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What Are the Most Common Questions Asked in Data Science and Machine Learning Interviews?

Posted on by Imarticus Learning

Data Science and Machine Learning have grown leaps and bounds in the last couple of years. Data science is essentially an interdisciplinary field that focuses on extracting data in different structured or unstructured forms by using various methods, algorithms and processes. Machine learning, on the other hand, is the ability to learn with data. It uses a mixture of artificial intelligence and statistical computer science techniques which help interpret data efficiently, without having to use explicit and large programs.
As more people look into these fields as prospective career choices, the competition to get recruited by companies in either of these fields is quite strong.
Thus, here is a list of a few frequently asked questions related to Data Science and Machine learning that you can expect in your interview.

1) Explain what data normalization is, and its importance.

This is one of the basic, yet relevant questions that are usually asked. Data normalization is a pre-processing step. It helps weight all the features that fit in a particular range equally. This prevents any kind of discrepancy when it comes to the cost function of features.

2) Highlight the significance of residual networks.

Residual networks and their connections are mainly used to facilitate easier propagation through any given network. Thus, residual connections allow you to access certain features present in the previous layers directly. The presence of residual networks helps make the network as more of a multi-path structure. This gives room for features to tread across multiple paths, thus helping with better propagation throughout the system as a whole.

3) Why are convolutions preferred over FC layers for images?

Though this is technically not a very common question, it is interesting because it tests your skills related to comparison and problem-solving. FC layers have one major disadvantage which is that they have no relative spatial information. On the other hand, convolutions not only use spatial information but also preserves and encodes it. Also, Convolutional Neural Networks (CNN) are said to have a built-in variance which makes each kernel a feature detector on its own.

4) What do you do if you find missing or corrupted data in any dataset?There are mainly two things that you can do if you find missing or corrupted data in a dataset.

  • Drop the respective rows or columns: This can be done by using two method functions, isnull() or dropna(). This will help you determine if any dataset is actually empty. If it is empty, you can simply drop it.
  • Replace the data with non-corrupted values: To replace any invalid value with another value, the fillna() method can be used.

5) Why are 3×3 convolutional kernels preferred over larger kernels?

Smaller kernels such as a 3×3 kernel generally use lesser computations as well as parameters. Thus, you can use several smaller kernels as opposed to a few larger ones. Also, larger kernels do not capture as much spatial content as smaller kernels do. Apart from this, smaller kernels use a lot more filters than larger kernels do. This, in turn, facilitates the use of more activation functions which can be used for discriminative mapping functions.

6) Why does the segmentation of CNN have an encoder-decoder structure?

The segmentation structure of CNN’s is usually in the encoder-decoder style so that the encoder can extract features from the network while the decoder can decode these features to predict the segments of the image under consideration.
Thus, looking into simple questions like this that focus on your knowledge of the concepts of Data Science and Machine Learning will really help you face an interview while applying for a position in the field.
People Also Ask:

  • What a Data Scientist Could Do?
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  • What is The Difference Between Data Analysis and Data Science?

 

How Should You Prepare For Statistic Questions for Data Science Interviews

Posted on by Imarticus Learning

Data Science has been the buzz word of the IT field for the past few years. Courses like data science course from Imarticus will equip you with all the skills required for a data science job. However, to ace the interviews for data science jobs, you should be well versed with the basic components of statistics too. This article discusses one of the key element in Data Science, statistics and its relevant topics to brush up before a data science job interview.
Preparing for Data science interviews
As in many interviews, the statistics are also going to start with technical questions. Many interviewers try to test your knowledge and communication skills by pretending to have no idea about the basic concepts and asking you to explain them. So, it is important to learn how to convey complex concepts without using the assumed knowledge.
Following are the few important topics you could brush off before attending the interview.
1. Statistical features
They are probably the most used statistics concept in data science. When you are exploring a dataset, the first technique you apply will be this. It includes the following features.

  • Bias
  • Variance
  • Mean
  • Median
  • Percentile and many others.

These features provide a quick, informative view of the data and are important to be familiar with.
2. Probability Distribution
A probability distribution is a function that represents the probabilities of occurrence of all possible values in the experiment. Data science use statistical inferences to predict trends from the data, and statistical inferences use probability distribution of data. So it is important to have proper knowledge of probability functions to work effectively on the data science problems. The important probability distributions in the data science perspective are the following.

  • Uniform Distribution
  • Normal Distribution
  • Poisson Distribution

3. Dimensionality Reduction
It is the process of reducing the number of random variables under consideration by taking a set of principle variables. In Data Science, it is used to reduce the feature variables. It can result in huge savings on computer power.
The most commonly used statistical technique for dimensionality reduction is PCA or Principal component analysis.
4. Over and Under-Sampling
Over and Under Sampling are techniques used to solve the classification problems. It comes handy when one dataset is too large or small relative to the next. In real life data science problems, there will be large differences in the rarity of different classes of data. In such cases, it is this technique comes to your rescue.
5. Bayesian Statistics
Bayesian statistics is a special approach to applying probability to the statistical problems. It interprets probability as the confidence of an individual about the occurrence of some event to happen. Bayesian statistics take evidence to account.
These topics from statistics are very important for a Data Science job and make sure you learn more about them before your interview. You can also try various data science training in Mumbai to begin your career at right note. Genpact data science course from Imarticus is an excellent choice to learn more about data science. Check out and join the course immediately.

How is MySQL Used In Data Science

Posted on by Imarticus Learning

Data Science is considered to be the most sought-after profession of the 21st century. With lucrative opportunities and large pay scales, this profession has been attracting IT professionals around the world. Various tools and techniques are used in Data science to handle data. This article talks about MySQL and how it is used in data science.
What is MySQL
In short words, MySQL is a Relational Database Management System or RDBMS that use Structured Query Language (SQL) to do so. MySQL is used for many applications, especially in web servers. Websites with pages that access data from databases use MySQL. These pages are known as “Dynamic Pages” since their contents are generated from the database as the page loads.
Using MySQL for Data Science
Data science requires data to be stored in an easily accessible and analyzable way. Even though there are various methods to store data, databases are considered to be the most convenient method for data science.
A database is a structured collection of data. It can contain anything from a simple shopping list to a huge chunk of data of a multinational corporation. In order to add, access and process the data stored in a database, we need a database management system. As mentioned MySQL is an open-source relational database management system with easier operations enabling us to carry out data analysis on a database.
We can use MySQL for collecting, Cleaning and visualizing the data.  We will discuss how it is done.
1. Collecting the Data
The first part of any data science analysis is collecting the massive amount of data of data. The Sheer volume of data often causes some insights to be lost or overlooked. So, it is important to aggregate data from various sources to facilitate fruitful analysis. MySQL is capable of importing data to the database from various sources such as CSV, XLS, XML and many more. LOAD DATA INFILE and INTO TABLE are the statements mostly used for this purpose.
2. Clean the Tables
Once the data is loaded to the MySQL database,  the cleaning process or correcting the inaccurate datasets can be done. Also deleting the dirty data is also part of this step. The dirty data are the incomplete or irrelevant parts of the data.
The following SQL functions can be used to clean the data.

  • LIKE() – the simple pattern matching
  • TRIM() – Removing the leading and trailing spaces.
  • REPLACE() – To replace the specified string.
  • CASE WHEN field is empty THEN xxx ELSE field END  – To evaluate conditions and return value when the first one is met.

3. Analyze and visualize data
After the cleaning process, it is time to analyze and visualize the meaningful insights from the data. Using the standard SQL queries, you can find relevant answers to the specific questions.
Some analysis examples are given below:

  • Using query with a DESC function, you can limit the results only to the top values.
  • Display details of sales according to the country, gender or product.
  • Calculate rates, evolution, growth and retention.

If you would like to know more about MySQL and its use in Data Science join the data science course offered by the Imarticus. This Genpact data science course offers a great opening to the career opportunities in Data Science. Check out the course and join right away.

How ML AI Is Allowing Firms to Know More About Customer Sentiment and Response?

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The importance of customer service for any industry just cannot be stressed enough. A recent study done by Zendesk showed that 42% of customers came back to shop more if they had an excellent customer experience, while 52% never returned once they had a single bad customer service experience.

The implementation of Machine Learning powered artificial intelligence is fast becoming the next significant revolutionizing change within the customer service sector. So much so that several studies have indicated that over 85% of all communications involving customer service will be done without the participation of a human agent by 2020.

Customer Service with AI
Customer service has become one of the most critical applications of artificial intelligence and machine learning. Here, the basic concept behind the service remains the same, with the implementation of AI making it far more sophisticated, easy to implement, and way more efficient than conventional customer support models. AI-powered customer service today doesn’t just include automated call center operation but a mixture of services including online support through chat.

Along with the diminished costs associated with using an AI, the other main advantage is that AI can dynamically adapt itself to different situations. These situations can change according to each customer and their related queries. By monitoring an AI during its initial interactions with customers and correcting them every time a wrong step is taken, we can permanently keep “teaching” the AI what is right and wrong in particular interaction with a certain customer.

Due to the AI being able to “learn” in this way, it will have the capability to accurately determine what needs to be done to rectify a particular complaint and resolve the situation to the customer’s satisfaction.
The AI can be trained to identify specific patterns in any customer interaction and predict what the customer will require next after each step.

No Human Errors
Another advantage of AI is that human error, as well as negative human emotions like anger, annoyance, or aggression, are non-existent. AI can also be trained to escalate an issue if it is out of the scope of its resolution. However, with time and increased implementation, this requirement will quickly decrease.

In today’s fast-paced world, more and more people prefer not having to waste time interacting with another human whenever it isn’t essential. A recent customer service survey targeted at millennials showed that over 72% of them prefer not having to resort to a phone call to resolve their issues. The demand for human-free digital-only interactions is at an all-time high.

Thus, it would be no surprise to find that savings would increase drastically with the implementation of AI-powered chatbots. One research by Juniper Research estimated that the savings obtained through chatbots would increase from $20 Million in 2017 up to more than $8 Billion by 2022. Chatbots are also becoming so advanced that according to the same report, 27% of the customers were not sure if they had interacted with a human or a bot. The same story also added that 34% of all business executives they talked to believe that virtual bots will have a massive impact on their business in the coming years.

Hence, the large-scale implementation of AI in customer service is inevitable and will bring drastic improvements in customer satisfaction and savings shortly.

Understand the Difference: Artificial Intelligence Vs Machine Learning

Posted on by Imarticus Learning

Artificial Intelligence and Computer Sciences, data sciences and nearly everyone today uses the terms Machine Learning/ML and AI/ Artificial Intelligence interchangeable when both are very important topics in a Data Science Course. We need to be able to differentiate the basic functions of these two terms before we do a data science tutorial where both ML and AI are used on another factor namely data itself.
AI is not a stand-alone system in the data science tutorial. It is a part of the programming that artificially induces intelligence in devices and non-humans to make them assist humans with what is now called the ‘smart’ capability. Some interesting examples of AI we see in daily life are chatbots, simple lift-arms in warehousing, smart traffic lights, voice-assistants like Google, Alexa, etc.
ML is about training the machine through algorithms and programming to enable them to use large data volumes, spot the patterns, learn from it and even write its own self-taught algorithms. This experiential learning is being used to produce some wonderful machines in detecting cancers and brain tumours non-invasively, spot trends and patterns, give recommendations, poll trends, automated driverless cars, foresight into possibilities of machine failure, tracking vehicles in real-time, etc. It is best learned at a formal Data science Course.

Difference Between Machine Learning And Artificial Intelligence

Here are the basic differences between ML and AI in very simple language.

  • ML is about how the machine uses the algorithm to learn. AI is the ability of machines to intelligently use the acquired knowledge.
  • AI’s options are geared to succeed. ML looks for the most accurate solution.
  • AI enables machines through programming to become smart devices while ML relates to data and the learning from data itself.
  • The solutions in AI are decision-based. Ml allows machines to learn.
  • ML is task and accuracy related where the machine learns from data to give the best solution to a task. AI, on the other hand, is about the machine mimicking the human brain and its behavior in resolving problems.
  • AI chooses the best solution through reasoning. ML has only one solution with which the machine creates self-learned algorithms and improves accuracy in performing the specific task.

Both AI and ML exist with the very life-breath of data. The interconnection is explained best through ‘smart’ machines to do such human-tasks through ML algorithms to scour and enable the final inferential steps of gainful data use. AI and ML are both essential to handle data which can run into a variety of complex issues in managing data. ML is the data science tutorial way you would train, imbibe and enable the computers and devices to learn from data and do all jobs using algorithms. Whereas AI itself refers to using machines to do the tasks which are in data-terms far beyond human computing capabilities. And in short, the data scientist/analyst is the one person who uses both AI and ML in his career to effectively use data and tools from both AI and ML suites.
One does not need a technical degree to choose the umbrella career of data science which teaches you both AI and ML. However, it is a must that you get the technical expertise and certification which is a validation of being job prepared from a reputed institute like Imarticus by doing their Data science Course. You will need an eclectic mix of personal traits, technologically sound knowledge of AI, ML, programming languages and a data science tutorial to set you on the right track. Hurry!
Conclusion:
The modern day trend of using data which is now an asset to most organizations and daily life can be put to various applications that can make figuring out complex data and life simpler by using AI achieved through ML programming.
The Data science Course at Imarticus Learning turns out sought-after trained experts who are paid very handsomely and never suffer from want of job-demand. Data grows and does so every moment. Do the data science tutorial to emerge career-ready in data analytics with a base that makes you a bit of a computer and databases scientist, math expert and trend spotter with the technical expertise to handle large volumes of data from different sources, clean it, and draw complex inferences from it.

Can You Integrate AIML with Android App?

Posted on by Imarticus Learning

 

Artificial Intelligence has quickly become one of the most important fields to humanity today. The subject of an increased amount of research, AI is currently one of the few fields which are soaring with no end in sight today. It can be said that the very future of humankind now depends upon AI, and how it develops in the future – such is the reach of Artificial Intelligence in the modern world.

With such a rapid rise in the field of AI, there is no doubt that the demand for talented people in the field is higher than ever. If you want a career which is challenging yet satisfying, Artificial Intelligence is definitely one of the best options. However, you should start learning more about AI quickly, and what better way to put your skills into test than building a chatbot?

Chatbots are one of the latest sensations sweeping over AI practitioners. Chatbots are now increasingly becoming a part of most companies, and most of the internet users have already interacted with a chatbot in some form or other. Being an AI aficionado or a prospective practitioner, you can surely try to build a chatbot from scratch in order to gain some practice in Artificial Intelligence. A conversational assistant is a challenge to create because it has to give a new answer to the same questions and learn from the answers of the user, too. You can build simple chatbots with ease, and port it into android apps too, in many ways.

AIML was one such language which was used in the development of early chatbots.

What is AIML?

Artificial Intelligence Markup Language or AIML was created by Dr Richard Wallace and is currently offered as an open source framework for developing chatbots. It is offered by the ALICE AI Foundation so that users can create intelligent chatbots for their use from scratch. AIML is an extremely simple XML, just like HyperText Markup Language or HTML.

It contains a lot of standard tags and tags which are extensible, which you use in order to mark the text so that the interpreter which runs in the background understands the text you have scripted.

Steps to Integrate Chatbots into Android Apps

The steps covered here are not comprehensive in any way, but only an outline which you can follow in order to make what you want. These do not contain any codes, because that would defeat the purpose of creating an android app chatbot from scratch.

However, you can always skip the parts you are uninterested in, like the design aspects of the app and the likes.

The first step is to create a chat UI and interface using Android Studio. Using XML, you can do this with only a basic understanding of the language. It should have an adapter too, for the different view types.

The, import the AIML files that you have written beforehand to your app. Then, the task you have is to modify the MainActivity.java in such a way so as to include the class Bot in it.

Obviously, there is a lot of coding involved if you want to build the bot from scratch. However, integration is definitely possible, too. If you find yourself interested in learning more, you should check out the artificial intelligence courses in India on offer at Imarticus Learning.

What Are Prerequisites to Start Learning Machine Learning?

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What Are Prerequisites to Start Learning Machine Learning?

There are few fields in technology which have risen as much as machine learning and data science have, in the past few years. The demand for professionals well versed in data science has more than tripled, while the field is also now one of the most lucrative profession options for any interested person.

Machine learning does require the user to have a modicum of understanding over mathematical concepts. Apart from the requisite programming skills, you will need to know some basic mathematical concepts in order to understand how various algorithms function in the backdrop. Here are some of the main topics that you need to know before you get into machine learning.

• Basic Maths
 The importance of mathematics in machine learning cannot be overstated, but the extent to which it is used depends upon the project at hand, really. Entry-level users may not need to understand a lot, because you may only have to learn how to implement the algorithms well using the tools at hand.

However, you would not understand the deeper workings of algorithms or libraries without knowledge about linear algebra or multivariable calculus. If you are serious about machine learning and want to explore how to start learning Machine Learning, there is no doubt that you will have to customize and build your own algorithms as you progress. This means that mathematics, especially linear algebra, and multivariable calculus is important.


• Statistics and Probability
Machine learning algorithms are all based on statistics and probability, at heart. Therefore, you would definitely have to have a deep understanding of statistical theory, like Bayes rule, independence, and the likes. Analysis models and distributions in statistics should also be covered, and you will have to be comfortable working with them for a long time.

Bayesian concepts to be covered while covering the basics include maximum likelihood, priors, posteriors and the entire concept of conditional probability. The frequentist way of thinking commonly used with datasets are discarded in this case – the statistical model is followed. You need to have statistical knowledge if you are planning to make a long, successful career in this.

• Data Modeling
 Data modeling refers to the process of estimating the structure of a data set, and this is done so that you can find out any variations or patterns within this. A lot of machine learning is also based on predictive modeling, so you would have to know how to predict the various properties of the data you have at hand. Iterative learning algorithms may result in errors being magnified in the set and the model, so a deep understanding of how data modeling functions is also a necessity.

If all of this seems intimidating in your quest to getting a machine learning certification India, make sure to remember that becoming a machine learning professional is not an overnight thing – it would require a certain amount of practice and experience. If you want to know more about how to learn machine learning, check out the machine learning courses available on Imarticus Learning!

However, you would not understand the deeper workings of algorithms or libraries without knowledge about linear algebra or multivariable calculus. If you are serious about machine learning and want to make a career in machine learning, there is no doubt that you will have to customize and build your own algorithms as you progress. This means that mathematics, especially linear algebra, and multivariable calculus is important.


• Statistics and Probability
Prerequisites for Machine learning are all based on statistics and probability, at heart. Therefore, you would definitely have to have a deep understanding of statistical theory, like Bayes rule, independence, and the likes. Analysis models and distributions in statistics should also be covered, and you will have to be comfortable working with them for a long time.

Bayesian concepts to be covered while covering the basics include maximum likelihood, priors, posteriors and the entire concept of conditional probability. The frequentist way of thinking commonly used with datasets are discarded in this case – the statistical model is followed. You need to have statistical knowledge if you are planning to make a long, successful career in this.

• Data Modeling
 Data modeling refers to the process of estimating the structure of a data set, and this is done so that you can find out any variations or patterns within this. A lot of machine learning is also based on predictive modeling, so you would have to know how to predict the various properties of the data you have at hand. Iterative learning algorithms may result in errors being magnified in the set and the model, so a deep understanding of how data modeling functions is also a necessity.

If all of this seems intimidating in your quest to getting a machine learning certification India, make sure to remember that becoming a machine learning professional is not an overnight thing – it would require a certain amount of practice and experience. If you want to know more about how to learn machine learning, check out the machine learning courses available on Imarticus Learning!

Should You Start With Big Data Training or Learn Data Analytics? Which One to Start First?

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It is always a better choice to learn Big data training rather than generalize with data-analytics which is a very large field. Today’s world deals with not just Big Data but the term for big have increased by many multiples of big in terms of data volume. Further, the tools that are used are fast evolving and learning the Big-Data tools first can be done online and through courses. Once you have proficiency in dealing with big data you can also do data analytics courses and understand better the concepts of analytics while applying them to databases classified as big and very, very big!

Difference Between Data Analytics And Big Data

The languages and tools used and the end purpose is different in the two courses one being used in managing large database sets while the other focuses on gaining and providing insights from such datasets. Data science covers courses to learn how to visualize data, make predictive models using R/Python and then use manipulation techniques on the data to get foresight and forecasts or trends. Big Data courses are about managing the data systems and databases. Tools used in Big data training are Hadoop, Tableau, R, NoSQL, and many others that deal with managing the data and integrating the results to give the desired dashboards, visualizations, graphics and summary of statistics.
The R language is taught in data sciences and includes R as its programming language because of its tool range to deal with statistical and analytical applications. The applications used need R programming and hence R developers would be more preferred. Big data training on the other hand, uses MapReduce for Java-based installation programs, needs to integrate and connect with R through Tableau from the Hadoop library and uses data processing tools like Flume, Hive, Sqoop, HBase etc
Learning Hadoop Course
You can use online resources and do it yourself using top10online courses.com. However, formal training has many advantages and is highly recommended. Join the Big data training Hadoop course at a reputed institute like Imarticus Learning. Hadoop has a vast array of subsystems which are hard to learn for the beginner without formal training. The course helps you assimilate the ecosystem and apply these systems to solving real-world industry-related problems in real-time through assignments, quizzes, practical classes and of course do some small projects to show off your newly acquired skills. The best part is that you have certified trainers leading convenient modes and batches to help you along even if you are already working.
Start building your project portfolio and get on GitHub.
The steps that follow are the Hadoop progressive tutorial in brief.
• Hadoop for desktop installation using the Ambari UI and HortonWorks.
• Choose a cluster to manage with MapReduce and HDFS.
• Use Spark, Pig etc to write simple data analysis programs.
• Work on querying your database with programs like Hive, Sqoop, Presto, MySQL, Cassandra, HBase, MongoDB, and Phoenix.
• Work the ecosystem of Hadoop for designing applications that are industry-relevant.
• Use to manage your cluster with Hue, Mesos, Oozie, YARN, Zookeeper, and Zeppelin.
• Practice data streaming with real-time applications in Storm, Kafka, Spark, Flume, and Flink.

Why do a data analytics course?

Today it would be exceptional if a company does not use Hadoop and data analytics in one form or the other. Among the ones that you can easily recollect are New York Times, Amazon, Facebook, eBay, Google, IBM, LinkedIn, Spotify, Yahoo!, Twitter and many more. Big Data, Data Analytics and Deep Learning are widely applied to build neural networks in almost all data-intensive industries.
However, not all are blessed with being able to learn, update knowledge and be practically adept with the Big data training Hadoop platform which requires a comprehensive ML knowledge, AI deep learning, data handling, statistical modelling and visualization techniques among other skills. One can do separate modules or certificate Big-Data Hadoop training courses with Imarticus Learning who offer such learning as short-term courses, MOOCs, online classrooms, regular classrooms, and even one-on-one courses. Choices are aplenty with materials, tutorials and options for training being readily available thanks to high-speed data and visualization made possible by the internet.
Doing a formal data analytics training course with certification from a reputed institute like Imarticus Learning helps because
• Their certifications are widely recognized and accepted by employers.
• They provide comprehensive learning experiences including the latest best practices, an updated curriculum and the latest training platforms.
• Employers use the credential to measure your practical skills attained and assess you are job-prepared.
• It’s a feather in your hat that adds to your resume and opens doors to the new career.
• Knowledge in Analytics is best imbibed through hands-on practice in real-world situations and rote knowledge gained of concepts may not be entirely useful.
The best Big data training courses for Advanced Analytics are available at the IIMs at Lucknow, Calcutta, and Bangalore or at the IITs of Delhi and Bombay. This is an apt course for people with lower experience levels since their curriculum covers a gamut of relevant topics in depth with sufficient time to enable you to assimilate the concepts.
The courses run by software training institutes like Imarticus are also excellent programs which cost more but focus on training you with the latest software and inculcating practical expertise. Very experienced professionals are likely to get corporate sponsorship and can avail training at competitive discounted rates. Face-to-face lab sessions, mandatory project work, use of role-plays, interactive tutoring and access to the best resources are also very advantageous to you when making the switch.
Job scope and salary offered:
Persons with up to 4 years experience can expect salaries in the range of 10-12 lakhs pa at the MNCs according to the Analytics India Magazine. Yes, the demand for jobs in this sector will never die down and is presently facing an acute shortage.
Conclusion:
In parting, there are plenty of options that you can research more on. It is worth it when your Big data training certification helps you land the dream career you want immaterial of the route you followed. Whether you prefer managing databases and then getting at the insights or choose to get the insights and then learn how to train and manage the datasets is your choice. Both choices will be in demand for jobs over the next decade. So don’t wait. Take that leap into data today!

Data Lake And Big Data Analytics

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If you have been in the IT space and data analytics space for some time now, you might have come across the term Data Lake at least once. But since the technology is in its early days, not a lot of people known what it is all about and thus in this article we will discuss all about data lakes, their benefits and how they are helping in data analytics.
What is a Data Lake?
In the most simplest of terms, a data lake is a centralized storage or repository that allows you to store all your structured and unstructured data, be it of any scale. The main significant difference between a data lake and other centralized repository options available in the market is the fact that a data lake will allow you to store your data without the need of any restructuring and also allows you to run various kinds of data analytics right on the repository.
The various data analytics option present in a data lake starts from dashboards and goes all the way up to visualisations and big data processing, and even real-time analytics and machine learning to help the user for making better decisions.
The Need For A Data Lake
As you might have already guessed, the need for access to a data lake is more important in this day and age than ever before, since the number of companies dealing with big data is constantly on the rise. A recent survey, conducted by Aberdeen found that companies which used data lake facilities were able to perform 9 per cent better to those who didn’t; this fact alone can contribute to the need of using a data lake.
The Benefits of a Data Lake
Similar to any other technology in the market, Data Lake too comes with a host of advantages which helps it stand apart from the rest. Some of the most significant ones are as mentioned below.

  1. Capability to store and run analytics, thus deriving results from unlimited data sources
  2. Capability to store all types of data, both structured and unstructured, thus covering everything from social media posts to CRM data
  3. Increased flexibility from other systems in the market
  4. Option to eliminate data silos
  5. Ability to run unlimited queries at any point in time

Data Lake and Data Analytics
As mentioned in the earlier paragraphs, data lakes in today’s world have multitude applications, one of the most significant being the ability to run data analytics on a host of different data types.
Companies which deal with a massive amount of big data, often face with the difficulty of storing different formats at different locations, thus making data analytics a virtually impossible option. But with data lakes, all forms of data, both structured and unstructured can be stored in one place, thus allowing the user to run analytics and visualization from one dashboard and derive results. On top of that, having a single data lake, companies save up on huge amounts of money and make higher profits in the long run.

Should a Scrum Master Know How To Code?

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Within the Scrum framework, there is no definition of the role and responsibilities of the Scrum Master needing to know how to code. As a matter of fact, the Scrum Master Responsibilities and role does not need any knowledge of coding. The Scrum framework treats the scrum master role as a person with scrum certification course and hence an ace coach of Scrum values.
data analytics courses

While without actual authority, the person in this role leads by example and infuses the team through the servant-leader example. So powerful is the effect of the Scrum Master role that the role is also known and called as Agile Manager or Coach, Iterative Coach or Manager and Team Coach.

Scrum Master(SM) abilities

The crucial abilities required to fulfill the role of a Scrum Master role are the orientation of team members, people-skills and diagnostic thinking. No technical skills, testing or coding knowledge is a prerequisite to such functioning. Yes, if by inclination the Scrum Master does possess such skills and technical knowledge it could be put to use intelligently and within the Scrum Framework.

To lead by example on the servant-master foundational principles of Scrum practices the SM needs to be an ace communicator able to transition from the business to technical aspects without hindrances. It needs pluck and exceptional diagnostic skills to be able to wear many hats, diagnose inter-team and interpersonal issues. More, it needs excellence in people management to be perceptive, empathetic and rein into the Scrum Framework diverse team members and clients.

Often the role of a coach involves cushioning and refereeing team-members communications. When should one be flexible, what are the non-variables, what will the impact of changeability lead to, what trade-offs will result and how will it impact productivity are normal issues dealt with within the Scrum framework. An effective SM ensures the removal of obstacles, distractions, disruptions and miscommunications allowing the team members to perfectly coordinate, communicate and collaborate.

Ex-programmers are rarely inclined to be effective Scrum Masters because of their default values leaning to be technically critical and less aware of business skills and acumen. Just as all business graduates do not become successful entrepreneurs, so also with programmers donning the role of a Scrum Master.

Team members with exposure to Project Management, Program Management, and Product Management with dynamic people skills and a high level of perception and communication skills are better suited for the responsibilities and role of an SM. They could be fine-tuned in an agile business analysis course 

Exceptions
Small DevTeams of less than five members who are all speaking coding would need a degree of familiarity with coding. Ideally, even such teams need an Agile Coach because of the additional responsibilities of being the Scrum Master are akin to traveling with your feet on two boats. Not only will there be confusion in the Scrum roles, but the clarity of roles envisaged in the Scrum Framework also fails.

In such an environment it would be wonderful to have an SM who understands coding speak to the DevTeam, reiterate understanding of the issues flagged, translate in non-technical plain speak to the product owner, business teams, and so on. That’s a creative use of coding knowledge at its best.

In parting, the Scrum Master will need managemental skills more than technical skills to function effectively in an Agile environment using Scrum practices to foster team communication and collaboration for achieving a common goal of productivity increase.