Here you can find what I have worked on so far. Mostly used my programming skills in Python for Finance topics but also playing around with Data Science Projects

This repository contains the commented and updated python code taken from the book 'Machine Learning and Data Science Blueprints for Finance'.

My projects for Udacity Machine Learning Nanodegree (MLND) Program. The Machine Learning, Data Science and Deep Learning projects contained in this directory make use of tools such as Python, PyTorch, Sci-Kit Learn, AWS SageMaker, AWS Lambda, Amazon S3, Amazon API Gateway and XGBoost to implement machine learning solutions to different types of problems in areas such as business, finance and academia to name a few.

Repo to hold jupyter notebooks and related files for learning data science techniques in python - numpy, pandas, matplotlib etc

Python for Data Science in Finance

Time Series Analysis with Python — covering ACF, PACF, stationarity, and ARIMA modeling with examples and visualizations. Ideal for finance, forecasting, and data science learners.

Coding projects I have worked on, in R and Python. Predominantly includes utilizing code to recreate the Black Sholes Model, Greek Option calculator, Stochastic Process and Brownian Motion and other data science applications for finance. Python was also used primarily for machine learning applications in finance, using various functions from sklearn, random forests, among others to perform predictive analysis on data such as forecasting bitcoin prices, predicting loan default probability, and building neural networks with TensorFlow. R project involves importing datasets from excel as well as using R functions to relabel and tweak datasets that were initially incompatible. R was predominantly used to perform econometric analysis of data as well as basic statistical functions like finding P value and T value.

Building a long-short strategy using self-made functions and backtest them via backtrader python modules

Learn data science with python for finance in one week.

Use cases for Data Science and Machine Learning method in Finance illustrated by standalone Jupyter notebooks (python)

"Educational Jupyter Notebooks on quantitative finance and data science. Available in both English and Spanish. For students and professors learning finance and Python." / "Cuadernos didácticos sobre finanzas cuantitativas y ciencia de datos. Disponibles en español e inglés. Para estudiantes y profesores de finanzas y Python"

This repository showcases projects and models demonstrating my quantitative analysis, data science, and machine learning expertise, specifically applied to finance and investment strategies. It includes Python-based tools for financial risk management, predictive modeling and portfolio optimization.

Welcome to my Data Science and Analytics Portfolio! In this repository, I'll be showcasing my recent projects across various domains including Marketing, HR, Finance, Product, and Supply Chain. Expect to find a diverse range of analyses and visualizations powered by tools such as Python, R, SQL, and PowerBI. Join me, I am always open for feedbacks.

Week 5: Data Science Prep Project Agenda Instructions Learning Outcomes Deliverables Assessment Submission Instructions During this week, we will get to test the skills that we learned during the Moringa Data Science Prep. More specifically, we will get the test our understanding of the following learning outcomes. Learning Outcomes I can understand what it takes to become a Data Scientist. I can adapt the project life-cycle of a typical data science project. I can demonstrate a sophisticated awareness of ethical implications relevant to the use of data. I can write code and document my workflow in a programming environment. I can recall the basics of Python programming for data science. I can obtain and manipulate data from various types of databases using the SQL language. I can evaluate the integrity of data by making decisions on data quality issues. I can perform the extraction, querying, and aggregation of data for analysis in multiple projects through common techniques and tools. I can understand mechanisms for missing data, outliers and analytic implications. Deliverables Our deliverables for this project will include; Presentation (Google Slides) Data Report (Google Docs) Notebook Files (Google Colab) Git Repository Assessment Overview As Mckinsey Consultants, we have been given several projects and asked to select one project that we will work on throughout this week and present on Friday. With our team, we will help one of the following clients identify, define, and solve for a major problem within their sector in Africa. Our TM will assign us to a team. These sectors and the clients include; Agriculture (World Food Organisation) Education (Bill and Melinda Gates Foundation) Health (World Health Organisation) Infrastructure, Water and Energy (UN-Water) Governance and Finance (World Bank) NB: We will select only one sector to work on. Below is a suggested outline of our Data Report. Problem Definition Objectives and goals Project Plan Data Sourcing Data Preparation and Quality Data Cleaning Analysis Conclusion, Recommendation, Next steps We will also be required to make use of tools that we learned during the Data Science Prep i.e CRISP-DM, Python, etc.

Introduction to the intellectual enterprises of computer science and the art of programming. This course teaches you how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Languages include C, Python, SQL, and JavaScript plus CSS and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. Designed for majors and non-majors alike, with or without prior programming experience. 68% of CS50 students have never taken CS before.

This is CS50x , Harvard University's introduction to the intellectual enterprises of computer science and the art of programming for majors and non-majors alike, with or without prior programming experience. An entry-level course taught by David J. Malan, CS50x teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Languages include C, Python, SQL, and JavaScript plus CSS and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. The on-campus version of CS50x , CS50, is Harvard's largest course.

### About this Project This is a partnership research between NYC Data Science Academy and HaystackAI. We used the latest Machine Learning sk-learn models for descriptive data analysis in Python. Objective of The Project: To provide insight on how to use clustering to identify outliers in the housing market, characterise and identify different kinds of anomalies, and identify opportunities for investment in each neighbourhood. To provide data backed insight for Single family Rensidence( SFR) PropTech investor who wish to invest in a place with long term growth by highlighting different areas with increased gentrification. To Provide diverse metrics to identify unappreciated opportunities and enable real estate professionals to be ahead of the market
. The Motivation of The Project: The traditional use of ZipCodes for demarcation of local areas have limited use in the business context as there can be different zip codes that represent the same market and vice versa. We try to use Machine learning Clustering techniques to determine the natural group of clusters for Single Family Residence. The Data: The data used for this project was provided by HaystackAI and protected under extant copy right laws. Other alternative sources of data that are publicly available were from Broker Listing Data,Crime_Diary, Census, Local News, Amenities, Finance and were downloaded from the assessors website as text files and contained both categorical and continuous data. We used unsupervised learning method for this project, created clusters of properties with geocoded locations to identify properties and their locations with similarities over a large number of features. Principal Component Analysis PCA, was used for dimensionality reduction, and unsupervised clustering was performed using k-means, hierarchical agglomerative clustering (HCA) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) using scikit-learn libraries. We identified seven natural clusters with unique descriptions and diverse investment potentials. We tested different clustering techniques including Kmeans with different initialisations, MiniBatchKmeans, Agglomerative Hierarchical Clustering and DBSCAN. We used the Elbow Method and Silhouette scores metrics for determining the optimum number of clusters. Cluster 1 , for example, has these characteristics, lower cost, very high crime rate, very high rate of growth, fewer amenities. It is likely attractive for investors that target lower income buyers and who would like to maximise ROI. Similarly, Cluster 3 has these characteristics, lower cost, stable crime rate, very high rate of growth-good amenities. It is ideal for medium term investors.

# Matplotlib Homework - The Power of Plots ## Background What good is data without a good plot to tell the story? So, let's take what you've learned about Python Matplotlib and apply it to a real-world situation and dataset:  While your data companions rushed off to jobs in finance and government, you remained adamant that science was the way for you. Staying true to your mission, you've joined Pymaceuticals Inc., a burgeoning pharmaceutical company based out of San Diego. Pymaceuticals specializes in anti-cancer pharmaceuticals. In its most recent efforts, it began screening for potential treatments for squamous cell carcinoma (SCC), a commonly occurring form of skin cancer. As a senior data analyst at the company, you've been given access to the complete data from their most recent animal study. In this study, 249 mice identified with SCC tumor growth were treated through a variety of drug regimens. Over the course of 45 days, tumor development was observed and measured. The purpose of this study was to compare the performance of Pymaceuticals' drug of interest, Capomulin, versus the other treatment regimens. You have been tasked by the executive team to generate all of the tables and figures needed for the technical report of the study. The executive team also has asked for a top-level summary of the study results. ## Instructions Your tasks are to do the following: * Before beginning the analysis, check the data for any mouse ID with duplicate time points and remove any data associated with that mouse ID. * Use the cleaned data for the remaining steps. * Generate a summary statistics table consisting of the mean, median, variance, standard deviation, and SEM of the tumor volume for each drug regimen. * Generate a bar plot using both Pandas's `DataFrame.plot()` and Matplotlib's `pyplot` that shows the number of total mice for each treatment regimen throughout the course of the study. * **NOTE:** These plots should look identical. * Generate a pie plot using both Pandas's `DataFrame.plot()` and Matplotlib's `pyplot` that shows the distribution of female or male mice in the study. * **NOTE:** These plots should look identical. * Calculate the final tumor volume of each mouse across four of the most promising treatment regimens: Capomulin, Ramicane, Infubinol, and Ceftamin. Calculate the quartiles and IQR and quantitatively determine if there are any potential outliers across all four treatment regimens. * Using Matplotlib, generate a box and whisker plot of the final tumor volume for all four treatment regimens and highlight any potential outliers in the plot by changing their color and style. **Hint**: All four box plots should be within the same figure. Use this [Matplotlib documentation page](https://matplotlib.org/gallery/pyplots/boxplot_demo_pyplot.html#sphx-glr-gallery-pyplots-boxplot-demo-pyplot-py) for help with changing the style of the outliers. * Select a mouse that was treated with Capomulin and generate a line plot of time point versus tumor volume for that mouse. * Generate a scatter plot of mouse weight versus average tumor volume for the Capomulin treatment regimen. * Calculate the correlation coefficient and linear regression model between mouse weight and average tumor volume for the Capomulin treatment. Plot the linear regression model on top of the previous scatter plot. * Look across all previously generated figures and tables and write at least three observations or inferences that can be made from the data. Include these observations at the top of notebook. Here are some final considerations: * You must use proper labeling of your plots, to include properties such as: plot titles, axis labels, legend labels, _x_-axis and _y_-axis limits, etc. * See the [starter workbook](Pymaceuticals/pymaceuticals_starter.ipynb) for help on what modules to import and expected format of the notebook. ## Hints and Considerations * Be warned: These are very challenging tasks. Be patient with yourself as you trudge through these problems. They will take time and there is no shame in fumbling along the way. Data visualization is equal parts exploration, equal parts resolution. * You have been provided a starter notebook. Use the code comments as a reminder of steps to follow as you complete the assignment. * Don't get bogged down in small details. Always focus on the big picture. If you can't figure out how to get a label to show up correctly, come back to it. Focus on getting the core skeleton of your notebook complete. You can always revisit old problems. * While you are trying to complete this assignment, feel encouraged to constantly refer to Stack Overflow and the Pandas documentation. These are needed tools in every data analyst's tool belt. * Remember, there are many ways to approach a data problem. The key is to break up your task into micro tasks. Try answering questions like: * How does my DataFrame need to be structured for me to have the right _x_-axis and _y_-axis? * How do I build a basic scatter plot? * How do I add a label to that scatter plot? * Where would the labels for that scatter plot come from? Again, don't let the magnitude of a programming task scare you off. Ultimately, every programming problem boils down to a handful of bite-sized tasks. * Get help when you need it! There is never any shame in asking. But, as always, ask a _specific_ question. You'll never get a great answer to "I'm lost." ### Copyright Trilogy Education Services © 2020. All Rights Reserved.

This is a backup repository for my Data Science and Visualization practice with Python.

This Folder contains the projects I built for Quantnet's Python for Finance and Intro to Data Science.

Python team project

A comprehensive Python learning repository documenting my journey from programming fundamentals to applied data science and machine learning. This repo combines: 📚 Coursework — structured modules from DataCamp and other learning tracks 🛠️ Hands-on Projects — Netflix analysis, NYC schools data, ML experiments 📊 Practical Applications

Python team project

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Python for Excel Use xlwings for Data Science and Finance

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Learning Python for Finance and Data Science

Python for Data Science - Pandas/Numpy and Finance

Advanced Data Science and Python for Finance Projects - Backtrader Python