Data Science Tools: Python, Pandas, Machine Learning, EDA (Learn Data Science Skills)

Data Science Tools: Python, Pandas, Machine Learning, EDA course is designed for anyone eager to dive into the world of data, whether new to programming or looking to expand their skills. You'll start by understanding data science and why it's important across different industries. The course will guide you through the basics of data science, data engineering, and data analysis, helping you see how these fields work together to drive innovation and improve decision-making.

You'll get hands-on experience with essential tools like Python and R, learning to manipulate data using Pandas, perform numerical operations with NumPy, and create visualizations with Matplotlib and Seaborn. The course "Data Science Tools: Python, Pandas, Machine Learning, EDA" emphasizes practical skills, including SQL for data querying and web scraping techniques, so you can gather, clean, and analyze data from various sources. We provide step-by-step instructions to set up your Python and Jupyter Notebook environment on Windows and macOS, ensuring you're ready to tackle data projects.

Most of the course focuses on applying your knowledge to real-world projects. You'll work on tasks like predicting house prices using regression models and building interactive data analysis web apps, helping you build a professional portfolio. Additionally, you'll explore the fundamentals of machine learning, learning to preprocess data, train models, and evaluate their performance. By the end of the course "Data Science Tools: Python, Pandas, Machine Learning, EDA", you'll have the confidence and skills to use data to make informed decisions and solve complex problems.

Data Science Tools: Python, Pandas, Machine Learning, EDA Table of Contents:

1. Introduction (02:37)
2. What is Data Science (01:36)
3. Data Science vs. Data Engineering vs. Data Analysis (02:02)
4. Applications of Data Science (02:11)
5. Overview of tools and technologies used in data science (02:53)
6. Basics of statistics for data analysis (03:10)
7. Introduction to Python for Data Science (00:52)
8. Structured vs. Unstructured Data (02:13)
9. Python installation on Windows (03:47)
10. What are virtual environments (01:51)
11. Creating and activating a virtual environment on Windows (06:43)
12. Python Installation on macOS (01:33)
13. Creating and activating a virtual environment on macOS (01:58)
14. What is Jupyter Notebook (02:17)
15. Installing Pandas and Jupyter Notebook in the Virtual Environment (01:06)
16. Starting Jupyter Notebook (05:23)
17. Exploring Jupyter Notebook Server Dashboard Interface (04:00)
18. Creating a new Notebook (02:55)
19. Exploring Jupyter Notebook Source and Folder Files (04:37)
20. Exploring the Notebook Interface (08:41)
21. Python Expressions (03:37)
22. Python Statements (04:42)
23. Python Code Comments (04:48)
24. Python Data Types (04:52)
25. Casting Data Types (02:57)
26. Python Variables (07:28)
27. Python List (09:33)
28. Python Tuple (07:11)
29. Python Dictionaries (10:17)
30. Python Operators (14:55)
31. Python Conditional Statements (08:03)
32. Python Loops (09:04)
33. Python Functions (07:59)
34. Overview of Pandas (01:07)
35. Creating a Pandas Series from a List (06:04)
36. Creating a Pandas Series from a List with Custom Index (02:28)
37. Creating a Pandas Series from a Python Dictionary (03:34)
38. Accessing Data in a Series using the index by label (02:14)
39. Accessing Data in a Series by position (02:15)
40. Slicing a Series by Label (02:41)
41. Creating a DataFrame from a dictionary of lists (06:31)
42. Creating a DataFrame from a list of dictionaries (05:03)
43. Accessing data in a DataFrame (07:45)
44. Download Dataset (01:23)
45. Loading Dataset into a DataFrame (03:40)
46. Inspecting the data (03:09)
47. Data Cleaning (06:30)
48. Data transformation and analysis (07:24)
49. Visualizing data (09:02)
50. What is Machine Learning? (02:06)
51. Installing and importing libraries (05:57)
52. Introduction to Data Preprocessing (01:04)
53. What is a Dataset (01:52)
54. Downloading dataset (04:15)
55. Exploring the Dataset (05:26)
56. Handle missing values and drop unnecessary columns (06:26)
57. Encode categorical variables (07:37)
58. What is Feature Engineering (01:37)
59. Create new features (08:15)
60. Dropping unnecessary columns (03:59)
61. Visualize survival rate by gender (06:55)
62. Visualize survival rate by class (04:05)
63. Visualize numerical features (04:11)
64. Visualize the distribution of Age (04:47)
65. Visualize number of passengers in each passenger class (03:49)
66. Visualize number of passengers that survived (03:48)
67. Visualize the correlation matrix of numerical variables (06:04)
68. Visualize the distribution of Fare (04:56)
69. Data Preparation and Training Model (01:49)
70. What is a Model (01:49)
71. Define features and target variable (04:56)
72. Split data into training and testing sets (02:55)
73. Standardize features (03:55)
74. What is a logistic regression model (01:25)
75. Train logistic regression model (04:13)
76. Making Predictions (03:28)
77. What is accuracy in machine learning (01:54)
78. What is confusion matrix (01:23)
79. What is a classification report (01:13)
80. What is a Heatmap (01:12)
81. Evaluate the model using accuracy, confusion matrix, and classification report (06:02)
82. Visualize the confusion matrix (03:29)
83. Saving the Model (09:18)
84. Loading the model (05:03)
85. Improving Understanding of the model's prediction (05:31)
86. Building a decision tree (07:45)
87. Building a random forest (09:51)
88. Importing Libraries and modules (07:28)
89. Loading dataset and creating a dataframe (04:08)
90. Checking for missing values (05:53)
91. Dropping column and splitting data (06:07)
92. Standardize the features for housing dataframe (04:00)
93. Initialize and train the regression model (02:54)
94. Make predictions on the test set (05:04)
95. Evaluating the model for the housing dataset (05:43)
96. Predicting a small sample of data (09:11)
97. Creating scatter plot (08:14)
98. Creating a bar plot (05:53)
99. Saving the housing model (06:35)
100. Loading the housing model (05:29)
101. What is Flask (01:20)
102. Installing Flask (01:37)
103. Installing Visual Studio Code (07:28)
104. Creating a minimal flask app (05:39)
105. How to run a flask app (01:24)
106. Http and Http Methods (02:54)
107. Loading the saved model and scaler into Python file (02:12)
108. Define the home route (01:57)
109. Define the prediction route (02:55)
110. Creating the template (05:30)
111. Adding a form to the template (03:17)
112. Displaying predictions and clearing form inputs (04:22)
113. Testing the prediction tool (01:51)
114. Exploring deployment and hosting options (01:59)
115. Create a new account on PythonAnywhere (02:05)
116. Creating a new web app in PythonAnywhere (01:53)
117. Uploading files to PythonAnywhere (02:30)
118. Creating and activating a virtual environment on PythonAnywhere (03:06)
119. What is a WSGI File (00:53)
120. Configuring WSGI File (04:21)
121. Running your app in a cloud hosting environment (03:48)
122. Project files (00:01)

Who is this course for?

1. Aspiring Data Scientists: If you're looking to start a career in data science and want to build a solid foundation in data analysis and machine learning, this course is perfect for you.
2. Professionals Seeking Career Growth: Whether you're a data analyst, engineer, or professional from any field wanting to add data skills to your toolkit and move into more data-focused roles, this course will help you advance your career.
3. Entrepreneurs and Business Owners: If you're a leader aiming to use data to make smarter decisions and stay ahead of the competition, this course will show you how data science can transform your business strategies.
4. Curious Learners: If you have a passion for data and want to explore how data science can bring about meaningful insights and change in various sectors, this course will satisfy your curiosity and expand your knowledge.

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