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Data science can add value to any business that uses it well. From finding statistics and insights across workflows to hiring new candidates and helping staff make better-informed decisions, data science is valuable to any company in any industry. The biggest reason for its looming popularity is its ability to allow brands to communicate their story in an engaging and powerful manner. When brands and companies comprehensively utilize data, they can share their goal with their target audience, thereby creating better brand connections. After all, nothing connects with consumers like an effective and powerful story that can inculcate all human emotions. Precisely, Data science algorithms help perform complex data science tasks like prediction, classification, clustering, and others. Paving the way for the success of businesses through its values,An Accomplished Big Data Leader Deciphering Business Challenges
Hill Climbing Algorithm: This algorithm boosts model performance. It searches for the optimal subset of features from a given list, subject to a user-defined performance metric as its objective function. The procedure uses the hill-climbing iterative modeling process by evaluating all combinations of n features before climbing up to n+1. Super Interactions: This algorithm captures non-linear relationships. It explores all possible n-way combinations of interactions. For n = 2 through 5, just 50 raw features can form over 2 million new variables! The procedure is suitably coupled with effective and efficient variable reduction techniques. Segmentation Recommender: The segmentation recommender algorithm enables data segmentation decision-making. It evaluates a set of pre-defined strategic scenarios on given data and makes a recommendation for a single overall model or multiple-segmented models. The procedure blends business needs with statistical tests such as correlation sign flip, over-dependence on a specific predictor, and error pattern analysis. Feature Clustering Enhancer: This algorithm selects predictive and representative features. It recommends variable selection based on joint analysis of unsupervised feature clustering outcome and supervised association analysis. The procedure provides flexibility to shortlist the top variables from each category. Statistical Model Assessment and Review Tool (S.M.A.R.T.): This is a .Net and SQL-based analytics product that serves as a one-stop-shop solution for model monitoring. Key features include a dashboard with multi-level views, on-demand monitoring, scheduler, model governance, and fully automated model assessment. Such analytics accelerators have translated into faster and improved outcomes at scale for Varun’s clients, thereby creating “speed to value” differentiation and enabling better decisions for the data-led businesses.Denting the Digital Space with Data Science Contributions
Varun has contributed to the data analytics field not only in the capacity of an individual consultant but also by leading large teams comprising 200+ data scientists and by training 1000+ analytics professionals on predictive modeling. In fact, he designed and authored a comprehensive data science methodology training course that feeds into his organization’s flagship training program for new hires.Data Segmentation
Should I Build a Segmented Model? A Practitioner’s Perspective, NYASUG Conference, January 14, 2010, Pace University, NY, USFeature Engineering and Feature Selection
PROC LOGISTIC Plus: The Power of Variable Transformations, NESUG Conference, September 14-17, 2008, Pittsburgh, PA, US
Feature Selection and Dimension Reduction Techniques in SAS, NESUG Conference September 11-14, 2011, Portland, ME, US
Feature Engineering Strategies: A Practitioner’s Guide, 5th IIMA International Conference on Advanced-Data Analysis, Business Analytics, and Intelligence, April 8-9, 2023, Ahmedabad, IndiaModel Training
Ensemble Hybrid Logit Model, KDD Cup 2010, Educational Data Mining Challenge, hosted by PSLC DataShop, July 2010
Solving the CECL Riddle through Risk Analytics, 6th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence, April 6-7, 2023, Ahmedabad, India
Credit Card Fraud Detection using Feature Engineering and Machine Learning, presented at Machine Learning Developers Summit 2023 organized by Analytics India Magazine and published by Association of Data Scientists, Lattice, The Machine Learning Journal, Volume-3, Issue-1, JanuaryMarch 2023Model Validation
Retail Credit Risk Model Validation: Performance and Stability Aspects, 4th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence, April 11-12, 2023, Ahmedabad, India
In addition, Varun has co-authored a series of EXL white papers on credit loss forecasting.Advanced Analytics at the Heart of Innovation
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The platform enables any data worker to turn the tremendous potential of data into something real and actionable faster than before. Thousands of companies of all sizes experience the thrill of solving game-changing problems using Alteryx. With a vibrant online and offline community, everyone from business analysts to data scientists are loving their jobs again because of the Alteryx platform. The platform is flexible enough to harness the power of data for a competitive edge and deliver real business impact.
In the past twelve months, Alteryx has introduced several new solutions that help round out the end-to-end platform—from data discovery to prep and blending, to visualization and insights. These solutions include:
• Alteryx Connect—a data exploration platform that allows users to discover and collaborate on data assets, and workflow visualizations, etc. that are typically siloed across departments. It is an outgrowth of the company’s acquisition of Semanta in early 2023.
• Alteryx Promote: a component of the Alteryx platform that empowers both data scientists and citizen data scientists to deploy predictive models directly into business systems through an API and then managing model performance over time. It is the result of the Alteryx’s acquisition of Yhat, a Brooklyn-based data science company, announced in June 2023.
A Dynamic Leader Who Knows the Way
Dean Stoecker is the Chairman, Chief Executive Officer, and a Founding Partner of Alteryx. Dean’s leadership and motivational skills, along with his ability to create, communicate and realize a vision, are a driving force behind bringing back the thrill of solving to analysts and data scientists across the globe.
Alteryx is a 20-year success story with Stoecker leading the company through solid organic growth, three rounds of funding and a successful IPO in March of 2023 to deliver a transformational experience to customers of all sizes. Dean’s unique vision and strategy around bringing a code-free and code-friendly experience unleashes the line-of-business analyst from the menial and mundane to truly loving their work again.
2023 was a banner year for the company. In addition to being recognized as one of the top performers in the class of 2023 IPOs, Stoecker received the EY Entrepreneur of The Year® 2023 Award in the Orange County Region, which recognizes entrepreneurs who excel in areas such as innovation, financial performance and personal commitment to their businesses and communities. In addition, Dean drives a philanthropic leadership with the creation of Alteryx for Good, a program that brings the charge of solving real-world problems to non-profits, educators, and local communities.
Success Doesn’t Come Overnight
Dean has said, “You can live without an arm or a leg, but you can’t live without a heart; analytics is the heart of the enterprise.” Alteryx’s rise to success is not your typical Silicon Valley story. Based in Irvine, CA., the 20-year-old company was founded as SRC in 1997 before changing its name to Alteryx in 2010. Dean and the rest of the co-founders never took a dollar of venture money until 2011. Alteryx ultimately raised US$163 million in three rounds, when Dean saw an opportunity for growth and a broader vision for an IPO.
Client Satisfaction at the Forefront
Alteryx has earned the trust of thousands of customers around the world, ranging from many of the world’s largest and best-known brands, including Kaiser, Ford, and McDonald’s, to growing organizations such as Rosenblatt Securities, Veritix, and Consumer Orbit, who all want to use the power of data for a competitive edge.
Awards and Recognition
Alteryx has been honored by several organizations for company growth, customer satisfaction, and impact on the industry and community. A few notable awards include:
Alteryx was traditionally known for its data prep and blending capabilities and the company is tasked with changing that perception. Its technology has evolved to a complete, end-to-end analytics platform, from data discovery to prep and blending, to visualization and insights.
Future Industry Predictions
Alteryx will continue to build its platform with the goal of revolutionizing businesses through data science and analytics. Below are a few predictions on where the data analytics industry is heading in general:
• Data science will break free from code-dependence. The company sees increased adoption of common frameworks for encoding, managing and deploying machine learning and analytical processes. The value of data science will become less about the code itself and more about the application of techniques. As analytics becomes more pervasive in organizations, and the number of data sources and statistical languages (R, Python, Scala, etc.) continues to expand and evolve, the industry will see the need for a common, code-agnostic platform where LOB analysts and data scientists alike can preserve existing work and build new analytics going forward.
• The CDO will come of age. Data is essentially the new oil, and the CDO is beginning to be recognized as the lynchpin for tackling one of the most important problems in enterprises today – driving value from data. Often with a budget of less than US$10 million, one of the biggest challenges and opportunities for CDOs is making the much-touted self-service opportunity a reality by bringing corporate data assets closer to line-of-business users. In 2023, the CDOs that work to strike a balance between a centralized function and capabilities embedded in LOB will ultimately land the larger budgets. Success will come to CDOs who put greater focus on agile platforms and methodologies that allow resources, skills, and functionality to shift rapidly between CoE and LOB.
Web site analytics provide valuable information about who is coming to your site and what they do once they get there. At the simplest level, a counter that displays the number of visitors to a page is a source of Web analytics, but analytics when interpreted properly, is much more than that. Knowing information about your site visitors and the actions they take can help you determine some important factors.
Many Web hosts provide a degree of analytical reporting as part of their service, but generally these are lacking in the amount of information they provide. For a single-page fledgling site they may provide enough information, but for a multiple page catalog you’ll need more. It isn’t necessary to pay large amounts for a service, though, and free services such as the highly-rated StatCounter provide an excellent basis from which to begin your analytical journey. Generally speaking, they all provide similar data, which can be used in the following ways:
By closely monitoring the keywords people use to find your site it is possible to adapt your service and your site to match their needs more closely. If searchers entering specific search strings are visiting your site then you know the kind of information they are seeking. It is possible to alter and optimize your pages, your products, and even your offers to increase revenue and performance.
Second, these statistics also offer a method of determining which of your visitors are more likely to purchase. In most cases, the more specific a search term or the more targeted a link, the more likely a person is to eventually purchase through your site. Because the visitor path displays the way a visitor found your site and shows if they made it to “checkout” or not, you can soon determine the most responsive sources of traffic.
A good way to encourage people to spend more money on your site is to offer them “related product” links. On product pages you can place links to related items that they may be interested in and the visitor path information is the ideal way to find the perfect combination of products.
Less than 5 seconds
Between 5 and 30 seconds
Between 30 seconds and 5 minutes
Between 5 minutes and 20 minutes
Between 20 minutes and 1 hour
Longer than an hour
Hopefully you will witness a small percentage of people that remain on your site for less than five seconds, but in reality it will depend on how targeted your traffic is. Well-targeted traffic is more likely to remain on the pages of your site whereas poorly targeted traffic will leave if not immediately interested.
Take a closer look at visitors that leave quickly and determine where they came from. If a PPC ad is providing you with visitors that only look at the resulting page and then leave, that’s an area you should spend time studying. Letting a poor-performing PPC campaign go unevaluated is one of the quickest ways lose money.
System statistics includes information like the browser that each of your visitors uses, or the resolution of their screen size, the operating system they use, and even whether their browser is Java enabled. This is invaluable for ensuring that all of your visitors are able to effectively view your site and provides you with potential reasons for any unusual activity on your pages.
While Internet Explorer is usually the browser of choice for 50 percent or more of site visitors, that still leaves a lot of shoppers using Firefox and other browser alternatives. If your site has only been tested in Internet Explorer and does not display correctly in other browsers, you could be losing as many as half of your potential customers.
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Data Analytics is tuff but tougher is to calculate the ROI behind the Data Science projects and ML models
It is incredibly tough for a lot of enterprises to make more informed decisions if the ROI behind data science models is less than the investment put behind it. A survey conducted byData analytics does not have enough Return over Investment (ROI). But is that true?
Return on investment is used as performance measurement and evaluation metric. Expressed mathematically as a ratio or a percentage, ROI= (Gains – Cost of Investment)/Cost of Investment There are several ways to calculate the ROI behind a data science project andMeasuring the success behind Data Science and ML algorithms-
• Investment Expenses How much investment goes behind building an • Opportunity Cost This simply means the decision-makers must think of how they could have deployed the investments if they didn’t invest it in a data science project. • Cost of Running an ML Algorithm Cost of running an ML algorithm means the transaction costs including the time spent on building, testing, and deploying a data science project or an ML model-based solution. Development costs include expenses on • Time Estimates The C-suite must determine how much time an investment behind building a data science project will start to pay off. The factor of time is useful when comparing two or more projects with the same expected ROI to be realized under the same circumstances. • Inflation vs Returns While calculating the ROI from Data Science projects and ML algorithms, the enterprise must take the underlying inflation into account to calculate and compare excess return over inflation or actual returns vs nominal returns to get a realistic ROI projection.Adapting to a Data Science Strategy
It is incredibly tough for a lot of enterprises to make more informed decisions if the ROI behind data science models is less than the investment put behind it. A survey conducted by Deloitte says that a staggering 67% of executives are not comfortable accessing or using data pipelines from their projects for strategic decision making. Instead, they prefer to stick to their legacy systems than know how to use data analytics. The worst part is- shielding away from data science investments, the most common excuse?Return on investment is used as performance measurement and evaluation metric. Expressed mathematically as a ratio or a percentage, ROI= (Gains – Cost of Investment)/Cost of Investment There are several ways to calculate the ROI behind a data science project and ML algorithm , but how do you measure a qualitative term in a mathematical equation? So, how does an enterprise deduce the matrices to measure Data Science project success or an ML algorithm accuracy?How much investment goes behind building an Data Science project and an ML algorithm? Investment expenses are all about forecasting these values as accurately as possible. That’s where the C-Suite brainwork begins. With the understanding of how much has to be spent to launch a new product or extend the functionality of a working system, enterprises can focus on the investment behind data science projects with the potential financial return that justifies the risks. This feasibility study of a data science project is directly connected with the return on investment (ROI) chúng tôi simply means the decision-makers must think of how they could have deployed the investments if they didn’t invest it in a data science chúng tôi of running an ML algorithm means the transaction costs including the time spent on building, testing, and deploying a data science project or an ML model-based solution. Development costs include expenses on IT infrastructure and employee compensation, required man-hours, and maintenance chúng tôi C-suite must determine how much time an investment behind building a data science project will start to pay off. The factor of time is useful when comparing two or more projects with the same expected ROI to be realized under the same circumstances.While calculating the ROI from Data Science projects and ML algorithms, the enterprise must take the underlying inflation into account to calculate and compare excess return over inflation or actual returns vs nominal returns to get a realistic ROI projection.According to Gartner, by next year 90% of big companies would hire a Chief Data Officer, a promising role that was almost non-existent a few years ago. Of late, the term C-Suite is gaining a lot of importance – but what does it mean? C-Suite gets its name from a series of titles of top-level executives whose job profile name starts with the letter C, like Chief Executive Officer, Chief Financial Officer, Chief Operating Officer and Chief Information Officer. The recent addition of CDO to the C-Suite has been channelized to develop a holistic strategy towards managing data science projects and unveil new trends and measure the ROI behind a data strategy that the enterprise has attempted to tab for years. In a nutshell, boosting ROI from data science projects and ML algorithms is crucial for business success but the best way to trigger it would be by getting a bird’s eye view of an organisation’s data science strategy, which will help in predicting success accurately and thus help it to strategize ROI-supported decisions.
“I have applied for various data science roles but I always get rejected because of a lack of experience.”
This is easily the most common issue I’ve heard from aspiring data scientists. They put in the hard work to learn the theoretical aspect of data science but when it comes to applying it in the real world, not many organizations are willing to take them on.
No matter how well you do in the interview round – the hiring manager always finds the lack of data science experience as the main sticking point.
So what can you do about this? It is a seemingly unassailable obstacle in your quest to become a rockstar data scientist.We at Analytics Vidhya understand this challenge and are thrilled to launch the Data Science Immersive Bootcamp to help you overcome it!
This is an unmissable opportunity where you will get to learn on the job from data science experts with decades of industry experience.
“In the Data Science Immersive Bootcamp, we are not only focusing on classroom training – we provide hands-on internship to enrich you with practical experience.”
So you get the best of both worlds – you learn data science AND get to work on real-world projects.Let’s Gauge the Benefits of the Data Science Immersive Bootcamp
This Bootcamp has been created by keeping Data Science professionals at heart and industry requirements in mind. Let’s dive in to understand the benefits of Data Science Immersive Bootcamp:
Learn on the job from Day 1: This is a golden opportunity where you can learn data science and apply your learnings in various projects manned by you at Analytics Vidhya during the course of this Bootcamp
Work with experienced data scientists and experts: The best experts from different verticles will come together to teach and mentor you at the Bootcamp – it is bound to boost your experience and knowledge exponentially!
Work on real-world projects: Apply all that you learn on the go! Real challenges are faced when you dive in to solve a practical problem and cruising through that successfully will hone and fine-tune your blossoming data science portfolio
Peer Groups and Collaborative Learning: Best solutions are derived when you learn with the community! And this internship gives you an opportunity to be part of several focused teams working on different data projects
Build your data science profile: You will get to present your work in front of Analytics Vidhya’s thriving and burgeoning community with over 400,000 data science enthusiasts and practitioners. You are bound to shine like a star after getting such an exhaustive learning and hands-on experience
Mock interviews: Get the best hack to crack data science interviewsUnique Features of the Data Science Immersive Bootcamp
There are so many unique features that come with this Bootcamp. Here’s a quick summary of the highlights:Curriculum of Data Science Immersive Bootcamp
Data Science Immersive Bootcamp is one of the most holistic and intensive programs in the data science space. Here’s a high-level overview of what we will cover during this Bootcamp:
Python for Data Science
SQL and other Databases
Statistics – Descriptive and Inferential
Structured Thinking & Communications
Basics of Machine Learning
Advanced Machine Learning Algorithms
Deep Learning Basics
Recurrent Neural Networks (RNN)
Natural Language Processing (NLP)
Convolutional Neural Networks (CNN)
Building Data Pipelines
Big Data Engineering
Big Data Machine Learning
Wholesome Data Science is what we call it – everything you need to learn is presented in a single platter!How to apply for the Data Science Immersive Bootcamp?
Here are the steps for the admission process to the Data Science Immersive Bootcamp:
Online Application – Apply with a simple form
Take the Fit Test – No knowledge of data science expected
Interaction with Analytics Vidhya team (Gurgaon) – Interview round to screen the best candidates
Offer Rollouts – The chosen candidates will be sent the official offer to be a part of the Bootcamp!
Welcome to AV’s Data Science Immersive Bootcamp!Fee Structure and Duration for the Data Science Immersive Bootcamp
Admission Fees: INR 25,000/-
Note: It is non-refundable and will get adjusted with the entire upfront payment (INR 3,50,000) or with 1st Installment (in case of Installment Payment Plan).
Option 1: (If paying all upfront)
Option 2: (Installment Payment Plan)
1st Installment – INR 99,000/- (30 days before the start date)
2nd Installment – INR 1,25,000/- (within 60 days of the start date)
3rd Installment – INR 1,25,000/- (within 120 days of the start date)Here are the details of the program:
Duration of Program: 9 months / 40 weeks
Internship Stipend (from month 1): Rs. 15,000/- per month
Number of Projects: 10+ real-world projects
No. of Seats in the Bootcamp: Maximum 30And The Most Awaited Aspect – You Get A Job Guarantee!
As mentioned above, this Bootcamp will enrich you with knowledge and industry experience thus making you the perfect fit for any role in Data Science. Bridging the gap between education and what employers want – the ultimate jackpot Analytics Vidhya is providing!
Build your data science profile and network
Create your own brand
Learn how to ace data science interviews
Craft the perfect data science resume
Work on real-world projects – a goldmine for recruiters
Harvard Business Review dubbed Data Scientist the sexiest job of the 21st Century.
And do not forget to register with us TODAY! Only 30 candidates will get a chance to unravel the best of Data Science in this specialized Bootcamp.
The data science and engineering fields are interacting more and more because data scientists are working on production systems and joining R&D teams. We want to make it simpler for data scientists without prior engineering experience to understand the core engineering best practices.
We are building a manual on engineering subjects like Git, Docker, cloud infrastructure, and model serving that we hear data science practitioners think about.Introduction to Git
A version control system called Git is made to keep track of changes made to a source code over time.
Typically, each user will clone a single central repository to their local system (referred to as “origin” or “remote”) which the individual users will clone to their local machine (called “local” or “clone”). Users “push” and “merge” their completed work back into the central repository once they have stored relevant work (referred to as “commits”) on their computers.Difference between Git and GitHub
Git serves as both the foundational technology, for tracking and merging changes in a source code, and its command-line client (CLI).
An online platform called GitHub was created on top of git technology to make it simpler. Additionally, it provides capabilities like automation, pulls requests, and user management. GitLab and Sourcetree are two additional options.Git for Data Science
In data science we are going to analyze the data using some models and algorithms, a model might be created by more than one person which makes it hard to handle and makes updates at the same time, but Git makes this all easy by storing the previous versions and allowing many peoples to work on the same project at a single time.
Let’s look into some terms of Git which are very common among developersTerms
Repository − “Database” containing all of a project’s branches and commits
Branch − A repository’s alternative state or route of development.
Merge − Merging two (or more) branches into one branch, one truth is the definition of the merge.
Clone − The process of locally copying a remote repository.
Origin − The local clone was made from a remote repository, which is referred to as the origin.
Main/Master − Common names for the root branch, which is the main repository of truth, include “main” and “master.”
Stage − Choosing which files to include in the new commit at this stage
Commit − A stored snapshot of the staged modifications made to the file(s) in the repository is known as a “commit.”
HEAD − Abbreviation for the current commit in your local repository.
Push − Sending changes to a remote repository for public viewing is known as pushing.
Pull − Pulling is the process of adding other people’s updates to your personal repository.
Pull Request − Before merging your modifications to main/master, use the pull request mechanism to examine and approve them.
As we have discussed above do for that we need some commands that are generally used, lets discussed them below −
git init − Create a new repository on your local computer.
git clone − begin editing an already-existing remote repository.
git add − Select the file or files to save (staging).
Show the files you have modified with git status.
git commit − Store a copy of the selected file(s) as a snapshot (commit).
Send your saved snapshots (commits) into the distant repository using the git push command.
Pull current commits made by others into your own computer using the git pull command.
Create or remove branches with the git branch.
git checkout − Change branches or reverse local file(s) modifications.
git merge − merges branches with git to create a single branch or a single truth.Rules for Handling Git Process Smooth
There are some rules for handling the smooth process of uploading a project over GitHubDon’t push datasets
Git is used to tracking, manage, and store the codes but it is not a good practice to put the datasets over it. Keep track of the data there are many good data trackers available.Don’t push secrets Don’t use the –force
−force method is used in various situations but it is not recommended to use it mostly because while pushing the code to git if there is an error, it will be displayed by the compiler or the CLI to use the force method to put the data on the server but it is not a good approach.Do small commits with clear descriptions
Beginners developers may not be as good with the small commits but it is recommended to do the small commits as they make the view of the development process much clear and helps out in future updates. Also writing a good and clear description makes the same process much easier.Conclusion
A version control system called Git is made to keep track of changes made to a source code over time. Without a version control system, a collaboration between multiple people working on the same project is complete confusion. Git serves as both the foundational technology, for tracking and merging changes in a source code, and its command-line client (CLI). An online platform called GitHub was created on top of git technology to make it simpler. Additionally, it provides capabilities like automation, pulls requests, and user management.
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