Top 10 reasons why Dimensionless is the Best Data Science Course Provider Online

Top 10 reasons why Dimensionless is the Best Data Science Course Provider Online

Introduction

Data Science was called “The sexiest work of the 21st Century” by the Harvard Review. Data researchers as problematic solvers and analysts identify patterns, notice developments and make fresh findings and often use real-time information, machine learning, and IA. This is where Data Science Course comes into the picture.

There is a strong demand for information researchers and qualified data scientists. Projections from IBM suggest that by 2020 the figure of information researchers will achieve 28%. In the United States alone, there will be 2,7 million positions for all US information experts. In addition, we were provided more access to detailed analyzes by strong software programs.

Dimensionless Tech offers the finest online data science course and big data coaching to meet the requirement, offering extensive course coverage and case studies, completely hands-on-driven meetings with personal attention to each individual. This assessment is a gold mine with invaluable insights. To satisfy the elevated requirement. We only provide internet LIVE instruction for instructors and not instruction in the school.

About Dimensionless Technologies

Dimensionless Technologies is a training firm providing online live training in the sector of data science. Courses include–R&P data science, deep learning, large-scale analysis. It was created in 2014, with the goal of offering quality data science training for an inexpensive cost, by 2 IITians Himanshu Arora & Kushagra Singhania.
Dimensionless provides a range of internet Data Science Live lessons. Dimensionless intends to overcome the constraints by giving them the correct skillset with the correct methodology, versatile, adaptable and versatile at the correct moment, which will assist learners to create informed business choices and sail towards a successful profession.

Why Dimensionless Technologies

Experienced Faculty and Industry experts

Data science is a very vast field and hence a comprehensive grasp over this subject requires a lot of effort. With our experienced faculties, we are committed to impart quality and practical knowledge to all the learners. Our faculty through their vast experience (10 plus industry experience) in the data science industry is best suited to show the right path to all students towards their success journey on the path of data science. Our trainer’s boast of their high academic career as well (IITian’s)!

End to End domain-specific projects

We, at Dimensionless, believe that concepts can be learned best when all the theory learned in the classroom can actually be implemented. With our meticulously designed courses and projects, we make sure our students get hands-on the projects ranging from pharma, retail, and insurance domains to banking and financial sector problems! End-to-end projects make sure that students understand the entire problem-solving lifecycle in data science

Up to date and adaptive courses

All our courses have been developed based on the recent trends in data science. We have made sure to include all the industry requirements for data scientists. Courses start from level 0 and assume no prerequisites. Courses make learners traverse from basic introductions to advanced concepts gradually with the constant assistance of our experienced faculties. Courses cover all the concepts to a great depth such that learners are never left wanting for more! Our courses have something or other for everyone whether you are a beginner or a professional.

Resource assistance

Dimensionless technologies have all the required hardware setup from running a regression equation to training a deep neural network. Our online-lab provides learners with a platform where they can execute all their projects. A laptop with bare minimum configuration (2GB RAM and Windows 7) is sufficient enough to pave your way into the world of deep learning. Pre-setup environments save a lot of time of learners in installing all the required tools. All the software requirements are loaded right in front of the accelerated learning

Live and interactive sessions

Dimensionless provides classes through live interactive classes on our platform. All the classes are taken live by instructors and are not in any pre-recorded format. Such format enables our learners to keep up their learning in the comfort of their own homes. You don’t need to waste your time and expenses in any travel and can take classes from any location of your preference. Also, after each class, we provide the recorded video of it to all our learners so that they can go through it to clear all their doubts. All trainers are available to post classes to clear the doubts as well

Lifetime access to study materials

Dimensionless provides lifetime access to the learning material provided in the course. Many other course providers provide access only till the time one is continuing with classes. With all the resources available thereafter, learnings for our students will not stop even after they have taken up our entire course

Placement assistance

Dimensionless technologies provide placement assistance to all its students. With highly experienced faculties and contacts in the industry, we make sure our students get their data science job and kick start their career. We help in all stages of placement assistance. From resume-building to final interviews, Dimensionless technologies is by your side to help you achieve all your goals

Course completion certificate

Apart from the training, we issue a course completion certificate once the training is complete. The certificate brings credibility to the resume of the learners and will help them in fetching their data science dream jobs

Small batch sizes

We make sure that we have small batch sizes of students. Keeping the batch size small allows us to focus on students individually and impart them a better learning experience. With personalized attention, we make sure students are able to learn as much possible and helps us to clear all their doubts as well

Conclusion

If you want to start a profession in data science, dimensionless systems have the correct classes for you. Not just all key ideas and techniques are covered but they are also implemented and used in real-world company issues.

Follow this link, if you are looking to learn data science online!

You can follow this link for our Big Data course! This course will equip you with the exact skills required. Packed with content, this course teaches you all about AWS tools and prepares you for your next ‘Data Engineer’ role

Additionally, if you are having an interest in learning Data Science, click here to start the Online Data Science Course

Furthermore, if you want to read more about data science, read our Data Science Blogs

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A Comprehensive Introduction to Data Wrangling and Its Importance

Top 5 Careers in Data Science You Need to Know About

Top 5 Careers in Data Science You Need to Know About

 

Reports suggest that around 2.5 quintillion bytes of data are generated every single day. As the online usage growth increases at a tremendous rate, there is a need for immediate Data Science professionals who can clean the data, obtain insights from it, visualize it, train model and eventually come up with solutions using Big data for the betterment of the world.

By 2020, experts predict that there will be more than 2.7 million data science and analytics jobs openings. Having a glimpse of the entire Data Science pipeline, it is definitely tiresome for a single human to perform and at the same time excel at all the levels. Hence, Data Science has a plethora of career options that require a spectrum set of skill sets.

Let us explore the top 5 data science career options in 2019 (In no particular order).

 

1. Data Scientist

Data Scientist is one of the ‘high demand’ job roles. The day to day responsibilities involves the examination of big data. As a result of the analysis of the big data, they also actively perform data cleaning and organize the big data. They are well aware of the machine learning algorithms and understand when to use the appropriate algorithm. During the due course of data analysis and the outcome of machine learning models, patterns are identified in order to solve the business statement.

The reason why this role is so crucial in any organisation is that the company tends to take business decisions with the help of the insights discovered by the Data Scientist to have an edge over the company’s competitors. It is to be noted that the Data Scientist role is inclined more towards the technical domain. As the role demands a wide range of skill set, Data Scientists are one among the highest paid jobs.

 

Core Skills of a Data Scientist

  1. Communication
  2. Business Awareness
  3. Database and querying
  4. Data warehousing solutions
  5. Data visualization
  6. Machine learning algorithms

 

2. Business Intelligence Developer

BI Developer is a job role inclined more towards the Non-Technical domain but has a fair share of Technical responsibilities as well (if required) as a part of their day to day responsibilities. BI developers are responsible for creating and implementing business policies as a result of the insights obtained from the Technical team.

Apart from being a policymaker involving the usage of dedicated (or custom) Business Intelligence analytics tools, they will also have a fair share of coding in order to explore the dataset, present the insights of the dataset in a non-verbal manner. They help in bridging the gap between the technical team that works with the deepest technical understanding and the clients that want the results in the most non-technical manner. They are expected to generate reports from the insights and make it ‘less technical’ for others in the organisation. It is noted that the BI Developers have a deep understanding of Business when compared to Data Scientist.

 

Core Skills of a Business Analytics Developer

  1. Business model analysis
  2. Data warehousing
  3. Design of business workflow
  4. Business Intelligence software integration

 

3. Machine Learning Engineer

Once the data is clean and ready for analysis, the machine learning engineers work on these big data to train a predictive model that predicts the target variable. These models are used to analyze the trends of the data in the future so that the organisation can take the right business decisions. As the dataset involved in a real-life scenario would involve a lot of dimensions, it is difficult for a human eye to interpret insights from it. This is one of the reasons for training machine learning algorithms as it easily deals with such complex dataset. These engineers carry out a number of tests and analyze the outcomes of the model.

The reason for conducting constant tests on the model using various samples is to test the accuracy of the developed model. Apart from the training models, they also perform exploratory data analysis sometimes in order to understand the dataset completely which will, in turn, help them in training better predictive models.

 

Core Skills of Machine Learning Engineers

  1. Machine Learning Algorithms
  2. Data Modelling and Evaluation
  3. Software Engineering

 

4. Data Engineer

The pipeline of any data-oriented company begins with the collection of big data from numerous sources. That’s where the data engineers operate in any given project. These engineers integrate data from various sources and optimize them according to the problem statement. The work usually involves writing queries on big data for easy and smooth accessibility. Their day to day responsibility is to provide a streamlined flow of big data from various distributed systems. Data engineering differs from the other data science careers as in, it is concentrated on the system and hardware that aids the company’s data analysis, rather than the analysis of data itself. They provide the organisation with efficient warehousing methods as well.

 

Core Skills of Data Engineer

  1. Database Knowledge
  2. Data Warehousing
  3. Machine Learning algorithm

 

5. Business Analyst

Business Analyst is one of the most essential roles in the Data Science field. These analysts are responsible for understanding the data and it’s related trend post the decision making about a particular product. They store a good amount of data about various domains of the organisation. These data are really important because if any product of the organisation fails, these analysts work on these big data to understand the reason behind the failure of the project. This type of analysis is vital for all the organisations as it makes them understand the loopholes in the company. The analysts not only backtrack the loophole and in turn provide solutions for the same making sure the organisation takes the right decision in the future. At times, the business analyst act as a bridge between the technical team and the rest of the working community.

 

Core skills of Business Analyst

  1. Business awareness
  2. Communication
  3. Process Modelling

 

Conclusion

The data science career options mentioned above are in no particular order. In my opinion, every career option in Data Science field works complimentary with one another. In any data-driven organization, regardless of the salary, every career role is important at the respective stages in a project.

Follow this link, if you are looking to learn data science online!

You can follow this link for our Big Data courseThis course will equip you with the exact skills required. 

Additionally, if you are having an interest in learning Data Science, click here to start the Online Data Science Course

Furthermore, if you want to read more about data science, read our Data Science Blogs

The Revolutionary Growth Rate of Python and R in 2019

The Revolutionary Growth Rate of Python and R in 2019

Python and R have been around for well over 20 years. Python was developed in 1991 by Guido van Rossum, and R in 1995 by Ross Ihaka and Robert Gentleman. Both Python and R have seen steady growth year after year in the last two decades. Will that trend continue, or are we coming to an end of an era of the Python-R dominance in the data science segment? Let’s find out!

Python

Python in the last decade has grown from strength to strength. In 2013, Python overtook R as the most popular language used for data science, according to the Stack Overflow developer survey (Link).

In the last three years, Python was the most wanted language according to this survey (25% in 2018, JavaScript was second with 19%). It is by far the easiest programming language to learn, the Julia and the Go programming languages being honorable mentions in this regard.

Python shines in its versatility, being easy to use for data science, web development, utility programming, and as a general-purpose programming language. Even full-stack development can be done in Python, the only area where it is not used being mobile (although that may change if the Kivy mobile programming framework comes of age and stops stalling all the time). It was also ranked higher than JavaScript in the most loved programming languages for the last three years (Node.js and React.js have ranked below it consistently).

Will Python’s Dominance Continue?

We believe, yes, definitely. Two words – data science.

Python
From https://www.digitaldesignjournal.com

 

Data science is such a hot and happening field right now, and the data scientist job is hyped as the ‘sexiest job of the 21st century‘, according to Forbes. Python is by far the most popular language for data science. The only close competitor is R, which Python overtook in the KDNuggets data science survey of 2016 . As shown in the link, in 2018, Python held 65.6% of the data science market, and R was actually below RapidMiner, at 48.5%. From the graphs, it is easy to see that Python is eating away at R’s share in the market. But why?

Deep Learning

In 2018, we say a huge push towards advancement across all verticals in the industry due to deep learning. And what is the most famous tool for deep learning? TensorFlow and Keras – both Python-based frameworks! While we have Keras and TensorFlow interfaces in R and RStudio now, Python was the initial choice and is still the native library – kerasR and tensorflow in RStudio being interfaces to the Python packages. Also, a real-life implementation of a deep learning project contains more than the deep learning model preparation and data analysis.

There is the data preprocessing, data cleaning, data wrangling, data preparation, outlier detection and missing data values management section which is infamous for taking up 99% of the time of a data scientist, with actual deep learning model work taking just 1% or less of their on-duty time! And what language is used for this commonly? For general purpose programming, Python is the goto language in most cases. I’m not saying that R doesn’t have data preprocessing packages. I’m saying that standard data science operations like web scraping are easier in Python than in R. And hence Python will be the language used in most cases, except in the statistics and the university or academic fields.

Our prediction for Python – growth – even to 70% of the data science market as more and more research-level projects like AutoML keep using Python as a first language of choice.

What About R?

In 2016, the use of R for data science in the industry was 55%, and Python stood at 51%. Python increased by 33% and R decreased by 25% in 2 years. Will that trend continue and will R continue on its downward spiral? I believe perhaps in figures, but not in practice. Here’s why.

R
From: RStudio

 

Data science is at its heart, the field of the statistician. Unless you have a strong background in statistics, you will be unable to process the results of your experiments, especially in concepts like p-values, tests of significance, confidence intervals, and analysis of experiments. And R is the statistician’s language. Statistics and mathematics students will always find working in R remarkably easy and simple, which explains its popularity in academia. R programming lends itself to statistics. Python lends itself to model building and decent execution performance (R can be 4x slower). R, however, excels in statistical analysis. So what is the point that I am trying to express?

Simple – Python and R are complementary. They are best used together. You will find that knowledge of both Python and R will suit you best for most projects. You need to learn both. You can find this trend expressed in every article that speaks about becoming a data science unicorn – knowledge of both Python and R is required as a norm.

Yes, R is having a downturn in popularity. However, due to the complementary nature of the tools, I believe that R will have a part to play in the data scientist’s toolbox, even if it does dip a bit in growth in the years to come. Very simply, R is too convenient for a statistician to be neglected by the industry completely. It will continue to have its place in the toolbox. And yes; deep learning is now practical in R with support for Keras and AutoML as well as of right now.

Dimensionless Technologies

Dimensionless Technologies

Dimensionless Technologies is the market leader as far as training in AI, cloud, deep learning and data science in Python and R is concerned. Of course, you don’t have to spend 40k for a data science certification, you could always go for its industry equivalent – 100-120 lakhs for a US university’s Ph.D. research doctorate! What Dimensionless Technologies has as an advantage over its closest rival – (Coursera’s John Hopkins University’s Data Science Specialization) – is:

  • Live Video Training

The videos that you get on Coursera, edX, Dataquest, MIT OCW (MIT OpenCourseWare), Udacity, Udemy, and many other MOOCs have a fundamental flaw – they are NOT live! If you have a doubt in a video lecture, you only have the comments as a communication tool to the lectures. And when over 1,000 students are taking your class, it is next to impossible to respond to every comment. You will not and cannot get personalized attention for your doubts and clarifications. This makes it difficult for many, especially Indian students who may not be used to foreign accents to have a smooth learning curve in the popular MOOCs available today.

  • Try Before You Buy Fully

Dimensionless Technologies offers 20 hours of the course for Rs 5000, with the remaining 35k (10k of 45k waived if you qualify for the scholarship) payable after 2 weeks / 20 hours of taking the course on a trial basis. You get to evaluate the course for 20 hours before deciding whether you want to go through the entire syllabus with the highly experienced instructors who are strictly IIT alumni.

  • Instructors with 10 years Plus Industry Experience

In Coursera or edX, it is more common for Ph.D. professors than industry experienced professionals to teach the course. If you are good with American accents and next to zero instructor support, you will be able to learn a little bit about the scholastic side of your field. However, if you want to prepare for a 100K USD per year US data scientist job, you would be better off learning from professionals with industry experience. I am Not criticizing the Coursera instructors here, most have industry experience as well in the USA. However, if you want connections and contacts in the data science industry in India and the US, you might be a bit lost in the vast numbers of student who take those courses. Industry experience in instructors is rare in a MOOC and critically important to your landing a job.

  • Personalized Attention and Job Placement Guarantee

Dimensionless has a batch size of strictly not more than 25 per batch. This means that unlike other MOOCs with hundreds or thousands of students, every student in a class will get individual attention and training. This is the essence of what makes this company the market leader in this space. No other course provider has this restriction, which makes it certain that when you pay the money, you are 100% certain of completing your course, unlike all the other MOOCs out there. You are also given training for creating a data science portfolio, and how to prepare for data science interviews when you start applying to companies. The best part of this entire process is the 100% job placement guarantee.

If this has got your attention, and you are highly interested in data science, I encourage you to go to the following link to see more about the Data Science Using Python and R course, a strong foundation for a data science career:

Data Science using R & Python

If you want to read about more data science applications and opportunities, please do go through the following articles:

Can you learn Data Science and Machine Learning without Maths?

and,

Data Science in Esports

As always, enjoy learning data science!

The Demand and Salary Of A Data Scientist

The Demand and Salary Of A Data Scientist

Data, Data Generated Everywhere

Mind-Blowing  Statistics

The amount of data that is generated every day is mind-boggling. There was an article on Forbes by Bernard Marr that blew my mind. Here are some excerpts. For the full article, go to Link

 

There are 2.5 quintillion bytes of data created each day. Over the last two years alone 90 percent of the data in the world was generated.

On average, Google now processes more than 40,000 searches EVERY second (3.5 billion searches per day)!

Every minute of the day:

Snapchat users share 527,760 photos

More than 120 professionals join LinkedIn

Users watch 4,146,600 YouTube videos

456,000 tweets are sent on Twitter

Instagram users post 46,740 photos

With 2 billion active users Facebook is still the largest social media platform.

Here are some more intriguing Facebook statistics:

1.5 billion people are active on Facebook daily

Europe has more than 307 million people on Facebook

There are five new Facebook profiles created every second!

More than 300 million photos get uploaded per day

Every minute there are 510,000 comments posted and 293,000 statuses updated (on Facebook)

And all this data was gathered 21st May, last year!

Data Scientist Salary

Photo by rawpixel on Unsplash

 

So I decided to do a more up to date survey. The data below was from an article written on 25th Jan 2019, given at the following link:

 

By 2020, the accumulated volume of big data will increase from 4.4 zettabytes to roughly 44 zettabytes or 44 trillion GB.

Originally, data scientists maintained that the volume of data would double every two years thus reaching the 40 ZB point by 2020. That number was later bumped to 44ZB when the impact of IoT was brought into consideration.

The rate at which data is created is increased exponentially. For instance, 40,000 search queries are performed per second (on Google alone), which makes it 3.46 million searches per day and 1.2 trillion every year.

Every minute Facebook users send roughly 31.25 million messages and watch 2.77 million videos.

The data gathered is no more text-only. An exponential growth in videos and photos is equally prominent. On YouTube alone, 300 hours of video are uploaded every minute.

IDC estimates that by 2020, business transactions (including both B2B and B2C) via the internet will reach up to 450 billion per day.

Globally, the number of smartphone users will grow to 6.1 billion by 2020 (this will overtake the number of basic fixed phone subscriptions).

In just 5 years the number of smart connected devices in the world will be more than 50 billion – all of which will create data that can be shared, collected and analyzed.

Photo by Fancycrave on UnsplashSo what does that mean for us, as data scientists?

Data = raw information. Information = processed data.

Theoretically, inside every 100 MB of the 44,000,000,000,000,000 GB  available in the world, today produced as data there lies a possible business-sector disrupting insight!

But who has the skills to look through 44 trillion GB of data?

The answer: Data Scientists! With Creativity and Originality in their Out-of-the-Box Thinking, as well as Disciplined Focus

data scientist daily wages

Here is a description estimating the salaries for data scientists followed by a graphic which shows you why data science is so hyped right now:

From Quora

Answer by Vidita Mehta

Salary Trends in Data Analytics

Freshers in Analytics get paid more than then any other field, they can be paid up-to 6-7 Lakhs per annum (LPA) minus any experience, 3-7 years experienced professional can expect around 10-11 LPA and anyone with more than 7-10 years can expect, 20-30 LPA.

Opportunities in tier 2 cities can be higher, but the pay-scale of Tier 1 cities is much higher.

E-commerce is the most rewarding career with great pay-scale especially for Fresher’s, offering close to 7-8 LPA, while Analytics service provider offers the lowest packages, 6 LPA.

It is advised to combine your skills to attract better packages, skills such as SAS, R Python, or any open source tools, offers around 13 LPA.

Machine Learning is the new entrant in analytics field, attracting better packages when compared to the skills of big data, however for a significant leverage, acquiring the skill sets of both Big Data and Machine Learning will fetch you a starting salary of around 13 LPA.

Combination of knowledge and skills makes you unique in the job market and hence attracts high pay packages.

Picking up the top five tools of big data analytics, like R, Python, SAS, Tableau, Spark along with popular Machine Learning Algorithms, NoSQL Databases, Data Visualization, will make you irresistible for any talent hunter, where you can demand a high pay package.

As a professional, you can upscale your salary by upskilling in the analytics field.

So there is no doubt about the demand or the need for data scientists in the 21st century.

Now we have done a survey for India. but what about the USA?

The following data is an excerpt from an article by IBM< which tells the story much better than I ever could:

From: Forbes magazine

 

Jobs requiring machine learning skills are paying an average of $114,000.

Advertised data scientist jobs pay an average of $105,000 and advertised data engineering jobs pay an average of $117,000.59% of all Data Science and Analytics (DSA) job demand is in Finance and Insurance, Professional Services, and IT.

Annual demand for the fast-growing new roles of data scientist, data developers, and data engineers will reach nearly 700,000 openings by 2020.

By 2020, the number of jobs for all US data professionals will increase by 364,000 openings to 2,720,000 according to IBM.

Data Science and Analytics (DSA) jobs remain open an average of 45 days, five days longer than the market average.

And yet still more! Look below:

 

By 2020 the number of Data Science and Analytics job listings is projected to grow by nearly 364,000 listings to approximately 2,720,000 The following is the summary of the study that highlights how in-demand data science and analytics skill sets are today and are projected to be through 2020.

There were 2,350,000 DSA job listings in 2015

By 2020, DSA jobs are projected to grow by 15%

Demand for Data scientists and data engineers is projectedto grow byneary40%

DSA jobs advertise average salaries of 80,265 USD$

81% of DSA jobs require workers with 3-5 years of experience or more.

For more, see: Link
  • Machine learning, big data, and data science skills are the most challenging to recruit for and potentially can create the greatest disruption to ongoing product development and go-to-market strategies if not filled.

So where does Dimensionless Technologies, with courses in Python, R, Deep Learning, NLP, Big Data, Analytics, and AWS coming soon, stand in the middle of all the demand?

The answer: right in the epicentre of the data science earthquake that is no hitting our IT sector harder than ever.The main reason I say this is because of the salaries increasing like your tummy after you finish your fifth Domino’s Dominator Cheese and Pepperoni Pizza in a row everyday for seven days! Have a look at the salaries for data science:

In India

From Quora

Do you know which city in India pays highest salaries to data scientist?

Mumbai pays the highest salary in India around 12.19L p.a.

Report of Data Analytics Salary of the Top Companies in India

  • Accenture’s Data Analytics Salary in India: 90% gets a salary of about Rs 980,000 per year
  • Tata Consultancy Services Limited Data Analytics Salary in India: 90% of the employees get a salary of about Rs 550,000 per year. A bonus of Rs 20,000 is paid to the employees.
  • EY (Ernst & Young) Data Analytics Salary in India: 75% of the employees get a salary of Rs 620,000 and 90% of the employees get a salary of Rs 770,000.
  • HCL Technologies Ltd. Data Analytics Salary in India: 90% of the people are paid Rs 940,000 per year approximately.

 

In the USA

Data Scientist salaries in united state

From glassdoor.com

 

To convert into INR, in the US, the salaries of a data scientist stack up as follows:

Lowest: 86,000 USD = 6,020,000 INR per year (60 lakh per year)

Average: 117,00 USD = 8,190,000 INR per year (81 lakh  per year)

Highest: 157,000 USD = 10,990,000 INR per year(109 lakh per year or approximately one crore)

at the exchange rate of 70 INR = 1 USD.

By now you should be able to understand why everyone is running after data science degrees and data science certifications everywhere.

The only other industry that offers similar salaries is cloud computing.

A Personal View

On my own personal behalf, I often wondered – why does everyone talk about following your passion and not just about the money. The literature everywhere advertises“Follow your heart and it will lead you to the land of your dreams”. But then I realized – passion is more than your dreams. A dream, if it does not serve others in some way, is of no inspirational value. That is when I found the fundamental role – focus on others achieving their hearts desires, and you will automatically discover your passion. I have many interests, and I found my happiness doing research in advanced data science and quantum computing and dynamical systems, focusing on experiments that combine all three of them together as a single unified theory. I found that that was my dream. But, however, I have a family and I need to serve them. I need to earn.

Thus I relegated my dreams of research to a part-time level and focused fully on earning for my extended family, and serving them as best as I can. Maybe you will come to your own epiphany moment yourself reading this article. What do you want to do with your life? Personally, I wish to improve the lives of those around me, especially the poor and the malnourished. That feeds my heart. Hence my career decision – invest wisely in the choices that I make to garner maximum benefit for those around me. And work on my research papers in the free time that I get.

So my hope for you today is: having read this article, understand the rich potential that lies before you if you can complete your journey as a data scientist. The only reason that I am not going into data science myself is that I am 34 years old and no longer in the prime of my life to follow this American dream. Hence I found my niche in my interest in research. And further, I realized that a fundamental ‘quantum leap’ would be made if my efforts were to succeed. But as for you, the reader of this article, you may be inspired or your world-view expanded by reading this article and the data contained within. My advice to you is: follow your heart. It knows you best and will not betray you into any false location. Data science is the future for the world. make no mistake about that. And – from whatever inspiration you have received go forward boldly and take action. Take one day at a time. Don’t look at the final goal. Take one day at a time. If you can do that, you will definitely achieve your goals.

Company wise salaries

The salary at the top, per year. From glassdoor.com. Try not to drool. 🙂

Finding Your Passion

Many times when you’re sure you’ve discovered your passion and you run into a difficult topic, that leaves you stuck, you are prone to the famous impostor syndrome. “Maybe this is too much for me. Maybe this is too difficult for me. Maybe this is not my passion. Otherwise, it wouldn’t be this hard for me.” My dear friend, this will hit you. At one point or the other. At such moments, what I do, based upon lessons from the following course, which I highly recommend to every human being on the planet, is:  Take a break. Do something different that completely removes the mind from your current work. Be completely immersed in something else. Or take a nap. Or – best of all – go for a run or a cycle. Exercise. Workout.  This gives your brain cells rest and allows them to process the data in the background. When you come back to your topic, fresh, completely free of worry and tension, completely recharged, you will have an insight into the problem for you that completely solves it. Guaranteed. For more information, I highly suggest the following two resources:

 

 or the most popular MOOC of all time, based on the same topic: Coursera

 

How to learn powerful mental tools

Learning How to Learn – Coursera and IEEE

 

This should be your action every time you feel stuck. I have completely finished this MOOC and the book and it has given me the confidence to tackle any subject in the world, including quantum mechanics, topology, string theory, and supersymmetry theory. I strongly recommend this resource (from experience).

Conclusion

Dimensionless | Data Science Courses

So Dimensionless Technologies (link given above) is your entry point to all things data science. Before you go to TensorFlow, Hadoop, Keras, Hive, Pig, MapReduce, BigQuery, BigTable, you need to know the following topics first:  

Data Science using R & Python

Python and R – the A, B, C, D, E, F, and G of data science!

Big Data Analytics NLP

Big Data and Analytics – this is what we talked about in this post!

Deep Learning

Deep Learning – the X, Y, and Z of data science today!

For further reading, I strongly recommend the following blog posts:

2019 Predictions for AI & Analytics

and:

Big Data : Meaning, Components, Collection & Analysis

All the best. Your passion is not just a feeling. It is a choice you make the day in and a day out whether you like it or not. That is the definition of character – to do what must be done even if you don’t feel like it. Internalize this advice, and there will be no limits to how high you can go. All the best!

AWS Big Data Prep Course – Why you Need it

AWS Big Data Prep Course – Why you Need it

What is AWS?

In very simple words, Amazon Web Services is a subsidiary of Amazon that provides on-demand cloud computing platforms to individuals, companies and governments, on a paid subscription basis. The technology allows subscribers to have at their disposal a virtual cluster of computers, available all the time, through the Internet.

Let us give a shot at a very technical description of AWS. Amazon Web Services (AWS) is a secure cloud services platform, offering computing power, database storage, content delivery and other functionality to help businesses scale and grow. Explore how millions of customers are currently leveraging AWS cloud products and solutions to build sophisticated applications with increased flexibility, scalability and reliability.

Capabilities?

  • Websites & Website Hosting: Amazon Web Services offers cloud web hosting solutions that provide businesses, non-profits, and governmental organizations with low-cost ways to deliver their websites and web applications. Whether you’re looking for marketing, rich media, or e-commerce website, AWS offers a wide range of website hosting options, and we’ll help you select the one that is right for you.
  • Backup & Recovery: AWS offers the most storage services, data-transfer methods, and networking options to build solutions that protect your data with unmatched durability and security
  • Data Archive: Amazon Web Services offers a complete set of cloud storage services for archiving. You can choose Amazon Glacier for affordable, non-time sensitive cloud storage, or Amazon Simple Storage Service (S3) for faster storage, depending on your needs. With AWS Storage Gateway and our solution provider ecosystem, you can build a comprehensive, storage solution.
  • DevOps: AWS provides a set of flexible services designed to enable companies to more rapidly and reliably build and deliver products using AWS and DevOps practices. These services simplify provisioning and managing infrastructure, deploying application code, automating software release processes, and monitoring your application and infrastructure performance.
  • Big Data: AWS delivers an integrated suite of services that provide everything needed to quickly and easily build and manage a data lake for analytics. AWS-powered data lakes can handle the scale, agility, and flexibility required to combine different types of data and analytics approaches to gain deeper insights, in ways that traditional data silos and data warehouses cannot. AWS gives customers the widest array of analytics and machine learning services, for easy access to all relevant data, without compromising on security or governance.

Why learn AWS?

DevOps Automation

You don’t want your data scientists spending time on DevOps tasks like creating AMIs, defining Security Groups, and creating EC2 instances. Data science workloads benefit from large machines for exploratory analysis in tools like Jupyter or RStudio, as well as elastic scalability to support bursty demand from teams, or parallel execution of data science experiments, which are often computationally intensive.

Cost controls, resource monitoring, and reporting

Data science workloads often benefit from high-end hardware, which can be expensive. When data scientists have more access to scalable compute, how do you mitigate the risk of runaway costs, enforce limits, and attribute across multiple groups or teams?

Environment management

Data scientists need agility to experiment with new open source tools and packages, which are evolving faster than ever before. System administrators must ensure stability and safety of environments. How can you balance these two points in tension?

GPUs

Neural networks and other effective data science techniques benefit from GPU acceleration, but configuring and utilizing GPUs remains easier said than done. How can you provide efficient access to GPUs for your data scientists without miring them in DevOps configuration tasks?

Security

AWS offers world-class security in their environment — but you must still make choices about how you configure security for your applications running on AWS. These choices can make a significant difference in mitigating risk as your data scientists transfer logic (source code) and data sets that may represent your most valuable intellectual property.

Our AWS Course

1. AWS Introduction

This section covers the basic and different concepts and terms which are AWS specific. This lays out the basic setting where learners are fed with all the AWS specific terms and are prepared for the deep dive.

2. VPC Subnet

A virtual private cloud (VPC) is a virtual network dedicated to your AWS account. It is logically isolated from other virtual networks in the AWS Cloud. You can launch your AWS resources, such as Amazon EC2 instances, into your VPC.

3. Route

A route table contains a set of rules, called routes, that are used to determine where network traffic is directed. Each subnet in your VPC must be associated with a route table; the table controls the routing for the subnet. A subnet can only be associated with one route table at a time, but you can associate multiple subnets with the same route table.

4. EC2

Amazon Elastic Compute Cloud (Amazon EC2) is a web service that provides secure, resizable compute capacity in the cloud. It is designed to make web-scale cloud computing easier for developers.

5. IAM

AWS Identity and Access Management (IAM) is a web service that helps you securely control access to AWS resources. You use IAM to control who is authenticated (signed in) and authorized (has permissions) to use resources.

6. S3

Amazon Simple Storage Service (Amazon S3) is an object storage service that offers industry-leading scalability, data availability, security, and performance. This means customers of all sizes and industries can use it to store and protect any amount of data for a range of use cases, such as websites, mobile applications, backup and restore, archive, enterprise applications, IoT devices, and big data analytics.

7. Lambda

AWS Lambda is a ‘compute’ service that lets you run code without provisioning or managing servers. AWS Lambda executes your code only when needed and scales automatically, from a few requests per day to thousands per second

8. SNS

Amazon Simple Notification Service (SNS) is a highly available, durable, secure, fully managed pub/sub messaging service that enables you to decouple microservices, distributed systems, and serverless applications. Amazon SNS provides topics for high-throughput, push-based, many-to-many messaging.

9. SQS

Amazon Simple Queue Service (SQS) is a fully managed message queuing service that enables you to decouple and scale microservices, distributed systems, and serverless applications. SQS eliminates the complexity and overhead associated with managing and operating message-oriented middleware and empowers developers to focus on differentiating work.

10. RDS

Amazon Relational Database Service (Amazon RDS) makes it easy to set up, operate, and scale a relational database in the cloud. It provides cost-efficient and resizable capacity while automating time-consuming administration tasks such as hardware provisioning, database setup, patching and backups.

11. Dynamo DB

Amazon DynamoDB is a key-value and document database that delivers single-digit millisecond performance at any scale. It’s a fully managed, multi-region, multi-master database with built-in security, backup and restores, and in-memory caching for internet-scale applications. DynamoDB can handle more than 10 trillion requests per day and support peaks of more than 20 million requests per second.

13. Cloud Formation

AWS CloudFormation provides a common language for you to describe and provision all the infrastructure resources in your cloud environment. CloudFormation allows you to use a simple text file to model and provision, in an automated and secure manner, all the resources needed for your applications across all regions and accounts. This file serves as the single source of truth for your cloud environment.

14. Projects

No learning can happen without doing any project. This is our mantra at Dimensionless Technologies. We have different projects planned for our learners which will help in implementing all the learners during the course.

Why Dimensionless as your learning partner?

Dimensionless Technologies provide instructor-led LIVE online training with hands-on different problems. We do not provide classroom training but we deliver more as compared to what a classroom training could provide you with

Are you sceptical of online training or you feel that online mode is not the best platform to learn? Let us clear your insecurities about online training!

  1. Live and Interactive sessions
    We conduct classes through live sessions and not pre-recorded videos. The interactivity level is similar to classroom training and you get it in the comfort of your home.

 

  1. Highly Experienced Faculty
    We have very highly experienced faculty with us (IIT`ians) to help you grasp complex concepts and kick-start your career success journey

 

  • Up to Data Course content
    Our course content is up to date which involves all the latest technologies and tools. Our course is well equipped for learners to grasp the knowledge required to solve real-world problems through their data analytical skills

 

  • Availability of software and computing resource
    Any laptop with 2GB RAM and Windows 7 and above is perfectly fine for this course. All the software used in this course are Freely downloadable from the Internet. The trainers help you set it up in your systems. We also provide access to our Cloud-based online lab where these are already installed.

 

  • Industry-Based Projects
    During the training, you will be solving multiple case studies from different domains. Once the LIVE training is done, you will start implementing your learnings on Real Time Datasets. You can work on data from various domains like Retail, Manufacturing, Supply Chain, Operations, Telecom, Oil and Gas and many more.

 

  • Course Completion Certificate
    Yes, we will be issuing a course completion certificate to all individuals who successfully complete the training.

 

  • Placement Assistance
    We provide you with real-time industry requirements on a daily basis through our connection in the industry. These requirements generally come through referral channels, hence the probability to get through increases manifold

Conclusion

Dimensionless technologies have the right courses for you if you are aiming to kick-start your career in the field of data science. Not only we cover all the important concepts and technologies but also focus on their implementation and usage in real-world business problems. Follow the link to register yourself for the free demo of the courses!

You can follow this link for our AWS course

Additionally, if you are interested in learning Data Science, click here to get started

Furthermore, if you want to read more about data science, you can read our blogs here

Also, the following are some suggested blogs you may like to read

Introduction to AWS Big Data

Top 5 Advantages of AWS Big Data Speciality

Introduction to Agent-Based Modelling