Valeria Levisohn’s Post

I'm excited to share that I've chosen to focus my 𝐦𝐚𝐬𝐭𝐞𝐫'𝐬 𝐭𝐡𝐞𝐬𝐢𝐬 on the topic of 𝐄𝐦𝐞𝐫𝐠𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐢𝐧 𝐅𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞. I've always had a passion for investing and, beyond my technical knowledge, I’ve also pursued learning in this area. I am now particularly interested in researching how my technical skills can intersect with finance. Some of the topics I will research are: 1. 𝐀𝐈 𝐚𝐧𝐝 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Improving predictive analytics and automating financial models. 2. 𝐁𝐥𝐨𝐜𝐤𝐜𝐡𝐚𝐢𝐧: Enhancing transaction transparency and security. 3. 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬: Enabling real-time decision-making. 4. 𝐑𝐨𝐛𝐨𝐭𝐢𝐜 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 (𝐑𝐏𝐀): Streamlining financial operations. I'm looking forward to starting my research!

🌟 Excited to Share My Academic E-Portfolio from the Post Graduate Program in Data Science and Business Analytics! 🌟 I’m thrilled to present my academic e-portfolio, which highlights the projects I’ve completed during my Post Graduate Program in Data Science and Business Analytics. Each project has given me hands-on experience in applying data science principles to real-world challenges. I look forward to expanding this portfolio as I continue growing in this field! #DataScience #BusinessAnalytics #Portfolio #LearningJourney #ContinuousImprovement #DataDriven

Embarking on a journey into Data Science and Business Analytics has been both challenging and rewarding. From analyzing complex datasets to delivering actionable insights, my projects have honed my skills in problem-solving and decision-making. I'm excited to apply these learnings to real-world business challenges and contribute to data-driven success.

Over the last six months, I've dedicated myself to deepening my knowledge in the field of data science, completing both the Machine Learning Specialization online course from the University of Washington and the IBM Data Science Specialization online course. During this time, I’ve explored key areas such as Machine Learning Foundations, Regression, Classification, and Clustering & Retrieval. I also worked on projects related to health and real estate, where I applied these techniques to real-world data. For example, a recent project on real estate data involved analyzing a correlation matrix, which revealed important relationships among variables. High correlations exist between variables such as total_rooms, total_bedrooms, population, and households, indicating that these factors move together and likely reflect housing density. Now, I'm excited to take this journey even further as I begin my Master's in Data Science and Advanced Analytics at NOVA IMS Information management school. 🚀 I can't wait to apply what I've learned and continue growing in this dynamic field. Here's to new challenges and opportunities ahead!

 Day 14 of My 30-Day Challenge 🚀 Today, I tackled some real-world problems to deepen my understanding of statistical concepts. Here’s a breakdown of what I learned:  📝 Problem 1: Constructing a 95% Confidence Interval (C.I.) Question: In the Quant test of the CAT exam, the population standard deviation is known to be 100. A sample of 25 test takers has a mean of 520. Construct a 95% C.I. for the mean. - Given:  - Population standard deviation (σ) = 100  - Sample size (n) = 25  - Sample mean (x̄) = 520 - Calculation:  - Standard Error (SE) = 100/√25 = 20  - Z-value for 95% confidence interval = 1.96  - Margin of Error (ME) = 1.96 × 20 = 39.2 So, the 95% confidence interval is: - Lower Fence = Point estimate - Margin of Error = 520 - 39.2 = 480.8 - Higher Fence = Point estimate + Margin of Error = 520 + 39.2 = 559.2 Therefore, the 95% confidence interval is from 480.8 to 559.2.  🧠 Problem 2: Testing the Effect of Medication on IQ Question: In the population, the average IQ is 100 with a standard deviation of 15. A team of scientists wants to test a new medication to see if it has a positive or negative effect, or no effect at all. A sample of 30 participants who took the medication has a mean of 140. Did the medication affect intelligence? 95% C.I. Choosing the Test: - Conditions for a z-test:  - We know the population standard deviation.  - The sample size is large (n ≥ 30). - Conditions for a t-test:  - We do not know the population standard deviation.  - The sample size is small (n < 30). Since we know the population standard deviation and have a large sample size (n ≥ 30), we use the z-test. Steps: 1. Hypotheses:   - Null Hypothesis (H0): Population mean = 100   - Alternative Hypothesis (H1): Population mean ≠ 100 2. Significance Level:   - α = 0.05   - Confidence Interval (C.I.) = 95% 3. Decision Boundary:   - With α = 0.05, we split it into 0.025 on each side.   - From the z-table, the critical value is ±1.96. 4. Calculating the Test Statistic:   - Formula: z = (x – μ) / (σ / √n)   - Here, z = 140 - 100 / 15/√30 = 14.60 Conclusion: - Since 14.60 > 1.96, the medication had a positive effect on intelligence.  Key Takeaways - Confidence Intervals help us estimate the range within which a population parameter lies. - Hypothesis Testing allows us to determine if a certain effect is statistically significant. - Z-Tests and T-Tests are chosen based on whether we know the population standard deviation and the sample size. It was a challenging day, but persistence paid off! A big thank you to Sahil Gogna for the inspiration. Let’s keep learning and growing together! 🌟 #Day14 #LearningJourney #DataScience #Statistics #DataAnalyst #CareerGrowth #30DayChallenge #Upskilling #ContinuousLearning #ConfidenceInterval #HypothesisTesting #ZTest #TTest #IQ

"Excited to share my latest journey into Data Science and Business Analytics! 🎓 Check out my e-portfolio to see the projects I've been working on through UT Austin’s DSBA program. Let’s connect and explore how data can drive innovation! #DataScience #Analytics #UTAustin #DSBA

I am glad to share some of my work from my program in Data Science and Business Analytics from McCombs School of Business. In this project I used statistical analysis, a/b testing, and visualization to decide whether the new landing page of an online news portal (E-news Express) is effective enough to gather new subscribers or not. The simulated dataset has certain important metrics such as converted status and time spent on the page that will help to conclude the effectiveness of the new landing page. Apart from that, the dependence of conversion on the preferred language was also analyzed in this project.

I'm thrilled to share that I've successfully completed the Data Analytics and Visualization Job Simulation course! 🚀 🔍 What I Learned: - Explored data visualization techniques 📊 - Mastered data preprocessing 📝 - Created insightful dashboards 📈 🌟 Why This Matters: As a data enthusiast, I'm committed to staying at the forefront of analytics. This certificate validates my skills and passion for turning data into actionable insights. 💡 👉 Next Steps: I'm excited to apply these skills in real-world projects and contribute to data-driven decision-making! 🌐

🎓 Milestone Achieved! 🎓 I'm thrilled to share that I've successfully earned my certification in Data Analysis! 🚀 This journey has been an incredible experience, deepening my understanding of turning raw data into actionable insights. From mastering statistical analysis to diving deep into visualization techniques, every step has brought me closer to harnessing the true power of data. 📊 As I continue to explore the ever-evolving world of data, I'm excited to apply these skills to solve real-world problems and drive impactful decisions.