Python for Data Science: A Comprehensive Journey to Mastery

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1 - Introduction

1 - Introduction.mp4

08:04

2 - Data types operators and data structures in Python

2 - Python Data Types.mp4

02:29

3 - Operators.mp4

00:59

4 - Arithmatic Operators.mp4

03:11

5 - Assignment Operators.mp4

06:03

6 - Comparison Operators.mp4

03:17

7 - More on Strings.mp4

08:14

8 - String Methods.mp4

27:25

9 - Lists.mp4

09:58

10 - Tuples.mp4

03:24

11 - Sets.mp4

06:28

12 - Dictionaries.mp4

07:55

13 - Identity Operators.mp4

02:17

14 - Compound Data Structures.mp4

06:00

3 - Python Loops and Comprehensions

15 - Python loops.mp4

04:13

16 - Range Understanding.mp4

07:20

17 - Creating and Modifying Lists.mp4

05:09

18 - Looping Through Dictionaries.mp4

02:38

19 - Enumerate Function.mp4

02:46

20 - List Comprehentions.mp4

01:45

21 - Adding Conditionals to List Comprehentions.mp4

03:27

4 - Comprehensive Guide to Python Functions

22 - Python Functions.mp4

02:34

23 - Functions Parameters.mp4

02:14

24 - Return values.mp4

01:51

25 - Default Parameters.mp4

01:41

26 - VariableLength Arguments.mp4

03:30

27 - Lambda Functions.mp4

01:19

28 - Higher Order Functions.mp4

05:30

29 - Recursive Functions.mp4

02:54

30 - Docstrings.mp4

01:50

31 - Functions Annotations.mp4

03:02

32 - Nested Functions.mp4

01:50

33 - Decorators.mp4

04:12

5 - NumPy for Efficient Numerical Computations

34 - Introduction to numpy.mp4

08:37

35 - Array Attributes.mp4

02:41

36 - Array Indexing and Slicing.mp4

04:29

37 - Array Operations.mp4

03:17

38 - Reshaping Arrays.mp4

02:41

39 - Stacking and Splitting Arrays.mp4

04:22

40 - Splitting Arrays.mp4

01:52

41 - Broadcasting.mp4

01:52

42 - Boolean Indexing and Filtering.mp4

04:07

43 - Advanced Array Manipulations.mp4

12:47

6 - Data Manipulation with Pandas A Comprehensive Guide

44 - Introduction to Pandas.mp4

04:41

45 - Pandas Series.mp4

04:43

46 - Pandas DataFames.mp4

06:58

47 - Loading Data Into a DataFrame.mp4

04:11

48 - Handling Missing Data NaN Values.mp4

06:06

49 - Basic DataFrame Operations.mp4

04:29

50 - Grouping Data in Pandas.mp4

06:08

51 - Merging and Joining DataFrames.mp4

07:58

52 - Data Cleaning.mp4

07:25

7 - HandsOn Machine Learning Exploring ScikitLearn

53 - Introduction to Machine Learning and Scikitlearn.mp4

03:50

54 - Data Preprocessing.mp4

07:47

55 - Handling Missing Values.mp4

03:55

56 - Features Scaling.mp4

05:22

57 - Encoding Categorical Variables.mp4

06:49

58 - Decision Trees.mp4

06:29

59 - Support Vector Machine.mp4

06:02

Description

Mastering Python, Data Analysis, and Machine Learning

What You'll Learn?

Python syntax, data structures, and libraries essential for data science, such as NumPy and Pandas.
Learn how to clean, organize, and transform raw data into usable formats for analysis and visualization.
Understand how to explore datasets to identify patterns, trends, and relationships.
Build predictive models using Scikit-learn, covering supervised learning algorithms.
Develop critical thinking and analytical skills to solve complex data challenges.

Who is this for?

Beginners: Individuals with no prior programming or data science experience who want to learn Python and data science from scratch.

Aspiring Data Scientists: Those looking to break into the data science field and build foundational skills needed for a successful career.

Software Engineers: Programmers or engineers who want to expand their knowledge into data analysis, machine learning, and data science techniques.

Data Analysts: Professionals looking to upgrade their Python skills to analyze and visualize data more efficiently.

College Students: Students pursuing degrees in fields such as computer science, statistics, or economics who want to strengthen their data science and Python skills.

Business Analysts: Professionals seeking to use data to drive better decision-making and extract actionable insights from datasets.

Professionals from Other Fields: Individuals from various industries (marketing, finance, healthcare, etc.) who want to enhance their analytical abilities and leverage data science in their work.

Entrepreneurs & Freelancers: Those who want to utilize data science to grow their business, gain insights into customer behavior, or enhance their services.

What You Need to Know?

Students should be comfortable using a computer, navigating files, and installing software.

This course is designed for absolute beginners, so no prior experience in Python or programming is necessary.

A positive attitude, curiosity, and the drive to learn new concepts and solve problems are essential.

More details

Description
Unlock the power of data with our comprehensive Python for Data Science course!
Expertly crafted to suit both beginners and experienced professionals, this course will guide you from the basics to advanced mastery in Python, a programming language that continues to dominate the data science landscape. Starting with fundamental concepts, youâ€™ll become proficient in Pythonâ€™s syntax and core libraries, and gradually progress to more advanced topics such as data manipulation, visualization, machine learning, and predictive modeling.
Our course is rooted in practical, hands-on learning, allowing you to work with real-world datasets and develop models that can drive meaningful decision-making. Whether your goal is to propel your career forward, transition into the rapidly expanding field of data science, or simply sharpen your analytical skills, this course provides everything you need to excel.
In addition to technical skills, youâ€™ll gain valuable insights into industry best practices, current trends, and the latest tools utilized by leading data scientists. With lifetime access to course materials, ongoing updates, and a supportive community of fellow learners, your journey to becoming a data science expert is both supported and sustained.
Enroll today and begin transforming your data into actionable insights that can shape the future of your career and industry!
Who this course is for:
Beginners: Individuals with no prior programming or data science experience who want to learn Python and data science from scratch.
Aspiring Data Scientists: Those looking to break into the data science field and build foundational skills needed for a successful career.
Software Engineers: Programmers or engineers who want to expand their knowledge into data analysis, machine learning, and data science techniques.
Data Analysts: Professionals looking to upgrade their Python skills to analyze and visualize data more efficiently.
College Students: Students pursuing degrees in fields such as computer science, statistics, or economics who want to strengthen their data science and Python skills.
Business Analysts: Professionals seeking to use data to drive better decision-making and extract actionable insights from datasets.
Professionals from Other Fields: Individuals from various industries (marketing, finance, healthcare, etc.) who want to enhance their analytical abilities and leverage data science in their work.
Entrepreneurs & Freelancers: Those who want to utilize data science to grow their business, gain insights into customer behavior, or enhance their services.

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