Mastering Data Science with Python [2023]

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Price: 19.99$

This course is designed to provide you with a comprehensive understanding of Python programming for data science and visualization. Starting with the basics of Python, you will learn fundamental programming concepts and techniques. You will then delve into data structures and explore how to manipulate strings, work with conditional statements, and implement loops. Once you have a solid foundation in Python, the course will focus on applying your programming skills to data science tasks. You will learn how to clean and preprocess data, handle missing values and outliers, and perform data exploration using various statistical and visualization techniques. The course will also introduce you to important libraries such as Num Py, Pandas, and Matplotlib, which are widely used in data analysis and visualization. Throughout the course, you will work on hands-on projects and exercises to reinforce your learning. You will also gain insights into real-world applications of Python in data science and develop the skills necessary to tackle data-related challenges. Course Curriculum: Introduction to Python Programming: Basics of Python Variables and data types Control flow statements: if, elif, else Loops: for loop, while loop Data structures: lists, tuples, dictionaries, sets Functions and lambda functions Object-oriented programming (OOPs) concepts Python for Data Science: Date and time manipulation using the datetime module Regular expressions (regex)Built-in functions in Python Introduction to Num Py and its advantages over lists Working with arrays and performing operations using Num Py Introduction to Pandas and its data structures (Series, Data Frame)Data cleaning techniques: handling missing values and outliers Data manipulation with Pandas: indexing, grouping, sorting, merging Visualization with Matplotlib: creating basic graphs Advanced Data Science with Pandas and Visualization: Exploratory Data Analysis (EDA) techniques Pandas Profiling and DABL for automated data analysis Using the Sweetviz library for visual data exploration Advanced data cleaning and preprocessing Creating insightful visualizations using Seaborn Plotting line, area, scatter, box, and violin plots with Plotly Geographical visualization using maps Statistics and Hypothesis Testing: Descriptive statistics: measures of central tendency, dispersion, etc. Inferential statistics: sampling, confidence intervals, hypothesis testing Performing hypothesis tests using Python libraries Capstone Project: Working on a real-world data science project Defining a business problem statement Conducting end-to-end analysis Creating a comprehensive report summarizing the findings By the end of this course, you will have gained a strong foundation in Python programming for data science and visualization. You will be equipped with the necessary skills to clean, analyze, and visualize data, as well as to draw meaningful insights and make data-driven decisions.