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Category: Analytics

How to Get a Data Science Job After A Career Gap?

Posted on by Imarticus Learning
Deepti recounts her journey from a career gap to be a stay-at-home mom to a landing a data science job after Imarticus’ data science course. It’s always challenging – getting back into the job market after an extended break. A business venture that went bust, prolonged illness, family responsibilities, the possible reasons are many. In my case, I wanted to take a career break for a couple of years to focus on raising my child who is now 9 years old. My name is Deepti, and I am a mathematics graduate with 6 years of experience in the IT industry, specifically Oracle applications and data warehousing. After my break, I experienced first-hand the challenges involved in restarting my IT career. The jobs I wanted were out of reach; the available jobs didn’t pay enough, and the jobs that paid decently pronounced me as overqualified. Imarticus’ data science course fit my needs perfectly!  In the midst of all this, I came across data science and realized that this industry is growing and, considering my background in IT and mathematics, would be the perfect fit for me. I began researching institutes with the right curriculum, faculty, infrastructure, and other pre-requisites you’d generally look for. I zeroed in on Imarticus’ data science course because it was the perfect blend of theoretical learning and hands-on industry-relevant practical experience. Big Data Analytics Courses One worry at the back of my mind was ‘how do I juggle between a full-time data science course and taking care of my 9-year old son?’ A quick call with Swapnil, the head of Imarticus’ Thane center, put my mind to rest. I spoke to him about my concern, and without a second thought, he offered to let my son accompany me to class. With my son in the room beside me, I was able to concentrate entirely on my lectures and complete the course successfully. The course itself is outstanding and warrants a 5-star rating. What I liked most about the course was a learning management portal – Learntron – that I was given 24/7 access to. I remember going back home at the end of a day of classes to put in some time working on the day’s assignments. I could always log on to the online platform, re-watch the lectures & videos, refer to the notes and clear any doubts & queries that arose. The learner was a great aid to my entire learning journey. I also want to make special mention of the faculty, Ms. Ritu Singh and Ms. Prachi Samant, who are amazing trainers. Being data science professionals themselves, they are incredibly proficient in every aspect of the industry and were able to help me relate the projects and case studies I worked on to real-world data science business problems. The final step was crucial: building my resume and preparing for interviews. Because of my career break, my resume was outdated and needed a complete overhaul. Imarticus’ career services team helped me hone my resume to reflect my new skills and turn it into a job-magnet. They also helped me run through the most probable data science interview questions through mock interviews. The professional transformation I’ve undergone is a testament to Imarticus’ data science program, and I am thrilled that I took up the course. Do you want to kick-start your data science career? Click Here to inquire about Imarticus’ Data Science course and speak to a career counselor today.

Why R Programming is better than SAS?

Posted on by Imarticus Learning

R Programming and SAS Programming have over the years, assumed the role of programming giants in the big data industry. And since the time of R programming’s inception, a debate between which tool is the best has been quite commonplace. For us to finally determine why one tool is better than the other, it would be very imperative to know what exactly the two ecosystems comprise of.
SAS by far has been the hands-down leader in the industry, mainly because of its long list of renowned clients and its time-honored existence in the industry. This tool or software environment offers its users with an enormous amount of varied statistical functions and has a great Enterprise Guide and Miner for its clients. This ensures that users learn faster with a continuous flow of technical support.
R, on the other hand, has come to be a crowd favorite, mainly because of its open-sourced nature. This means that it can be downloaded free of cost as opposed to its licensed counterpart SAS. This free mature and an overactive community, R programming have to offer quite a number of latest statistical functions and a number of solutions as well.
Related Article: R Programming in Business Analytics
It may seem that R has come to lead the race in this commercial analytics space and rightly so. Here we present, in a nutshell, a few very strong reasons which tip the scale in R Programming’s favor.

  1. The Cost

SAS programming, since its inception, has been a licensed software environment. While it has a plethora of private companies under its belt, very few individuals are able to get access to it on their own. This is the fundamental difference that gives R the cutting edge. Being free and easily available to download makes R a novice’s dream.

  1. Application

R programming wins here with quite a large margin too, mainly owing to its community. All the latest features and new abilities have already reached the users, both faster and more frequently when compared to SAS. On the other hand, SAS has a team, which upgrades its features in variation roll outs, which have major intervals of time between them.

  1. Transparency 

R happens to have the largest community available for any kind of open-sourced programming tool out there. As this proves a greater advantage for all those looking for small tricks and tips to solve any sort of bugs in their programs. Fast and free help is always appreciated by many of us in the society today. This is not quite the case with SAS though.
These three qualities unearth stark differences between the two tools. At the same time, it is also true that many professionals today don’t just work with either one of these tools. Many reasons are touted for this, it could be the amazing customer support that SAS offers, the extensive amount of statistical facilities, and the fact that SAS has only reviewed features in it and so on. But at the same time, there has been a popular trend among data science aspirants of opting for courses, which give them the benefit of the best of both worlds. Institutes like Imarticus Learning thus become their go-to places for the same.

Why You Should Become a Certified Data Analyst?

Posted on by Imarticus Learning

So you are looking into taking up a career in the field of data analysis? Assuming that you have already done your required share of research and have interacted with a number of people who were influential enough to guide you in your journey, now you are seeking out the exact path to follow.

One thing to remember before you opt for a certification is to narrow down on your field of choice in the large and varied spectrum of the data analysis.

Most of the times, when it comes to the conventional courses in terms of data analytics, it is seen that there are bachelor degrees available to students in areas of management information systems, computer science and the likes. These may seem like a broad-based strategy to make it work for you. Which is exactly why you need to take additional steps to learn how to be a great communicator, a proficient problem solver and someone who knows very well how to drive the point home.

Related Article: What is Data Analysis and Who Are Data Analysts?

Data Analysts are professionals who generally do the job of collecting, maintaining and troubleshooting issues which are usually encountered when a company stores digital information. While it is important to have a bachelor’s degree to enter this field, it is not quite mandatory for you to have a certification.

But at the same time, being a certified data analyst increases your prospects of employment at the top gun companies of the industry.

Here’s what a real data analyst has to say to all of those looking to make a career out of this field. He says, “Taking statistics and research methodology courses are a great way to start off. Also, it is very important to be a consumer of statistical analysis.

Find some topics or issues that interest you and read whatever analytical works that you are able to find. Sports happens to be one natural avenue for learning about data analysis because they happen to be so very data oriented.”
Getting certified would help broaden your perspective and increase your avenue of expertise as well.

This would make you competent enough to:

  1. Work with amazingly equipped IT teams, management staff and key players of the company in order to reach the set goals.
  2. Be an expert at mining the data from primary as well as secondary sources.
  3. Be a master cleaner of data and a pro at dispensing all the irrelevant information.
  4. Become a smart enough employee to pinpoint trends, correlations and patterns in a number of complicated data sets.
  5. Become a magician at designing, creating and maintaining relational databases and data systems.

A certification is clearly a way to upgrade and update yourself professionally. Mainly because it can help open doors for you which would not even exist had you chosen the path of just a conventional degree. These and many motivational factors lead quite a lot of data science aspirants to take up professional training certification courses from institutes like Imarticus Learning.

Related Article : How to Differentiate Between Data Analysts and Data Scientists?

How is Machine Learning Helping Businesses Grow?

Posted on by Imarticus Learning

Machine learning (ML) technologies represent one of the exciting new aspects of the digital age. These technologies are premised on sophisticated algorithms that empower modern enterprises to tackle a variety of business problems. Computers and digital systems that use Machine learning are designed to gain experience from various processes and apply certain rules and data sets to perform complex calculations.

Modern machine learning systems also leverage the use of cloud technologies in a bid to maximize speed and cost-effectiveness.

  • Recent advances in the commercialization of cloud computing services allow business organizations to utilize huge offerings in compute and storage services. Large cloud players are offering modern enterprises an opportunity to use cloud computing solutions powered by machine learning technologies. These systems are also “creating new opportunities for innovators to offload labour-intensive research and analysis to the cloud.”
  • Machine learning systems are enabling business decision makers to visualize data more efficiently. The use of these technologies enables business analysts and business managers to access and utilize data analysis paradigms. This means that machine learning systems are essentially crunching huge volumes of data and electronic information and presenting patterns, analysis, and insights to modern businesses. This personnel can analyze these patterns to quickly initiate business decisions in response to evolving market conditions.
    • Digital technologies have emerged as a major enabler in modern societies. Machine learning and artificial intelligence technologies are no exceptions. Cloud-based machine learning algorithms can process current data from business environments to predict future consumer requirements, market trends, etc. This enables business organizations to process unprecedented amounts of data in the ultimate pursuit of growing and expanding their commercial footprints. Companies and brands that can effectively anticipate future requirements are better positioned for future market performance.
    • Certain industries such as logistics and transportation can gain clear benefits by implementing machine learning technologies. Vehicles can be fitted with digital devices and transmission systems that generate data regarding the performance of vehicle systems and sub-systems. Analysis of such data can help vehicle designers and engineers to refine and improve the performance of each vehicle over time. Higher mileage from each vehicle and fewer maintenance hours can help these businesses to earn larger profits.

 

  • Machine learning algorithms can help the banking and insurance industry to spot and prevent instances of fraud. Certain insurance service providers are using the technology to scan the faces of loan applicants and insurance policy applicants. These algorithms have access to huge databases that enable them to detect any scope or intent of fraud ahead of time.
    Thus, machine learning systems help these service providers to expand the scope of their business while cutting scope for malfeasance and thereby reducing losses. Such use of machine learning technologies is expected to gain momentum in time.
  • Artificial intelligence technologies and machine learning algorithms are helping businesses to make decisions that are more efficient. Retailers can use these technologies to analyze sales data from the past and other points of market information to control their inventories and supplies. This approach removes the scope of guesswork in certain aspects of business operations while creating scope for efficient operations and greater profits.

Related Article : Skills Required to Learn Machine Learning

What Are The Best Way to Prepare For SAS Statistical Business Certification?

Posted on by Imarticus Learning

For any individual to become successfully certified in the above-mentioned certification, there are quite a number of things they need to be proficient at.
These are:

  1. Being able to generate descriptive statistics and knowing how to explore data with graphs.
  2. Being able to perform analysis of variance, linear regression and so on.
  3. Being able to score new data with the help of developed methods and having an expertise in the process of fitting a logistical regression model.

Apart from these, SAS programming is a prerogative for the candidate to be extremely strong in the fields of both mathematics and statistics. The beginning of anything must be punctuated with proper research about the same. Similarly, before starting off on your journey to gain this certification, it is important to have a proper in-depth understanding of all the statistical concepts and also hone your modelling skills.Data Analytics Banner
Once you’ve cemented your base, there are quite a number of way you could opt for.

  1. You could always get hired by a company which has a policy of on job training of their employees. Not every company believes in training their recruits from scratch mainly because it is not really the prerogative. But there are some companies who make extensive use of data analytics in their daily activities. Such companies are willing to invest in training their employees quite thoroughly.
  2. Get this certification in the most formal way there is. Through joining a professional training course. This way you will be able to get the required technical skills and help in structuring your overall personality in a way where it would get you closer. There are many institutes today like one Imarticus Learning which actually has a series of courses called Prodegree course, which train you thoroughly enough for you to become eligible to achieve the Statistical Business Certification.
  3. Enrolling yourself for internships by far is one of the best ways touted by many professionals. This is mainly because it’s the first instance where you are able to get all the first-hand experience that you need in the future as well as when you will be looking to actually make this your proper career.
  4. Networking is one thing that works best too. Mainly because you get to interact with people who actually have either gotten the certification or pursuing it. This makes it very important as they are able to provide you with insights and tips that you would not have been aware of if you want through this path alone.
  5. One very important thing to do is read, read and keep on reading. There are many books available both offline as well as online. Apart from that, there are books specific to your syllabus topics which would help as well.

Of these ways, it is believed that the best way would always be to take up a course which gets you closer to the certification. Which is why institutes like Imarticus Learning have actually become quite popular recently.

 

The 5 biggest mistakes in Business Writing

Posted on by Imarticus Learning

It’s quite surprising how after teaching you Accounting, Marketing, organizational behaviour and even business psychology, universities and schools forget to tutor students on something they will spend a significant part of their careers doing- writing. If you go into consulting, banking or law, writing is perhaps your biggest weapon or weakest link. If your writing is crisp, so will be your communication. If sloppy, then you might suddenly find yourself responsible for the break down in a big deal negotiation because you decided to leave out a comma in an important email or forget to check up on your numbers. Remember the example:’ Eats, shoots and leaves?’
Here are the top 5 dumbest mistakes people make in business writing.

  1. Er…your numbers don’t add up: You could have done every calculation right and you’re probably the brains behind the entire idea, but it takes only one table that doesn’t add up to question the sanctity of your entire report. This usually happens when excel sheets are linked and don’t update properly. So don’t trust excel. Make sure your original excel sheets are not linked to anything and have no reference value issues. Two: Paste your table as a graphic if you can. That way if something changes in your excel sheet, it won’t on your document. 
  2. Unsourced data: Business writing is not a thesis. No one is looking for original thought, we are looking for coherent analysis. Everybody knows you didn’t travel to Sierra Leone to find out what percentage of their diamonds are blood diamonds. But someone did. And your data and the following analysis is much more valuable if you make a reference to them.
  3. Over-reliance on spell and grammar check: Is ‘colour’ also spelt as ‘color’? Not according to my spell check. Spell check is good but doesn’t blindly accept every change. Understand the context. Sometimes Word thinks it knows better than you; it doesn’t.
  4. Less is more: Keep the body of your main powerpoint presentation or report as short as possible. The best business writing gets to the point immediately. Try to put all the supporting documentation in the appendix. That said, don’t put in your own personal thoughts on why the stock market is about to crash. A good appendix is a useful appendix that enhances the main study. It is not the place where you dump research just because you spent four hours looking for it.
  5. Passive writing: Write in the active voice.

Example of active voice:  Mr Prabakar leads the team.  
Example of passive voice: The team is led by Mr Prabakar.

You might think it sounds the same but look at it again. In the first instance, your focus is on Mr Prabakar. The passive voice is clunkier and leaves room for ambiguity. The active voice is more assertive and sure of itself. Only use the passive tense if you want to emphasize something in the sentence. So if you wanted to emphasize the team, you can say.

The team is now led by Mr Prabakar.

But as a rule, stick to the active tense.
Finally, get someone to read your document before you present it. This should take care of language, cross-references, acronyms that need to be expanded and odd ‘xxx%’ which will irritate your MD so much that he might take it into your consideration when paying your bonus.
Click to check out Investment Banking Courses in Mumbai
For more tips on writing, check out the following books. There is no shame in keeping a small reference library on your desk.

 

How to Learn SAS Online For Beginners?

Posted on by Imarticus Learning

SAS or Statistical Analysis System as its technical name goes has by far been an undefeated champion when it comes to the data programming tools. In the industry of data science, this licensed tool has been ruling the roost both in terms of the number of users and the recommendations. Although today, the momentum of its popularity is facing roadblocks majorly in terms of an increasing number of open sourced tools and super interactive community forums.
Whatever may be the case, this tool still happens to hold a huge chunk of market share in the industry. A steady growth has been documented, especially in terms of the jobs in countries like the US, UK and India, where employers are on the lookout for professionals trained in SAS. Another major factor that works immensely in favor of SAS is that it is one tool which can be learnt very easily. This may be the reason why many aspirants are advised to start off their journey with SAS owing to its flexibility and user-friendly nature.
Related Article: What is SAS?
So if you happen to be one such aspirant who wants to learn how to work with SAS but doesn’t know how to make the head or tail of it, then you’ve come to the right place. Here we arm you with an ideal set of tips which you must follow to gain momentum with SAS. A point of disclaimer is that this is an ideal way and could very well be customised by you.

  1. Find out the reason why exactly you want to learn SAS, talk to people who you know are involved in the industry and get their valuable inputs. Once you have cemented your base, go ahead and download a university edition by creating a SAS profile. You will also be required to download VMware or Oracle Virtual Box to make the software compatible.
  2. A few things to remember about the university edition of SAS would be that firstly, it works only on 64-bit machines and you will have to download the Oracle Virtual Box well before you begin downloading the software.
  3. Once you’re done with this, you would find it really helpful to go through the Base SAS training program that is provided on sas.com. This is free and promises to teach you the basics of the tool in about 24 hours. Just remember to solve all the quizzes.
  4. It is usually advised that once your basics of SAS are clear, you must try and look into learning SQL. Once you do that it would help you out with accessing the data. There is no hard and fast rule about this as you can, even go about your data managing jobs without learning SQL.
  5. Take up the learning of descriptive statistics, look into online free courses or paid courses which would teach you the same. Once you are done with the primary path, you can diversify your learning into inferential statistics, linear and logistic regression, decision trees, clustering and segmentation and so on.

If you happen to feel that going through this whole process on your own could be quite tedious, then don’t fret at all. Today there happen to be many esteemed institutes like Imarticus Learning which offer many comprehensive courses in tools like SAS Programming which you can opt for.

Why doing a Data Science course is the Right choice?

Posted on by Imarticus Learning

Recently a renowned survey ranked India at 100th place especially when it comes to the concept of ‘ease of doing businesses in the economy’. Similarly, a corresponding survey found out that 1% of the wealthiest, richest population of the country is in possession of close to 73% of the monetary and financial assets of the country. This clearly showcases India in good light and confirms that we are a rich country.

With so much affluence around, it is but obvious that it would trickle down into the Indian business industry. And this has indeed happened, as today the business world is following three watchwords with full determination. These are innovation, competition and productivity.

Today technology has overtaken the role of a perfect launching pad, for various aspects of Bitcoin, Artificial Intelligence, and Machine Learning and most importantly for the field of Data Science.

Related Article: How Beneficial is Data Science Prodegree for Your Career?
Data or the congregation of information that is generated by us citizens on a daily basis has become the primary unit, the irreplaceable cog, if you may of this machine of a industry. Data is like this treasure trove, this unlimited source of energy, which if harnessed in a proper manner can lead to explosive benefits and growth.

This is why technology giants today are on the lookout for the right kind of talent to fill in the gaps left open by this field.
One sure way to ensure this is the introduction of various certification and vocational, professional courses. This is why many candidates today are being seen taking up various professional courses and getting certified in certain skills.

For instance, Imarticus Learning is one acclaimed institute which offers candidates with immense industry endorsed knowledge and equips them with the technical skills to make it big in the field of their choice in the future.
Doing these courses is quite advantageous mainly because of the wide spectrum of career opportunities they offer.

Getting certified opens up a doorway to a number of varied opportunities and verticals that a professional can explore. The fact that this certification stands as a testament to your credibility and reliability, quickens the career-building process.

Similarly, the career of big data is touted to be quite lucrative in the future. A recently conducted study forecasts that the big data market is supposedly predicted to be worth $46.34 billion by the year 2018. This most definitely shows that there is going to be great money in the field soon enough.

With more and more organizations taking up the route of big data analytics, it is important to note that a certification or a course in data analytics will be a highly valued intangible asset to have. It is predicted that the demand for such trained professionals will soon outlast the supply for the same.

One big advantage here would be that the more the opportunities available to you, the more your value would also go on increasing. Thus high paying jobs, great diversification in career, short-term investment and long-term benefits are the various advantages of a data analytics course in India.

Related Article: Average Salary of a Data Scientist

Trends That Will Shape The Future of Data Analytics

Posted on by Imarticus Learning

The human race since the beginning of time has been fascinated with all phenomena that seem magical. It all began with the discovery of fire and today we humans have made such progress, which could not have been imagined of in the past centuries. Our obsession with robots and machines working for us, follow our every command and scaling new heights of innovation is no longer just restricted to movies. Today with the advent of artificial intelligence, machine learning and the emergence of data science, the future is full of boundless possibilities.
Many are mistaken to think that data science as a field is fairly new. That is not so really, as its origin can be traced back to more than a few decades ago in time. While the research in this field was definitely at its peak, but there was no possible outlet for putting any of it to practical use which was why it took such a long time to make its debut on the technology scene. But all of this changed drastically when circumstances became quite conducive to their growth and development. With huge data sets emerging, people were able to actually work with them. This led to the development of powerful machine learning algorithms as well as computers which could both analyse and operate on these datasets.
Here are a few trends that are all set to change how we will be looking at data analytics in the future.

Internet of Things

Internet of things is simply all the things that are related to the internet today. They refer to all those appliances which work within wireless networks. The market of this field is all set to grow up to $561.04 billion by the year 2022. This would mainly be because of the emergence of advanced analytics and various other data processing techniques which will change the future of data science.

Personalization

Today’s markets are gradually moving away from mass-produced trite goods, to knowing their customer thoroughly in order to find out a way to offer them services. The simple logic put to use here is that the better you know about your customer, the better are your chances of selling your product successfully. So many new websites have emerged which define the whole concept of ‘hyper-personalization’. These websites range from Google, Amazon and so on.

Artificial Intelligence

AI or artificial intelligence is one field which is believed to be a major future show runner in the field of data analytics in the future. For all of those who have to seem movies like Her, they would be definitely excited about how amazing the work of artificial intelligence will become soon enough. There is something called as augmented intelligence which is usually used in the more assistive role of AI. This progress would help enhance human intelligence and not focus on the whole replacement idea of the same.
Keeping in mind these current trends, many candidates have begun to approach institutes like Imarticus Learning which offer comprehensive professional training courses in the field of data analytics.

How to Differentiate Between Data Analysts and Data Scientists?

Posted on by Imarticus Learning

In the past couple of years, the field of data science seems to have rapidly shot to fame. The main reason for this is ‘data’. In our information-driven world, all of us play quite a crucial role. Since the moment we wake up and glance at our phones, to the last moment of the day, we are all digital labourers, trying to generate the very data that acts as a fodder material for the companies over at Silicon Valley.
All of these high tech companies today are all for the increasing demands of data scientists and data analysts. Jobs in this sphere have been steadily increasing and have taken up permanent residence at the top job search engines all over the web. The various titles that beckon data aspirants are the likes of Data Scientists, Data Analysts, Data Engineers and many others. While the prefix of all of these job titles may lead you to believe that all of these professionals carry out the same functions, it is not really so. As data science happens to be a vast field with so many diverse verticals and untapped areas, there is always something new to do with someone new.
Coming back to how these similar sounding positions are actually quite different. Let us start with Data Scientists, these professionals are popularly known as the rock stars of the Information Technology industry. They are usually in charge of making accurate predictions, which help the businesses take the most lucrative decisions. These individuals have a treasure trove of educational qualifications and experience.
They usually belong to a background of computer science applications, modelling, statistics or math. They have an ‘IT’ factor in the form of a combination of brilliant business skills and excellent communication skills that set them apart from the general public involved in the industry. Further division of roles for a Data Scientist could be becoming a Data Researcher, Data Developer, Data Creatives and even Data Businessmen.
Apart from Data Scientists, there is another career option which is called as Data Analyst. These professionals perform a wider spectrum of functions like collecting, organizing and analysing of data in order to derive important information from the same. They are also known as Data Visualizers as they are supposed to present this collected and processed data in the form of charts, graphs and tables and go on to build other related databases for their firms. They could diversify their careers by going into roles like Data Architect, Database Administrators, Analytics Engineers, and Operations and so on.
The major differences between these two positions are that a Data Scientist usually is required to be familiar with database systems like MySQL as well as Java, Python and so on. Whereas a data analyst must be familiar with other data warehousing and business intelligence concepts and must have an in-depth knowledge of SQL and analytics.
If we put the differences between the two aside, then we would infer that both the positions require a professional to do a thorough course in programming tools like Python, Big Data Hadoop, SAS Programming, and R Programming and so on. While these tools could be learnt through self-study, but most prefer institutes like Imarticus Learning to help them along their journey.