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Tag: data science career

How Can You Prepare for The Data Science Interview?

Posted on by Imarticus Learning
Reading Time: 3 minutes

How Can You Prepare for The Data Science Interview?

Do you have the jitters before every interview? Everyone does! Besides trying to run through the probable questions mentally, you need to stand well-placed with three fundamental attributes. They are aptitude, mathematical knowledge, and proficiency in technical skills. To explain and convince the other person does call for excellent communicative skills and a presence of mind! Commonly, data science courses will include learning of techniques in Big Data, Machine Learning, and programming languages like R and Python.
Before you try and prepare for a data science interview, you need to be honest with yourself and identify your key strengths and weaknesses.
What do you think the questions asked to you will be? Let’s have a look at the best techniques to conquer those butterflies in your stomach advocated by Imarticus Learning to get ahead of the crowd and ensure you emerge successful with a Data science Course.
Task 1: Understand your skill set, job profile, and application:
The essentials for any post in data sciences though, are the practical implementation-skills of your domain knowledge, the tools, and techniques you have competency in, great aptitude and comprehension attributes in quantitative, analytical analysis, programming languages and your confidence in answering questions on them.
Task-2: Crack the technical round:
Cover conceptual understanding of important topics needing the application of programming languages like Tableau, TensorFlow, Scala, Python, SQL, and R. You can expect most interviews to have a skill-test round where questions will be a case-study or assignment based on your skill-sets and implementation values of your learning. This is probably where all your tasks, test cases, project work, and case studies will be the litmus tested.
Task-3: Revise your basic topics well:
Since time and explanations need to be concise and succinct, you would do well to revisit supportive topics of data science like –

  • Concepts in Probability, Bayes Theorem, the distribution probability, etc.

·  Modelling techniques, Linear and non-linear Regression, Statistical Models, Time series,  Models for Non-Parametric data, popular algorithms, data tools, and libraries, etc.

  • Deep learning, database best practices, ML, ConvNets, LSTM, and other neural networks

You will need to make effective presentations of an industrially-relevant scenario through discussions or case-studies. It is a challenge to present the problem, cite research undertaken by you or others, suggest a valid solution and discuss business outcomes. Ensure you use and showcase your capability to solve problems, reinforce your learning, display solution finding, presentation, and team skills in this round.
Task-4: It’s perfectly valid to not have all the right answers in the personal round:
Data science is a vast field, and innovations happen every day through newer and more optimized models and statistical techniques. There are ten ways to do one thing, and at the end of the day, nobody has all the correct answers. So it’s fine if you do not know anything. However, the flexibility to adapt to teams and accept other’s views, the vision to add value to the employing organization, and learn-on-the-job are non-negotiable in this round.
Task-5: Your resume is the basis of measuring you:
Most times, it is best to mention what matters most in resume writing. Questions asked during interviews will silently explore your admissions. Be prepared to link your learning to your job experiences and prepare for justification of career decisions and choices made and stated in your resume.
Task-6: Continued Learning and practice counts:
An excellent Data science course certification, webinars, community learning, MOOCs and internships are good validations and endorse your desire for continued learning, focus on applications and job-suitability as well. Practice and repeat the reinforcement of your learning curve.
Conclusion:
Especially for first-timer career aspirants, the interview can prove to be very stressful. It is okay to stumble and fail, but the ability to get back up on your feet and justify your strengths is crucial. A Data Science career is a juggling of multiple domains and soft skills, a strong persona, dedication, and intent.
At Imarticus Learning, the methodology is to practically train you as a generalist on all the above tasks and includes resume-writing, personality-development and interview-training modules leading to assured placements. Their certification is widely accepted in industry circles as a skill-endorsement and being job-ready. So, why wait? Enroll today.

What Exactly is The Field and Type of Work That a Data Scientist has to Perform?

Posted on by Imarticus Learning
Reading Time: 3 minutes

The field of data science gained prominence when technology enabled Google to introduce the ranking systems for searches. Recommendations were looked at in LinkedIn and data suddenly began to influence all types of newsfeeds.

Currently, the fine art of data science has permeated through every known vertical including Human Resources. Can you even imagine a world devoid of mobile phones, digital payments, or self-driven cars? Yet, just over a decade ago the scenario was very different.

The evolving segment of Data Science Training behind the huge volumes of data generated every second today implies a large number of tasks being powered by insights generated by powerful statistical models. In spite of all this hype, it’s still unclear what this field entails. What exactly does it take to become a data scientist with firms like Google and Apple?

 

What the data analyst and scientist does:

If we look at the most trending and lucrative Data Science Career, the one difference that sets these two categories apart is perhaps the areas of data science they operate in. It is a very huge and diverse field and demands the individual to have a strong understanding of advanced statistics and programming. The scientist’s role is more to clean, organize and make the data available in the desired format first.

Such data is then leveraged to train algorithms which are specifically used to execute the task on hand with maximum accuracy. The models are optimized, tested and re-engineered to provide desirable output in the form of products like forecasting engines.

In a way, the data scientist ensures the sustainable development and growth of the entire system and could also be called the architects behind a decision. Some firms like Mu Sigma Inc based out of Bangalore and Chicago have been the pioneers in this field in India.

The poorer cousin per se to the data scientist is the data analyst, who uses the created system so engineered to do the final live data-analysis and produce those forecasts and predictions to further particular goals and business outcomes.

So, whether the need is for a product-framework model being developed, a self-taught solution for optimization in a production process, or using the ML algorithm to provide and make decisions on a flight or hotel bookings, one definitely needs the services of both a data scientist and analyst.

Data science today in plain tech-speak is all about the latest technological infrastructure, analysis and repeated testing of pipelines, ML, AI, deep-learning algorithms, neural networks, modeling, decision-making ML, and innovative personalized data end-products.

The field is evolving rapidly:

Companies like Amazon, Airbnb, Etsy, Twitter, Facebook, Google, Apple and many more have greatly contributed to making data science a high-paying career. And, the sheer volumes of data being produced, is so large, that it seems unlikely that the Data Science Career aspirants will face a shortage of jobs, for the next decade or so.

Today data science is contributing to cancer cure and treatment needs, powering investigative tools for the law enforcement, high-tech medical imagery, and technologically advanced MRIs and CAT scans, as also the numerous uses of data in self-drive cars, making recommendations to leverage possible business markets and outcomes, in AI and ML-driven production technology, and providing the latest fintech digital solutions and multi-vertical end-products.

The requisite skills needed:

Since data analysts and scientists make a living off the collection, cleaning and modeling of data, testing and creating dashboards, visualizing petabytes of data, making statistical forecasts and inferences, and providing key verticals and stakeholders with the required decision-making prowess, the skills required of them are also multi-dimensional.

A course in data science training should educate the scientist in very short time-periods with practical skills, latest technology, and an ethical code besides non-transferable soft-skills and managerial experience. Though interest in data-analytics is the prime requisite, a sound degree in finance, economics, CE and engineering is a definite plus.

It also pays to specialize in performing areas like ML, Business Intelligence, and Decision Science which form the how, and whys of data science. Besides the practical aspects of techniques and best practices, you will need to be good at business modeling, data analysis, and communicative skills.

Parting notes:

Making a career in data sciences is definitely a good career choice. To succeed at it you will need a supportive learning partner like Imarticus Learning where the short-term courses are succinct, highly skilled and offer measurable certification. Besides who can refuse the offer of certified-trainers mentorship, assured placements, personality development and being job-ready from the first day?

Sandeep’s Review of Imarticus’ Data Science Course

Posted on by Imarticus Learning
Reading Time: 2 minutes

We caught up with Sandeep, a recent graduate of the Post Graduate program in Analytics, for a quick chat to get his perspective on the program, the curriculum, Imarticus Learning’s placement process and more.
Tell us a little bit about yourself.

Sandeep: My name is Sandeep Singh. I recently completed my B.Sc. in Computer Science and was looking for an avenue to enhance my analytics skills and start my career.

Data Science Course in MumbaiI came across Imarticus’ data science course and, after thorough research, decided to enroll for it. I completed the course and have been placed at M Technologies through Imarticus.

How has your experience been with Imarticus Learning?
Sandeep: My experience with Imarticus Learning was super! The course focused on practical training with hands-on learning of various analytical tools and thorough practice with numerous datasets.

Looking back, I see the importance of actually applying Analytical tools and techniques to the projects I worked on because it gave me a running start when I began working.

What has changed since you joined Imarticus Learning?
Sandeep: Since the day I joined Imarticus my confidence has been boosted to a very high level. Through the practice of various analytical tools such as R, Python, SAS, Tableau, etc. I’ve come to believe in myself. My soft skills have also been elevated with the help of business communication workshops, mock interviews, and soft skill sessions throughout the course.

Would you recommend the program to someone else?
Sandeep: While researching various institutes, I came across some reviews that say Imarticus Learning is fake. Well, I wanted to see for myself and now that I have, I would definitely recommend Imarticus. If you’re looking for an institute, the first thing that comes to mind is the faculty and the learning material.

The faculty and staff are very cooperative and help you both inside and outside the classroom. The learning material is extensive and covers every aspect of data analytics. The best part is all of the lectures, notes, datasets, and quizzes are stored in an online Learning Management system and is available to students anytime, anywhere.

What do you like most about Imarticus?
The best thing about Imarticus Learning was the course content, the cooperative staff and the informative notes that are easily accessible. The resume building workshops and mock interviews definitely prepared me for the placement drives and I was able to crack the interview and land a job at M Technologies.

Looking to get started on your data science career, Speak with a counselor and get matched with the best course for you.

Become Data Scientist in 90 Days

Posted on by Imarticus Learning
Reading Time: 2 minutes

Data science is similar to any other field of science. The scientists involved conducted their own research and based on the information available form hypothesis and theories. However, in the case of data science, these hypotheses are created based on the data made available to the concerned scientists. The primary factor which an individual must consider in order to become a data scientist within a span of 90 days is to understand and to have a knack for analyzing data.
A career in data science is a hot topic in the present market. Organizations all around the globe are relying on big data, and for that skilled data, a scientist is required. Analysis of collected data involves the visualization of the data which is then backed up by creating reports after identifying specific patterns. However, what sets Data Science apart from the more traditional business analysis is the use of complex algorithms. The advanced algorithms such as neural networks, machine learning algorithms, and regression algorithms are used to scan the available data in order to identify the meaning and the purpose of the numbers and codes.
To become a data scientist an individual must have adequate knowledge about the fundamentals and the framework of these algorithms. This can only be possible when the concerned individual has a tremendous foundation for mathematics and statistics. So if you are aspiring to be a data scientist, make sure to get the basics right by keeping track of your mathematics as well as statistic skills.
Another foremost fundamental of data science is to know and understand the purpose of this study. The sole objective of a data scientist is to answer various questions. The study of data is carried out so that the probable questions can be answered by going through and analyzing a large set of recorded data. Let us consider the example of the popular entertainment network Netflix. In 2017, Netflix put forth a petition where a million dollars would be paid to a data scientist who would successfully improve the suggestion algorithm of the network.
Such is the demand and the requirement of the data scientist in the current market. Now for beginners, it is essential not to get into complex codes and a large amount of data. Analysis of large data would automatically mean the use of multiple algorithms. In order to become an efficient data scientist within a span of 90 days, it is critical to know personal strengths and weaknesses. Taking small steps helps as it builds confidence as well as enhances skill gradually. By considering these subtle factors, an individual can learn data science in no time and become proficient at it.
Another essential factor of becoming a data scientist is to go beyond the learning of Hadoop. There are many data science courses which not only helps you to be efficient with Hadoop but also assists you to gain real knowledge about reading and understand the various algorithms which are part of this data science game.
So to conclude, data science is a field which requires knowledge from all domains. A combination of mathematics, statistics, and algorithms give rise to data science. The job of a data scientist is not only to create a hypothesis, but also to find data which proves the formulated hypothesis to be correct. Thus, all these elements make the study of data science unique and challenging to master. However, with the right guidance made available through data scientist courses, an aspiring individual can surely reach the pinnacle of the data science industry.

Where Data Science Will Be 5 years From Now?

Posted on by Imarticus Learning
Reading Time: 3 minutes

Data is everywhere and data science is the perfect m mixture of algorithms, programming, deploying statistics, deductive reasoning, and data interference.

Data is the amalgamation of statistics, programming, mathematics, reasoning, and more importantly, a data scientist is a field that comprises everything that related to data cleaning, preparation, and analysis.

But when thinking about where data science will be 5 years from now, it’s useful to know how data science has made its unique position in the science field over the past five years.

Why is it hard to imagine a world without data?

As of late, advanced data have become so unavoidable and essential that we’ve nearly turned out to be unwilling to deal with anything that isn’t in data. To request that an information researcher takes a shot at something that isn’t digitized. Give them a table scribbled on a wrinkly bit of paper. Or then again, to more replicate the size of what we will discuss, whole libraries of thick books, flooding with tables of data.

Stack them around their work area and they’d most likely run away and never return. It is because the digital codes of information have become essentials and valuable. We cannot do modern work without them.  That’s the reason digitalization of the data is the whole story that makes our business work easier.

What data scientists do on a regular basis?

Data scientist begins their day by converting a business case into the algorithm, analytic agenda, develop codes, and exploring pattern to calculate which impact they will have on the business. They utilize business analytics to not just clarify what impact the information will have on an organization later on, however, can likewise help devise solutions that will assist the organization in moving forward.

So if you are perfect in statistics for data science, mathematics calculations, algorithms, and resolve highly complex business problems efficiently than the position of a data scientist is a round of clock available for you.

If we talk about data science salary, the job, and salary of the data scientist always on the top on in India but all over the world. A career in information particularly appeals to the youthful IT experts due to the positive relationship between the long periods of work experience and higher data science salary.

What does a data scientist actually need?

If you want to explore your career in data science, you are in the right place. Here we suggest you how to learn data science and statistics for data science along with the kind of skills recruiters expecting from you.

First and foremost, before entering in the data science choose the best data science online course. Because with the help of online courses you can build your skills easily and efficiently. Secondly, there are many roles in data science, so pick the one that depends on your background and work experience.

So, now you have decided on your job role and subscribed to the data science online course. The next thing you need to do is when you take up the course is learn data science go through actively, always follow the instructor instructions, the reason we are saying to follow the course regularly because it gives you a clear picture regarding data science skills.

The demand for data science is enormous and businesses are putting huge time and money into Data Scientists. So making the correct strides will prompt an exponential development. This guide gives tips that can kick you off and assist you in avoiding some expensive mistakes.

Data science is the core of the business because all the operations related to the business depend on the data science from statistics to decision making companies are using data science and its story not end here.

Do Data Scientist Use Statistics?

Posted on by Imarticus Learning
Reading Time: 2 minutes

Do Data Scientists Use Statistics?

Data science has been the buzzword of the tech industry for the past few years. Everyone is aware of the endless opportunities and large pay scale awaiting the data scientists. But when the question becomes “what do they do?” or “how do they do it? ” Only a few people know it. This article discusses whether data scientists use statistics in their operations. Read on to find out.

Statistics in Data Science
Statistics can be a very powerful tool in data science. It is simply the use of mathematics to analyse the data technically. The following are the few important instances where data scientists use statistics.

  1. Design Experiments to Inform Product Decisions.
    Data scientists use Frequentist Statistics and experimental design to determine whether or not the difference in the performance of two types of products are significant to take action. This application help data scientists to understand the experimental results especially when there are multiple metrics being measured.
  2. Models to Predict the Signal
    Using Regression, Classification, Time series analysis and casual analysis, data scientists can tell the reason behind a change of rate of sales. They use these techniques to predict the sales of upcoming months and point out the relevant trends to be careful of.
  3. Turning Big Data Into Big Picture
    Consider a large group of customers buying products. The data about each person’s shopping list is worthless if it stays like that. Data scientists can label each customer and put similar ones into a group and understand the buying pattern. It helps to identify how each group of people affect the business development. Statistic techniques such as clustering, latent variable analysis and dimensionality reduction are used to achieve this.
  4. Understand User Engagement, Retention, Conversion and Leads
    It is known that many customers would be lost from the signing-in stage to the actual regular use stage. Data science use techniques such as regression, latent variable analysis, casual effect analysis and survey design to find out the reason behind this loss. It also identifies the successful leads the company is using to engage more customers.
  5. Predicting the Customer Needs
    Statistical techniques such as latent variable analysis, predictive modelling, clustering and dimensionality reduction help data scientists to predict the items a customer might need next. A matrix of users and their interactions with the company product is all that is needed to obtain this.
  6. Telling the story with Data
    It is the end product of all operations of data scientists. He acts as the ambassador between the company and data. All the findings from data should be properly communicated with the rest of the company without losing any fidelity. Rather than summarizing the numbers, a data scientist has to explain why each number are significant. To do that properly, data visualisation techniques from statistics are used. Clearly, data scientists use statistics to solve various problems in their day to day life. If data science seems the right career choice for you, don’t wait for long. Imarticus  Learning is now providing course on data science prodegree. This Genpact data science course will equip you with all the necessary skills for a successful data science career.

Are Data Scientists Useful at Pharmaceutical Companies?

Posted on by Imarticus Learning
Reading Time: 2 minutes

Data science has been disrupting the industries for the past couple of years. With the advent of technology, data and the hidden insights in them are widely being used to improve every industry. The industries like finance and health care have already made their way up using data science technology. This article discusses whether data scientists are important to pharmaceutical companies.
Well, data in the hands of a skillful data scientist have great value. The data in Pharmaceutical companies are no different. With the aid of a data scientist, these companies hold great opportunities to improve their operations. How exactly do they help? Keep reading to find out.
The Drug Development
Unlike many challenging problems resolved by engineers, drug development has no overall model serving as a basis for optimization. This multidisciplinary and complicated process is in great due for a significant improvement. Pharmaceutical companies nowadays are generating great amounts of data, driven mostly by the sequencing of human genomes. These companies are now realizing the importance of data scientists in developing algorithms that can uncover efficiencies by analyzing this data. Since the time of its inception, pharmaceutical companies are striving to meet the new clinical needs without affecting their financial health. The data science approach is expected to play a critical role in meeting this requirement.
Clinical Trial Planning
The phase III clinical trials have always been a headache for the pharmaceutical companies, especially in the light that the patent exclusivity of a drug starts roughly around the time of its first clinical trial. The practical complications with recruiting and randomizing a sufficient number of patients result in increased costs and delays. It also erodes the time over which the company can recoup the costs during the period of patent exclusivity. Data-driven approaches are now taking over this issue. They are now resorting to data science to build precision into the way they calculate the feasibility of successful clinical trials within the time and expense constraints.
Drug Repurposing
Drug repurposing is an attempt to find an altogether different use for a drug. It can be attempted on both drugs that are on the market and the ones stopped developing. It is usually done by evaluating a hypothesis put together by a scientist in a laboratory. It eliminates the time and cost incorporated with developing a medicine. With the recent update brought by data science, this discovery can be made quicker by computation of complementary “drug-disease” pair on large public repositories of sequencing and gene expression data. It will ultimately result in cheaper medicines.
Wearables 
Wearables technology provides a method of unobtrusively capturing continuous physical measurements. They are aimed at replacing the expensive instruments that are traditionally used. With the aid of data science, pharmaceutical companies are aiming to solve the problem in medication adhering in clinical trials.  So, it is clear that pharmaceutical companies are also in need of trained data scientists.  You can start your journey to a successful data science career by taking the data science prodegree by Imarticus Learning. It provides with all the necessary skills required for your career. It is one of the best data science course in Mumbai.

The Data Science Vacancy Gap In India

Posted on by Imarticus Learning
Reading Time: 3 minutes

Quick. At the top of your head, think about a profession that has many open positions in the market. It’s not engineering. It’s not commerce or the arts. It’s data science and its better-known subset machine learning.
Did you know that data science was once termed to be the hottest job creator for the 21st century. It goes without saying that the large data generation coupled with the technology to play around with the data has given birth to a profession where the demands overrun the supply by a large margin.

Getting Straight To The Facts

  • In India alone, you will find that data science courses and machines learning are the hottest and trending topics of interest not limited to the IT sector alone but also for other industries.
  • Platforms like Thomson Reuters and Great Learning indicate that more than 50,000 positions related to data and data analytics are currently open for the taking in India.
  • The number of postings reached an all-time high towards the end of 2017 and had been rising to a constant range over the following months.
  • The reports also indicate that India currently contributes nearly 12 per cent of all data science positions when compared to the rest of the world.
  • Sectors like banking, finance, e-commerce, media, marketing and healthcare account for more than 67 per cent of the positions spanned mainly over eleven primary Indian states with the highest concentration being from metropolitan centers and urban districts.
  • Similar reports suggest that the banking and financial services are the biggest market for data analytics and data science professionals and are expected to create at least 58 per cent of all jobs for the year 2018. E-commerce is accounted for 17 per cent of the total job share.

Looking At The Factors

  • It is undeniable to say that there has been a rapid rise in the number of colleges and institutions offering data science training, data analyst training but the striking fact is that such skills are becoming increasingly common with completely unrelated professions as well.
  • Employers are no longer looking for candidates with a working level of understanding of computers but rather those who can utilize and leverage the data towards producing meaningful results.  
  • The large gap in the employment sector has been linked to the lack of institutions that properly prepare candidates for the tasks at hand. While there may have been an explosion of data science learning institutes all over the country, quick studies of the curriculum show a stark difference from what the industry is currently looking from.  
  • Government statistics, on the other hand, argue that while more and more data scientists and data analysts are being funneled out, nearly 44 per cent of fresh graduates choose

to work elsewhere outside the country or shift to another position due to dissatisfactions with the pay.

  • It should, however, interest people that average pay scales for data science positions are heavily influenced by the industry the candidate chooses to work in. Top level startups offer initial pays ranging from 89-91 K per year but tend to be higher for well-established companies.

Where The Road Leads Ahead   

  • Software and data science tools like Hadoop, SPSS, SAS, Python and IBM Watson are expected to be the next frontier for leading changes to the data science career as a whole and will likely be the most sought-after skills in the future.
  • More than 39,000 analytics jobs are anticipated to be created in India by 2020 with cyber-security taking an 11 per cent share, healthcare taking up 55 per cent, engineering studies taking up 8 per cent and space exploration 16 per cent.
  • Besides these, agriculture and aviation too will be engaged heavily with data analytic jobs as well as automation and the new buzzword driver less transportation.

How Beneficial is Data Science Prodegree For Your Career?

Posted on by Imarticus Learning
Reading Time: 3 minutes

Data Science is one of the most sought after career tracks at the moment. There is a reason that the hype on data science exists. The fundamental focus of data science is that it assists human being on taking better decisions, quicker decisions. And it’s not that this is a requirement of only a handful of industries from a particular segment. This is true across industries, even where decisions are automated for e.g. in online shopping, retail etc.,

There is a rapid growth in the data science field. Its prominence is directly proportionate to the record level of increase in the raw material i.e. structured and unstructured data.

There are a number of other factors that are adding significance to this field. The number of sensors that accumulate information like internet, phones etc.., along with advanced and sophisticated machine learning techniques that help give better insights with the help of better extraction algorithms.

All these forces are working in one direction, the direction to ensure that the skills of using available data to extract actionable insights for business to impact better decision making which in turn will impact the revenue of the company is here to stay. Recognising this most MBA’s have also introduced Data Science into their MBA curriculum.

What skills does one learn in order to become an effective Data Scientist?

Large bits of unstructured data are not easy to interpret, one needs a unique skill set, one needs to develop useful auxiliary skills, some technical attributes required to apply is the top line. One needs to create a perfect balance of various skills. Predictive modelling, analytics, organisation skills and above all communication skills.

Besides the above to be able to secure a lucrative job in the organisation of your choice one needs to develop excellent and valuable coding skills. Efficiency in SAS Statistical Analysis System, R programming language, Python programming language etc.., further aids your skills as a data scientist or analyst. It helps you to think logically in terms of algorithms, which in turn allows you to better manage irrelevant data.

Another additional set of skills that are essential to have academically and through experience are contextual understanding of possibly any given situation, skills in probability and statistics. And finally the most important of all the skills is the ability to communicate, explain, in the method and language of the audience, your findings. So storytelling and presentation skills become imperative.

Why Data Science Prodegree at Imarticus Learning?

To begin with the Data Science Prodegree at Imarticus is designed in association with Genpact as the knowledge partner. It essentially covers all foundational concepts and offers hands-on learning of leading analytical tools such as SAS, R, Python, Tableau etc., and the learning is integrated with relevant industry case studies and projects, which is essential in gaining in-depth problem-solving capabilities.

The course is divided into four semesters and is focused on ensuring that the candidate not only gain the theoretical knowledge of the tools but also learns best industry practices and business perspectives through live interaction with the gurus of the corporate world through guest lectures and regular project submission.

To ensure maximum learning efficacy the course ranges over 200 hours and is delivered in two modes, online and classroom. The course offers career readiness assistance too, at Imarticus the Career Assistance Services provides you customized industry specific mentorship, with assistance in resume building workshops and one on one mock interviews.

The Data Science Prodegree is a power packed course endorsed by Genpact, which has a comprehensive coverage aided by project based learning, with effective and efficient program delivery along with career assistance. Thus preparing you to confidently apply your newly learned skills and excel in your given role right from day one, making you a sought after data driven decision maker.