Difference Between Data Science & Business Analytics

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Data Science and Business Analytics, often used interchangeably, are very different domains. A layman would probably be least bothered with this interchangeability, but professionals need to use these terms correctly as the impact on the business is large and direct. In this article, we will elaborate on the difference between the two.  

Overview

Data Science and Business Analytics are unique fields, with the biggest difference being the scope of the problems addressed. Simply put, The science of data that uses algorithms, statistics, and technology is known as Data Science. It provides actionable insights on a range of structured and unstructured data solving a broader perspective such as customer behaviour. 

Difference between Data Science and Business Analytics

On the other hand, the statistical study of mostly structured business data is known as Business Analytics. It provides solutions to specific business problems and roadblocks. 

These two terms are interchangeably used in either of the above scenarios, i.e., a business analytics problem could be wrongly addressed to be solved with the help of Data Science. The implications of carelessly using the term ‘Data Science’ in this context could be adverse because the tools and techniques used in Business Analytics are different than Data Science and using wrong tools to assess a data set will yield imperfect and undesirable results. 

Data Science is an umbrella term for all things dedicated to mining large data sets. An intersection of programming, statistics, and data analytics, Data Science is not limited to only statistical or algorithmic aspects. Business Analytics is the end-product of data science. It includes two broad categories, that are Statistical Analysis and Business Intelligence. 

Difference between Data Science and Business Analytics

Business Intelligence

Another term often confused with Data Science is Business Intelligence. It is also an umbrella term that portrays ideas and strategies to improve decision making by utilizing fact-based support systems. Modern Business Intelligence is much beyond just business reporting. It is a mature framework that encompasses intuitive dashboards, mobile analytics, what-if planning, etc. It additionally incorporates enormous back-end machinery for maintaining control around reporting.

Although it sounds similar to Data Science, it is not. The principal difference lies in the type of problems that they address. Business Intelligence deduces the new unknown values of previously known elements using a formula that is already available. On the other hand, Data Science works with unknown scenarios without any formula or algorithm in hand, to solve data queries that nobody has ever answered in the past. Data Science problems are solved by exploring data, finding the best method, building a model around it, and finally operationalizing the model. 

Conclusion

Business Intelligence is well established with deep roots in a typical corporate landscape. Corporate professionals are familiar, comfortable, and confident with the BI concepts and framework. As BI projects work on known unknowns, the projects can be planned well in advance and timelines could be efficiently followed. Also, there is minimal trial and error with several successful BI projects in a company’s kitty, who would have developed good project expertise over the years. 

There is a massive career scope in the fields of Business Intelligence and Business Analytics. Professionals who are genuinely thinking of making a shift in the BA and Data Science roles can consider upskilling with the right course. Great Learning’s PG program in Data Science & Business Analytics and helps working professionals make a smooth and successful transition. The course offers the choice of online or classroom-based learning with Dual Certificate from University of Texas at Austin, McCombs School of Business (world rank #2 in Analytics), and Great Lakes (India rank #1 in Analytics). It helps you with hands-on practical learning with case studies and projects, without the need of quitting your job. The course is also tailor-made keeping in mind the professionals from the non-IT background. With our career guidance and support, you can easily land your dream job in Business Intelligence and Business Analytics.  

 

The course offered an exhaustive curriculum – Dipankar Neogi, Sr. Data Analyst at Indegene.

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There are times when professionals in a specific domain reach saturation and do not enjoy working in their domain. At such times, one can either succumb to the monotony or rise up to look out for opportunities in other domains, sometimes drastically different from their current role. This is what Dipankar Neogi did when his marketing career reached its saturation point. Read how he transitioned to a data analyst role from being a marketing professional.  

What is your professional background?

I had completed my post-graduation as an MBA from Ramaiah Institute of Management Science in 2010 and worked for 7 years in Marketing with a firm in Bangalore. In order to upskill and upscale my career, I took a course in Business Analytics and Business Intelligence at Great Learning. Presently, I am working with Indegene as Senior Data Analyst.

Why did you choose Business analytics for upskilling and why did you choose GL?

I was not happy with the role I was working in previously. There were no growth opportunities and the remuneration not satisfactory. I had two options; one was to take up a full-time technical course and the other was to pursue an Executive MBA or get through GMAT to ISB or IIM. There was a lot of buzz for Analytics and Data Science and some of my friends suggested some courses. I did research and came across the GL’s BABI course. Since it was more business-related than a tech-driven program, I took this course as it suited me the best.

How was your overall experience with GL?

The course offered an exhaustive curriculum. Now since I am working in an Analyst role, I see how well-versed the course is. It provided detailed learning in Statistics, Regression, Conjoint analysis, etc., all of which I am using in my current role. The classroom experience made me realise the dream of being from a reputed firm. Working with peers and coming together on weekend classes or for projects, is what I recall as the most memorable time at GL.

Share your experience at the Interviews?

I got interviewed and shortlisted by many companies. I realised some patterns in the questions asked during the interviews. I figured out that the interviewer mainly focussed on my learning of the project that I had mentioned in my CV. In some interviews, I was given a real-life situation and was asked to give predictions for them. They judged me not only on my answers but also on my perception of the situation and the approach towards it.

Any advice to future aspirants?

Firstly, you need to focus more on projects. Apart from the Capstone project offered by GL, work end to end on some projects of your own. This will give you clarity and confidence with the subject matter. And secondly, focus on mastering one domain at a time. This will give you a stronghold in the domain.

Upskill with Great Learning’s PG program in Business Analytics and Business Intelligence and unlock your dream career.

Payment Options – Online PG Program in Data Science and Business Analytics (PGP-DSBA Online)

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Great Lakes’ Online PG Program in Data Science and Business Analytics (PGP-DSBA Online) provides the most flexible payment options to reduce your financial stress while deciding on upskilling. With us, you need not worry about the financing options, and just need to focus on learning and output. Our program offers the below payment structure for maximum flexibility:

  1. Easy Installments – Initially you just need to pay the admission fee to confirm your candidature. The rest of the amount is divided into 3 equal instalments to minimize the stress of one-time payment.
  2. Multiple Payment Options – We offer multiple payment options including payment through debit card, credit card, net banking, demand drafts and cheques. Fuss-free, right?
  3. Pre-Approved Education Loans – We do not want the financial aspect becoming a road-block in your learning process. Therefore, we have partnered with various third party lenders like HDFC Credila, Avance Education, and Zest Money (0% EMI) providing a substantially lower interest rate as compared to other financial institutions in the market.
  4. Fee Waiver on One-Time Payment – A fee waiver up to INR 10,000/- is given if someone chooses to pay the full program fee in one go.

With the online PGP-DSBA’s Flexi-payment options, you no longer need to wait for a fulfilling and rewarding career in Business Analytics. Join us now!

21 Open Source Python Libraries You Should Know About

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The probability that you must have heard of ‘Python’ is outright. Guido Van Rossum’s brainchild – Python, which dates back to the ’80s has become an avid game changer. It is one of the most popular coding languages today and is widely used for a gamut of applications. In this article, we have listed 21 Open Source Python Libraries you should know about.

What is a Library

A library is a collection of pre-combined codes that can be used iteratively to reduce the time required to code. They are particularly useful for accessing the pre-written frequently used codes, instead of writing them from scratch every single time. Similar to the physical libraries, these are a collection of reusable resources, which means every library has a root source. This is the foundation behind the numerous open-source libraries available in Python. 

Let’s Get Started!

1. Scikit- learn: It is a free software machine learning library for the Python programming language and can be effectively used for a variety of applications which include classification, regression, clustering, model selection, naive Bayes’, grade boosting, K-means, and preprocessing.

Scikit-learn requires:

  • Python (>= 2.7 or >= 3.3),
  • NumPy (>= 1.8.2),
  • SciPy (>= 0.13.3).

21 open source python libraries you should know

Spotify uses Scikit-learn for its music recommendations and Evernote for building their classifiers. If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip.

2. NuPIC: The Numenta Platform for Intelligent Computing (NuPIC) is a platform which aims to implement an HTM learning algorithm and make them public source as well. It is the foundation for future machine learning algorithms based on the biology of the neocortex. Click here to check their code on GitHub.

3. Ramp: It is a Python library which is used for rapid prototyping of machine learning models. Ramp provides a simple, declarative syntax for exploring features, algorithms, and transformations. It is a lightweight pandas-based machine learning framework and can be used seamlessly with existing python machine learning and statistics tools.

4. NumPy: When it comes to scientific computing, NumPy is one of the fundamental packages for Python providing support for large multidimensional arrays and matrices along with a collection of high-level mathematical functions to execute these functions swiftly. NumPy relies on BLAS and LAPACK for efficient linear algebra computations. NumPy can also be used as an efficient multi-dimensional container of generic data.

21 open source python libraries you should know

The various NumPy installation packages can be found here.

5. Pipenv: The officially recommended tool for Python in 2017 – Pipenv is a production-ready tool that aims to bring the best of all packaging worlds to the Python world. The cardinal purpose is to provide users with a working environment which is easy to set up. Pipenv, the “Python Development Workflow for Humans” was created by Kenneth Reitz for managing package discrepancies. The instructions to install Pipenv can be found here.

6. TensorFlow: The most popular deep learning framework, TensorFlow is an open-source software library for high-performance numerical computation. It is an iconic math library and is also used for machine learning and deep learning algorithms. Tensorflow was developed by the researchers at the Google Brain team within Google AI organisation, and today it is being used by researchers for machine learning algorithms, and by physicists for complex mathematical computations. The following operating systems support TensorFlow: macOS 10.12.6 (Sierra) or later; Ubuntu 16.04 or later; Windows 7 or above; Raspbian 9.0 or later.

7. Bob: Developed at Idiap Research Institute in Switzerland, Bob is a free signal processing and machine learning toolbox. The toolbox is written in a mix of Python and C++. From image recognition to image and video processing using machine learning algorithms, a large number of packages are available in Bob to make all of this happen with great efficiency in a short time.

8. PyTorch: Introduced by Facebook in 2017, PyTorch is a Python package which gives the user a blend of 2 high-level features – Tensor computation (like numpy) with strong GPU acceleration and developing Deep Neural Networks on a tape-based auto diff system. PyTorch provides a great platform to execute Deep Learning models with increased flexibility and speed built to be integrated deeply with Python.

9. PyBrain: PyBrain contains algorithms for neural networks that can be used by entry-level students yet can be used for state-of-the-art research. The goal is to offer simple, flexible yet sophisticated and powerful algorithms for machine learning with many pre-determined environments to test and compare your algorithms. Researchers, students, developers, lecturers, you and me – we can all use PyBrain.

21 Open Source Python Libraries you should know

10. MILK: This machine learning toolkit in Python focuses on supervised classification with a gamut of classifiers available: SVM, k-NN, random forests, decision trees. A range of combination of these classifiers gives different classification systems. For unsupervised learning, one can use k-means clustering and affinity propagation. There is a strong emphasis on speed and low memory usage. Therefore, most of the performance-sensitive code is in C++. Read more about it here.

11. Keras: It is an open-source neural network library written in Python designed to enable fast experimentation with deep neural networks. With deep learning becoming ubiquitous, Keras becomes the ideal choice as it is API designed for humans and not machines according to the creators. With over 200,000 users as of November 2017, Keras has stronger adoption in both the industry and the research community even over TensorFlow or Theano. Before installing Keras, it is advised to install TensorFlow backend engine.

12. Dash: From exploring data to monitoring your experiments, Dash is like the frontend to the analytical Python backend. This productive Python framework is ideal for data visualization apps particularly suited for every Python user. The ease which we experience is a result of extensive and exhaustive effort. 

13. Pandas: It is an open-source, BSD licensed library. Pandas enable the provision of easy data structure and quicker data analysis for Python. For operations like data analysis and modelling, Pandas makes it possible to carry these out without needing to switch to more domain-specific language like R. The best way to install Pandas is by Conda installation 

21 open source python libraries you should know about

14. Scipy: This is yet another open-source software used for scientific computing in Python. Apart from that, Scipy is also used for Data Computation, productivity, and high-performance computing and quality assurance. The various installation packages can be found here. The core Scipy packages are Numpy, SciPy library, Matplotlib, IPython, Sympy, and Pandas.

15. Matplotlib: All the libraries that we have discussed are capable of a gamut of numeric operations but when it comes to dimensional plotting, Matplotlib steals the show. This open-source library in Python is widely used for publication of quality figures in a variety of hard copy formats and interactive environments across platforms. You can design charts, graphs, pie charts, scatterplots, histograms, error charts, etc. with just a few lines of code. 

21 open source python libraries you should know

The various installation packages can be found here.

16. Theano: This open-source library enables you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. For a humongous volume of data, handcrafted C codes become slower. Theano enables swift implementations of code. Theano can recognise unstable expressions and yet compute them with stable algorithms which gives it an upper hand over NumPy. Follow the link to read more about Theano. The closest Python package to Theano is Sympy. So let us talk about it.

17. SymPy: For all the symbolic mathematics, SymPy is the answer. This Python library for symbolic mathematics is an effective aid for computer algebra system (CAS) while keeping the code as simple as possible to be comprehensible and easily extensible. SimPy is written in Python only and can be embedded in other applications and extended with custom functions. You can find the source code on GitHub. 

18. Caffe2: The new boy in town – Caffe2 is a Lightweight, Modular, and Scalable Deep Learning Framework. It aims to provide an easy and straightforward way for you to experiment with deep learning. Thanks to Python and C++ API’s in Caffe2, we can create our prototype now and optimize later. You can get started with Caffe2 now with this step-by-step installation guide.

19. Seaborn: When it comes to visualisation of statistical models like heat maps, Seaborn is among the reliable sources. This Python library is derived from Matplotlib and closely integrated with Pandas data structures. Visit the installation page to see how this package can be installed

20. Hebel: This Python library is a tool for deep learning with neural networks using GPU acceleration with CUDA through pyCUDA. Right now, Hebel implements feed-forward neural networks for classification and regression on one or multiple tasks. Other models such as Autoencoder, Convolutional neural nets, and Restricted Boltzman machines are planned for the future. Follow the link to explore Hebel.

21. Chainer: A competitor to Hebel, this Python package aims at increasing the flexibility of deep learning models. The three key focus areas of chainer include :

a. Transportation system: The makers of Chainer have consistently shown an inclination towards automatic driving cars and they have been in talks with Toyota Motors about the same.

b. Manufacturing industry: From object recognition to optimization, Chainer has been used effectively for robotics and several machine learning tools.

c. Bio-health care: To deal with the severity of cancer, the makers of Chainer have invested in research of various medical images for early diagnosis of cancer cells.

The installation, projects and other details can be found here.

So here is a list of the common Python Libraries which are worth taking a peek at and if possible familiarizing yourself with. If you feel there is some library which deserves to be in the list do not forget to mention it in the comments.

 

Keep Calm and Let the Business Analyst Handle it

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A Day in the Life of a Business Analyst

Business Analytics has emerged as a much sought after skill set. Professionals with business analytics expertise can work in different analytical profiles in companies to help them grow – but what exactly do they do? This article breaks down the essentials of a BA profile by looking into a typical day in the life of a business analyst. 

Equipped with strong analytical skills and a sound knowledge of the market, business analysts take care of a range of tasks to help companies meet their business goals. While collecting and interpreting data is core to a business analytics profile, stakeholder communication, meeting management and report collating are equally important. Aspiring business analytics professionals can refer to this detailed account of a day in an analyst’s life to understand the requirements and set their expectations. 

For a business analyst, a typical day starts by pinning up tasks on a storyboard to plan and prioritise them. As with most other roles, business analysts also have team meetings to discuss pain-points, pending tasks, obstacles and priorities. After having the tasks planned out, the workflow looks somewhat like this:

Prioritization & Business Context: Analysts typically like to plan their day and week meticulously in advance. Iteration planning meetings (IPM) are an excellent way to interact with the whole team and stay updated on all the ongoing projects. This iterative method ensures that objectives and plans are clearly explained. IPM also helps the team to understand the business context of any particular project and prioritize accordingly.  

Investigate Goals and Issues: An important part of an analysts job is to identify the problem. Research, interviews, analytical observations are few of the ways in which analysts investigate the situation to recognise the issues. Analysts look at past data, and try to make projections based on inferences.

Analysing Information:  It is after collecting data points concerning the issue at hand, that analysts finally get down to the analysing part. Data sets are thoroughly examined for recurring patterns and anomalies. Analytic reports are then shared internally for teams to understand the problem areas. These reports break down series of data sets into comprehensible explanations so that they are easily interpreted by the leadership team to help them arrive at business decisions unanimously.

Documenting Information: It is important to record all the analytical findings since they can act as future reference points. Analysts spend a considerable part of their day collecting and documenting all the analytical results, inferences and new developments. Considering documentation techniques are specific for each report, analysts also spend time looking into different documentation methods to choose the best option for any given report. 

Backlog Grooming:  This is an ongoing task for analysts- analysing and distributing the backlog. Resource optimization is crucial for any business and analysts aid that by efficient backlog management. Analysts go through the task lists and plan resource allocation according to priority.

Meetings and Communication: A major part of an analyst’s day is spent in active communication- internally with the team or externally with stakeholders. Business communication is not limited to just speaking, but it also means non-verbal communication in the form of emails and presentations to make sure that information is properly relayed, agreed to and acted upon.

Client Interaction: Client feedback is an integral part of any business plan. The best way to ensure that you are proceeding in the right direct and your business goals are met is by getting direct feedback from the clients. Feedback sessions can be used to evaluate the progress of the projects and analyse its success. Incorporating feedback in the project proceedings will lead to more satisfactory results and improve project success rates.

Business analysts act as a bridge between problems and solutions, trying to understand the former and planing the latter. However, a business analyst needs to work closely with the development teams, operations teams and the service teams to make any business project successful. A typical day in a business analyst’s life involves different kinds of tasks like extensive communication, reporting and documentation. While knowledge of analytical tools is an absolute necessity for an analyst, interpersonal communication skills coupled with strong business acumen is also required to deliver results.

The quality of faculty at GL is unmatched compared to other institutes – Venkatesh Radhakrishnan, Sr. Research Analyst at RRD

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From an amateur in Data Science to a Hackathon winner, Venkatesh has come a long way in his career. He was a newbie in this field since he’s from a commerce background, but he still didn’t lose confidence or direction throughout the course. Here’s how he did it: 

What is your professional background?

I completed my graduation with B.Com in Informations System Management from Ramakrishnan Mission Vivekananda College, Chennai by 2015. Then, I started as a Research Associate in RRD – Global outsourcing solutions, APAC. In 2017, I took a BABI course with GL. Currently, I am working as a senior research analyst at RRD.

How did you develop an interest in this course? Why did you choose Great Learning?

As soon as I started working in RRD as a research associate, I got aware of the Analytics field in terms of information & intelligence value. As a market researcher, I developed an immediate interest in this field and started looking for institutes that provided courses in this stream. I came across GL and it’s brand value. I got in touch with the team, got interviewed for the course and cracked admissions in BABI course at GL. 

How did you transition from a Research to a Data Science role?

During my time at GL, I spoke to my organization about my course and requested them to let me explore my skills in order to check applicable areas. After their approval, we started developing a proof of concept for clients; which turned pretty well. In the past 2 years, as a company, we have come a long way in terms of our practice and implementation of analytical tools. I feel privileged to be a part of an organization where through the help and support of management, I could transition to the Data Science field.

What did you think was the best thing about this program?

The best thing about this course is it provides flexibility in terms of learning things at one’s own pace. The course and curriculum are designed to promote learning and not spoon-feeding. There is an ample amount of time to learn, understand, practice and implement the concepts. The quality of faculty at GL is unmatched compared to other institutes.

What would be your advice to the future aspirants?

The advice would be to develop key skills in identifying areas where analytical tools can be applied to make things easy and profitable. One needs to study and research properly to understand the deployment of tools in favour of Business.

Upskill with Great Learning’s PG program in Business Analytics and Business Intelligence and unlock your dream career.

Your essential weekly guide to Data and Business Analytics 

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By 2020, 80% of organizations will initiate deliberate competency development in the field of data literacy, according to Gartner. The march of analytics into the collective consciousness of businesses around the world is unstoppable now, and the implications are far-reaching. From a glut of new skills that employees need to learn, to the shiny new applications of Analytics that’s changing the way humans live, there’s quite a lot of activity going on here. We try to make sense of all that news in our digest that encapsulates the Analytics landscape.

Here are some articles that will take you through recent advancements in the data and analytics domains. 

Businesses Face Three Biggest Challenges While Leveraging Big Data

According to a report from Dun & Bradstreet, the three biggest challenges businesses still face when it comes to leveraging big data are protecting data privacy, having accurate data, and Analysing/processing data. The global big data market was estimated at $23.56 billion in 2015 and now is expected to reach $118.52 billion by 2022.

Big Data & Business Analytics Market to Rear Excessive Growth During 2015 to 2021

Due to the tremendous increase in organizational data the adoption of big data and business analytics has been increased within organizations to better understand their customer and drive efficiencies. Read more to know about Drivers and Challenges of Big Data and Business analytics market. 

‘Jeopardy!’ Winner Used Analytics to ‘Beat the Game’

An aggressive strategy, mathematical finesse, a sharp mind, and a willingness to take risks were some of the factors that spurred ‘Jeopardy!’ game-show contestant James Holzhauer to win 32 consecutive games and rake in more than $2.4 million. Read more to know how this happened. 

The Age of Analytics: Sequencing’s New Frontier is Clinical Interpretation

Today, genomic data is being generated faster than ever before. And those on the frontier of this field are trying to make sure that data is as useful as possible. While the surge in sequencing has benefited many patients, the genomic data avalanche has caused its own problems. Read more about the challenges and proposed solutions to manage and analyze the volumes of genomic data. 

Times Techies: Upskilling is Key to Meeting Demand For Analytics

An exhaustive Nasscom-Zinnov report released last year flags a huge talent demand-supply gap in the artificial intelligence (AI) and big data analytics (BDA) family of jobs. By 2021, the total AI and BDA job openings in India is estimated to go up by 2,30,000. But the fresh employable talent or university talent available will be just 90,000, leaving a huge gap of 1,40,000. 

Happy Reading!

Developing industry-relevant skills through Experiential Learning

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At Great Learning, we believe in imparting a holistic education experience through exposure to real-world problems, and a mentor-driven approach to solving them. We strive to provide a great learning experience to build capabilities that are driven by an efficient mix of theoretical sessions and practical ‘learn by doing’ approach. 

As a testimony to our philosophy of education, the ‘Cricket World Cup Challenge’, and ‘Hack of All Trades’ are an integral part of our learning journey, organized for Great Learners across programs. Here’s how these initiatives help our learners achieve career success:

The Cricket World-Cup Challenge

Overview:

The Great Learners were required to form a team of 2 with someone who is not a Great Learning program participant. The teams had to predict outcomes of each match, based on which they were assigned scores. Finally, using machine learning models, they had to predict the finals and submit a report on the same. 

Here’s a snapshot of the event in numbers:

Engagement statistics for the Cricket World Cup Challenge

 

Based on the match outcome predictions, a leader board was posted on Social Media each Monday.

Cricket World Cup Challenge Experiential Learning

Special sessions were conducted with Mr Gaurav Sundaraman, Data Scientist at ESPNCricinfo, on Cricket Analytics

After 50 days of altering leaderboard dynamics, we finally got our winners:

Cricket World Cup Challenge Experiential Learning

Here’s what the participants had to say about this initiative:

– Thanks for the innovative exercise. It created a lot of interest in the games as well as finding stats to predict the winner.

– It was a thrilling competition.

– Congratulations on the event being a success, and thanks for organizing.

– I would like to see more such competition based on some social impact data. That would help us understand our social environment. Thanks.

– All the instructions and scoring system were very transparent. Keep rocking team very good job. Looking forward to more such initiatives.

– This gave us a lot of learning and exposure.

 

Hack of all Trades 

The initiative was run exclusively for the online batches of the Business Analytics and Business Intelligence PG program, for both Indian and International participants. It was a 3-day online Hackathon where the participants had to predict the annual turnover of the restaurants across India based on the restaurant details, aggregated rating from social media, and customer survey data.

A state of the art Hackathon platform was devised with integrated leaderboard, customized view for each user with login credentials, FAQs and rewards on the same page.

hack of all trades experiential learning

 

There were 4 different rewards to raise the fun quotient of the experience

Hack of all Trades - Experiential Learning

Here’s what the participants had to say about this initiative:

– Thank you for the opportunity to showcase our talent to the world!

– Thrilling!

– It was a fantastic event and will look forward to more.

– Good initiative to check one’s skillset.

– Good practical experience.

 

Hack of all trades experiential learning* Social Media Mentions

 

When we talk about active engagement and learning through digging solutions to practical real-life challenges, this is just the tip of an ice-berg. At Great Learning, we put immense thought and effort in pushing such initiatives across programs and derive meaningful learning outcomes through them.

Such projects and hackathons have been the core of the teaching methodology at Great Learning and will continue to be so. The purpose is to nurture students to become job-ready professionals who are capable of acing interviews in their respective domains and areas of interest. These methods are replicated across courses to give a similar experience to students and professionals enrolling for any given program.

 

 

What are the best Data Science and Business Analytics Courses for working professionals in India?

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The term analytics and data science have garnered a lofty prominence in the past decade mostly used interchangeably. As it stands strong today, business analytics is finding applications across functions ranging from marketing, customer relationship management, financial management, supply chain management, pricing and sales, and human resource management among others. It has also made a place for itself across industries, spanning its wings even to the most traditional ones such as Manufacturing and Pharma. 

A bachelor’s degree with a minimum of 2-3 years of work experience is mandatory to enrol for almost all business analytics or data science programs out there. It holds a great career scope for graduates in the field of engineering, business management, marketing, computer science and information technology, finance, economics, and statistics among others. 

All things said and done, there are certain challenges that professionals might face while looking out for the perfect business analytics or data science course to steer their career on the growth path:

– Lack of time and issues with balancing work and course schedules

– Financial Barriers

– Inflexibility in the course structure

– Obsolete curriculum or irrelevant modules

– Inaccessibility to the course

An institution or a course that focuses on combating these challenges and provide a comfortable, valuable, and manageable learning experience is the ideal course for professionals. At Great Learning, we strive to focus on these issues and design courses to suit the needs and resources of the aspirants.

Here is a comparative study of various Data Science and Business analytics program with Great Learning’s PG program in Business Analytics and Business Intelligence:

What are the best Data Science and Business Analytics Courses for working professionals in India?

Great Learning has been changing the lives of professionals across domains for over 5 years now. Having imparted more than 5 million hours of learning, we have touched professionals in 17 different countries, and are working towards reaching more geographies to transform careers of professionals across the globe. 

The post-graduate program in Business Analytics and Business Intelligence was the first program to be launched by Great Learning in the year 2014. Since then, there have been more than 50 batches with 5000+ professionals enrolled and successfully completed the course. The program has been ranked #1 Analytics program in India for 4 years in a row by Analytics India Magazine and has involved 300+ Industry Experts and 25+ India’s Best Data Science Faculty to impart quality skills and practical learning. Having propelled more than 2,500 career transitions, the success of the program can also be gauged by the fact that 90% of our alumni refer the course to other professionals. 

Best business analytics and business intelligence course for professionals

Our alumni have been placed with some of the top Analytics firms and reputed MNCs such as IBM, Accenture, HSBC, KPMG, LatentView, Myntra, Rakuten, RBS, Shell, Tiger Analytics, UST Global, and many more, with an average salary hike of 48%. This alone speaks a lot about the value and industry relevance of the program. Know more about Great Learning’s PG program in Business Analytics and Business Intelligence here. 

Here are a few testimonials by our PGP BABI Alumni. Read-Along:

GL puts a lot of effort to make the curriculum up to date matching world-standards Sowmya Vivek, Independent Consultant – Data Science, ML, NLP

The best part of GL is its experienced faculty – Sriram Ramanathan, Associate Director for Data Products at Scientific Games

The best takeaway is the approach with which I now perceive business problems – Pratik Anjay, Data Scientist at Walmart

The course aided my old desire to pursue finance as a career – Sahil Mattoo, Data Scientist, DXC Technologies

The guidance from the GL faculty is an important driver of my success – Priyadarshini, Analyst at LatentView

 

Book a call with us at +91 84480 92400 and our learning consultants will guide you through the program details and the specific queries that you might have.