Master Python & Data Science: From Zero to Advanced Skills

About Master Python & Data Science: From Zero to Advanced Skills

This comprehensive Python course is designed to take you from the basics of Python programming to advanced data science techniques, including data manipulation, visualization, statistics, and machine learning. Whether you're a complete beginner or looking to enhance your Python and data science skills, this course will provide you with practical knowledge through hands-on exercises and real-world examples.

By the end of this course, you'll be able to write Python programs confidently, manipulate data using Pandas, create insightful visualizations using Matplotlib, and even build simple machine learning models. This course is ideal for aspiring developers, data analysts, or anyone who wants to dive deep into Python for data-driven tasks.

Course Content

Section 1: Getting Started with Python

Lecture 1: Data Types in PythonOverview of Python’s data types, including integers, floats, strings, lists, tuples, sets, and dictionaries. Learn through practical examples and exercises, and explore common operations for each data type.

Section 2: Python Basic Constructs

Lecture 2: FunctionsDiscover how to define functions in Python, pass parameters, return values, and understand variable scope. Hands-on exercises will help you build and use functions in real-world scenarios.

Section 3: Introduction to NumPy

Lecture 3: Performing Mathematical Functions Using NumPyLearn about NumPy arrays and their significance in scientific computing. Explore basic operations and mathematical functions through hands-on exercises.

Section 4: NumPy Advanced

Lecture 4: NumPy vs. ListUnderstand the key differences between NumPy arrays and Python lists through performance comparisons and practical examples.

Lecture 5: SciPy IntroductionExplore SciPy and its ecosystem, focusing on its use in scientific computations with examples.

Lecture 6: Sub-Package ClusterDive into SciPy’s cluster sub-package and apply clustering techniques on real datasets.

Section 5: Data Manipulation Using Pandas

Lecture 7: Introduction to PandasGet introduced to Pandas and its powerful data structures, Series and DataFrame. Learn the importance of data manipulation.

Lecture 8: DataFrame in PandasLearn how to create, manipulate, and filter data using Pandas DataFrames. Hands-on exercises will deepen your understanding.

Lecture 9: Merge, Join, and ConcatenateMaster data combination techniques with merge, join, and concatenate functions.

Lecture 10: Importing and Analyzing Data SetsDiscover methods to import and explore data from various sources.

Lecture 11: Cleaning the Data SetLearn techniques for handling missing data, duplicates, and outliers.

Lecture 12: Manipulating the Data SetExplore advanced manipulation techniques using apply, map, and groupby.

Lecture 13: Visualizing the Data SetCreate insightful data visualizations with Pandas’ built-in functions.

Section 6: Data Visualization Using Matplotlib

Lecture 14: What Is Data Visualization?Understand the importance of data visualization and explore different types of visualizations and their use cases.

Lecture 15: Introduction to MatplotlibGet hands-on with Matplotlib and learn basic plotting techniques.

Lecture 16-22: Creating Different Types of PlotsMaster creating line, bar, scatter, histogram, box, violin, pie, doughnut, and area charts using step-by-step guides and practical exercises.

Section 7: Statistics

Lecture 23: What is Data?Learn the basics of data, its types, and data collection methods.

Lecture 24: Introduction to StatisticsUnderstand core statistical concepts, including descriptive vs. inferential statistics.

Lecture 25: SamplingDive into sampling methods and their importance in statistics.

Lecture 26: ProbabilityLearn basic probability concepts and rules.

Lecture 27: Probability DistributionExplore types of probability distributions and their applications.

Lecture 28: Inferential StatisticsMaster hypothesis testing and confidence intervals for making data inferences.

Section 8: Machine Learning Using Python

Lecture 29: Types of Machine LearningGet an introduction to supervised, unsupervised, and reinforcement learning.

Lecture 30: What Can You Do With Machine Learning?Explore real-world applications of machine learning across industries.

Lecture 31: Machine Learning DemoFollow a step-by-step guide to building and evaluating a simple machine learning model.

Why Enroll?

By enrolling in this course, you will:

Build a solid foundation in Python programming and data science.

Gain hands-on experience with industry-standard libraries like NumPy, Pandas, and Matplotlib.

Develop the skills to clean, manipulate, and visualize data.

Learn essential statistical concepts to analyze and interpret data.

Get started with machine learning using Python.

Work through practical projects and exercises that will prepare you for real-world scenarios.

Enroll now and kickstart your Python programming and data science journey!

This course outline emphasizes the key learning outcomes, hands-on exercises, and structured progression that Udemy learners expect, providing both beginners and intermediates with the practical skills to advance in their Python and data science careers.