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Category: Analytics

Do Data Scientist Use Object Oriented Programming?

Posted on by Imarticus Learning

It is estimated that there are 2.5 quintillion bytes of data produced every day in our world. In this data-driven world, the career opportunities for a skilled data scientist are endless. With the data production rate predicted to go higher than of now, the career opportunities for those who can manage data are not going anywhere. This article discusses whether data scientists are using Object-Oriented Programming. Let’s find out.

What is Object-Oriented Programming
Object-Oriented Programming or OOP is a model of the programming language organized around objects rather than the actions. It also emphasizes data rather than the logic. Traditionally, a program is considered to be a logical procedure that converts input data into output.

In such cases, the challenge was to come up with a logic that works. The OOP model redefined that concept. It takes the view that we should care more about the objects we are trying to manipulate rather than the logic we use. These objects could be anything from humans defined by names and addresses to little widgets such as buttons on the desktop.

The main advantages of OOP are:
• Programs with a clearer modular structure.
• Codes are reusable through inheritance.
• Flexibility through polymorphism.
• Very effective problem-solving.

Object-Oriented Programming in Data Science
Using Object-Oriented Programming for data science may not always be the best choice. As we said, the OOP model cares more about the objects than the logic. This type of approach is most suited for GUI, interactive application, and APIs exposing mutable situations. When it comes to data science, functional programming is preferred more due to superior performance than compared to the OOP model. The advantage of better maintainability offered by OOP is sacrificed in the data science for the sake of performance.

Polymorphism is an important feature of OOP. It allows a loosely coupled architecture, where the same interface can be easily substituted for different implementations. This feature is very helpful when dealing with applications of large size. However, data scientists seldom use large codebase. They always use small scripts and prototypes. So, OOP would be far too much overhead with no significant benefits.

Although, machine learning libraries are a must needed thing for data scientists. Most of these libraries make use of object-oriented programming, at least the ones in Python. Machine learning libraries such as Scikit-learn heavily make use of OPP. Data scientists who work with R and SQL will never have to use OOP.

Conclusion
It is clear that even though Object-Oriented Programming Offers a lot of benefits, it is not exactly what data science need. So in general, object-oriented programming is seldom used by the data scientists.

If the data science career seems to suit you, wait no more. Imarticus is offering courses on data science prodegree, which will provide you with all the skills and knowledge to excel in your career. This Genpact data science course allows you to start your journey on the right foot with placement assistance at so much more.

Want To Learn Artificial Intelligence And Machine Learning? Where Can You Start?

Posted on by Imarticus Learning

These are exciting times to be a part of the technology industry, what with newer and fascinating fields being discovered every day. With the tech companies leading the way, the newer fields like Artificial Intelligence and Machine Learning are finding takers in multiple domains. Probably, this is the reason the demand for machine learning courses is at an all-time high.
Every second person, in the technology domain, you interact with would wax eloquent about how they are going to learn machine learning or how they have been taking the latest and most difficult artificial intelligence courses. Hearing this can be quite an unnerving experience, especially if you are a newbie in the field and looking to find out some machine learning courses that can help you unravel this mysterious world.
But, fear no more, as in this article, we try to help you find your initial foothold in this domain and slowly but surely come up to speed in your quest to learn machine learning. The first thing that confuses newcomers and throws them off-track when they begin to learn machine learning is the different terms and their interrelationships. What do the terms – Artificial Intelligence, Deep Learning, Neural Network Programming, Machine Learning – mean and how are they related.
If you somehow can navigate through the maze of big data and machine learning courses and get a hang of these terms, another big question comes up. Do I need to learn programming, statistics as well as calculus? Is this the right direction for my career, even though I do not understand either of this? Is there a way to learn machine learning without being proficient in all these?

There are no straight forward answers to these questions. But below are a few pointers that may help you to take your first step as well as identifying the correct artificial intelligence courses for yourself.
First and foremost, we need to understand the interrelationship between artificial intelligence and machine learning. Artificial intelligence is mostly trying to mimic human intelligence and behaviour by the machines including creativity, learning and reactions to any situation. As you start to learn machine learning with the help of machine learning courses, you realize that machine learning is nothing but a subset of artificial intelligence, dealing with pattern recognition and self-learning.
Now, the next fundamental question – do you need to learn programming language or statistics to complete artificial intelligence courses? You definitely need to have some basics in both as the statistics help you understand what you are doing, and the programming language shows how it is done. In case either of these is unknown to you, it is recommended you start your quest to learn machine learning with the courses for these.
You can find some excellent courses to learn programming languages like R or Python, models and algorithms basics through – imarticus.org Or also can contact on – info@imarticus.com or 1-800-267-7679 Or else you can visit our different training locations in India – Mumbai, Pune, Thane, New Delhi, Banglore, Chennai, Gurgaon, Hyderabad and Ahmedabad.
Keep Learning…!!

The Complete Guide on Choosing The Best Data Analytics Course

Posted on by Imarticus Learning

Are you interested in data analytics and application and data analysis tools? Well, just having an interest in this field will not take you any further. When it comes to choosing any data analytics course, you should know that there are two different phases of your chosen specialization. The first phase will help in finding out the apt type of analytics training you want, while the second phase would tell you the proper understanding of the practical implications regarding the analytics training. In other words, you need to understand the data analytics jobs and check the perspective of the same, which will further define your training.
The right approach
 After you have the answer for the question, what is data analytics?, the next common question that is posed by most of the people include, what kind of analytics training will be appropriate for me as per the educational background?
This will be your starting phase when it comes to choosing a Big Data data analytics course. However, a majority of people end up failing to find the right training institute since they tend to follow the modern trends. One can see many courses and degrees dealing with data analytics; however, your choice should not be based as per the nice ads or marketing gimmicks but finding out the answer of the question, what do I need to get success in this field and get the data analyst jobs.
The 1st Phase – Choosing the best Data Analytics Course that you require! 
First things first, have your analytics aptitude assessment test. This assessment test may not be a fun thing, but if you are really keen on pursuing your career in this field, then you should have some basic aptitude for data. Once you are clear on this part, you are then supposed to find answers to the following questions and then think of joining any data analytics course:
Are you sure about why you really need this course?
Your perfection should be apparent when it comes to choosing any training on data analytics. This will help you in choosing the right program and course content. So, depending upon the requirements of your chosen domain or data analyst jobs, you should select the course. In other words, you should be crystal clear about what you want from your course.

Check the skills where you lag behind 
You know better about your strengths and weakness. This will eventually help in getting the insight about the kind of skills that you need for your career path. The data analytics jobs would need good technical skill sets along with a good understanding of mathematics for being competent in your work. The popular analytical skills required for the job including the following:
• Good exposure and experience with Data-to-Decision Framework
• The Basic knowledge of business and data analytics
• SQL skills
• Learning skills in working with stakeholders
• Good exposure to predictive analytics
• Experience in handling stats and data analysis tools including SAS, Knime, R, to name a few
Besides these, a working professional would need focusing on stat tools, DTD framework, advanced stat methods, collaborations with analysts, etc. So, depending upon the gap you have in your skill sets, you can further choose the apt data analytics course. You have three key options when it comes to choosing any course, which includes the following:
• The master’s program in data analytics
• A short-term semester program
• Enrolling for any professional workshop
The 2nd Phase- The Analytics Career
If you are keen on making a career in data analytics, you should know the fact that these are not the same as the IT industry in terms of requirement, placement and perks. Since the data analytics courses and subjects are not covered under any undergraduate program, hence getting direct placement in campus is not at all possible. Companies advertising data analyst jobs look for candidates having prior experience in this field.
In other words, you should know how to use the data analysis tools then only you will be called for the interview. Hence once you go beyond the question what is data analytics and start pursuing any program make sure you keep on getting some hands-on experience by being an intern in any company or try connecting with people working with real-time data. This will add you credibility.
On your Toes
Data Analytics is constantly growing and with every passing day, there is something experimented and added to it. If you are choosing any data analytics course without any consideration, then you are committing a blunder. Data is becoming complex with every passing day. You can find a good number of data analysis tools being added to the list that help in handling the data with great security. Data is the future currency of any company, hence if you are considering this career just for fun think again. This is because it would be difficult for you to crack any interview for data analyst jobs.
Different domains have different requirements
Every industry is different, and so are the requirements. This goes without saying that the data analyst jobs you need in one domain would be different than that of other domain. The techniques and data analytics tools you one in one could be outdated in the other. You should know this reality before you join any data analytics course. Be very sure about the domain you choose and get an edge over it rather than trying different things at one time.
Wrapping up
Choosing a data analytics course is not less than rocket science provided you do not know anything about it. If you have a fair about understanding about what is data analytics, and its various other aspects, the above tips can help you in choosing the best course.

Exploring the Potential of AI in Healthcare!

Posted on by Imarticus Learning

To begin with, let us start with AI and its potential. What exactly is AI?
Over the last two decades, we have built huge data resources, analyzed them, developed ML both unsupervised and supervised, used SQL and Hadoop with unstructured sets of data, and finally with neural and deep learning techniques built near-human AI robots, chatbots and other modern wonders with visualized data.

We have found, optimized pattern-based techniques, exploited tools of ML, deep-learning, etc to create speech, text, image applications used pervasively today in games, self-driven cars, answering machines, MRI image processing and diagnosis of cancer, creating of facial IDs, speech cloning, facial recognition tools, and learning-assimilation products like Siri, Google Assistant, Alexa and more.

AI potential in healthcare:
The AI potential to use intelligent-data both structured and unstructured makes it a very effective diagnostic tool. Their processing speeds, ability to find anomalies and volumes of data AI can handle makes it irreplaceable, un-replicable, and the most effective tool in the healthcare sector’s diagnosis and cost-effective treatment sectors for masses.

EyePACS aided-ably by technology has been used to integrate symptoms, diagnosis, and lines of treatment in diabetic types of retinopathy. Beyond diagnosis AI in the UK helps diagnose heart diseases, cardiovascular blockages, valve defects etc. using e HeartFlow Analysis and CT scans thereby avoiding expensive angiograms.

Cancer detection at the very cellular stages is a reality with InnerEye from Microsoft which uses the patient scans to detect tumors, treating cancer of the prostate, and even find those areas predisposed to cancers. Babylon health and DeepMind are other query-answering apps that have saved multiple doctor-visits by answering queries based on patient-records, symptoms, and other data.

All that AI needs to retransform healthcare services is the appropriate and viable infrastructure that is so vital but expensive today, keeping it out of reach of the masses at large.

Cautions with use of AI:
The first issue with AI is that its ability and potential is often misused. Some doctors fear AI may one day take over their roles. The use of laser-knives, surgical implements, tasers for immobilization, and deep-neural stimulation of the brain to prevent seizures are being used to take healthcare to the underprivileged masses. Harnessing the potential of AI is an issue of ethics, the patient’s privacy, and lives. Doctors need to use these tools to aid and not replace human-intervention in healthcare.

The issue of data transfers, privacy issues, legal responsibilities, misuse, and selling of data is of high importance. Another issue that looms large is the inertia to change and sufficient testing before large-scale adoption in healthcare for the masses. An approach that is amply cautious is always the best when millions are being spent on healthcare, and the lives of masses are at stake.

The right way to implement AI into the healthcare system would then hinge on education and training, sufficient testing and trial-runs before implementation, assuring and involving all stakeholders, and rediscovering ways and means of optimizing human-intervention in diagnosis, treatment, and care of patients.

The industrial sector will see thousands of opportunities thrown up which in turn when exploited create growth and employment opportunities galore. AI in healthcare is truly a panacea-producing tool. Perhaps in the near future immortality-quest will also take its place in the fields being explored. For now, the Government, doctors, researchers, industries, and patients are all set to accept the positive impact of AI on the healthcare sector readily.

How do Data Scientists Use Programming?

Posted on by Imarticus Learning

 

There are few fields in the world which have grown in stature as data science has in recent years. As a discipline, the growth of data science has been unparalleled – machine learning and AI are now implemented in a host of large organizations around the world. 

With every passing second, new data is created by millions of users around the world. Companies try to leverage this data by analyzing it, and data science is used to get business value from the findings. This means that using data science as both a business tool and a product, a number of companies have made business decisions which have made them grow quicker than ever. 

With the rise of data science, the demand for capable data scientists has also improved. It is now one of the most lucrative career options and one which is extremely accessible too. However, how do you become a data scientist, and is programming truly required for being successful in the discipline?

The Importance of Programming for a Data Scientist

Programming remains to be one of the main technical skills which any data scientist requires to succeed. In the initial stages of learning, one does not need to know much programming. This is because the focus is one using the tools at hand to implement algorithms, in order to analyse the data at hand. 

However, when you grow in your career, you will find that you need to modify algorithms or even create new ones for analysis. This means that you will have to modify the codes behind them, or even write a new algorithm from scratch. This requires you to have a command over at least one programming language – be it Python, R or Java. 

Knowledge in programming and statistics can go a long way in helping you implement your own algorithms and analyse the data in a more efficient manner. It will help you manipulate the data set, and bring any unconventional ideas you have to life. If programming is something you can’t do, you will be forced to use the tools at hand without the option of trying to change those tools themselves. While you may be able to manage with this, it will have an impact on your career in the long run. 

Can Data Scientists Be Good Without Programming?

Of course, if you do not know how to program, the obvious thing would be to start learning it since it can improve your career over the long term. However, there are some other ways in which you can excel at your job too. You should try and master a GUI tool, like Tableau so that you are able to visualize your data in a much more efficient manner. You should become a storyteller, and must be able to convey the story the data tells you in a concise manner to the client. Excelling at sales would also help, since you may have to interact with the client a lot. 

If you find yourself interested in machine learning and data science, you should definitely check out the data science course at Imarticus Learning. It is one of the best data science training in Banglore. 

Do Data Scientist Use Statistics?

Posted on by Imarticus Learning

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.

How Can You Learn About Healthcare Data Analytics?

Posted on by Imarticus Learning

 

Data analysis positions are becoming more and more hard for the organisations to fill, thanks to the skyrocketing demand for data professionals. With a tons opportunities lying ahead, the time and effort spent to learn data analytics is worth every penny. Wondering how to start learning it? keep reading the article.

What is Data Analytics?
In simple words, data analytics is the process of sorting massive chunks of unstructured data and delivering important insights. The insights play a crucial role in making important decisions in business. Be it a small or large organisation, the service of a data analyst is vital to their operations.

The Data Scientists are an entirely different job than Data Analyst. The former one is involved with more programming, creating algorithms and building predictive models while analytics is much less complex. With proper knowledge and certification in data analytics, you will be able to pursue careers such as Data Analyst, Business Analyst, Product Manager, Digital Marketer and Quantitative Analyst.

Learning Data Analytics
 Data analytics demands problem solving and communication skills to be successful. However, you will also need some technical skills to perform the jobs relating data analytics. Some most common skills you will need are the following: 

• Excel (Spreadsheets)- Microsoft Excel is a spreadsheet program widely used for complex data analysis. The built-in pivot tables in Excel are one of the most popular analytic tools.
• SQL (Database Language) – SQL or Structured Query Language is used to add and retrieve data quickly from a database. It allows operations on millions of rows of data.

• R (Programming Language) – R is a programming language built for statistical computation and graphics. It is a popular tool among statisticians, data miner, data scientists, data analysts and business analysts. It is an essential tool for developing statistical software, machine learning and data analysis. Many high profile companies like Google and Facebook have adopted R as their preferred language to analyse data.

• Data Visualization – Data visualisation is an important part of communication in data analysis. It helps the key decision makers of organisations to identify the insights and trends easily and understand the complex information. With a touch of creativity, this skill is comparatively easy to have.

Other few important skills you can consider for a better leg in data analytics role are:
 • Google Sheets – It is similar to Excel but a Cloud version.
• Tableau – It is a GUI data visualization tool. allows easier data visualization.
• Data Studio – A Data Visualization Tool from Google
• Google Analytics – It is a free web analytics tool from Google.
• Math Skills – Linear algebra and multi variable calculus are an added advantage.
• Primary understanding of Machine learning.

Conclusion
 With companies flooding with data, there is a large gap between the demand and availability of people who can use data right. With technologies like the Internet of Things coming up, the demand for such people will only go up.

So, it is the best time to start learning data analytics. Imarticus Learning is providing a data analytics course to help those who aspire a career in this field. Be sure not to miss this opportunity.

Why Do People Often Use R Language Programming for Artificial Intelligence?

Posted on by Imarticus Learning

Why Do People Often Use R Language Programming for Artificial Intelligence?

All over the world, machine learning is something which is catching on like wildfire. Most of the large organisations now use machine learning and by extension, AI for some reason or other – be it as a part of a product or to mine business insights, machine learning is used in a lot of avenues. Even the machine learning future in India seems all set to explode in the next couple of years.

All this has led companies to be on the lookout for proficient practitioners, and there are a lot of opportunities existing currently in this field. You might have started to wonder how you can make your mark in this science field – machine learning and AI are something which you can learn from your home, provided you have the right tools and the drive for it.

Many students have already started learning R, owing to the availability of R programming certification course on the internet. However, some are still not sure whether they want to learn R or go for Python like many of their peers are. Let us take a look at why R certification course is a great choice for machine learning and Artificial Intelligence programming and implementation. 

Features of R
R is a multi-paradigm language which can be called a procedural one, much like Python is. It can also support object-oriented programming, but it is not known for that feature as much as Python is.

R is considered to be a statistical workhorse, more so than Python. Once you start learning, you will understand that statistics form the base of machine learning and AI too. This means that you will need something which can suit your needs, and R is just that. R is considered to be similar to SAS and SPSS, which are other common statistical software. It is well suited for data analysis, visualisation and statistics in general. However, it is less flexible compared to Python but is more specialised too. 

R is an open source language too. This does not simply mean that it is free to use, for you – it also implies that you will have a lot of support when you start to use it. R has a vast community of users, so there is no dearth of help from expert practitioners if you ever need any.

One other thing that differentiates R and Python is the natural implementation and support of matrices, and other data structures like vectors. This makes it comparable to other stats and data-heavy languages like MATLAB and Octave, and the answer that Python has to this is the numpy package it has. However, numpy is significantly clumsier than the features that R has to offer.

Along with the availability of a lot of curated packages, R is definitely considered to be better for data analysis and visualisation by expert practitioners. If you think that you want to try your hand at machine learning and AI, you should check out the courses on machine learning offer at Imarticus Learning.

Which is better for data analysis: R or Python or else?

Posted on by Imarticus Learning

 

Data sciences have become a crucial part of everyday jobs. The availability of data, advanced computing software, and a focus on decisions that are analytics-driven has made data sciences a booming field. Jobs abound in this field and hence large interest also exists on which languages to learn. 

R and Python are the most popular tools for data science work. Both are flexible, open source, and evolved just over a decade ago.R is used for statistical analysis while Python is a programming language that can be termed general-purpose. These are both in combination essential for data analysis where you are involved in working with large data sets, machine learning, and creating data visualization insights based on complexities involving data sciences.

The process of Data Science:
 Very simply put the course on data science involve the four subdivisions discussed below. Let’s compare the two for the following.

Data Collection:
Python is supportive of different data formats. You can use CSVs, JSON and SQL tables directly in your code. You can even find Python solutions when stuck on Google. Rvest, magrittr, and beautiful soup packages in Python resolve issues in parsing, web scraping, requests etc.

Data can be imported from CSV, Excel, text files etc. Minitab or SPSS file formats can be converted into R data frames. R is not as efficient in getting web information but handles data from common sources just as well.

Data Exploration:
One can hold large volumes of data, sort, display data and filter large amounts of data using Pandas without the lag of Excel. Data frames can be redefined and defined throughout a project. You can clean data and scan it before you clean up empirical sense data.

R is an ace at numerical and statistical analysis of large datasets. You can apply statistical tests, build probability distributions, and use standard ML and data mining techniques. Signal processing, optimization, basics of analytics, statistical processing, random number generation, and ML tasks are easy to perform from its rather limited libraries.

Data Modeling:
 Numerical modelling analysis with Numpy, scientific computing with SciPy and the scikit-learncode library with machine learning algorithms are some excellent working features in Python.

The R’s core functionality and specific modelling analysis are rather limited and compatible packages may have to be used.

Data Visualization:
 The Anaconda enabled IPython Notebook, the Matplotlib library, Plot.ly, Python API, nbconvert function and many more are great tools available in Python.

ggplot2, statistical analysis abilities, saving of files in various formats like jpg, pdf etc, the base graphics module and graphical displays make R the best tool for statistical analysis complexities.

Before choosing, ask these questions
• Do you have programming experience?
• Do you want to do a Python course for business analytics or a business analytics course?
• Do you want to go into research and teaching or work in the industry?
• Do you want to learn ML or statistical learning in data sciences?
• Do you want to do software engineering?
• Do you want to visualize data in graphics?

Research well and you will find that depending on what functions you need both are excellent languages to learn for a career in data science.

What is a Data Scientist Internship Like?

Posted on by Imarticus Learning

 

Over the past few decades, Data science has emerged as one of the most admired career fields in the world. Currently, it is estimated that 2.5 quintillions bytes of data are produced every day. The value is expected to keep growing and growing in the future. With such a forecast, the high demand for skilled data scientists is expected to stay. There are various sources to acquire data science skills and knowledge. But it is always better to have a real-life experience before the actual job.

The internships are the best source of real-life experience for any profession and the same goes for data science too. This article describes a typical data scientist internship and provides you with a basic idea about it. The actual experience may vary according to the company you go for.

Messy and Complicated Real Life Data Mining Projects
 If you get to work on any data mining project, don’t expect it to be anywhere near the problems you faced in the classroom. The projects you get will be messy and complicated, unlike the controlled environment in the university lecture.

However, with the help of your teammates, you will be able to do all the complex work. It will help you improve your mining skills and provide you with a taste of real-life mining problems. Before heading to an internship, make sure you are equipped with the right level of skills for such messy data.

Being A Trusted member of the Team
 Most companies provide you with excellent exposure and guidance. You will be taken to many meetings and entrusted with various details. This is intended to inspire you to perform better. The meetings you attend within the several departments will help you to understand how the business runs and how departments are interacting with each other. This knowledge is vital to business understanding.

Developing the Essential Data Science Skills
 You will be facing numerous challenges at each stage of every project. It will lead you towards the skills paramount for a career in data science. You will be required to engage with various staff for information and it adds to your communication skill. Through various projects, you will gain experience in many aspects of organisational operations and project management. Some of them are listed below.

• The need for business understanding
• Importance of feasible project plans and aims
• Value of correct data collection methods
• The need for documentation of a project to make it transparent and repeatable
• Importance of having iteration and feedback from the team to ensure the project progress.

During a Data Science Internship, you will gain very valuable technical experience in various segments of data science. The consultancy experience you obtain through tackling real-life problems is also very vital to your data science career. To equip you with all the necessary skill sets to take on such a career, Imarticus is providing a data science prodegree. This Genpact data science course will help you start the data science career on the right foot.