Master Python for Data Science: A Hands-On Approach
What you will learn:
- Real-world Python applications and versatility.
- Python installation on Mac and Windows.
- Core Python programming concepts: variables, data types, operators.
- Data structure manipulation: lists, tuples, sets, dictionaries.
- Function creation and usage with parameters and arguments.
- Data processing with filter, map, and zip functions.
- Data analysis using analytical and aggregate functions.
- String manipulation and regular expressions.
- Control flow using loops and conditional statements.
- Object-Oriented Programming (OOP) principles.
- Date and time manipulation in Python.
- Statistical concepts and their importance.
- Descriptive statistics: measures of central tendency, spread, and shape.
- Probability theory: basic probability, conditional probability, Bayes' Theorem.
- Inferential statistics: hypothesis testing, t-tests, chi-squared tests, ANOVA.
Description
Unlock your data science potential with our immersive Python course! This practical guide is perfect for beginners, aspiring developers, and anyone eager to master Python for data analysis and beyond. We'll take you from zero to hero, building a strong foundation in Python programming and equipping you with the statistical skills essential for data-driven decision-making.
This course goes beyond the basics. You'll learn to install Python, work with core data structures (lists, dictionaries, sets), build efficient functions, and master essential data manipulation techniques. We delve into regular expressions, conquer loops and conditionals, and unravel the secrets of object-oriented programming (OOP).
But that's not all! Prepare to unlock the power of statistics. You'll explore descriptive and inferential statistics, covering measures of central tendency, spread, and shape. We'll uncover the intricacies of probability, including Bayes' Theorem and various probability distributions. Finally, you'll gain practical experience with hypothesis testing, including t-tests, chi-squared tests, and ANOVA.
Each concept is reinforced with hands-on assignments and real-world examples. Whether you aspire to a career in data science, want to automate tasks, or simply improve your programming skills, this course will provide you with the knowledge and confidence you need to excel. Enroll today and start your data science journey!
Curriculum
Python Fundamentals
This section lays the groundwork for your Python journey. You'll start by exploring practical Python applications and installing Anaconda on both Windows and macOS. We'll then cover fundamental programming concepts such as variables, their scope, data types, type casting, and various operators. A quiz will test your understanding of these essential basics. We also delve into efficient ways to handle different data types and perform operations, building a solid base for future sections.
Data Structures and Algorithms
Dive into the world of data structures. You'll learn about lists, tuples, sets, and dictionaries—essential tools for manipulating and organizing data. We'll also explore stacks and queues and how they are applied in solving problems. You’ll get a grasp on the importance of time and space complexity and gain proficiency in sorting and searching algorithms to efficiently manage data. A comprehensive quiz will reinforce your understanding.
Python Functions and Advanced Techniques
Master the art of functions! You'll learn how to define functions with parameters and arguments, build modular applications using Python modules, and use powerful techniques such as filter, map, and zip for efficient data processing. We'll show you how to leverage list, set, and dictionary comprehensions, along with the elegance of lambda functions, for streamlined code. You'll also use aggregate functions for data analysis, all culminating in a quiz to assess your mastery.
String Manipulation and Regular Expressions
Become proficient in handling strings. You'll learn essential string functions, string formatting, and user input methods for creating interactive programs. We’ll explore the power of regular expressions, covering metacharacters, built-in functions, and special characters and sets. A quiz will test your skills in string manipulation and regular expression usage.
Control Flow: Loops and Conditionals
Learn to control the flow of your programs. We'll cover conditional statements, for and while loops, and the effective use of break and continue statements. You'll learn how to combine these elements to solve more complex programming challenges. A quiz will reinforce your understanding of loop structures and conditional logic.
Object-Oriented Programming (OOP) and Date/Time
Grasp the fundamentals of object-oriented programming (OOP). We cover key concepts like inheritance, encapsulation, and polymorphism. You'll learn to work with dates and times using the Date and Time class and perform precise time manipulations with the TimeDelta class. A quiz will check your understanding of OOP principles and your ability to handle date and time operations.
Introduction to Statistics
This section introduces the importance of statistics in data analysis. Learn how statistics shapes our understanding of information, sets the stage for meaningful analysis, and is vital for effective data manipulation and visualization. The section concludes with a quiz to consolidate your understanding of the fundamental role of statistics.
Descriptive Statistics
Delve into the core of descriptive statistics. You'll learn about various types of data, measures of central tendency (mean, median, mode), measures of spread (range, variance, standard deviation), measures of dependence (correlation, covariance), measures of shape and position (quartiles, percentiles), and how to standardize data and calculate z-scores. A quiz will assess your understanding of these key concepts.
Probability and Basic Inferential Statistics
Explore fundamental probability concepts. We will cover set theory, conditional probability, Bayes' Theorem, permutations, combinations, random variables, and various probability distributions. This section lays the groundwork for inferential statistics and includes a comprehensive quiz to test your knowledge.
Inferential Statistics and Hypothesis Testing
This section introduces you to inferential statistics, covering topics such as the normal distribution, skewness, kurtosis, statistical transformations, sampling methods, confidence intervals, and correlations. You'll then learn about hypothesis testing, including t-tests, z-tests, chi-squared tests, and ANOVA. The section culminates in a quiz to assess your understanding of inferential statistics and hypothesis testing techniques.
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