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Great way to start an analytics career

🧐 COMPUTER SCIENCE OR DATA SCIENCE? 🧐 Have you ever thought about which is the best path for you? Don't worry, we've got you! We've created a comprehensive guide comparing the two fields. Discover their key differences, potential career paths, and the educational requirements needed for each. Learn everything about them here 👉 https://lnkd.in/er-u6Swt #Unosquare #DigitalTransformationBlog

🚀 Exciting News! I'm thrilled to announce that I've officially enrolled in the Applied Data Science Program at WorldQuant University. This opportunity marks a significant step in advancing my career. Under the expert guidance of Nicholas Cifuentes-Goodbody. I'll be diving into eight comprehensive projects covering a wide range of techniques and challenges: 1. 🇲🇽 Housing in Mexico: Analyzing 21,000 properties to determine the impact of size vs. location on real estate prices. 📊 (Real Estate-Data Visualization) 2. 🇦🇷 Apartment Sales in Buenos Aires: Building a linear regression model to predict apartment prices in Argentina. 📈 (Predictive Modeling-Data Pipeline) 3. 🇰🇪 Air Quality in Nairobi: Building an ARMA time-series model to predict particulate matter levels. 🌱 (Time Series-Hyperparameter Tuning) 4. 🇳🇵 Earthquake Damage in Nepal: Utilizing logistic regression and decision tree models to predict damage outcomes. (ML-MongoDB) 5. 🇵🇱 Bankruptcy in Poland: Implementing random forest and gradient boosting models to predict company bankruptcy. ⚖️ (Gradient Boosting Classifier-Imbalanced Data) 6. 🇺🇸 Customer Segmentation in the US: Employing a k-means model to cluster US consumers. 🗺️ (Feature Selection-PCA, Metrics) 7. A/B Testing at WorldQuant University: Conducting a chi-square test to boost program enrollment. 🆎 (A/B Testing-ETL) 8. 🇮🇳 Volatility Forecasting in India: Creating a GARCH time series model to predict asset volatility. 📊(Stock Data-API) Each project consists of four self-paced lessons followed by programmatically graded assignments, requiring a score of 90% or better to pass. I'm looking forward to tackling these challenges and sharing my journey and the insights I gain along the way with my network. Let's connect and discuss the transformative power of data science! #DataScience #DataVisualization #Python #SQL #Statistics #ApiDesign #MachineLearning #WorldQuantUniversity

Today's is my 1st day of online Indternship. I learn about use of Data science in ML. And real life applications.

Have you ever found yourself asking how you ended up here? This seems to be the question I have asked myself a lot over the last few months, especially after coming to the realization I wanted to leave academia behind and transition into data science. Doing a Ph.D was all I had ever wanted to do. I was beyond grateful for the opportunity to start one in 2015. Ph.Ds are difficult, there are no two ways about it. “They don't just give those things away,” someone once told me. And boy were they right! I struggled a lot during the first two years of my Ph.D: nothing worked, I had no idea how to fix it; it seemed as though I was going round in circles. Fortunately, however, this was not the case. Despite it looking as though I was not making progress in my experiments, I kept showing up. I kept trying things. I kept trying to find ways to fix the many issues I was having with my experiments. Then, luckily for me, everything suddenly clicked into gear and I had more successful experiments than I had time to analyze. I realize now that during these difficult periods at the start of my Ph.D, I learned how to think, how to research and how to solve difficult problems. These are skills I still cherish and rely on today. More importantly, however, I am immensely proud of the way in which I overcame these challenges. This is a story that is quite common in my discussions with other people following the same path. Nothing works before its time, but the most important thing is to keep experimenting, keep innovating and just keep going. It was, and still is, the proudest moment of my life when I graduated with my Ph.D in 2019. Not necessarily because I had done what I set out to do, but because I had learnt so much about myself along the way. 

Hello Connections, I hope this message finds you well. I'm here to announce that I had join as Data Science virtual internhip through Data Oasis Infobyte. Here I was given a task of Iris Classification whcih as according to three species. So here my task was to train machine learning model that can learn from this species of measurements and then classify accordingly with the datset which was given with using different libraries. Github link: https://lnkd.in/dKXNCgZM #DataOasisInfobyte #DataScience Task 1: Iris Flower Classification

This is a helpful framework to think about the overall benefits of working across academia and industry. I've gone back and forth during my career, sometimes doing both at the same time, and it is one reason I have such unique and transferable skills. I encourage everyone to look at all their options post-Ph.D.

Day 97-100 of 100DaysOfStudyingDataScience Finally Done I'm thrilled to announce that I've successfully completed my #100DaysOfStudyingDataScience challenge! This journey has been filled with countless learning opportunities, from working with housing data in Mexico to predicting bankruptcy in Poland. Over the course of this challenge, I tackled 8 diverse projects that allowed me to hone a wide range of data science skills: Analyzing housing prices in Mexico using data visualization and regression techniques. Building a linear regression model to predict apartment prices in Buenos Aires, Argentina. Forecasting air quality levels in Nairobi, Kenya using time series analysis and ARMA modeling. Applying logistic regression and decision trees to predict earthquake damage in Nepal. Leveraging random forests and gradient boosting to predict company bankruptcy in Poland. Utilizing K-means clustering to segment customers in the US. Conducting A/B testing and chi-square analysis to evaluate program enrollment at WorldQuant University. Creating a GARCH model to forecast asset volatility in India. This challenge has been an overwhelming journey, filled with both challenges and immense growth. I'm grateful for the opportunity to have pushed my limits and expanded my data science expertise. As I move forward, I'm excited to continue learning and improving my skills in this dynamic field. Data science is a never-ending journey, and I can't wait to see what the future holds. Tomorrow, I'll be sharing my celebratory batch with everyone. And if anyone has any opportunities where I can continue learning and growing my data science skills, I'd be thrilled to hear about them. Thank you to all who have supported me along the way. I'm excited to share this milestone with you all! #100DaysOfStudyingDataScience #DataScience #CustomerSegmentation #KMeans #100DaysOfCode #ZiloTech #WorldQuant

The most important part of any successful project/business as a data scientist is customer interaction. It definitely helps in better project planning, execution and management when you know about the end customer requirements. Here’s a free live session to know more on how to create an impact in the business and get recognized as a data scientist.