Master the Art of Data Science

Uncategorized

Course Content

NumPy Introduction
In this chapter, you will learn about NumPy library, ndarray creation, random module, axis argument, and array indexing and slicing.

NumPy Array Shape Manipulation And Operations
In this chapter, you will learn about array shape and reshape, array vectorization and broadcasting, arithmetic functions and statistical methods of array, and searching and sorting.

Pandas Introduction
In this chapter, you will learn about pandas, data structures in pandas- series, data frames, import and export data, operations on data frame, and handling missing data.

Pandas Manipulating And Querying DataFrame
In this chapter, you will learn about manipulating data frames, statistical methods, indexing, manipulate index , and querying the data frame.

Pandas Aggregation Methods And Other Important Modules
In this chapter, you will learn about aggregation methods, merge on keys, concatenate or append, and DateTime data type.

Seaborn
In this chapter, you will learn about seaborn library in python, kinds of plots, and styling the plots.

A course by

HC

H

Aspiring data scientists or analysts who want to learn the fundamentals of data science and gain practical skills for data analysis and interpretation.
Students studying data science, statistics, or related fields who need to acquire hands-on experience in data analysis, machine learning, and data visualization.
Professionals in non-technical roles seeking to transition into the field of data science and enhance their analytical capabilities using Python and popular data science libraries.
Software developers interested in expanding their skill set to include data science and machine learning techniques to build intelligent applications.
Business analysts or decision-makers who want to develop a solid understanding of data science concepts and techniques to make data-driven decisions.
Researchers or scientists looking to leverage data science tools and methodologies for conducting data analysis and drawing insights from their research.
Data engineers or database administrators who want to complement their technical skills by learning data science concepts and techniques to enhance data management and processing.
Statisticians or mathematicians interested in applying their mathematical knowledge to real-world data problems using data science methodologies.
Entrepreneurs or startup founders looking to utilize data science techniques for market analysis, customer segmentation, or predictive modeling to drive business growth.
Data enthusiasts or hobbyists who have a keen interest in data science and want to explore and experiment with data analysis and machine learning on their own projects.