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Category: Management

Analytics & Sports – How Data Analysts Be Asking in The Glory in The Sports

Posted on by Imarticus Learning

Sports have seen their fair share of analysts and pundits ever since the first game was played. Traditionally, the analysis made by these pundits and analysts is based on hunches rather than concrete mathematical and statistical foundations. Even if they tried to factor in the statistics, there is no way that a human brain would calculate the entire complex, behind the scene calculation, which goes into a typical mathematical or statistical model.

So, it was thought that data analytics in the sector of sports had reached saturation. It all changed with Machine Learning and Artificial Intelligence.

In case you are not aware, machine learning and artificial intelligence are the fields that employ complex statistical models with a colossal amount of data to analyze the data, derive insights, and make predictions from it. So, when these advanced modeling techniques are fed the data generated from sports, we derive more accurate results, wisdom, and forecasts than all the pundits and legacy sports analysts.

Let us now see precisely what makes data analysis so lucrative in this sector.

How is sports analytics changing the paradigm?

There are only two ways in which sports analysis is being used in today’s paradigm. One is when the sports teams ask for data analytics, and the second is when it is used for sports betting.

data analytics certification course in IndiaThe first step of the Data Analytics course in sports involves mining various data like stats of any individual player, match conditions, and the teams’ recent performances. Then, all the information is passed down to Data Analysts.

It is their job to run machine learning models on this data to generate new insights. Now, these insights could be as simple as whether the team would win the next match or not.

To something more specific like, will Ronaldo be able to score his third goal with his left foot in the 69th minute?

Analytics is not limited to in-game events. By analysis of the data generated by the fans, the team would be able to cater to their needs better and thus improve their following. The sports team can also use data analysis to enhance merch sales.

How are big teams using data analytics?

While there are many examples available out there, we choose the ones from football.

Manchester United: We all have seen the Red Devils wear the AON branded shirts for a long time now. ManU has been using AON’s data analysis to stay ahead of the curve.

Real Madrid: This might come as a surprise to many, but Real Madrid has partnered with Microsoft (the Azure platform owner) to provide the Madridistas with all the analytics they need. They use Microsoft services to plan out the training, performance, and even fitness regimes of their players.

Conclusion:

There are a lot of apparent benefits when it comes to using data analytics training in the paradigm of sports. We have barely touched the surface here. However, we hope that we were able to provide you with some new information about data analysis in sports today.

Explainable AI: Escaping The Black Box of AI and Machine Learning

Posted on by Imarticus Learning

With the introduction of machine learning, the vertices of Artificial Intelligence (AI) developed manifold and established their presence across multiple industries. Machine learning helps understand an entity and its behaviors through interpretations and detections of patterns. It has endless potential. But its difficulty is in forming a decision in the first place through a machine learning algorithm.

artificial intelligence and machine learning coursesThere are often concerns about the reliability of machine learning models because of the questions about processes adopted to arrive at an anonymous decision. AI and Machine learning courses help in comprehending extensive data through intelligent insights.

It is useful in applications like weather forecasting, fraud detection, etc. But there is a crucial requirement to understand the processes of ML because it can form decisions using insufficient, wrong, or biased information.

This is where Explainable AI comes into the picture. It is the bridge between the ML Black Box and AI. Experienced AI is a model that explains the logic, goals, and responsible decisive process behind a result to make it understandable to humans.

As per reports by Science Direct, certain models of AI developed early in the process were easy to interpret since they had a certain amount of observability and clarity in their processes. However, with the advent of complicated decision systems like Deep Neutral Network (DNN), the process has become more difficult.

The success of DNN models is a result of productive ML models and their parametric space. It comprises muliple parameters that result in making DNN a black-box model too complicated for users. The search for an understanding of how this mechanism works is at the other end of the black-box model.

A machine learning course makes the process a lot easier. As the need for transparency is rising, the information utilized in ML is no longer justifiable, as it does not provide any detailed explanations for their behavior. Explainable AI along with ML helps in addressing the partial innate of AI. These biases are detrimental in industries like healthcare, law, and recruitment.

Explainable AI consists of three basic core concepts, which are:

  1. Inspection
  2. Accurate predictions
  3. Traceability

Accurate predictions refer to the process of explanation of models about the results and conclusions reached that enhance decision understanding, and trust from users. The traceability factors help humans to intervene in the decision-making of AI and control their functioning in case of need. Because of these features, explainable AI is becoming more and more important these days. A machine learning career is on the rise. In recent predictions from Forrester, it was reported that 45% of AI decision-makers find trusting an AI system is very demanding.

To assist developers to understand ML and explainable AI in detail, IBM researchers open-sourced AI Explainability 360. Google also announced an advanced explainable AI tool. The field of explainable AI is growing. And with it, it will bring enhanced explainability, mitigation of biases, and greater results for every industry.

How Artificial Intelligence Training Can Be Helpful For Any Voice Content Purpose?

Posted on by Imarticus Learning

Introduction

The technological change and digitization that is taking place across the globe are being accelerated by the day. Growth in Innovations and Inventions in all the major domains is Multi-fold. This major chunk of this growth can be attributed to the advances that are being taken in Artificial Intelligence. Artificial Intelligence had contributed to almost all the major domains in some or another way.

Artificial Intelligence as a domain is very comprehensive in both depth and breadth. Some of the major domains that comprise artificial intelligence are Neural Networks, Fuzzy Logic, Expert Systems, Natural Language Processing, Robotics, etc., In these domains, one of the fastest-growing domains is Natural Language Processing.

Natural Language Processing

The ability to understand and read human language through artificial intelligence is Natural language processing (NLP). NLP can be classified in terms of Text, Voice, and Audio. In all three, voice recognition is the fastest growing and widely known through various voice assistants in our smartphones.

Not only on the consumer side but also there are many applications on the enterprise and industry domains. Voice recognition/speech recognition is used for many purposes such as customer insights, in meetings, Security features, etc.,

Speech Recognition and its multiple applications:

Speech recognition is the Comprehension of the voice by the Computer to perform a task. It involves various modules such as speech to text, text to speech, Speech processing, and many more. Speech recognition can be found in many applications such as

  • Car Navigation
  • Navigation and Control such as GPS
  • Voice Assistants

Considering the advantages let’s consider how Artificial Intelligence Training in Speech Recognition/Voice recognition will help us to pursue a career in Artificial Intelligence.

 Artificial Intelligence Training

To Pursue natural language processing, there must be a holistic knowledge of Artificial Intelligence. Artificial Intelligence is one of the most important skills for any high-paying job. To have a holistic knowledge of artificial intelligence some key topics are to be concentrated upon such as

  • Python and Statistics
  • Data Analysis and Python
  • Machine Learning
  • Deep learning
  • Natural Language Processing
  • Computer Vision

To have a successful career in Artificial Intelligence there should be a basic understanding of python, TensorFlow, and other similar technologies. Though not all the domains are equally important, if an individual prefers to have a career in natural language processing then knowing other domains listed above will complement.

Skills Required to become an NLP Expert

Skills like Fundamentals in Machine Learning such as regression, probability estimation, data integration can be learned through training in Artificial Intelligence. In addition to this, there are additional skills that help an individual to become an NLP Expert. Skills such as parts of speech, Sentence Fragmentation, Information Extraction are needed from the linguistics point of view.

When seen from the programming side, NLP research scientists should be well equipped with at least one of python, java, ruby as a known language. Also, knowledge of probability & statistics and Recursive Neural Networking are essential for NLP. Artificial Intelligence Training will develop all these necessary skills

Prospects of Artificial Intelligence and Natural language processing

Rapid Growth in the technology space had made way for many new opportunities. Artificial Intelligence is one such field where there are umpteen opportunities in multiple domains. There are many High paying, exciting jobs that are available when Artificial Intelligence skills are possessed. Especially in Natural Language processing, the job prospects are surging. The job profiles that are offered for a career in Artificial Intelligence and Natural Language Processing are

  • Machine Learning Engineer
  • Natural Language Processing- machine learning Developer
  • Data Scientist
  • Software Engineer
  • Research Scientist
  • ML Specialist

Conclusion

Artificial Intelligence is a growing field and the opportunities that it offers are ever-increasing. In artificial intelligence, Natural Language Processing (NLP) is an essential part of high job prospects. A wholistic Artificial Intelligence Training and NLP will help an individual in pursuing a great career in Artificial Intelligence.

Artificial Intelligence is Modernizing Restaurant Industry

Posted on by Imarticus Learning

John McCarthy coined the term ‘Artificial Intelligence’ more than 6 decades ago. We have been dreaming of a technology-enabled future for decades and it is turning into reality now. Artificial Intelligence is the programming of machines to imitate human intelligence and their actions. It helps in minimizing human efforts along with improving speed, accuracy, and effectiveness.

Today, there are huge numbers of data created in almost every sector. There is a need for Advanced Algorithms, huge data management, storage, analysis, and several other complex tasks. Artificial Intelligence and machine learning training is gaining popularity due to this increasing need for accuracy, precision, and effectiveness.

It’s interesting how Artificial Intelligence has grown over a small period in several sectors. Education, Transportation, Sports, Media, Marketing, Banking and Finance, and so on goes the list of industries that use Artificial Intelligence.

How Artificial Intelligence Helps in the Restaurant industry

Below are a few examples of the application of Artificial Intelligence in the Restaurant Industry.

Forecast Sales. People indeed make orders based on a specific climate. You would not go for a hot cup of cocoa on a hot humid day. You would go for something that is cooler. The climate makes an impact on the food and beverages sold by an eatery. AI with Machine Learning helps the restaurant to project their sales based on the orders made, current weather conditions, and past sales performance.

Scheduling Capabilities. Predictive Scheduling is fundamental these days. Staffs need to be informed in advance of their schedules, whom to be switched with, shifts, etc. Doing this manually can get quite tiresome for the manager. Artificial Intelligence provides help with this kind of function. It can perform a lot of scheduling tasks in an efficient time that would normally take up a lot of human effort and time.

Smart Virtual Assistant. Virtual Assistants and related technologies have been gradually growing over the past few years. It gathers various information related to the restaurant and presents it to the user when they search about it. Based on the information customers pick a suitable restaurant. Therefore they play a direct role in the growth and sales of the restaurant.

Robots. Is it possible? Yes, definitely. It won’t be a while before we may actually be delivered food from robots. A restaurant chain in Pasadena named Caliburger was one of the firsts to bring in a robot to their kitchen. Though it is yet to come into effect in India, it could take the restaurant sector to a whole new level of functioning.

AI-Driven Kiosks reduce waiting time and improve the eatery experience for customers. These systems act as POS systems and make the ordering process simpler for the customers.

Inventory and Purchasing. Pos systems integrated with Inventory and Purchasing can improve efficiency and helps in reducing losses. It helps to track and order based on sales, menus, and recipes. One can cross-verify between the actual food preparation and the portion of food left, thereby helping to find out sources of waste and theft in your kitchen. AI-powered restaurant management solutions ensure the right supply of materials and help to remove one more tedious work from your list.

Mapping And Management Tools. Delivery is gaining popularity these days. It is important to optimize the delivery routes for quick, efficient, and quality food delivery. Management Tools helps to keep a check on the drivers’ progress, guide on routes and on how to save miles and time, and keeping the customers in the journey throughout.

Self Serve Options powered by Artificial Intelligence could work wonders in the Restaurant Sector. Though it has not come into effect in India, self-serve kiosks could speed up the services without actually sacrificing the human touch. Customers can browse through menus, make orders, and make payments online without having to stand in long queues. This helps in improving customer satisfaction.

Artificial Intelligence is changing our lives in more ways than we know in this 21st century. So is Artificial Intelligence helping to reshape the Restaurant Industry. The AI-powered tech advancements these days are critical in boosting sales and increasing customer satisfaction to a much higher level. Since most individuals have already embraced digital technologies, it would only make it smarter and easier to inculcate Artificial Intelligence in the Restaurant Industry.

Data Analytics in Healthcare: Can a Techie Succeed in The World of Medicine?

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In the modern-day, Information Technology has seeped into all sectors. Industries have adopted Data Science and Analytics to drive their work and have reaped the rewards for it. In the paradigm where Data Analysts are highly sought after in all sectors, how do they fare in the field of Healthcare?

 

Big Data Analytics courses are the perfect way for techies to break into the medical sector. It has become a tool with unlimited potential, and a Data Analytics career in healthcare is a very real and prospective opportunity.

Data Analytics is generally used to draw meaningful interpretations, find trends and predict possible outcomes from Data.

Healthcare Analytics specifically aids in avoiding preventable diseases, conducting an accurate diagnosis, predicting and combating epidemics and coming up with effective treatment strategies for diseases.

A Healthcare Analyst contributes to improving the quality of healthcare and reducing the treatment costs by automating tedious processes.

Applications of Data Analytics in Healthcare

There are many applications of Data Analytics in healthcare, and a career in the field works to implement these uses. Some of the most popular applications are:

Patients’ prediction: Healthcare Analytics can be leveraged to improve patient predictions. The past admission records can be used to discover hospitalization trends, peak times and deploy personnel accordingly.

Strategic Planning: The existing data can be used to identify patterns, conduct studies and map out strategies for extensive care and in community medicine.

Electronic Health Records:

It is one of the most popular uses of Data Analytics in Healthcare. It is used to track patient medications, treatments, progress, and medical history. Data Analytics on these can reveal more details about their medical conditions and lead to more accurate prognoses.

Predictive Analytics: Predictive Analytics is the practice of recognizing patterns and predicting probable outcomes by studying the data presently available. This can be used to improve care delivery for patients with complex medical history by using history to try and predetermine the conditions they may face in the future.

Data Analytics Careers in Healthcare

The applications of Big Data analytics in medicine are vast, and more are being uncovered every day. In this scenario, techies are being welcomed into the sector with open arms. Healthcare employs techies as Data Analysts, Informatics Consultants, Clinical Data Managers in the Quality and Performance improvement sector.

One can also get promoted to leadership positions for other Analysts as Informatics Director or Chief Medical Information Officers. All these positions come with their responsibilities as well as perks. The roles and responsibilities would also entail assistance in the proper integration of data analytics within specific healthcare areas.

Conclusion

A career as a healthcare analyst is a door that opens infinite possibilities. Not only do you get to save lives as a part of the field, but you also get to innovate and change medicine in your way.

What Is a Real-time Processing in a Big Data Use Case?

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Breaking down Real-time Processing

In today’s digital era, people are accustomed to real-time information that helps them make more informed decisions by factoring in the latest updates. Businesses in contemporary need real-time information to remain competitive in the market and improve their offerings.

best Data Analytics courses in IndiaLeveraging big data has become indispensable for businesses as it helps them provide valuable insights into the market. A big data career is also considered one of the most sought-after career choices given the demand for the same.

Real-time data processing systems help to factor in rapidly changing variables and relevant data to provide instant output so that any unnecessary delays can be avoided.

Some of the most prominent examples of real-time processing include order management for e-commerce websites, online ticket booking, financial fraud detection, radar range system, etc. A continuous stream of data is required to produce instant output in real-time. The most prominent benefit of real-time processing is that it helps to provide instant results and ensures that everything is up to date.

Real-time Big Data Processing Applications

Some of the most prominent applications of real-time big data processing in the contemporary include the following.

Online Commerce

Online commerce or e-commerce platforms heavily rely on real-time big data processing given the nature of business. It helps to solve issues related to customer service, inventory management, purchase pattern detection, personalized offerings, reduction of churn rate, etc. E-commerce companies also use real-time data processing to improve their logistics; they identify the nearest warehouse to effectively reduce delivery rates. It also helps to optimise the price and increase the sales volume by analyzing tons of data in real-time.

Social Media Networks

Gone are the days where people used to receive news the next day after the occurrence of a particular event. Social media networks have improved the flow of information and one can instantly receive any new update in real-time avoiding any delay in communication. Given the flow of information on social media platforms, real-time processing of data is almost a necessity. Some of the important functions performed by real-time data processing in the case of social media networks include classification of user-generated data or content, speeds up real-time trending, server functionality analysis, etc.

Healthcare

The healthcare industry also heavily relies on the use of real-time data processing systems. Some of the most common applications in this sector include real-time patient monitoring to assess crucial changes, wearable sensors and devices help to make life-saving decisions using real-time data. A majority of the data in the healthcare domain is usually unstructured and real-time big data processing helps to provide a detailed overview. It also assists in prescribing relevant medicines and avoids any unnecessary expenses.

Conclusion

Real-time big data processing has become popular in the last decade and is almost a necessity for a business that wants to create a sustainable brand in the long term.

best data analytics certification courses in IndiaThe big data career is also gaining traction with the growing demand for data analysts who can assist in the real-time processing of big data.

One can opt for data science courses from reputed institutions like Imarticus Learning to boost their job prospects and build a career in this field.

Mining and Visualizing Data: Important Skill required in Data Analytics

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Data analytics is one of the fastest-growing fields in today’s business landscape. Data analysts across industries are being tasked with data mining and visualization for different purposes like data interpretation, data analysis, etc.

data analytics certification course in IndiaAs a result of this, there’s a high demand for professionals with the data analytical skillset. There are data analytics courses that teach you data mining and data visualization.

These skills are indispensable in the data analytics landscape. If you want to be successful, it is absolutely necessary to learn this skill set.

In this blog post, we will talk about the important skills required for data mining & visualization and how to find a good data analytics course that offers them!

Key skills required for data mining and data visualizing

As a data analytics specialists, important skills you must have to apply for data mining & data visualizing jobs are:

Data Mining: Evaluating data to determine what kind of information it has and how a particular data can be used for various purposes (i.e., analysis). To analyze the data that you want to extract from a database or spreadsheet, tools like SQL queries help find specific patterns within large sets of data. You need to know about the kinds of algorithms available such as linear regression for analyzing relationships between variables recorded over time or clustering techniques applied when investigating groupings in your data set.

Data Visualization: Achieving insights into complex datasets with accurate representation through graphs, charts, etc. This includes designing insightful visualizations without sacrificing data accuracy. For this, you must have knowledge of data visualization techniques like data smoothing to make data trends more visible or data clustering for creating informative thematic maps.

Data Analytics: The application of statistical analysis to data in order to reveal patterns and trends that can be used for decision making, modeling future data, etc. For this, you need knowledge about the various analytical techniques like linear regression or time series forecasting, which are then applied to data sets in order to make predictions with mathematical models.

Understanding these important skills will help you think critically and understand how data analytics is done!

How to find a good data analytics course?

– The course should teach data mining, visualization, and advanced SQL skills.

– It should also have a good rating and be well-reviewed by existing students.

– A course with thorough programming exercises is preferable to one that just offers lectures or videos.

– The institution needs to provide a certificate of completion for the student in order for them to become certified data analysts, as many institutes are providing the courses without any certification.

If you are looking for a data analytics course that has all the above-mentioned features, then Imarticus Learning is for you!

A Rewarding Data Analysts Career with Imarticus!

best data analytics certification courses in IndiaKnowing the need for new-age careers & the potential of online education, Imarticus offers certification courses in data analytics for beginners and advanced levels.

If you want to further your career in data mining & to visualize data, and have a leg up over other applicants, getting a certification in data analytics is your best option. An advanced degree with various specializations at Imarticus will add credibility to your profile and position you as an efficient professional.

For any further details, contact us through the Live Chat Support system or visit any of our training centers in Mumbai, Thane, Pune, Bengaluru, Chennai, Hyderabad, Delhi, Gurgaon, and Ahmedabad.

Most Essential Asset for Professionals: Data Analytics Tools!

Posted on by Imarticus Learning

To perform data analysis, professionals use tools & software that ensure the best results. There are many of them in the market, but a few are on the top list for data analysts that need to be chosen wisely to extract the best benefit. 

This article discovers the best data analyst tools and highlights key features of each based on different types of analysis processes. But first, here’s a basic definition & introduction.

What Are Data Analyst Tools?

Data analyst tools are applications & software that data analysts develop & perform analytical processes to make better, informed business decisions while keeping costs low & profits high.

Top Tools Used by Data Analysts?

1. Business Intelligence Tools

BI tools are the most represented means to perform data analysis. Specialized in business analytics, they prove beneficial for every data analyst to monitor, analyze, and report essential findings. Self-service, advanced SQL modes, and predictive analytics make them easily adjustable to every level of knowledge without immense IT involvement. 

DATAPINE covers most requirements from beginners & advanced users.

Key Features:

  • Visual drag-&-drop interface
  • Powerful predictive analytics & interactive charts & dashboards
  • Intelligent alarms triggered for anomaly

2. Statistical Analysis Tools

There exist multiple programming languages that make work easier & effective. With the expansion of languages, some rules and scenarios need special attention for statistical data analysis & modeling.

R PROGRAMMING is the most popular tool for data analysts.

Key Features:

  • An ecosystem of more than 10k packages & extensions for data analysis
  • Statistical analysis, modeling, & hypothesis testing
  • Active community of researchers, statisticians, & scientists

3. General-Purpose Programming Languages

Programming languages help to resolve a variety of data problems. Tools like C#, Java, PHP, Julia, Ruby, and Python are many reliable solutions.

PYTHON is the most trusted tool.

Key Features:

  • Open-source solution with simple coding processes
  • Easy integration with other languages
  • Advanced analysis processes through ML & text mining

4. SQL Consoles

SQL is a programming language used to manage data held in relational databases, effectively handling structured data for analysts. Highly popular in the data science community, these tools are used in various business cases & data scenarios. 

MYSQL WORKBENCH is the most popular one.

Key Features:

  • Unified visual tool for data modeling, administration, SQL development, backup, etc.
  • Instant access to the database via Object Browser
  • SQL Editor offers color syntax highlighting, reuse of SQL snippets, & execution history

5. Unique Predictive Analytics Tools

Predictive analytics is an advanced technique to combine data mining, AI/ML, and predictive modeling to predict future events. It is an essential data analysis tool as it simplifies predictive analytics processes. 

SAS FORECASTING is an analysis software helpful for predictive analytics processes & helps analysts to predict future scenarios.

Key Features:

  • Automatic forecasting for entities or products, including hierarchical forecasting
  • Scalability & modeling to create an ensemble
  • An unlimited model repository including time-series & casual methods

Apart from the tools mentioned above, there are industry-specific analytics tools & data science platforms that assist professionals. A business analytics course can help you better understand what data analytics is!

Build New-Age Career with Imarticus:

data analytics coursesImarticus Learning offers Data Science Courses, the most extensive, industry-approved experiential learning program ideal for Data Analysts. 

Industry experts have developed the programs to help students learn real-world applications of Data Science from scratch & build robust models to generate valuable business insights and predictions.

Take a giant career leap with this analytics program.

For more details, contact us through the Live Chat Support system or visit our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Hyderabad, Delhi, and Gurgaon.

What are Roles and Responsibilities of Scrum Master

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Who is Scrum Master

Scrum master is the moderator for an Agile Development team. These people have to look for a management task like information interchange between team members. Scrum masters manage that how people can exchange their information. Also, their responsibility includes promotion and supporting scrum. These are usually done by making people understand Scrum theory, rules, and values.

Scrum Master is the leader who makes people engaged or interactive so that this interaction results in maximized values. The scrum master takes care of the scrum tasks that needed to be done during a project. They hold processes that are going on in the organization, scrum framework, and scrum team. The project can get into failure if no scrum master is present to do promotion and support the process.

Scrum Master Responsibilities

The scrum master has many different roles in a process or a project. A scrum master ensures the goal, scope, and product information that is cleared to all the team members. They take care of product planning. They are knowledgeable about how to approach a project and meetings required for those. Some of the responsibilities include:

  • Helping the team to achieve the desired goal in a specific time.
  • Helping the team for the agreement during the scrum meets.
  • Ensuring the team to agree upon the rules of the daily scrum.
  • Ensuring there is no distraction for the team during the project.
  • Removing the problems and obstructions faced by the team.
  • Developing a good environment so that team can be constructive.
  • Taking care of the relationship between team and product owner.

Challenges Faced By Scrum Master

A scrum master is a person that ensures that everything in the scrum process is followed by the team. It can be said that scrum master’s role is easiest but there are still a few challenges that are faced by the scrum masters that are stated below:

1. Resisting Change- It can be in different forms.
2. Misunderstanding process – There can be different confusions in the daily implementation tasks. Often, there can be confusion between old terms and methods.
3. Failure in an adaption of changing roles- Changes in the role can take place between team and department. You should be able to adapt to your role.

How To Become Scrum Master

The way to be a scrum master you need to get a certification. Scrum Alliance offers CSM that gives the person knowledge to get their teams more effective. The certification talks about the concepts of the Scrum framework and you will gain understanding about events and team roles. If you are certified you can expand your career in various industries. A scrum certification gives you in-depth knowledge about scrum.

There are very few requirements that are required for CSM. You should be aware of the scrum framework. There is a session that talks about an overview of how to support a team. CSM conducts exams and If you pass and accept their license then you would graduate with the two-year member in the Scrum Alliance. There are also other exams apart from CSM.

Certified Scrum Master earns between $70-100K yearly which is dependent upon in the industry. Having certifications makes a scrum master a more preferable candidate for the job.

In this article, I have discussed who are scrum masters, what are their roles and responsibilities in the organization, how they help their team during projects and processes. I have discussed the challenges and problems faced by the scrum master and in the last, I have explained a few certifications that a person willing to do to become a scrum master.

What is The Salary Of A Data Scientist?

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One of the biggest drivers of data science as a career in India is its earning ability. In India alone, the average annual pay of a data scientist (analyst) is upwards of INR 8 lakh. This can definitely vary depending upon the location, the type of the job, and the employer.

While you can improve the chances of earning a higher salary by signing up for a data science course, it is a good idea to look at how the remuneration looks like through the cycle of a data science career.

Hierarchy of Salaries of a Data Scientist

This is a quick look at how your salary will grow as you gain more experience and get trained for a data science career.

  • Beginner – An entry-level data scientist from a reputed institution can look at earning upwards of INR 5 lakh a year. This is a better salary compared to those that engineering graduates receive in India. Here, as a beginner, your goal should be to gain more hands-on experience in your specific field to demand a better salary. For example, a data science training course can improve your chances by 2x.
  • Analyst With 1-4 Years of Experience – Although there is a possibility that your data science salary can touch the INR 10 lakh mark in this period, it will depend on your projects and overall work experience. This is a good time to shift from your existing employer and look at greener pastures.
  • Professional – This is when you achieve more than 10 years of experience. Here you can dictate a salary in the range of INR 15 lakhs to INR 25 lakhs. With several credentials and projects in your name, you can really carve out a prosperous life for yourself and your family. This is also where you can think of spending some time on research.

Just remember that when you start off, make sure you are well trained for a data science career. As a fresher, your academic qualifications will play a vital role during interviews. A data science training course that will help you choose a specialty and make it a focal point is the way to go.

Promotions and Salary Hikes in Data Science

As with every other professional field, a promotion in a data science career is entirely dependent on performance. However, a salary hike of 30% to 50% can be expected. Again, this will be influenced by how well you perform in your role, but given that skilled data scientists are hard to find in India, you can be optimistic.

In any case, if you don’t feel you are professionally growing in your current company, you can always look at upskilling or switching jobs. A data science training course can make you a potential candidate then.

As noted above, getting trained for a data science career can boost your earning and promotion capabilities. While data scientist intrinsically is a well-paying job, a training course in the field will do no harm.

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