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Day: December 17, 2018

Importance of Data Analysis and why you should learn it?

Posted on by Imarticus Learning

Inspection,cleansing, transformation and modelling of data in order to achieve information that further suggests conclusions and assists with decision making is what data analysis is all about. It’s a rapidly booming field of study for the youth, and companies are always on the hunt to find people who are masters at this procedure so as to increase their growth.

Analytical and logical tools are used to determine and accurately learn data analysis. These skills need to be learnt and honed over time in order to land yourself a good position in this field.              

Analyzing data is important for any business, old or new. It provides a clear understanding of customer behavior and much more essential business intelligence to promote growth and rectify mistakes if any. The first step in this huge process is defining an objective, without which the purpose of the study is lost.

Posing questions is the next step, after which comes data collection through various online and offline tools and techniques. This is the most crucial part of the process, as you need to define your objectives to learn data analysis as accurately as possible.

Learn data analysis by learning the essential tools and the most basic ones used in this line of work. One of the most widely used programs for data analysis is Excel. The other ones are Python, SQL & R. It is easy to get defocused with so many programming languages available and not knowing which one to learn first.A road map always helps while learning something new. R is a good place to start in terms of programming language. R Studio is an essential program to have to learn data analysis.   

If you want to learn data analysis, do not get intimidated by the courses available. You can look up educational websites and just by investing a few bucks, and you can know all there is to it. The most important part to remember before starting is having a fair idea of which software or program does what. It is always better to practice till you’re perfect, rather than spend time only on reading about it. There are also a lot of offline courses available to keen learners in order to learn data analysis.

If you’re sure about pursuing this field, then investing in a good college, institute, or course can help bring out the best in you. While there are many crash courses for the same, not many degree courses are available to learn data analysis. Interning under data analysts in your city of choice and your company of choice will also contribute largely towards technical and practical knowledge. Companies generally welcome promising interns and are willing to work towards their progress as a professional while seeking a fresh approach to business from them in return.        

The best way to excel in this line of work is choosing a specific skill you want to take forward professionally. It is the best bet for making the most of there sources available to you to learn data analysis.             

We offer data analytics courses at our centers in Mumbai, Thane, Pune, Ahmedabad, Jaipur, Delhi, Gurgaon, Bangalore, Chennai, Hyderabad, Coimbatore.                                     

Top tips on how to apply data analytics in your project

Posted on by Imarticus Learning

Data science and analytics are two extremely useful tools that can give accuracy to your project and help automate repetitive tasks. With the demand and scope of data analytics growing with each passing day, companies are trying to integrate everything and get as much information on it as they can.

Data science techniques and analysis are quite helpful because they can be used to enhance the decision-making capacity of your manager, predict future revenues, understand market segments, and produce better content. In the healthcare sector, this technology can be used to diagnose patients correctly.

But how do you integrate data analytics into your professional projects? For that, a sound knowledge of the same is required. Even if you learn the basics of data analytics, it will give a major boost to your career. The entire world is moving towards digitization, and so data analytics is required to gather, analyse, and make sense of the data in front of you.

In order to become an expert in data analytics, and incorporate it seamlessly into your project, you need to have a data analytics training.There are many data analytics courses that you can take for a better understanding of data science and analysis. Here is a list of some of the best data analytics courses available online.

  • Introduction to Data Science

This data analytics training course requires a basic understanding of R programming language and provides an in-depth insight into the necessary tools and concepts used in the data science industry. They also work with powerful techniques for analyzing data and use real-world examples to help you gain clarity over the concepts.

  • Applied Data Science with Python

It is being offered by the University of Michigan. It aims to introduce learners to the specialized version of data science through Python. It is for learners with an understanding of Python, and want to expand their knowledge by incorporating the essentials of statistical,machine learning, information visualization, text analysis, and social network analysis techniques into their projects.

  • The Python Mega Course: Build 10 Real World Applications

This data analytics training is aimed at people with no background of Python, but are interested in learning basic as well as advanced skills of Python and data analysis. It is for people with no previous or little programming experience.

It does not rely on a lot of theoretical teaching but focuses instead on giving problems to the students that they can solve by doing. This course uses video, quizzes, real-world examples to familiarize learners with Python in the beginning and then enhance their skills later.

  • Social Media Data Analytics

This is one of the best data analytics courses available online that especially caters to social media. It is for people who want to use their data analysis skills to get the best out of social media.This course involves giving assignments and mini-projects, which would require you to use your data analytics skills to leverage your social media presence.

How to Work on Deep Learning programming?

Posted on by Imarticus Learning

Learning Algorithms

Algorithms are at work all around us. Right from suggestions displayed in a text box while using Whats App to time boxing traffic signals, algorithms greatly improve the quality of human life these days. The more efficient the algorithm, the better the quality of service. Imagine an elevator system for a skyscraper with a thousand floors.

An adaptive machine learning algorithm can change the way it works depending on the demand and timetable of people going to different floors and dramatically reduce the waiting time for a person taking the elevator when compared to a static algorithm with no feedback loop.

Machine Learning is nothing but the improvement in performing a task with experience.The more the experience, the better is the performance of a machine learning algorithm. It can also be used for predicting the outcome of an event based on the historical data available. Filtering spam from your mailbox, Commute time predictions, Suggestions in social media, digital assistants are a few examples of the applications of machine learning algorithms.

Deep Learning and the Complexities involved

The fundamental rule in computer science is the use of abstractions. All concepts act as building blocks to another seemingly advanced concept, which is nothing but a layer of abstraction added over the older concept.

Algorithms, data structures, machine learning, data mining are the building blocks of Deep learning which is Machine learning and the concept of feature wise classification. Deep learning defines which feature characterizes a pattern and then uses data mining to classify, compare and define a feature.

Deep learning algorithms typically take more time to train but are more accurate and dependable as experience increases. They are used for speech recognition. NLP. Computer vision, Weather pattern analysis etc. They are usually implemented using neural networks. Deep learning is a subset of machine learning.

How to Learn Deep Learning programming

Below are few ways to understand and work on Deep learning:

  1. There are several machine learning courses, and deep learning courses available online,mostly in Python and R. Python training is usually a prerequisite for these courses. Some of the best ones are available in Udemy, Course Era, edX etc. These courses can be completed online and are prepared by the best minds in the field.  

  2. Understanding the inbuilt Python libraries: The future of machine learning and deep learning depends greatly on the inbuilt library support python provides. Tensor Flow, Thea nos, Pandas etc. are a few powerful libraries which it provides for programmers to explore deep learning concepts.

  3. Knowledge of Machine Learning or doing a machine learning course is generally preferred before diving into deep learning because conceptually machine learning is a general form of learning compared to the more specific deep learning. But based on the programmers understanding of the basic concepts, exposure to Python and R libraries, deep learning can also be started directly.

  4. However, the classic order is, do a python course -> Do a machine learning course -> Do a deep learning course and then contribute to the deep learning community after practice and execution.

  5. All the tools involved are opensource, so with sufficient interest, programming expertise and Python knowledge, cracking Deep Learning should be an easy task. Take part in the community and practice, practice, and practice to excel.

All the very best for your journey into Deep Learning..!!

Should You Do Masters in Fintech or Digital Marketing? Why?

Posted on by Imarticus Learning

Fintech and Digital Marketing are the latest buzzwords in new professions. These are coming-of-age roles ushered by the advent of the internet. Since the internet is the go-to medium for every geography and demography, creating the medium first and then the content has become a large and significant part of this eco-system.

Fintech

The study of technology that is related to finance is known as Fintech. Fintech comes as there placement from the traditional financial services. This technology is dedicated to improving the finance sector. Now it has to be noted Fintech is being used in established organizations and startups to have a growth in their financial sector. 

Thus a person with knowledge of Fintech courses is likely to get a job over a person who only knows the basics of traditional marketing. Some of the subjects that are involved in Fintech courses are:

Robotics
Machine Learning
Artificial Intelligence
Cryptocurrency
Advanced Analytics
Digital cash and open banking
Hadoop

Further, if you don’t have time to learn in all these subjects by attending an institution,then you can always opt for Fintech online courses. Some universities like the offer these courses online which comes with a 15-week time frame. 

Fintech online courses offered by this university focus on important areas like implications,valuations, risks, and startups.

Digital Marketing

The use of electronics and the internet to promote a business can be termed as digital Marketing.  In this type of marketing channels like email, social media, the search engine is used to attract traffic. Start-ups who need potential leads need digital marketing at all cost since they have limited resources and are fixed with a tight budget.Some of the courses that are available in digital marketing are:

Website planning
Search engine optimization
Lead generation
Social media  and email marketing
Knowledge of Blog through Ad Sense

Choosing the Right Program for Yourself

Digital marketing and Fintech both have their USPs but if a choice has to be made, then going with Fintech would be a better choice. The things that you will get to know by studying Fintech are all related to future employment. Nowadays companies are in dire need of hiring professionals who know robotics or data analysis. 

There are so many digital marketers but a very few people who have the knowledge of Fintech. Over the next few years,Fintech will have some massive growth and salaries for this job will reach sky high. 

Some of the companies now regret the fact they have invested too much in digital marketing rather than paying attention to Fintech.  Big data management is very important since the internet is growing at a nonstop rate, for this knowledge of Hadoop is important which is again offered by Fintech courses.  Furthermore, digital marketing is not as difficult as learning Fintech. So concentrating on a tougher course is a good option for being successful later.

Conclusion

From the above points, the reader gets a clear idea about both the courses and which one is better. While one is ruling the present market (digital marketing) the other is likely to rule the future market (Fintech).

Can Artificial Intelligence be self-aware?

Posted on by Imarticus Learning

Artificial Intelligence has gradually been spreading its wings to more and more sectors wherein only humans could work until now. A prime example of this is the introduction of an artificial intelligence process to vehicles, making self-driving cars a reality.

While this is a significant development, it pales in comparison to the possibilities that could be unlocked if we have a computer that is completely aware of itself and its surroundings. These machines could be sent to do a variety of jobs that humans find difficult.

In fact, it could reach such a point wherein robots replace humans at every job, leaving humans to live their lives in lazy bliss. This could completely shake up society as we know it and leave humans without a sense of purpose. This also raises the question of legality when it comes to robots. Would be held under the same accountability as humans, or scarily still, would they band together and eliminate humans altogether?

There are varying views among researchers about what consciousness is and whether machines could someday achieve it. Some researchers say that consciousness develops with constantly accepting new information, retrieving the old and processing all of it into thoughts and, subsequently actions. If this assumption is true, then any consciousness developed by computers will be the most advanced one, even more so than human consciousness.

They will be able to access millennia worth of information within a fraction of a second and be able to make decisions which are both more complex and more logical than what any person could accomplish. However, some researchers disagree with this opinion,saying that some factors that contribute to consciousness, such as creativity and compassion, are not the result of calculations and will always be exclusive to humans.

Another view of this topic is the quantum view, which takes into account the quantum theory of physics. According to this view, all the physical aspects of this world and consciousness complement each other all the time. It goes on to say that whenever a person observes or manipulates any physical object, noticeable change could be observed due to that person’s conscious interaction with the object.

This can be explained by considering consciousness as something that exists by itself and isn’t derived through physics, merely needing a medium such as a brain to manifest itself. If this is true, it seems highly unlikely that machines would be able to tap into this consciousness.

Another factor to talk about here is computational programming, which is the basis on which every computer on the planet runs. For a computer to be self-aware, it should have the capability to come up with its own language as a means of processing thought through artificial intelligence training course. However, while this has been done by artificial intelligence processes in recent years, these languages still depend on and are based on the instructions given to these machines by humans.

As such, these machines will never be able to reach the same level of thought complexity that would be required to say, write a poem, or even understand empathy and a sense of belonging to the world. So in short, the answer to the question of whether artificial intelligence could become self-aware is, yes but only to an extent.

The likelihood of these machines reaching the level of thought complexity that humans enjoy is extremely less unless there is a breakthrough in our knowledge of how our minds work and we are able to translate that into the code for these computers.

What makes Hadoop so Powerful and how to Learn it?

Posted on by Imarticus Learning

Why Hadoop?

With today’s powerful hardware, distribution capabilities, visualization tools, containerization concepts, cloud storage and computing capabilities, huge amounts of raw data can be stored, processed, analyzed, and converted into information, used for decision making, historical analysis and for future trend prediction.

Understanding Big data and converting into knowledge is the most powerful thing any entity can possess today. To achieve this, Hadoop is currently the most used data management platform. The main benefits of Hadoop are:

  1. Highly scalable
  2. Cost-effective
  3. Fault-tolerant
  4. Easy to process
  5. Open Source
  1. What is Hadoop?

Hadoop is a Highly distributed file system (HDFS), maintained by Apache Software Foundation. It is a software to store raw data, process it by leveraging the distributed computing capability and to manipulate and filter it for further analysis.

Several frameworks and machine learning libraries like python and Operate on the processed data to analyze and make predictions out of it. It is a horizontally scalable, largely distributed, clustered, highly available, and reliable framework to store and process unstructured data.

Hadoop consists of the file storage system (HDFS), a parallel batch processing engine Map Reduce and a resource management layer, YARN as standalone components. Open source software like Pig, Flume, Drill, Storm,Spark, Tez, Hive, Kafka, HBase, Mahoot, Zepplin etc. can be integrated on top of the Hadoop ecosystem to achieve the intended purpose.

How to Learn Hadoop?

With interest in Big Data growing day by day, learning it can help propel your career in development. There are several Big data Hadoop training courses and resources available online which can be used to master Hadoop theoretically.

However, mastery requires years of experience, practice, availability of large hardware resources and exposure to differently dimension ed software projects. Below area few ways to speed up learning Big Data.

  1. Join a course: There are several Big Data and Hadoop training courses available from a developer, architect, and administrator perspective. Hadoop customization like MapR, Horton Works, Cloud era etc. offer their own certifications.
  2. Learning marketplaces: Virtual classrooms and courses are available in Course Era, Udemy, Audacity etc. They are created by the best minds in the Big Data profession and are available at a nominal price.
  3. Start your own POC: Start practice with a single node cluster on a downloaded VM. Example: Cloud Era.com quick start.
  4. Books and Tutorials on the Hadoop ecosystem: Hadoop.apache.org, Data Science for Business, edurekha,digital vidya, are a few examples apart from the gazillion online tutorials and videos.
  5. Join the community: Joining the big data community, taking part in discussions and contributing back is a surefire way to increase your expertise in big data.

Points to remember why Learning Hadoop:

Below are the things to keep in mind while working on large open source Big Data projects like Hadoop:

  1. It can be overwhelming and frustrating: There will always be someone wiser and more adept than you are.Compete only with yourself.
  2. Software changes: The ecosystem keeps shifting to keep up with new technology and market needs. Keeping abreast is a continuous process.
  3. Always Optimize: Keep finding ways to increase the performance, maturity, reliability, scalability, and usability of your product. Try making it domain agnostic.
  4. Have Fun: Enjoy what you are doing, and the rest will come automatically!

All the Best on your foray into the digital jungle!