Python for Data Science with Assignments

Python for Data Science with Assignments, available at $54.99, has an average rating of 4.73, with 67 lectures, 11 quizzes, based on 131 reviews, and has 19574 subscribers.

You will learn about Real-world use cases of Python and its versatility. Installation of Python on both Mac and Windows operating systems. Fundamentals of programming with Python, including variables and data types. Working with various operators in Python to perform operations. Handling data using essential data structures like lists, tuples, sets, and dictionaries. Utilizing functions and working with parameters and arguments. Employing filter, map, and zip functions for data processing. Exploring analytical and aggregate functions for data analysis. Using built-in functions for regular expressions and handling special characters and sets. Iterating over elements using for loops and while loops. Understanding the object-oriented programming (OOP) concepts and principles. Working with date and time classes, including TimeDelta for time manipulation. Fundamental concepts and importance of statistics in various fields. How to use statistics for effective data analysis and decision-making. Introduction to Python for statistical analysis, including data manipulation and visualization. Different types of data and their significance in statistical analysis. Measures of central tendency, spread, dependence, shape, and position. How to calculate and interpret standard scores and probabilities. Key concepts in probability theory, set theory, and conditional probability. Understanding Bayes' Theorem and its applications. Permutations, combinations, and their role in solving real-world problems. Practical knowledge of various statistical tests, including t-tests, chi-squared tests, and ANOVA, for hypothesis testing and inference. This course is ideal for individuals who are Beginners with no prior programming experience. or Students or professionals in various fields, including business, science, social sciences, and healthcare, who want to enhance their data analysis skills. or Anyone interested in automating tasks or data analysis. or Data analysts, researchers, and scientists seeking to strengthen their statistical foundations and Python programming skills. or Anyone interested in gaining a deeper understanding of statistical concepts and their practical applications. or Beginners with no prior statistical knowledge but with a curiosity to learn and apply statistical methods. or Professionals looking to advance their career by acquiring valuable statistical and data analysis skills. or Individuals preparing for standardized tests or exams that include statistical and data analysis components. It is particularly useful for Beginners with no prior programming experience. or Students or professionals in various fields, including business, science, social sciences, and healthcare, who want to enhance their data analysis skills. or Anyone interested in automating tasks or data analysis. or Data analysts, researchers, and scientists seeking to strengthen their statistical foundations and Python programming skills. or Anyone interested in gaining a deeper understanding of statistical concepts and their practical applications. or Beginners with no prior statistical knowledge but with a curiosity to learn and apply statistical methods. or Professionals looking to advance their career by acquiring valuable statistical and data analysis skills. or Individuals preparing for standardized tests or exams that include statistical and data analysis components.

Enroll now: Python for Data Science with Assignments

Summary

Title: Python for Data Science with Assignments

Price: $54.99

Average Rating: 4.73

Number of Lectures: 67

Number of Quizzes: 11

Number of Published Lectures: 67

Number of Published Quizzes: 11

Number of Curriculum Items: 78

Number of Published Curriculum Objects: 78

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Real-world use cases of Python and its versatility.
Installation of Python on both Mac and Windows operating systems.
Fundamentals of programming with Python, including variables and data types.
Working with various operators in Python to perform operations.
Handling data using essential data structures like lists, tuples, sets, and dictionaries.
Utilizing functions and working with parameters and arguments.
Employing filter, map, and zip functions for data processing.
Exploring analytical and aggregate functions for data analysis.
Using built-in functions for regular expressions and handling special characters and sets.
Iterating over elements using for loops and while loops.
Understanding the object-oriented programming (OOP) concepts and principles.
Working with date and time classes, including TimeDelta for time manipulation.
Fundamental concepts and importance of statistics in various fields.
How to use statistics for effective data analysis and decision-making.
Introduction to Python for statistical analysis, including data manipulation and visualization.
Different types of data and their significance in statistical analysis.
Measures of central tendency, spread, dependence, shape, and position.
How to calculate and interpret standard scores and probabilities.
Key concepts in probability theory, set theory, and conditional probability.
Understanding Bayes' Theorem and its applications.
Permutations, combinations, and their role in solving real-world problems.
Practical knowledge of various statistical tests, including t-tests, chi-squared tests, and ANOVA, for hypothesis testing and inference.

Who Should Attend

Beginners with no prior programming experience.
Students or professionals in various fields, including business, science, social sciences, and healthcare, who want to enhance their data analysis skills.
Anyone interested in automating tasks or data analysis.
Data analysts, researchers, and scientists seeking to strengthen their statistical foundations and Python programming skills.
Anyone interested in gaining a deeper understanding of statistical concepts and their practical applications.
Beginners with no prior statistical knowledge but with a curiosity to learn and apply statistical methods.
Professionals looking to advance their career by acquiring valuable statistical and data analysis skills.
Individuals preparing for standardized tests or exams that include statistical and data analysis components.

Target Audiences

Beginners with no prior programming experience.
Students or professionals in various fields, including business, science, social sciences, and healthcare, who want to enhance their data analysis skills.
Anyone interested in automating tasks or data analysis.
Data analysts, researchers, and scientists seeking to strengthen their statistical foundations and Python programming skills.
Anyone interested in gaining a deeper understanding of statistical concepts and their practical applications.
Beginners with no prior statistical knowledge but with a curiosity to learn and apply statistical methods.
Professionals looking to advance their career by acquiring valuable statistical and data analysis skills.
Individuals preparing for standardized tests or exams that include statistical and data analysis components.

Are you ready to embark on an exciting journey into the world of Python programming? This comprehensive course is designed to take you from a Python novice to a proficient programmer, equipping you with the skills to tackle real-world projects, automate tasks, perform data analysis, and excel in coding interviews.

In this course, you’ll explore the following key topics through practical hands-on exercises and real-world examples:

– Discover real-world use cases of Python and understand its versatility in various domains.

– Learn how to install Python on both Mac and Windows operating systems to kickstart your programming journey.

– Grasp the fundamentals of Python programming, starting with variables and their scope.

– Dive into data types and type casting to effectively manage different kinds of data.

– Gain insight into essential Python operators to perform various operations with ease.

– Explore essential data structures like lists, tuples, sets, and dictionaries for efficient data manipulation.

– Learn about stacks and queues and their applications in solving real-world problems.

– Understand the space and time complexity of algorithms and their impact on code performance.

– Study sorting and searching algorithms to efficiently organize and retrieve data.

– Master the concept of parameters and arguments to write flexible and reusable functions.

– Uncover the power of Python modules and their significance in building modular applications.

– Utilize filter, map, and zip functions for streamlined data processing.

– Harness the flexibility of lambda functions to write concise and efficient code.

– Master list, set, and dictionary comprehensions for elegant data manipulation.

– Perform data analysis using analytical and aggregate functions to gain valuable insights.

– Handle strings and discover important string functions for text manipulation.

– Learn string formatting and user input techniques for interactive programming.

– Gain proficiency in working with meta characters and implementing regular expressions.

– Unlock the potential of built-in functions for regular expressions, and handle special characters and sets.

– Implement conditional statements for decision-making in your code.

– Iterate over elements using for loops and while loops to process data efficiently.

– Control loop flow with break and continue statements for better program control.

– Combine conditional statements and loops effectively to tackle complex problems.

– Grasp the fundamentals of object-oriented programming (OOPs) and its role in building robust applications.

– Understand inheritance, encapsulation, and polymorphism, and leverage them to write efficient code.

– Explore the Date and Time class for working with dates and times effectively.

– Utilize the TimeDelta class for precise time manipulation in your Python programs.

– Delve into the world of data-driven insights and discover how statistics plays a pivotal role in shaping our understanding of information.

– Equip yourself with the essential Python skills required for effective data manipulation and visualization.

– Learn to categorize data, setting the stage for meaningful analysis.

– Discover how to summarize data with measures like mean, median, and mode.

– Explore the variability in data using concepts like range, variance, and standard deviation.

– Understand relationships between variables with correlation and covariance.

– Grasp the shape and distribution of data using techniques like quartiles and percentiles.

– Learn to standardize data and calculate z-scores.

– Dive into probability theory and its practical applications.

– Lay the foundation for probability calculations with set theory.

– Explore the probability of events under certain conditions.

– Uncover the power of Bayesian probability in real-world scenarios.

– Solve complex counting problems with ease.

– Understand the concept of random variables and their role in probability.

– Explore various probability distributions and their applications.

Join us on this enriching journey, and let’s unlock the power of Python together! Whether you’re an absolute beginner or looking to expand your programming skills, this course provides you with a solid foundation and practical expertise to succeed in your Python endeavors. Enroll now and start your Python programming adventure!

Course Curriculum

Chapter 1: Basics of Python

Lecture 1: Real world use cases of Python

Lecture 2: Installation of Anaconda for Windows and macOS

Lecture 3: Introduction to Variables

Lecture 4: Introduction to Data Types and Type Casting

Lecture 5: Scope of Variables

Lecture 6: Introduction to Operators

Chapter 2: Introduction to Data Structures

Lecture 1: Introduction to Lists and Tuples

Lecture 2: Introduction to Sets and Dictionaries

Lecture 3: Introduction to Stacks and Queues

Lecture 4: Introduction to Space and Time Complexity

Lecture 5: Introduction to Sorting Algorithms

Lecture 6: Introduction to Searching Algorithms

Chapter 3: Introduction to Functions in Python

Lecture 1: Introduction to Parameters and Arguments

Lecture 2: Introduction to Python Modules

Lecture 3: Introduction to Filter, Map, and Zip Functions

Lecture 4: Introduction to List, Set and Dictionary Comprehensions

Lecture 5: Introduction to Lambda Functions

Lecture 6: Introduction to Analytical and Aggregate Functions

Chapter 4: Strings and Regular Expressions

Lecture 1: Introduction to Strings

Lecture 2: Introduction to Important String Functions

Lecture 3: Introduction to String Formatting and User Input

Lecture 4: Introduction to Meta Characters

Lecture 5: Introduction to Built-in Functions for Regular Expressions

Lecture 6: Special Characters and Sets for Regular Expressions

Chapter 5: Loops and Conditionals

Lecture 1: Introduction to Conditional Statements

Lecture 2: Introduction to For Loops

Lecture 3: Introduction to While Loops

Lecture 4: Introduction to Break and Continue

Lecture 5: Using Conditional Statements in Loops

Lecture 6: Nested Loops and Conditional Statements

Chapter 6: OOPs and Date-Time

Lecture 1: Introduction to OOPs Concept

Lecture 2: Introduction to Inheritance

Lecture 3: Introduction to Encapsulation

Lecture 4: Introduction to Polymorphism

Lecture 5: Introduction to Date and Time Class

Lecture 6: Introduction to TimeDelta Class

Chapter 7: Introduction to Statistics

Lecture 1: Introduction to Statistics and its importance

Lecture 2: Explain the role of statistics in data analysis

Lecture 3: Introduction to Python for Statistical Analysis

Chapter 8: Statistics: Introduction to Descriptive Statistics

Lecture 1: Types of Data

Lecture 2: Measures of Central Tendency

Lecture 3: Measures of Spread

Lecture 4: Measures of Dependence

Lecture 5: Measures of Shape and Position

Lecture 6: Measures of Standard Scores

Chapter 9: Introduction to Basic and Conditional Probability

Lecture 1: Introduction to Basic Probability

Lecture 2: Introduction to Set Theory

Lecture 3: Introduction to Conditional Probability

Lecture 4: Introduction to Bayes Theorem

Lecture 5: Introduction to Permutations and Combinations

Lecture 6: Introduction to Random Variables

Lecture 7: Introduction to Probability Distribution Functions

Chapter 10: Introduction to Inferential Statistics

Lecture 1: Introduction to Normal Distribution

Lecture 2: Introduction to Skewness and Kurtosis

Lecture 3: Introduction to Statistical Transformations

Lecture 4: Introduction to Sample and Population Mean

Lecture 5: Introduction to Central Limit Theorem

Lecture 6: Introduction to Bias and Variance

Lecture 7: Introduction to Maximum Likelihood Estimation

Lecture 8: Introduction to Confidence Intervals

Lecture 9: Introduction to Correlations

Lecture 10: Introduction to Sampling Methods

Chapter 11: Introduction to Hypothesis Testing

Lecture 1: Fundamentals of Hypothesis Testing

Lecture 2: Introduction to T Tests

Lecture 3: Introduction to Z Tests

Lecture 4: Introduction to Chi Squared Tests

Lecture 5: Introduction to Anova Tests

Instructors

Meritshot Academy
Providing Best-in-class Education and Upskilling Courses.

Rating Distribution

1 stars: 1 votes
2 stars: 3 votes
3 stars: 13 votes
4 stars: 33 votes
5 stars: 81 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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