services.

Applications of big data architectures

Using and implementing big data applications is vital to big data architecture. In particular, the following big data applications are used and applied by the big data architecture:

. Due to its data ingestion process and data lake storage, the big data architecture's structure enables the deletion of sensitive data from the beginning.

A big data architecture involving batch or real-time ingests data in both formats. There is a regular schedule and frequency for batch processing. 

Data from the table is divided using SQL, U-SQL, or Hive queries. By splitting the tables, the query performance is enhanced. Since data files can be segmented, the ingestion process and job scheduling for batch data are more straightforward.

Distributed batch files can be further divided using parallelism and quicker work times. Workload allocation across processing units is also employed.

The static batch files are built and saved in further-splittable file formats. The formats used to create and store the static batch files might be further divided. The Hadoop Distributed File System (HDFS) can process files simultaneously across hundreds of nodes, reducing job times over time. The Hadoop Distributed File System (HDFS) may group hundreds of nodes and process files in parallel, thus reducing job times.

Conclusion

It is important to understand the different components of big data architecture and how each of the components can impact a big data strategy. With a proper understanding of big data architecture, companies can prepare to handle structured and unstructured data. It helps them make strategic decisions about what actions they should take with those data sets.

How Data Science Can Change The World

Data scientists worldwide are extremely beneficial, and Big Data would not exist without the thoughts and efforts of these experts who employ technology to generate useful data. The value of a data scientist who understands how to turn massive amounts of information into lots of data for feed has increased as more organizations accept the concept of big data and see its possibilities.

Data processing involves a wide range of values, which is where a data scientist comes in. Most people still don't understand what a data scientist does or why a firm needs one, even though many have heard that data science is a formidable profession and that data scientists are superheroes that assist every part of today's society. Any available data scientist has received advanced programming, arithmetic, and analysis training. They have never-ending experience that includes data collection, data extraction, and management training. To become a certified data scientist and analyst, join the data analytics course in Chennai. 

Every data scientist must be smart and offer a strategy for the firm's future objectives by ensuring that the company supports its efforts. Through various measuring activities, data scientists use the institution's data to improve performance throughout the entire organization.

An organization's data is handled by a data scientist, who then recommends and prescribes specific instructions that will aid in the expansion of the entire business and engage every available employee for increased productivity. A data scientist's duties include ensuring that the employees are treated fairly and accepting the company's production factor. They use the system to extract data and motivate action in a way that makes everyone in the company successful.

The key challenges can now be the members' main emphasis after they better understand the production procedures. Data scientists interact with the company's system, questioning and making assumptions solely to create new adjustments to the calculation algorithm. As part of their duties, they must consistently increase the benefit that comes from data collection.

All that Google has shown us is that a lot of businesses have a source of customer information that is being recorded, but if the information isn't used properly, the data isn't useful. The capacity to use available data, which might not be relevant on its own, and integrate it with additional data to create a platform from which the business can thoroughly understand its clients, is one of the numerous benefits of data science.

Making clear decisions and putting those changes into action are the battles here. YouWhat about the opposite side of the war, you might be asking. It is crucial to know how such adjustments have affected the company's development." A data scientist is crucial in this situation. It merits having someone who can assess all of the crucial factors and guarantee the candidates' success in obtaining a data science certification.

HR professionals spend their days hiring new employees, but data science is changing this. Data science professionals can sort through mountains of data using the necessary information found in social networks, databases, and search engines, and all of this data is only useful in identifying the job prospects that best suit the firm's demands.

Data scientists' work involves retrieving the vast amounts of data that are currently at their disposal, as well as reports, job applications, even more sophisticated data, and games that are simple to analyze. Your team can use data science to create quicker and more appealing decisions.

Data science is advantageous to any organization that uses its data appropriately. It is beneficial to many businesses, from analysis and ideas across workflows and employing new applicants to helping other members make better judgments.