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How to Build a Career in Data Science?

In order to start off on your journey of building a career in the field of data science, it is important for you to thoroughly analyse both the field and yourself in order to ensure the compatibility of the two. Generally speaking, any Data science professional, or Data Scientist as they are commonly known is supposed to have a certain skillset.

This skill set must include various skills and techniques like business expertise, data manoeuvrability, knowledge of SQL and other programming languages, a curious outlook towards their job, knowledge about the nuances of IT, receptiveness when it comes to teamwork, and most importantly the statistical expertise and ability to access to data.

Also, Read Data Science – Things You Should Know

Thus, this skillset provides a candidate with the perfect amount of capabilities and opportunities to build a rewarding career in data science for themselves. Customization is the rule of the day in today’s economic setup, with every single firm out there trying their best to provide accurate and astute services to their target audiences. With the access to social media and the internet increasing manifold, simultaneously increasing is the need of professionals who are required to go through tons of information in order to find out the exact insights which would be needed to help the company achieve massive profits.

This is probably why many job searching sites reflect an increasing number of position offerings for professionals who are interested in the field of Data Science. So if you happen to be one such professional, then there are a lot of options for you to try out for in this field. These positions could range anything from a Business Intelligence Analyst, Data Mining Engineer, and Data Architect and of course the Data Scientist and many others. All of these positions do require an individual to be able to demonstrate all of the skills mentioned above.

With more and more big named companies like Oracle, Apple, Microsoft and Walmart demanding professionals working in the data science industry increases, at the same time there is also a great demand in general for such professionals. If we are to go by what a number of surveys are saying, by this year there are estimated to be more than 20,000 openings for data science professionals all over the world.


In order to grow your career in Data Science, it is necessary for you to have certain educational requirements. These don’t really refer only to the fact that you need to belong to a certain educational background, but they actually refer to the amount of knowledge that you have when it comes to certain data programming or data analytical tools.

These tools include R Programming, SAS Programming, Hadoop, SQL, Python, Hive and so on. Today there are so many institutes out there, both internationally and nationally that offer short-term training courses, in order to train professionals in the usage of such tools.In India especially there Imarticus Learning which is one such institute that offers industry endorsed courses in Data Science and Analytics.

Related Article: Optimise Your Workflow – Tips for Future Data Scientist

Which Skills are Required for Machine Learning Jobs?

Machine language is generally related artificial intelligence, which provides the machine or computers with the ability to complete certain tasks like diagnosis, planning, prediction, recognition or robot control. It consists of different algorithms, which you can use to teach the machines to change and grow when exposed to new data.

The process of implementing machine language is somewhat similar to data mining because the process looks through data and searches for the same pattern. Now that you have an idea of what machine learning is, let’s have a look at the skills that are required to get a machine learning job.

Also Read: Future of Machine Learning in India

#1: Computer Science & Programming Skills

Some of the fundamentals of computer science are essential when you are looking to learn machine learning. Concepts like data structure, algorithms, complexity and computability, along with computer architecture are essential for artificial intelligence. In fact, you should also have knowledge of programming languages like C, C++, Java, Python and R, among others. A little bit knowledge of assembly language doesn’t hurt either.

#2: Probability & Statistics

Conditional probability, and its characteristics and the techniques derived from it plays a key role in the machine learning algorithms. Moreover, you should also know about the different terms of statistics like mean, median and mode along with variance and standard deviation. These are all necessary to not only observe the pattern but also validate the data that is received through different means. Some machine learning algorithms are in essence an extension of the common statistical operation procedures.

#3: Applied Mathematics & Algorithms

You need to know not only how to solve a problem but also how to implement it in short executable steps when it comes to machine learning. Algorithms help you to understand how to break down a problem into executable steps, and that is why this is important. In addition, you also need to know about gradient, convex optimization and its application in daily life, so that you can implement it in machine learning.

#4: Operating Systems

When it comes to machine learning, most of the coding is done in Linux or some version of it. So, you need to be versatile with Unix or a version of Linux, which is in use presently. You also need to know about the Linux tools, which will make your life easier in the long run. Some examples include grep, find, sort and tr.

#5: Software Engineering & Designing of Systems

When you are designing a machine learning tool, you are also designing an advanced software. So, at the end of the day, you need to know how to design system, and how you can implement your ideas in that. You also need to understand how different algorithms interact with your system, and how you can speed up the process without compromising on the resource space.

Now that you know about the skills required for machine learning jobs, it is time to get started on acquiring these skills. In case you have some of these skills, make sure you hone them so that you can implement it and build a great system when the time comes. Good luck!

Related Article: What is The Easiest Way To Learn Machine Learning?


How to Become A Data Scientist?

A data scientist is a new trend, and everyone has been working to find a stable place in it. It is ranked on the top as one of the hottest jobs in the Harvard business review. This guide will help you to know about the details of becoming a data scientist.

Who is a Data Scientist?

Before going towards the steps, it is mandatory to know what it means: Data scientists are people expert in analyzing data and have practical technical skills to tackle complex technical problems. They are a unique blend of a mathematician, computer scientist, and trend spotter.
They are professionals in both it and business sector, and that’s why they earn more than enough.

Also Read: What is the Role of a Data Scientist?

How to become one?

Learn statistics, ml, and algebra. A good data scientist can solve any problem better than a computer science engineer. They are well learned in the statistics and algebra. That is why deepening the knowledge about the mentioned subject is a fundamental need to start the journey as a data scientist.

Learn more about databases

The database is a frequent subject in computer science. A data scientist, however, makes you safer in securing your high paying job. This can only happen if a thorough study of databases is done.


It is a common part of the computer science world. It is, however, important to note that unless one becomes a better coder, they cannot become a data scientist. A data scientist has efficient experience in coding, and they have deep knowledge about it.

Practice and work on projects

Whosoever has achieved the status of becoming a data scientist; they will recommend you to start practising your coding and programming skills in real time. Practice makes a man perfect, and a data scientist is an ideal computer science engineer.

Practice on big data software

The data scientist has to deal with various non-segregated and segregated data. To make things easy, many data scientist use big data software such as MapReduce or Hadoop. Becoming an expert in using the software can help you in achieving your desired goal.

Become expert in data munging

It is a process in which raw data is further converted into easy to study and well-analyzed form. A data scientist is an expert in it. You have to practice more to become one.

Learn more!

It is one of the critical processes of becoming a data scientist. It is known to everyone that a computer scientist has to be updated about the new languages in the field. A data scientist is also the same. They have to be well learned in their area and become experts. This can happen by being in the association of like-minded people and developing the curiosity to learn more.

Development of powerful communication skills

How will you communicate with people if you fear to go in front of an audience? Communication skills, therefore, most important part of any job profile. An expert data scientist has practical communication skills that separate them from others.

Apply for jobs

After learning, practising and studying thoroughly, if you are sure of your talent and skills, you can start applying for the jobs by making an attractive portfolio.


Becoming a data scientist is not an easy task.
However, the situation can be tackled by using the process of learning and practice. The more you practice, the better you become. Even if you fail in interviews, you can still practice and learn. Who knows? You can be the next data scientist expert.

Related Article: What a Data Scientist Could Do?

What is The Easiest Way To Learn Machine Learning?

Machine Learning is applied to enable machines to process and make decisions by figuring patterns without explicit programming. This can be achieved via multiple techniques one of them is through training machines on a large dataset called training dataset that is used to create models to help machines in making decisions when exposed to real-time data.

There is no shortcut to learning, and when it comes to Machine Learning the process is definitely not quick but if you are inclined to Artificial Intelligence then there is a smarter way of ensuring quality learning with little investment.

Also Read: Future of Machine Learning in India

Machine Learning is about optimization and to optimize data mining learners should have a decent level of programming knowledge and skills. There are many languages that provide Machine Learning capabilities and there are various online courses available to learn them, but it is imperative to choose a language you already have some background with to make sure you pick up fast. Python is easy to learn and is optimal for data manipulation and repeated tasks while R caret is a little elusive but is good for ad-hoc analysis and exploring datasets.

Before you really embark on your journey to become a Machine Learning specialist you need to understand the concepts of Machine Learning and invest in the theory of it via specific online courses like Machine Learning course from Andrew Ng and Learning from Data course by Prof. Yaser Abu-Mostafa. Learning from videos has proven to be more efficient and quick, although the power of books should never be undermined since in this article our focus is to make learning quicker I recommend videos and slideshows over books and papers.

As you acquire deeper knowledge of Machine Learning you would come across various Machine Learning algorithms, these are broadly classified into three categories based on the amount of “feedback” provided to a system to enforce learning, these categories are:

1.) Supervised Learning.

2.) Un-Supervised Learning.

3.) Reinforcement Learning.

To acquire a better understanding of these algorithms you need to have the fundamental knowledge of Linear algebra, Probability theory, Optimization, Calculus and Multivariable calculus etc.

Machine Learning works on raw unstructured big data so it is important for you to understand data statistics including descriptive and inferential statistics. There are a number of online courses on statistics, Udacity and Edx provide courses that cover descriptive stats, probability, and inferential stats.

You also need to have a deep understanding of various Data Cleaning techniques and different stages of data explorations to deal with a large number of unstructured data bits. Most of the times Machine Learning systems need to process incomplete or damaged/scrambled data, for such scenarios handy knowledge of techniques like Variable Identification, Univariate and Multivariate analysis, Missing values treatment, Outlier treatment becomes very useful.

Once you have undergone the basic courses for Machine Learning foundation building it is time to practice what you have learned, Kaggle Knowledge competition is a good place to start. By experimenting more you can polish your skills well and know your level, and shortcomings on which you can work on. Popular Machine Learning communities to help you further in learning are as follows:



Related Article: What are The Skills You Need to Become a Machine Learning Engineer?

Why is There A Huge Demand For Data Science Jobs in India?

Huge Demand For Data Science Jobs in India

Are numbers your game? Do you love coding and do algorithms excite you? If the answer to that was yes, you are in for a huge professional growth. All you need to do is step into the fast-emerging field of data sciences and your future is secured. Be it retail, manufacturing or healthcare, the demand for data analytics and scientists has never seen such a rise before in India. The number of jobs available increased to 42% from 2014 to 2015, 52% from 2015 to 2016 and has almost doubled from 2016 to 2017. Right now, there are close to 50,000 date sciences jobs available in India.

Analysing and reorganisation of data to reveal human patterns and interactions is what data science involves in simple terms.

Also Read: Average Salary of a Data Scientist

Organisations like PayPal, AIG, Target, Walmart, and Mercedes-Benz Accenture are few renowned global companies that have today put up their data science centres in India. According to a report by Simplilearn, Bangalore has the highest number of jobs in data sciences, followed by Hyderabad, Pune, Delhi, Mumbai, and Chennai.

20 years ago when the technology was all about slow computers and primitive computer languages, there was no absolute scope for data sciences to exist. Now that it has progressed, there are still limited resources for people to enhance their data sciences skill – including self-help books, research papers and online courses. So the labour available in this profession is low and the salary very high. This is one of the biggest reasons there is a boom in the professional opportunities of data sciences.  According to Glassdoor, the average pay of a person with the right skills is 19 lacs per annum.

Also, it is not just the big guns of the tech world that has openings for data sciences jobs. Even the smaller startups are open to hiring entry level data analysts and scientists for reorganising the data they started out with. This enables newbie’s to find a substantial ground to enhance their skills and the company can afford to pay them less. OYOFit, Oliveboard, CollegeDekho are few startups who have largely indulged in data analytics.

Data science is also a broad field that requires people with varied skill sets, belonging to different professional areas. Someone who is into computers will get into the database storing while those proficient in statistics will be a good analyst. Professionals who are today entering this field have a degree in computer science, engineering, economics, social sciences and business. Such people have mastered the skills of data sciences through self-study and an unending spirit of problem-solving.

Apart from these factors, data science has been termed by the Harvard Business Review as the “sexiest job of the 21st century. Top data scientists of the world today are working with companies like Google, Amazon, Facebook, LinkedIn and Twitter. The responsibilities that a data scientist has are very unique. Its sub-roles include – data architect, business analyst, data engineer, database administrator, and data and analytics manager. This helps professionals to grow in their career at a faster pace.

Related Article: What is The Difference Between Data Analysis and Data Science?

The Advantages of Learning Fintech

In any market, when a certain industrial product or service is launched, there are a variety of reasons why it may work or may not work for the industry. This holds especially true in terms of any of the new technologies entering the market. The growth of such technological industries determines the influx of new players, some of whom may come in with a silver spoon in their mouths, basically turning everything they touch into gold, great turnovers, fabulous mergers and acquisitions and so on. At the same time, there are also a number of failures and flounders in the industry.

Also Read: Introduction to Fintech

The fintech industry is not a very new one and has been through its fair share of ups and downs. In spite of a little while of a slump in the middle, there are many ways in which the fintech industry has strived and turned its trade positive in order to come to be thriving in the present times. One of the main reason why this industry has seen more ups is that it catered not just to the big guns but also to the small business owners which included all the disadvantaged sections of the society, women, minorities and immigrants.

This industry is going to grow on and be stronger, which is why there are many experts who believe it is advantageous to get certified and get into this industry.


Here are a few reasons why we believe there are great advantages in learning Fintech;

  1. Financial technology has been criticised in the sense that it would not see a large number of growth and developments in the future. But the majority thinks otherwise. The reason that fintech is actually going to be there in the industry for a much longer time is the very fact that banks are getting digitalized and they are doing so at an alarmingly fast rate. The industry is no longer at the innovator stage and is more at an adoption stage. Thus getting certified to work in this industry will prove extremely advantageous.
  2. Fintech companies or those organizations that are involved in this industry don’t really have the rules of capitalism apply to them. This is mainly because of the fact that, in capitalism, there is the chance of companies getting explosive dividends and later on also going bust. But in case of fintech whenever a void is created, there would be a number of alternative lenders to jump in to fill it up. This is the reason why the field of fintech which does not follow a ‘growth at all costs’ mentality will definitely flourish both in terms of growth and job opportunities.
  3. The world is getting digitalized on a massive scale and today no one really has any time for actually getting their transactions done from slower, larger financial corporations anymore. Technology has touched every single sphere of life including the sphere of banking. This is why learning the various nuances of fintech would be most definitely an advantage for candidates entering into this new world of technology.

The above reasons are why learning fintech is a great option for candidates looking to get into the industry today.

Related Article: What are the career opportunities in the Fintech sector in India?

What Are Data Science Courses?

Data scientists today are in great demand for this fast-paced world. With immense data to be handled and dealt with, data scientists are a comfort. The profession is surely touching the sky. Well, gone are the days when people lined up for an engineering or medicine degree to make some money! As obvious as it could be, data scientists are paid really well. Being a data scientist can make your dreams of a luxury home and financial independence come true. For fast growth, companies need good data scientists and management of big data is no more a task.

Being a data scientist doesn’t require you to have a purely technical background. Even if you are from a non-tech background you can still fetch high paying data scientist jobs. All you need is learning professional skills and command of programming languages. This is because a lot of automated tasks are involved in data cleaning, preparation and analytics. Several tools are designed to carry out such operations and without making use of coding the complex algorithms. Many of the tools for big data management have inbuilt algorithms. One needs some building blocks to understand those algorithms. However, enrolling in data science courses can truly be more effective and can add a charm to your CV.

Also Read: Average Salary of a Data Scientist

The following are the list of courses useful for being a professional data scientist-

  1. Fundamentals of Data and Its Management
  2. Statistical Modelling
  3. Mathematical Algorithm
  4. Data Science
  5. Data Mining
  6. Data Analysis
  7. Data Engineering
  8. Machine Learning

You don’t need expertise in all of these, so you can calm down. There is the very slight difference between all these courses and they all are interlinked. However, it is a must to know fundamentals of statistics and mathematics. Data engineering and machine learning require good programming skills. Data analysis and mining are like visualizing the data from beginning till the end applying the statistical concepts using the tools and interpret the results.

You can learn Data Analytics using programs like:

  • R Programming
  • Python
  • SAS
  • SPSS
  • Minitab
  • Stata
  • Matlab

Online portals can definitely ace your career in data science by providing free data science courses. As these courses are re-defining segments and spreading across knowledge base for management of big data and other elements, it becomes imperative for the general populace to be aware of the data science courses. Conceptualities like Data Science, Statistical Modelling, Data Engineering and Machine Learning have already created ripples in the industry with respect to their advent in technology and knowing about them will definitely help.

Related Article: Top 5 Data Science Trends in 2018

Big Data Soon To Cure Infectious Diseases

big data in Healthcare

2016 has seen a massive spurt of popularity for the field of Big Data Science. With concepts like Artificial Intelligence and predictive analytics, coming into the limelight, the whole world experienced the dawn of a new era in terms of the field of information technology. We are all aware of the various marvels that can be performed with the help of data analytics. Some very popular examples could be the growth and development in Artificial Intelligence, the various employee enhancements strategies of the Human Resources Departments, the fact that the entire world could be going cashless soon enough and so on. But did you know, that big data is now can be advanced enough in healthcare industry, to stop and fight infectious diseases?

big-data-healthcareEarlier in the early, post modern times, it was a huge task to be able to track any kind of disease at all. Probably this was why there were a majority of outbreaks of epidemic diseases. Medical information was either unavailable or there was no medium through which it could be kept a track of. With the advent of technology it became very possible to store huge amounts of medical records on the PC’s. But then, as there was no way to connect countries wirelessly, it was impossible to inform everyone about certain outbreaks. This was because you would have to print every single piece of information and then take the pains of actually going ahead and transferring it to different quarters. But with the advent of the internet and now with the advent of Big Data Science, the scene has changed drastically. What earlier seemed a fable like thought, today seems a possible reality. With the help of various data analytics tools today, it is soon going to be possible to map various diseases, their impact as well as, make it possible for asserting predictions.


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healthcare and Big DataOutbreaks are usually found in places with high chances of exposure, like for example hospitals or places with a large number of infected people. Big data would be able to track such occurrences, which are also known as “healthcare associated infections”, the benefit of which will help us then map the spread of such diseases. While on one hand, it was difficult to map these diseases in the early days, mainly because of the fact, that with time they changed as well. But today, with the concept of evolving maps in place, in addition to being interactive, these can map the progress as well as intensity of the diseases. These diseases are usually known to make rapid shifts in terms of how it spreads and where it spreads to, this is where the maps will be able to serves perfectly, mainly because this will help professionals to go on and predict of the next place, that the disease might affect, thus in a way forewarning. There are many efforts being taken to develop such data simulation systems by a number of Data Scientists. This is another reason why the field of Big Data Science has begun to attract more aspirants.

If you are planning to earn career in data science, then Imarticus Learning offer various business analytics courses in both online and classroom mode



Why Hadoop?

The term “big data” certainly isn’t just referring to the mounting volumes of information companies are collecting. It is just as much about the variety and velocity matters i.e. the massive amounts of unstructured data that needs to be stored, managed, cleaned and then dashed around to talk to other data or move in near real-time.
One cannot overlook the issue of volume. Estimates contend that financial and securities organizations are juggling around 3.8 petabytes per firm. Following behind the investment institutions, the banking industry is contending with around 1.9 petabytes.
The variety and need for speed on this data is the real crux of the issue—and big banks are starting to see clearer paths as they look to advanced analytics platforms outside of the traditional databases as well as to frameworks like Hadoop.
To put the real challenges in some context, we take a look at a few examples of large financial and banking institutions that are hitting the upper limits of their traditional systems and looking beyond to new analytics and framework solutions.

Morgan Stanley’s Big Data Approach

As one of the largest global financial services organizations in the world with over $300 billion in assets under its care, Morgan Stanley keeps close tabs on new frameworks and tools to manage the complex information pools that back high-stakes decisions.
The financial services giant has been vocal about how it is solving the challenges of the industry, most recently by looking to the Hadoop framework.
The limitations of the traditional databases and grid computing paradigms that served the financial giant for years were stretched to the limit.
Like several other investment banks, Morgan Stanley started to look to Hadoop as the framework of choice to support growing data size, but more importantly, data complexity and the need for solid speed. The adoption of Hadoop allowed Morgan Stanley to “bring really cheap infrastructure into a framework” that let them install Hadoop and let it handle the tasks.
The company now has a “very scalable solution for portfolio analysis. At the core of the Hadoop future at Morgan Stanley is the matter of scalability. It allows management of petabytes of data, which is unheard of in the traditional database world.

Bank of America Tackles Big Data

As one of the largest banks in the States, Bank of America has been in good company with others of the same ilk that are seeking to tap into Hadoop to manage large amounts of transaction and customer data.
Big data will create a new era for businesses of all types, spawning a “second Industrial Revolution” which will be driven by open source frameworks, including Hadoop which has the potential to be as disruptive as Linux was 20 years ago.
Hadoop enables the bank to be good custodians of cash, & increase transparency & in the larger system to drive positive change.
Hadoop in finance has fast emerged as the preferred choice for financial big data as Hadoop financial analysis has the following advantages:

  1. Segregation of data and computation to save network bandwidth and faster calculations
  2. Financial big data requires performing thousands and millions of calculations in a matter of seconds. Traditional solutions import data over the server which causes unnecessary bottlenecks over the network and interferes with other processes
  3. Hadoop in finance can be leveraged to perform calculations locally and use the network only for transmitting the results which saves precious network space for more important processes.

The differentiator that Hadoop brings is by enabling doing the same things on a much larger scale and gets better results.
To sum it up, Hadoop and Big Data in financial services has been acknowledged by world’s leading financial institutions as the way for the future.

Leading Institute in India (SAS & Python) – Analytics Vidya Read More

Imarticus Learning’s analytics program Certification in SAS & Python have been rated best analytics institute for Certification Courses in SAS Programming, Certification in Data Science Using SAS, and Certification Courses in Python by Analytics Vidya.
The assessment criteria for these rankings were for the best online and hybrid courses in India that would help students make the right decision.


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