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| CVID | Entry Id | Upload Date | Country | State | District | Home Town | Phone | Username | Connect With Me | Experience Level | Total Experience | Career Gap | Total DS/ML/AI Experience | How Did You get Your First DS/ML/AI Job ? | Job Consultancy Name Which Helped You ? | Total IT (S/W) Experience | Total IT (H/W) Experience (Years/ Months) | Total Non IT Experience | Total Exp. (Years /Months)- Intern- Non (DS/ML/AI) | Total Exp. (Years /Months)- Intern-(DS/ML/AI) | Exp in Detail | 10th % | 12th % | % in Grad | Qualification | UG Degree | UG Branch | UG Degree College Name | UG Passout Year | Master's Degree Branch | Master's Degree College Name | Master's Degree Passout Year | PG Diploma/PGP Course Name | PG Diploma/PGP College | PG Diploma/ PGP Passout Year | PhD Detail | PhD Passout Year | Qualification | Have You Done/Are You Doing Any Course ? | Mode of Learning ? | List of Training Courses Did ? | Online offline course Detail-1 | Online offline course Detail-2 | Online offline course Detail-3 | Online offline course Detail-4 | Online offline course Detail-5 | Online offline course Detail-6 | Online offline course Detail-7 | Online offline course Detail-8 | Online offline course Detail-9 | Online offline course Detail-10 | Best Course As Per you ? | Why do you think Above Course is The Best ? | Project Details (Related to DS/ML/AI Only): | Key Responsibilities/Project work related to DS/ML/AI | My Linkdin.com Profile URL is | My GitHub.com repository URL is | My Kaggle.com Profile URL is | My Medium.com profile URL is | My Own ( DS/ML/AI ) Blog/Website URL is | How to Contact me? | Why do you want to become a Data Scientist/ ML/AI Engineer? | CV Available ? | cv quality | Download my CV | Download CV | Placement Status | Placement Year | See my day to day Challenges & How I am Dealing With Them | My Success Story | |
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| CVID100 | 100 | 13/02/2022 | Bharat (Earlier India) | Maharashtra | Thane | Mumbai | (766) 681-1246 | Aakashrajbhar19@gmail.com | Akashrajbhar | Akashrajbhar | Fresher | NA | >70% | Less Than 60% | >80% | UG Only | B.Sc | Information Technology | R.D.National College | 2021 | B.Sc Information Technology R.D.National College 2021 | Yes | Offline | ExcelR Solution | Data Science Certification|ExcelR Solution|2022,Data Analytics|ExcelR Solution|2022 | ExcelR Solution | Teaching is good... Faculty is Awesome | House Price Prediction (Personal) Fraud Detection (Personal) Air Quality Forecasting (Institute) Bank Loan Analysis (Institute) | Honestly, For Money this field is currently blowing the roof, but also thinking about what AI & ML can do these day, gives me goosebumps, so want to learn more & contribute to it. | Yes | Download My CV | Download My CV | I am still searching for jobs | ||||||||||||||||||||||||||||||||||||||
| CVID1011 | 1011 | 04/09/2022 | Bharat (Earlier India) | Uttarakhand | Nainital | Haldwani | (953) 611-5999 | meenapargain8@gmail.com | meenapargain | meenapargain | Experienced ( Non IT ) | 5 Yrs | 3 Years | 5 Yrs | Jyoti electronics and printers|Haldwani|Owner|2018|2020 Freelancer|Haldwani|Freelancer|2016|2018 Karvy Pvt Ltd|Noida|Technical Support Officer|2015|2016 | Less Than 60% | Less Than 60% | >70% | UG, PG Diploma/PGP | B.E/ B.Tech | Information Technology | Amrapali Institute of Technology and Sciences | 2014 | Post Graduate program in Data Science and Engineering | Great Lakes Gurugram Haryana | 2021 | B.E/ B.Tech Information Technology Amrapali Institute of Technology and Sciences 2014 Post Graduate program in Data Science and Engineering Great Lakes Gurugram Haryana 2021 | Yes | Offline | No one | Post graduate program in data science and engineering|Great Lakes|2021 | No one | I want to suggest to get into industry there we can learn more rather than taking any courses. | Covid-19 prediction model based on symptoms Objective: Based on the symptoms shown by an individual, predict whether the individual is covid positive or negative, using a Machine Learning driven approach. Outcome: This test is meant as an initial buffer to help ease the burden on the global health care system during a covid spike. The model acts as an Ml-based solution for initial diagnosis. Key skills: Data cleaning, Exploratory Data Analysis, Machine Learning, Supervised Learning, Classification | Responsible for the planning, organizing, and directing as well as managing and a thorough evaluation on a daily basis of the tasks within the organization/company. 1. Al Product/Service Prototyping 2.Market Segmentation using Machine Learning and Data Analysis 3.AlProduct/Service Business and Financial Modelling | I want to be a data scientist/ML/AI Engineer because I am very passionate about data and its working, I want to be part of it and increase my domain knowledge and also change my knowledge into actions by applying that in real world industry. I want to contribute to the country by becoming a part of data science community, as we have lots of potential to change the country from developing to developed. Data is everywhere, now we have to use that data in a great way so that it contribute to the country's economy. | Yes | Download My CV | Download My CV | I am still searching for jobs | |||||||||||||||||||||||||||||||
| CVID1012 | 1012 | 04/09/2022 | Bharat (Earlier India) | West Bengal | Kolkata | Kolkata | (081) 006-6842 | mananbaid908@gmail.com | Manan28 | Manan28 | Fresher | NA | >60% | >70% | >80% | UG & Masters | B.Sc | Economics | Amity University Kolkata | 2020 | M.Sc. Economics | St. Xavier's College (autonomous) Kolkata | Pursuing | B.Sc Economics Amity University Kolkata 2020 M.Sc. Economics St. Xavier's College (autonomous) Kolkata Pursuing | Yes | Online | Coursera | Introduction to data Analytics Essential|IBM (Coursera)|07/07/2022 | Great Lakes | It has wide range of scope in business analytics with lots of practical experience like Marketing analytics, Supply chain Analyst, Retail Analytics etc. | Not done any projects. | I want to pursue and build my career ahead in that field as I have a Required prior skills and knowledge and want to improve a lot more on that field. | Yes | Download My CV | Download My CV | I am still searching for jobs | |||||||||||||||||||||||||||||||||||
| CVID121 | 121 | 17/02/2022 | Bharat (Earlier India) | Maharashtra | Pune | Nanded | (845) 910-2748 | umesh.rathod1307@gmail.com | Umesh1307 | Umesh1307 | Experienced ( DS/ML/AI ) | 1 Yr | 1 Year | 6 Months | I got placed through My College Placements | Fukoku India Pvt Ltd|Pune|Maintenance Analyst|2021|2022 | >70% | >80% | >70% | UG Only | B.E/ B.Tech | Mechanical | Dr. Babasaheb Ambedkar Technological University, Lonere, Raigad, Maharashtra | 2019 | B.E/ B.Tech Mechanical Dr. Babasaheb Ambedkar Technological University, Lonere, Raigad, Maharashtra 2019 | Yes | Online | iNeuron.ai, AlmaBetter | Full Stack Data Science|AlmaBetter|Pursuing | iNeuron.ai | Before choosing any Institute one should consider following my own filtering criteria 1) Affordability:- Nowadays everything is available on the internet free of cost, one with strong will power can create a customized roadmap for them and learn, it doable and people have done it and doing it. So one should opt affordable education. 2) Content: Content wise it should be rich and relevant to cater the need of industry. Because content is that driver which navigates our career. 3) Industrial behaviour training and placement assistance: There should be a mandatory extensive training for behavioural science, and they should help in articulating the appropriate resumes to get shortlisted for hiring process step one. These all things can be accomplished at ineuron.ai and AlmaBetter. These are my own personal opinions and endeavours, these could be different for different candidates. | 1) Book Recommendation System Tags: Popularity based recommendation, Content based filtering, Collaborative filtering Project Description: ∆ Developed a book recommendation system using memory and model based collaborative filtering by utilising the ratings of books and the various user feature ∆ Implemented singular value decomposition based Matrix factorization to obtain user-item interactions and employed cosine similarity as a distance metric to measure user-item and item-item similarities and create models. ∆ Created profiles for the top active user hy leveraging interaction strength with the recommended items and achieved recall @5 of 42% and recall @10 of 53% ∆ Handled the cold start problem based on global and demographic-specific book popularity and improved the efficiency of the user recommendation engine by 33% 2. Email Campaign Effectiveness Prediction(Multiclass Classification) 𝐓𝐚𝐠𝐬: 𝐂𝐥𝐚𝐬𝐬𝐢fi𝐜𝐚𝐭𝐢𝐨𝐧, 𝐊𝐍𝐍 𝐈𝐦𝐩𝐮𝐭𝐞𝐫, 𝐇𝐲𝐩𝐞𝐫-𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫 𝐓𝐮𝐧𝐢𝐧𝐠, 𝐒𝐌𝐎𝐓𝐄, 𝐗𝐆 𝐁𝐨𝐨𝐬𝐭, 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐆𝐚𝐢𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐃𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧: ✅ Developed a multi-class XGBoost model to characterize the email and predict its effectiveness by reader actions such as ignore, read, and acknowledge the mail. ✅ Performed missing value imputation using KNN-Imputer, implemented SMOTE boosting, and carried out hyperparameter tuning using RandomisedSearchCV. ✅ Leveraged the SHAP summary plots to determine the most important features such as limit of word count, keywords, communication time, and personalization. ✅ Obtained F1 scores of 89% & 84% on train and test data respectively and estimated an overall increase in the customer acquisition rate by 15% using model predictions. 3. Appliances Energy Prediction 𝐓𝐚𝐠𝐬: 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧, 𝐄𝐱𝐭𝐫𝐚 𝐓𝐫𝐞𝐞 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐨𝐫, 𝐏𝐂𝐀, 𝐇𝐲𝐩𝐞𝐫-𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫 𝐓𝐮𝐧𝐢𝐧𝐠, 𝐒𝐇𝐀𝐏 𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐝 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐃𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧: ☑ Extra Trees Regressor to predict the appliances energy consumption of a building using sensor data available from the building and local weather reports. ☑ Engineered attributes themed across the hourly session, external weather, internal temperature, and humidity to capture patterns that affect the energy consumption. ☑ Performed dimensionality reduction using PCA, feature selection through Lasso Regressor, and carried out hyper-parameter tuning using Bayesian Optimization. ☑ Leveraged SHAP plots to identify factors that affect household energy consumption such as office hours, appliance type, past trend, external humidity, etc. ☑ Obtained the best adjusted R2 score of 89% and 77% on train and test data respectively and estimated an increase in power conservation rate by a whopping 18%. 4) Play Store App Reviews Analysis(Data Analysis) 𝐓𝐚𝐠𝐬: 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬, 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐬𝐚𝐭𝐢𝐨𝐧, 𝐑𝐢𝐝𝐠𝐞 𝐚𝐧𝐝 𝐋𝐚𝐬𝐬𝐨 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧, 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐓𝐫𝐞𝐞. 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐃𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧: 🔴 The objective of this project is to deliver insights to understand customer demands better and thus help developers to popularize the product. 🔵 Explored and analyzed the data to discover the key factor responsible for app engagement and success. 🔴 Understood feature importance for predicting the rank of an application over google play store. 🔵 Acknowledged the working mechanism of ASO (App Store Optimization Engine) | Maintenance Analyst 𝐌𝐲 𝐑𝐨𝐥𝐞𝐬 & 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 ----------------------------------- 💡 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐞𝐝 𝐜𝐨𝐥𝐥𝐞𝐜𝐭𝐞𝐝 𝐝𝐚𝐭𝐚 𝐨𝐧 𝐫𝐞𝐨𝐜𝐜𝐮𝐫𝐫𝐢𝐧𝐠 𝐦𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 𝐢𝐬𝐬𝐮𝐞𝐬/𝐫𝐞𝐩𝐚𝐢𝐫 𝐩𝐫𝐨𝐠𝐫𝐚𝐦𝐬 𝐭𝐨 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐢𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭𝐬. 📊 𝐀𝐧𝐚𝐥𝐲𝐳𝐞𝐝 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 𝐨𝐟 𝐭𝐡𝐞 𝐦𝐨𝐥𝐝𝐢𝐧𝐠 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐟𝐨𝐫 𝐝𝐚𝐢𝐥𝐲 𝐫𝐞𝐣𝐞𝐜𝐭𝐢𝐨𝐧 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 𝐮𝐬𝐢𝐧𝐠 𝐫𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬. 🔗 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐳𝐞𝐝 𝐭𝐢𝐦𝐞𝐥𝐲 𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 𝐨𝐟 𝐦𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 𝐚𝐜𝐭𝐢𝐯𝐢𝐭𝐢𝐞𝐬 𝐜𝐨𝐨𝐫𝐝𝐢𝐧𝐚𝐭𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐫𝐞𝐚𝐬. 🎯 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐞𝐝 𝐫𝐞𝐩𝐨𝐫𝐭𝐬 𝐨𝐧 𝐄𝐱𝐜𝐞𝐥 𝐚𝐧𝐝 𝐏𝐨𝐰𝐞𝐫𝐏𝐨𝐢𝐧𝐭 𝐭𝐨 𝐜𝐨𝐧𝐝𝐮𝐜𝐭 𝐦𝐨𝐥𝐝𝐢𝐧𝐠 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐝𝐞𝐟𝐞𝐜𝐭 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬. ☛ 𝐋𝐞𝐯𝐞𝐫𝐚𝐠𝐞𝐝 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬 𝐟𝐨𝐫 𝐩𝐫𝐞𝐯𝐞𝐧𝐭𝐢𝐯𝐞, 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞, 𝐚𝐧𝐝 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 𝐦𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞. | It's quite an intriguing question, to answer this question i'd love to walk you through my career timeline, I have done my bachelor in mechanical engineering passed in 2019 then I have had one year of career gap due to several reasons, after that I got a chance to work as maintenance analyst wherein i was working a team which take care of all three kind of machine maintenance with help of heuristic and machine learning approach, that was the first i had kick in mind about the potential of state of the art technologies like Machine Learning , NLP and Deep Learning, my curiosity never failed to motivate me to learn and delve deep into this dynamic field, then commenced learning about it , i have undergone a proper training and projects, that boosted my confidence about this field, to be specific following are the reasons push force or driving force which attracts me, 1) Versatility: Data Science skills has to boundaries, It can be extended efficiently to any problem of any domain, it could be health care, E-commerce, Automobile, Banking and finance and much more. 2) Challenging and continuously evolving: This is very strong reason that keep me self satisfied that I'm doing something which is ever evolving. | Yes | Download My CV | Download My CV | I am still searching for jobs | Please follow 3P formula: 1) Planning: Plan your roadmap with time bound. 2) Persistent: Be persistent in everything that utmost important like learning and applying for jobs. 3) Patience: Have a patience guys, we will get what we are trying to get. | ||||||||||||||||||||||||||||||||
| CVID1020 | 1020 | 06/09/2022 | Bharat (Earlier India) | Telangana | Hyderabad | Hyderabad | (901) 447-9008 | raju.ssr365@gmail.com | Rajesh65 | Rajesh65 | Fresher | NA | >60% | >70% | Less Than 60% | UG Only | B.E/ B.Tech | Mechanical | Adams Engineering college palvancha | 2014 | B.E/ B.Tech Mechanical Adams Engineering college palvancha 2014 | No | NA | I am sincere passion for data science and mention specific areas of interest. | Yes | Download My CV | Download My CV | I am still searching for jobs | |||||||||||||||||||||||||||||||||||||||||||
| CVID1026 | 1026 | 07/09/2022 | Bharat (Earlier India) | Karnataka | Bengaluru Rural | Bangalore | (805) 068-0191 | mallikarjunreddy76@gmail.com | Mallikarjun | Mallikarjun | Experienced [ IT (S/W) ] | 1 Yr | 2 Years | 1 Yr | Unified Gateways|Bangalore|Junior Member Technical Team|2019|2020 | >80% | >80% | >60% | UG Only | B.E/ B.Tech | Electrical & Electronics Engineering | Sir M Visvesvaraya Institute of Technology, Bangalore, Karnataka | 2019 | B.E/ B.Tech Electrical & Electronics Engineering Sir M Visvesvaraya Institute of Technology, Bangalore, Karnataka 2019 | No | NA | NA | I had training on Azure Data Engineering and I liked the work and felt that I have to choose career in Data science field moving ahead. | Yes | Download My CV | Download My CV | I am still searching for jobs | |||||||||||||||||||||||||||||||||||||||
| CVID1028 | 1028 | 07/09/2022 | Bharat (Earlier India) | Rajasthan | Jodhpur | NA | (876) 994-6735 | akash01.panwar@gmail.com | akashpanwar | akashpanwar | Experienced [ IT (H/W) ] | 6 Yrs | 1 Year | >30 | Capgemini|Pune|Consultant|2020|2022 Lowes India|Bangalore|Senior Software Engineer|2017|2020 Maxxton India|Pune|Software Engineer|2016|2017 | >60% | >70% | >70% | UG Only | B.E/ B.Tech | Computer Science & Engineering | Rajasthan Technical University | 2014 | B.E/ B.Tech Computer Science & Engineering Rajasthan Technical University 2014 | Yes | Online | Coursera | IBM Data Science|Coursera|2022,IBM Data Analyst|Coursera|2022,Google Data Analytics|Coursera|2022 | Coursera | The course was detailed one. | NONE | NONE | I want to become Data Engineer/ Data Analyst | Yes | Download My CV | Download My CV | I am still searching for jobs | I am having 6+ years of working experience in IT due to some personal tragedy I had to leave my job few months ago, now I am jobless and looking for job in my interest field. I apply everyday but I only get rejection emails. | |||||||||||||||||||||||||||||||||
| CVID1037 | 1037 | 08/09/2022 | Bharat (Earlier India) | Andhra Pradesh | Nellore | Nellore | (995) 995-0466 | sannareddymidhunreddy@gmail.com | Midhun | Midhun | Fresher | NA | >80% | >80% | >80% | UG Only | B.E/ B.Tech | Mechatronics Engineering | Manipal University, Manipal, Karnataka. | Pursuing | B.E/ B.Tech Mechatronics Engineering Manipal University, Manipal, Karnataka. Pursuing | No | Learning Python | Want to pursue and build skill for this field. | No | Download My CV | NA | I am still searching for jobs | |||||||||||||||||||||||||||||||||||||||||||
| CVID1601 | 1601 | 18/04/2023 | Bharat (Earlier India) | Haryana | Gurugram | Dewas | (913) 127-0638 | pthksaket.007@gmail.com | pthksaket | pthksaket | Experienced ( Non IT ) | 4 Yrs | No Career Gap | 4 Yrs | FILO EdTech|Gurugram|Team lead - International Supply operations|2022|2023 Caterpillar Signs Pvt. Limited|Gurugram|Senior Advisor|2022|2022 Amazon Development Centre|Remote|Customer Service Associate|2018|2022 | >80% | >80% | >60% | UG Only | B.E/ B.Tech | Electronics & Telecom/ECE | Malwa Institute of Science & Technology, Indore, Madhya Pradesh | 2019 | B.E/ B.Tech Electronics & Telecom/ECE Malwa Institute of Science & Technology, Indore, Madhya Pradesh 2019 | Yes | Online | Applied AI, Coursera, Trainity | Data Analyst Trainee|Trainity|2023 | Applied AI | No matter what factor you consider, I now feel like I have lost a golden opportunity when I wasn'table to complete the Applied AI course. But it's the best. | Instagram User Analytics Description: This research examines how Instagram users utilize and interact with the platform. We will analyze these users in an effort to provide marketing, product, and development teams with business insights. Teams from throughout the company utilise this information to develop new marketing campaigns, choose which features to include in apps, gauge the performance of the apps by looking at user interaction, and generally, improve the user experience while assisting in business expansion. Findings ● The five oldest Instagram users ● Instagram users who have never shared a single image ● the individual whose photos receive the most likes ● The top 5 hashtags that are used the most ● When the day of the week do most users sign up? ● Instagram user posts on average ● People that have liked each and every picture on the website (bots) Approach: We are working with the product team of Instagram and the product the manager has asked us to provide insights on the questions asked by the management team. We use SQL to derive different insights from the dataset provided by the management team. First, we run the necessary commands for creating the database to work on. Then, we performed an analysis to generate valuable insights for the company. Insights: ● There is a total of 100 users using Instagram clones. ● Around 26% of the users are inactive on Instagram. We can remind the ● inactive users by sending them promotional emails to post their 1st photo. ● The most liked photo on Instagram is posted by Zack_Kemmer93, which is liked by 48% of the users. The team can start the contest for the most liked photos. This will make the users post more such good posts. ● The most used hashtag is “smile”. Around 59% of the users use the “smile” hashtag. If a partner brand uses the “smile” hashtag, it will be able to reach the most users on the platform. ● The best days to launch ads are Sunday and Thursday. As most users register on Instagram on Sunday and Thursday. ● 13% of Instagram IDs are fake and dummy accounts Operation Analytics & Investigating Metric Spikes Description: Operation analytics is the analysis done for a company's end-to-end operations. This helps the business identify the areas where it needs to make improvements. As one of the most crucial components of a business, this form of analysis is also utilized to improve workflow efficiency and understanding across cross-functional teams. Analyzing metric spikes is a crucial component of operational analytics because as data analysts, we must be able to answer questions such as, "Why is there a decline in daily engagement?" or at least help other teams answer these questions. Why have sales decreased? Etc. Daily answers to questions like these are required, thus it's crucial to look into metric spikes. Findings: ● Number of jobs reviewed ● Throughput ● Percentage share of each language ● Duplicate rows ● User Engagement ● User Growth ● Weekly Retention ● Weekly Engagement ● Email Engagement Approach: I am working for a company like Microsoft designated as Data Analyst Lead and is provided with different data sets, and tables from which I must derive certain insights out of it and answer the questions asked by different departments. Firstly, I spent some time understanding the data/table given. I cleared the questions in my mind and what are the things to consider while reviewing the data. I use SQL to derive different insights from the dataset provided by the management team. I first created a database “operation_analytics” and then the tables using the structure and links provided by the team. Then, we performed an analysis to generate valuable insights for the company. Insights: 1. Case Study 1 (Job Data): ● The number of distinct jobs reviewed per hour per day for November 2020 is 83%. ● We used the 7-day rolling average of throughput as it gives the average for all the days right from day 1 to day 7 whereas, the daily metric gives the average for only that particular day itself. ● The percentage share of the Persian language is the most (37.5%). ● There are two duplicate rows if we partition the data by job_id. But if we look at the overall columns, all the rows are unique. 2. Case Study 2 (Investigating metric spike): ● The weekly user engagement increased from week 18th to week 31st and then started declining from then onward. This means that some of the users do not find much quality in the product/service in the last of the weeks. ● There are in total of 9381 active users from 1st week of 2013 to the 35th week of 2014. ● The overall count of weekly engagement per device used is the most for MacBook users and iPhone users. ● The email opening rate is around 34% and the email clicking rate is around 15%. The users are engaging with the email service which is good for expanding the company. Hiring Process Analytics Description: The hiring process is the foundational and crucial part of a business. The MNCs learn about the key underlying trends relating to the hiring process here. Before hiring freshmen or anybody else, a corporation should consider trends such as the number of rejections, interviews, sorts of jobs, openings, etc. As data analysts, it is our responsibility to examine these trends and provide insights that the hiring department may use. Findings: ● Hiring: How many males and females are Hired? ● Average Salary: What is the average salary offered in this company? ● Class Intervals: Draw the class intervals for salary in the company. ● Charts and Plots: Draw a Pie Chart / Bar Graph ( or any other graph ) to show the proportion of people working in a different departments? ● Charts: Represent different post tiers using chart/graph. Approach: As a lead data analyst for a multinational corporation like Google, I have been given access to the recruiting history data and expected to make sense of it in order to respond to specific inquiries. In order to develop new insights and respond to the company's inquiries, we will deploy EDA. Details on individuals who applied for a certain position in one of the firm's departments are included in the dataset provided by the company. To examine the data with various tables and columns, I utilized MS Excel. Insights: ● The rejection rate of male applicants is 6% higher than that of female applicants. ● The average salary paid in this company is 50K. ● Most of the employers are in the Operations Department and then in the Human Resource Department. ● The applicant is most likely to get hired if he/she is applying for the HR Department as the rejection rate here is the least. ● There are only 3 candidates in the company who are paid more than 100K. IMDB Movie Analysis Description: The dataset provided by the company contains various columns of different IMDB Movies. We are required to Frame the problem. For this task, we will need to define a problem we want to shed some light on. We can do this by asking the following 'What?' : ● What do we see happening? ● What is our hypothesis for the cause of the problem? (this will be broadly based on intuition initially) ● What is the impact of the problem on stakeholders? What is the impact of the problem not being solved? Findings: The things that we find out through the project are: movies with the highest profit ● top movies as per IMDb rating top directors ● most popular genres ● top foreign language films Approach: Firstly, I cleaned the data. Then, we used Five 'Whys' approach to determine its root cause by repeatedly asking the question “Why”. While asking Why is easy, what we're interested in is the answer. Each time we answer why the next time gets more difficult as we must think deeper about the reasons for this. As we ask why, we may find that we have multiple answers for the same question. Insights: ● In the profit columns, there may be up to 5 outliers. ● Avatar is the film with the most revenue, followed by Jurassic Park, Titanic, and so forth. ● The Shawshank Redemption is the film with the highest rating on IMDB. ● Italian film The Good, the Bad, and the Ugly is the most popular foreign film. ● Tony Kaye is the next best director, followed by Charles Chaplin. ● Comedy is the second most popular genre after drama. ● Leonardo DiCaprio is a popular actor with both the audience and the critics. ● In the 2000s, there were the most votes cast, while in the 1940s, there were the fewest. Bank Loan Case Study Description: This case study aims to give us an idea of applying EDA in a real business scenario. In this case study, we will develop a basic understanding of risk analytics in banking and financial services and understand how data is used to minimize the risk of losing money while lending to customers. Business Understanding: The loan-providing companies find it hard to give loans to people due to their insufficient or non-existent credit history. Because of that, some consumers use it to their advantage by becoming a defaulter. Suppose we work for a consumer finance company that specializes in lending various types of loans to urban customers. We have to use EDA to analyze the patterns present in the data. This will ensure that the applicants capable of repaying the loan are not rejected. Findings: ● Our aim is to identify the patterns which indicate if a client has difficulty paying their installments which may be used for taking actions such as denying the loan, reducing the amount of the loan, lending (too risky applicants) at a higher interest rate, etc. ● The driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default. ● Presenting the overall approach of the data analysis, cleaning the dataset, finding outliers, data imbalance, univariate, segmented univariate, bivariate analysis, etc. ● The top 10 correlations for the Client with payment difficulties and all other cases (Target variable). Approach: ● Imported the NumPy, pandas, matplotlib, and seaborn Python libraries. ● Imported the datasets (Application_Data & Previous_Application) ● Identification: We have identified how we will approach the data, finding the missing dataset and working on it accordingly to gain the required results. ● Outliers: Identified outliers and showed how they play if any role in our dataset. ● Imbalance: Understanding the ratio of imbalance in our data. ● Correlation Analysis: Find the correlation between the variables with respect to the target variables and find the top three correlations. ● Visualisation: Visualized the data with the help of charts and graphs. Insights: 1. Non-Default: ● Academic degree has fewer defaults. ● Students and Businessmen have no defaults. ● Clients with Trade Type 4 and 5 and Industry Type 8 have defaulted less than 3%. ● People above the age of 50 have a low probability of default. ● Applicants with Income more than 700,000 are less likely to default. ● People with zero to two children tend to repay the loans. 2. Default: ● Men are at a relatively higher default rate. ● Clients who are either on Maternity leave OR Unemployed default a lot. ● Not approving the loan of young people who are in the age group of 20-40 as they have a higher probability of default. ● When the credit amount goes beyond 3M, there is an increase in defaulters. ● People who have less than 5 years of employment have a high default rate. XYZ Ads Airing Report Analysis Description: Advertising is a strategy we use to promote our company and grow sales or enhance audience awareness of our goods and services. Our advertising may contribute to a customer's initial perceptions of our company before they interact with us and make a purchase. Businesses may have a local, regional, national, or international target audience, or a combination of them. So, they market in various methods. Internet/online directories, trade and technical press, radio, movies, outdoor advertising, national papers, magazines, and television are a few examples of the different sorts of advertising. The advertising industry is particularly cutthroat because many players would spend a lot of money to target the same market. The analytical capabilities of the business are to target those audiences from those media platforms, where they can cheaply convert them to clients. Findings: ● Describe Pod Position. Does a company's spending on advertisements over a given time period depend on the Pod position number? ● What percentage of different brands air on TV, and how has that percentage changed from Q1 to Q4 of 2021? ● Do a comparative analysis of the brands, defining each one's advertising approach and outlining how it varies from brand to brand. ● In Q1 2022, Mahindra and Mahindra plans to launch a digital advertising campaign to supplement its current TV advertising. Provide the Mahindra & Mahindra CMO a media plan based on the data from 2021. Which demographic should they aim for? Approach: ● Scatter chart w.r.t different brands is used to know if the pod position affects the amount spent on Ads for a specific period of time by the company. ● Bar chart and column chart are used for answering the share of various brands in TV Airings. ● We used a pivot table to conduct the competitive analysis for the brands. ● Clustered Column chart is used to suggest a media plan to the CMO of Mahindra and Mahindra Insights: ● The brand’s money spent on the advertisement is the least for the last quarter pod position and the highest for the first quarter pod position. ● The money spent by Mahindra and Mahindra is the most for the pod position ads. ● The money spent by Honda Cars is the least for the pod position ads. ● The money spent by Maruti Suzuki is the most consistent for all the Quarters of the year. ● People watch the most in prime time and on weekends. ● The Ads are shown the least in the prime access and evening news parts of the day. ABC Call Volume Trend Analysis Description: The attached dataset is of Inbound calls of an ABC company from the insurance category consists of a Customer Experience (CX) Inbound calling team for 23 days. Data includes Agent_Name, Agent_ID, Queue_Time [duration for which customers have to wait before they get connected to an agent], Time [time at which call was made by the customer in a day], Time_Bucket [for easiness we have also provided you with the time bucket], Duration [duration for which a customer and executives are on call, Call_Seconds [for simplicity we have also converted those times into seconds], call status (Abandon, answered, transferred). Findings: ● The average call time duration for all incoming calls received by agents (in each Time_Bucket). ● The total volume/ number of calls coming in via charts/ graphs [Number of calls v/s Time]. ● Propose a manpower plan required during each time bucket [between 9 am to 9 pm] to reduce the abandon rate to 10%. ● Propose a manpower plan required during each time bucket in a day[9 pm to 9 am]. The maximum Abandon rate assumption would be the same 10%. Approach: ● We used pivot tables and pivot charts to get valuable insights into the data. ● We assumed an agent work for 6 days a week; ● On average total unplanned leaves per agent is 4 days a month; An agent's total working hrs are 9 Hrs out of which 1.5 Hrs go into lunch and snacks in the office. ● On average an agent occupied 60% of his total actual working Hrs (i.e. 60% of 7.5 Hrs) on a call with customers/ users. ● We also assumed the total number of days in a month is 28 days for easy calculation. Insights: ● The customers call the least in the evening. So, the company can reduce ● the number of agents at that time for answering the calls. ● The company can hire 17 customer support agents for the night shift work. ● The company can shift some of the day workers to the night shift. The employees who are working 9 am to 9 pm. The manager can change some of the worker's shifts from 5 am to 2 pm and some workers from 2 pm to 11 pm to get the most calls answered. ● The company can make the employers divide into 3 parts too so that the agents are always available 24/7. | Same as Above Skills Used: Google Suite, Python, Excel, Statistics, SQL, Exploratory Data Analysis, Data Mining, Data Cleaning, Data Visualization. | My only aim is to becaome a data magician, because data was past then it assists you in present to predict the future. This is why I am obsessed with data and want to become a data scietist/expert/magician. | Yes | Download My CV | Download My CV | I am still searching for jobs | Searching for Data Analyst/ Data Scientist jobs and applying for the same. | |||||||||||||||||||||||||||||||||
| CVID1805 | 1805 | 06/07/2023 | Bharat (Earlier India) | Maharashtra | Pune | Jalgaon | (940) 360-5972 | bhushan.borse2797@gmail.com | Bhushan.borse2797@gmail.com | Bhushan.borse2797@gmail.com | Experienced [ IT (H/W) ] | 3 yrs | No Career Gap | 3 | Birlasoft Ltd|pune|data analyst|2020| | >70% | >70% | >60% | UG Only | B.com | Commerce (Account) | Pune | 2020 | B.com Commerce (Account) Pune 2020 | No | Domain - Healthcare
Project Description:To make data-driven decisions, this project should provide a visual representation of hospitals chain. It should be useful for the client to understand the data in a clearer way and make informed decisions as a result of the visualization. Responsibilities:
Project Description: The main objective of project was to analyze the loan portfolio of a client, including the types of loans issued, their distribution across different categories (personal loans, mortgage loans, student loans, etc.), and the performance of the loan portfolio over time. This analysis can help identify trends, potential risks, and opportunities for portfolio optimization Responsibilities:
Project Description: A key objective of the project is to determine whether the client is able to provide coverage that is both affordable and financially viable. Business management can use it to make decisions and change strategies. Responsibilities:
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I am intreted in this position because I am experienced person in this field and I like to so such logical work | Yes | Download My CV | Download My CV | I am still searching for jobs | 1. Understanding the data and KPI's or view which will be shown on final dashboard. 2. Creating a wireframe / paper design/ design documents to finalize the number of dashboards or graphs, graph type and colour code. 3. Connecting tableau to database and creating dashboard 4. Iterative work on formatting, alignment and look in feel of dashboards 5. Data quality testing by comparing output of dashboards with database. 6. Functional testing by checking filters, navigation, etc. 7. Publishing dashboard on server and giving row level or user level security and getting review from users and modifying the dashboard accordingly. 8. This all is dependent on business requirements. |
Maine 2018 main B.sc. Statistics (54%) kiya hai, 3 sal odd job kiya , mere paas koi bhi skill nahi hai , muze data science main entry level ka job chahiye , bohot confusion hai. Mera cv is website par hai. please guide me sir.
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