Learn Python for Data Science from Scratch -with 10 Projects

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Damilare Abolaji

13:20:02

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1 - Introduction to Python and the Jupyter Notebook

1 - Introduction.mp4

02:17

2 - Python-for-data-science-course.docx

2 - What is Python.mp4

03:29

3 - Overview of the Jupyter Notebook.mp4

06:20

4 - The Print Function.mp4

04:44

5 - Basic Arithmetic Functions.mp4

05:31

6 - Variables.mp4

11:33

7 - Project 1.mp4

01:53

8 - Project 1 Solution.mp4

06:51

2 - Data Types and Structures in Python

9 - Strings.mp4

18:29

10 - Strings Numerical Data Types.mp4

10:20

11 - Lists.mp4

22:20

12 - Tuples.mp4

16:19

13 - Dictionaries.mp4

20:52

14 - Project 2.mp4

00:56

15 - Project 2 Solution.mp4

05:45

3 - Control Flow in Python

16 - Overview of Control Flow.mp4

06:08

17 - Conditional Statements.mp4

22:58

18 - For Loops.mp4

10:48

19 - While loops.mp4

07:37

20 - Project 3.mp4

01:48

21 - Project 3 Solution.mp4

08:27

4 - Functions and Modules in Python

22 - Functions.mp4

16:12

23 - Lambda Functions.mp4

06:35

24 - Modules.mp4

12:08

25 - Project 4.mp4

01:13

26 - Project 4 Solution.mp4

04:52

5 - Introduction to Numpy

27 - Introduction to Numpy.mp4

04:43

28 - Creating arrays in Numpy.mp4

16:15

29 - Indexing and Slicing Arrays.mp4

11:43

30 - Copy and View in Numpy.mp4

05:15

31 - Shape and reshaping arrays.mp4

14:49

32 - Basic Operations in Numpy Arrays.mp4

10:40

33 - Data Analytics operations in Numpy.mp4

14:56

34 - Project 5.mp4

02:24

35 - Project 5 Solution.mp4

14:24

6 - Introduction to Pandas

36 - Introduction to Pandas.mp4

10:44

37 - Reading in Files in Pandas.mp4

11:00

38 - Looking at data in the dataframe.mp4

08:39

39 - Accessing filtering and Sorting data.mp4

14:05

40 - Indexing loc and iloc in Pandas.mp4

08:08

41 - Groupby and aggregate functions.mp4

11:05

42 - Merge Join and Concatenate.mp4

15:58

43 - Data Cleaning in Pandas 1.mp4

18:20

44 - Data Cleaning in Pandas 2.mp4

12:24

45 - Data Visualization in Pandas.mp4

14:05

46 - Project 6.mp4

01:32

47 - Project 6 Solution.mp4

11:17

7 - Introduction to Matplotlib

48 - Introduction to Matplotli.mp4

05:49

48 - Python-for-data-science-course.docx

49 - Basic Plots in Matplotlib.mp4

18:48

50 - Project 7.mp4

01:59

51 - Project 7 Solution.mp4

17:57

8 - Basic Machine Learning with ScikitLearn

52 - Introduction to Machine Learning.mp4

04:42

52 - Python-for-data-science-course.docx

53 - Supervised Unsupervised Learning.mp4

05:18

54 - Machine Learning Techniques.mp4

07:51

55 - Introduction to ScikitLearn.mp4

04:24

9 - Regression Models with ScikitLearn

56 - Github repo of resources.txt

56 - Introduction to Regression Models.mp4

08:07

57 - Building your First Linear Regression Model 1.mp4

09:35

58 - Building your First Linear Regression Model 2.mp4

23:29

59 - Building your First Linear Regression Model 3.mp4

20:38

60 - Building your First Linear Regression Model 4.mp4

08:43

61 - Project 8.mp4

01:25

62 - Project 8 Solution.mp4

22:27

10 - Classification Models with ScikitLearn

63 - Github repo of resources.txt

63 - Introduction to Classification Models.mp4

07:02

64 - Building your First Classification Model 1.mp4

18:44

65 - Building your First Classification Model 2.mp4

16:27

66 - Building your First Classification Model 3.mp4

15:31

67 - Building your First Classification Model 4.mp4

16:44

68 - Project 9.mp4

00:50

69 - Project 9 Solution.mp4

26:38

11 - Clustering Models with ScikitLearn

70 - Github repo for resources.txt

70 - Introduction to Clustering Models.mp4

06:19

71 - Building your First Clustering Model 1.mp4

22:49

72 - Building your First Clustering Model 2.mp4

11:51

73 - Project 10.mp4

01:11

74 - Project 10 Solution.mp4

23:24

12 - Wrap up

75 - Wrap Up.mp4

02:29

Description

Unleash Data Potential: Master Python for Data Science, Visualization, and Machine Learning from Ground Zero to Pro!

What You'll Learn?

Foundations of Python Programming for Data Science: Students will gain a solid understanding of Python, the programming language widely used in the field of da
Data Manipulation and Analysis Skills: Participants will acquire proficiency in handling data by exploring various data types (integers, floats, strings, boole
Visualization Techniques with Matplotlib: Students will develop the ability to visually represent data using Matplotlib, a popular data visualization library.
Introduction to Machine Learning with Scikit-Learn: The course will introduce students to the fundamentals of machine learning using the Scikit-Learn library.
By the end of the course, students will have acquired a strong foundation in Python programming, data manipulation, visualization, and the basics of machine lea

Who is this for?

This course is designed for individuals who are interested in entering the field of data science and want to build a strong foundation in Python programming for data analysis and machine learning

What You Need to Know?

Basic Computer Literacy.

Critical Thinking and Problem-Solving Skills.

No Prior Programming Experience Required

More details

Description
Unlock the Power of Data with Python!
Embark on a transformative journey into the dynamic world of data science with our Udemy course, "Learn Python for Data Science from Scratch." Whether you're a coding novice or looking to elevate your skills, this course is your gateway to mastering Python and unleashing its potential in data analysis and machine learning.
What You'll Learn:
Python Foundations: Grasp the essentials with an in-depth introduction to Python and the Jupyter Notebook, culminating in a hands-on project to create a personalized calculator program.
Data Manipulation Mastery: Dive into data types, structures, and learn the art of sorting with a practical project, setting the stage for your journey into the heart of data science.
Visualization Wizardry: Harness the power of Matplotlib to craft captivating visualizations, creating line charts and bar charts from real-world datasets.
Machine Learning Magic: Explore Scikit-Learn to understand supervised and unsupervised learning, predict housing prices, customer behavior, and more. Elevate your skills with hands-on projects that bridge theory and application.
Projects: Conclude your learning adventure with 10 captivating projects. From data preparation and model training to evaluation and deployment, you'll showcase your newfound skills in a real-world scenario.
Who Is This For?
Beginners eager to enter the exciting field of data science.
Professionals looking to transition into data-driven roles.
Students and graduates seeking practical skills for their careers.
Enthusiasts exploring Python's potential in data analysis and machine learning.
Why Enroll?
Structured curriculum designed for seamless learning progression.
Real-world projects to reinforce theoretical concepts.
Engaging and interactive content for an immersive learning experience.
Join a supportive community of learners passionate about data science.
Ready to embark on your data science journey? Enroll now and equip yourself with the tools to transform raw data into actionable insights!
Who this course is for:
This course is designed for individuals who are interested in entering the field of data science and want to build a strong foundation in Python programming for data analysis and machine learning

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Senior Business Intelligence Developer with a proven track record of leveraging data analytics and strategic insights to drive business growth. Strong expertise in data modeling, visualization, and reporting, with a deep understanding of advanced analytics and data governance. Recognized for exceptional communication skills and ability to present complex data in a concise and meaningful manner.

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