Data Science

In this GitHub repository, I am uploading my learnings in Data Science.

Contents

Python

Python is the most important and basic requirement in Data Science. I have included fundamental and advanced concepts to build a solid foundation. Python PDF Notes Python Code

Numpy

Numpy is a powerful library for numerical computations in Python. Here, you'll find various operations on arrays, mathematical functions, and more. NumPy

Pandas

Pandas is essential for data manipulation and analysis. This section covers data structures like Series and DataFrame, along with data cleaning, transformation, and aggregation techniques. Pandas

Matplotlib

Matplotlib is a widely used plotting library in Python. I've included examples and tutorials on creating various types of visualizations to help you understand data better. Matplotlib

Seaborn

Seaborn is built on top of Matplotlib and provides a high-level interface for drawing attractive and informative statistical graphics. This section showcases how to create different plots and perform exploratory data analysis. Seaborn

Plotly

Plotly is a versatile graphing library that allows you to create interactive plots. In this section, I've explored various types of interactive visualizations, from basic charts to complex dashboards, which can be extremely useful for data analysis and presentation. Plotly

Data Analysis Process

Data Analysis Process, it is iterative and non-linear but highly structured. From asking the right questions to effectively communicating results, each step plays a crucial role in deriving meaningful insights.

• Asking the Right Questions - Starting with strong questions is key.
• Data Wrangling/Munging - Transforming raw data into a valuable format.
• Exploratory Data Analysis (EDA) - Unveiling insights through data exploration.
• Drawing Conclusions - Using techniques like Machine Learning and Statistics.
• Communicating Results - Sharing outcomes effectively through reports, presentations, and more. Data Analysis Process

Contact

If you want to contact me, you can reach out through the following channels:

• Email: [email protected]
• LinkedIn: Dilkush Singh
• Portfolio: Dilkush Singh