Arun

Some EDA Project

• Category: About Data visualiaztion in python programming, Power Bi and Google looker studio
• Resources: at GitHub
• Python URL: Go to the repository
• Power Bi project URL: Go to Blog post

Basics of Python

• Welcome to my Python Programming repository, your gateway to mastering one of the most versatile and widely-used programming languages. This collection of Jupyter notebooks and tutorials is thoughtfully structured to take you on a journey from Python basics to more advanced topics, making your learning experience both enriching and enjoyable.
• Explore my comprehensive GitHub repository, where I share my passion for Python programming and provide in-depth tutorials on essential Python concepts and powerful libraries.
• From the fundamentals of Python syntax, including variables, data types, functions, and control flow, to more advanced topics like NumPy, Pandas, Seaborn, Matplotlib, Plotly, and scikit-learn, you'll find a wealth of knowledge to help you excel in Python programming.
• Dive into detailed guides, practice assignments, and real-world examples that cover everything from data manipulation and visualization to machine learning. Whether you're a beginner taking your first steps with Python or an experienced coder looking to expand your skills, my GitHub repository has something for everyone.
• Don't miss out on the chance to elevate your Python expertise and enhance your programming journey. Explore the files and tutorials below to start your Python adventure today.

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Content

• Python syntax, Data types
• Operators, Functions, Modules
• Conditional statements, Loops
• First-steps-with-python
• Numpy
• Pandas
• Matplotlib, Seaborn
• Plotly
• Scikit-learn

Object object-oriented-programming

Advanced Python

Logging

Multithreading and multiprocessing in python

Importance of Configuration file

Configuration Files in Python

OS python module & Configuration files

Data-analysis-projects

Some of the selected data-science projects that I have been doing can be found at My Extrapolatory Data Analysis repository:

• E-commerce: In today's digital age, E-commerce has revolutionized the way we shop and conduct business. With the rise of online platforms, enormous amounts of data are generated daily. This treasure trove of information holds valuable insights that can drive strategic decision-making. In this blog, we delve into a Python data analysis of an E-commerce sales dataset, unearthing meaningful patterns and trends that can inform business strategies and optimize performance. In this Jupyter notebook, I employed a publicly available dataset from the E-commerce sales dataset at Kaggle. Utilizing this dataset, I crafted captivating visualizations using Python libraries. Additionally, I harnessed the power of Google Looker Studio, Power BI, and Tableau to construct an informative dashboard (which can be found at my medium blog).
• Exploratory data analysis: Roller Coster introduced in USA since 1925: Here, I have explored various aspects of a dataset of 1000 rollercoasters in the USA introduced from 1925 to 2020. Here I have used Pandas, Matplotlib, and Seaborn as major python libraries. Data can be obtained from dataset at Kaggle. Here I have briefly divided projects into 5 major steps:
  • Importing main python libraries
  • Data Understanding (.shape, head(), .columns, .dtypes, .describe())
  • Data Preparation: Dataset cleaning, and dataset filtering is done
  • Featuring Understanding (Histogram, KDE, Boxplot)
  • Feature Relationships (scatter, pairplot, heatmap, etc).
• Data analysis on risk factors involved in investment by Deutch Bank:
  • Objective: To perform data analysis on the investment products offered by Deutsche Bank and identify potential risks associated with each product.
  • The technical chart for the DB Bank contains: MACD, Momentum, Stochastic (STO), CCI
  • Conclusion:Through data analysis, Deutsche Bank is able to identify potential risks associated with its investment products and take appropriate measures to manage those risks. This helps the bank make informed decisions about which products to invest in and which to avoid, ultimately reducing the overall risk of its investment portfolio.
• Exploratory data analysis: Indian national elections for 1977-2014: The Indian elections are known for their scale and significance as they involve the world's largest democratic exercise. With a vast and diverse population, India holds national elections periodically to determine the composition of the Lok Sabha (lower house of Parliament) and state elections to determine the regional governments. In the repostory, you can find the Jupyter python notebook containg the various visalizations. I have also wrote a blog, where I have included various graphs in Power Bi and Google Looker studio (which can be seen at: blog post. In this blog post, we have explored the election results of various political parties in India over the years. The datasets were taken From Harvard dataverse.
• Smoking habits in United Kingdom: an exploratory data analysis:The purpose of conducting exploratory data analysis on smokers in the United Kingdom is to gain comprehensive insights into various aspects related to smoking behaviors, prevalence rates, associated health risks, and socioeconomic factors. By analyzing relevant datasets, such as national surveys, health records, and demographic information, this study aims to uncover patterns, trends, and correlations within the smoking population. The findings from this analysis can contribute to a better understanding of the current state of smoking in the UK, inform public health policies and interventions, and support efforts to reduce smoking rates and improve overall population health. This is done at Power Bi and Google looker studio