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Day: January 9, 2019

What Are Prerequisites to Start Learning Machine Learning?

Posted on by Imarticus Learning

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?

Posted on by Imarticus Learning

 
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

Posted on by Imarticus Learning

 
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?

Posted on by Imarticus Learning

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.

What is the difference between data science and data analytics?

Posted on by Imarticus Learning

 

One of the biggest jobs in the technological center is working with big data. There are plenty of roles within this sector and two of the most popular ones include data science and analytics. While a lot of companies tend to hire similar candidates for these roles, there is still a difference between the two.

It is important that you understand the two roles before you choose a career path in either. If you’re looking to kickstart a career in the field of big data, then knowing the difference between data science and analytics is a good pointer to keep in mind.

What is data science?

Data science is a broader term for different methods and models used to get information. Under data science are the statistics, scientific methods, and math along with other tools which can be used to manipulate and analyze data. If there is a process or a tool that can be used on data to analyze it and extract information from the same, then it falls under the umbrella of data science.

As a practitioner of data science, you’ll have to connect data points and information to figure out connections which can be more useful for a business. It requires you to explore the unknown and find newer patterns or insights which can then be turned into actionable decisions from a business perspective. Data science attempts to delve into connections and figure out methodologies which work for the betterment of a business.

What is data analytics?

Data analytics is more concentrated and specific than data science. It is focused on achieving a specific goal by sorting through large data sets and looking for ways to support the same. Analytics are more automated as they can help in providing better insights in certain areas. Data analysis also involves analyzing large data sets to find smaller, more useful pieces of information to fulfill an organization’s goals.

Analytics basically sorts data into things that an organization knows or doesn’t know and can be used to measure any event in the present, past or even future. It moves from insights to impact and connects patterns and trends along with the true goals of a company, keeping the business aspect in mind.

Knowing the difference:

Data analysts and scientists perform different roles and companies must know exactly what they’re looking for. Data analytics usage in industries such as travel, gaming or healthcare, where analysts can extract specific data to improve business, data science is used in more broader categories such as digital advertising or internet searches.

Data science also plays a role in developing machine learning and Artificial Intelligence. Companies are looking at systems which allow computers to go through large amounts of data. They then formulate algorithms, developed by analysts which can sift through the same and find connections which can help them reach their objectives and thus, bring in more revenue.

Imarticus provides the best data analytics course to make it easier for anybody looking to enter the world of big data science and kickstart their career. 

For more details regarding this, you can directly visit – Imarticus Learning and can drop your query by filling up a simple form through the site or can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Banglore, Hyderabad, Delhi, Gurgaon, and Ahmedabad. 

What are the best practices for training machine learning models?

Posted on by Imarticus Learning

As we all know, Machine learning is a popular way of learning at your own pace. Machine learning also facilitates learning based on your likes and interests. For example, you are a person who is interested in space and astronomy, a machine learning driven course to learn mathematics for you, will first ask you few basic questions about your interest.

Once it establishes your interest, it will give examples of mathematical calculations using objects of space to keep you engaged. So, how are these machines able to establish your interest? What are the best practices for training machine learning models is something that we will see in this article.

Machine learning is based on three important basics.
• Model: A Model is responsible for identifying relationship between variables and to make logical conclusion.
• Parameters: Parameters are the input information that is given to Model to make logical decisions.
• Learner: Learner is responsible for comparing all the Parameters given and deriving the conclusion for a given scenario.

Using these three modules, machine is trained to handle and process different information. But it is not always easy to train the machine. We need to adopt best practices for training machines for accurate predictions.

• Right Metrics: Always start the machine learning training or practice with a problem. We need to establish success metrics and prepare a path to execute them. This is possible when we ensure that the success metrics that have been established are the right ones.

• Gathering Training Data: The quality and quantity of data used is of utmost importance. The training data should include all possible parameters to avoid misclassifications. Insufficient data might lead to miscalculated results. The quantity of data also matters. Exposing the algorithms to a small set of humongous data can make them responsive to a specific kind of information again leading to inaccurate results when exposed to something other than the test data.

• Negative sampling: It is very important to understand what is categorized as negative sampling. For example, if you are training your data for a Binary classification model, include data that requires other models like multi class classification model. By this, you can train the Machine to handle negative sampling too.

• Take the algorithm to the database: We usually take the data out from the database and run the algorithm. This takes lot of effort and time. A good practice would be to run the training algorithm on the database and train it for the desired output. When we run the equation through the kernel instead of exporting the data, we not only save hours of time but we also prevent duplication of data.

• Do not drop Data: We always create pipelines by copying an existing pipeline. But what happens in the background is, the old data gets dropped many a times to provide place for the fresh data. This can lead to incorrect sampling. Data dropping should be effectively handled.

• Repetition is the key: The Learner is capable of making very minute adjustments for refining the model to obtain the desired output. To achieve this, the training cycle must be repeated again and again until the desired Model is obtained.

• Test your data before actual launch: Once the Model is ready test the data in a separate test environment till you obtain the desired results. If your data sample is all the data up to a particular date for which you have all predictions, the test should be conducted on upcoming data to test the predictions.

Finally, it is also important to review the specifications of the Model from time to time to test the validity of the sample. You may have to upgrade it after a considerable amount of time depending on the type of model.

There is a lot to learn about ML(Machine Learning) that cannot be explained in a simple article like this. The Machine learning future in India is very bright. If you have the desired machine learning skills and need to pursue big data and machine learning courses in India, learn from pioneers like Imarticus.

Is Data Analytics An Interesting Career Field?

Posted on by Imarticus Learning

One of the biggest job sectors of the last few years, data analytics is seen as one of the most lucrative career options today. In the United States, an estimated 2.7 million jobs are predicted to be taken by data science and analytics by 2020. The value that big data analytics can bring companies is being noticed and companies are looking for talented individuals who can unearth patterns, spot opportunities and create valuable insights.

If you’re someone who’s good at coding and looking to make the next jump from a career perspective, then data science could be your calling. Here are a few reasons you should look out for a career in data analytics:

Higher Demand, Less Skill:
India has the highest concentration of data scientists globally, and there is a shortage of skilled data scientists. According to a McKinsey study, the United States will have 190,000 data scientist jobs vacant due to a lack of talent, by 2019. This opens the door for a good data analyst not just to make money, but own the space.

Good data analysts can take complete control of their work without having to worry about interference. As long as you can provide crucial insights which contribute to the company’s business, you’ll find yourself moving up the ladder faster than expected.

Top Priority in Big Companies:
Big data analytics is seen as a top priority in a lot of companies, with a study showing that at least 60% of businesses depend on it to boost their social media marketing ability. Companies vouch by Apache Hadoop and its framework capabilities to provide them data which can be used to improve business.

Analytics is being seen as a massive factor in shaping a lot of decisions taken by companies, with at least 49% believing that it can aid in better decision making. Others feel that apart from key decisions, big data analytics can enable Key Strategic Initiatives among other benefits.

Big Data Is Used Almost Everywhere:
Another great reason to opt for big data or data analytics as a career option is because they are used pretty much everywhere! With the highest adopters of the technology being banking, other sectors which depend on big data include technology, manufacturing, consumer, energy and healthcare among others.

This makes big data an almost bulletproof option because of the wide range of applications it can be used for.

Most Disruptive Technology In The Next Few Years:
Data analytics is also considered as one of the most disruptive technologies to influence the market in the next few years. According to the IDC, the big data analytics sector is touted to grow to up to $200 billion by 2024.

Thus, big data analytics is going to be the future of computing and technology. The sector is seeing massive growth and a lot of demand. The more you’re able to provide insights that can make a difference in this sector, the higher are your chances of getting a lucrative job.
Whether it’s a data analytics course in Bangalore or any other city, Imarticus will be able to provide you with the right kind of training and knowledge with data analytics courses to help your career soar.

How Does Scrum Work?

Posted on by Imarticus Learning

 

Software development uses Scrum. We shall explore Scrum from definition to practice taking into account its pros and cons.

Scrum Definition:
 Scrum is a strategy for software product development helping software developers to work as collaborative teams for the achievement of common business goals like the creation of a market-ready product.

Scrum is also a game strategy borrowed from rugby. Ultimately the aim of a Scrum is to use the team performance to achieve a common goal. In software development, Scrum practices help the team communicate and collaborate for bettering productivity in project development.

The Scrum environs:

The Scrum framework works on three roles.
• The Scrum team who work in Sprints to produce market-ready products.
• The Scrum Master is not the manager and ensures the team uses Scrum practices.
• The Product Owner or client prioritizes the backlog, manages and coordinates the team efforts.

How it works:

Scrum software development involves the Scrum team collaborating to resolve complex issues. The product backlog is discussed by the team to priorities Product Owner needs and fixed deadlines.

An Agile concept or Sprint is defined specifying the time for the chosen item from the backlog and could last a week or month to produce a market-ready product. Each Sprint is reviewed in daily Scrums and on completion a new Sprint begins.

The process continues till the deadlines or budget is complete. Each daily Scrum reviews tests and corrects the previous day’s progress. All Scrum team members are involved, contribute and communicate towards the Sprint completion. The Scrum Master ensures the environment, practices and Scrum framework requirements are diligently met.

Scrum Advantages:
 DevTeams of software developers work at high speeds and make use of Scrum to better their functioning with the following advantages.


• Scrum developers with decision-making capacities have higher motivation and morale.
• Every Sprint produces a market-ready product. Prioritizing ensures a low-risk, high-quality product goes to the market even as the project is still on-going.
• The time to market is reduced by ensuring the Scrum Product Owner is serviced on a need-basis.
• Scrum projects have better ROI due to effective feedback and corrections, decreased time to market, lesser defects, regular testing and early disbanding.
• Better testing is possible as each Sprint is reviewed before the next is taken up.
• Change of evolving goals and focus areas is feasible.

Scrum Disadvantages:
Scrum practices do not work well for all teams. Some disadvantages in the implementation of projects following Scrum practices are


• Scrum teams turn dysfunctional when micromanagement occurs by the Scrum Master’s interference.
• Adding functionalities to the backlog and fixed deadlines can cause creep in the scope of the project.
• The greatest impediment to project progress is the loss of a team member.
• Software developers work quickly and in small teams. Scrum practices work well for such teams.

Scrum Best Practices:
Quality products are created daily by winning teams using these simple Scrum practices.


• Specify relevant product features and requirements on time.
• Daily test and provide feedback to the Product Owner.
• Hold regular sprint-reviews.
• Use sprint retrospectives as constructive feedback.
• Avoid missed and miscommunications through face-to-face discussions.
• Trust your team performance.
• Allow team members to self-organised around their personalities, team skills, and work styles.
• Prevent burnouts through professional and personal conflicts and stress.

In conclusion, Scrum works well at all levels and in both personal and professional lives and environments. An Agile business analyst and Scrum software prodegree with SAP will empower you to use Scrum, Agile and SAP effectively.