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Course Content
Section 1: Course overview
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Course Overview
01:49
Course Files
Section 2: Introduction to Python
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Overview of Python for Data Science
08:03
Installation and setting up the environment (Anaconda, Jupyter Notebooks).
19:12
Python basic syntax, variables, and data types.
32:35
Control structures: Loops (for, while), conditionals (if, elif, else).
17:32
Functions and modular programming.
09:15
Section 3: Python Data Structures
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Lists in data Science
39:44
Tuples in data Science
11:44
Dictionaries in data Science
08:34
Sets in data Science
03:59
Section 4: Python Libraries for Data Science (NumPy)
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Introduction to NumPy Library.
10:50
Numpy Arrays
05:58
NumPy Built-in Methods
12:26
Array Attributes and Methods
07:13
Section 5: Python Libraries for Data Science (Pandas)
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Introduction to pandas.
21:15
Data Frame operations: indexing, slicing, filtering, sorting.
11:10
Data Frame Manipulations: Renaming Columns
04:52
Loc and iloc functions in Pandas
06:39
Section 6: Data Preprocessing and Statistical Analysis
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Data Preprocessing
26:34
Data Descripting
08:01
Retrieving and Manipulation
05:50
Handling-missing values.
08:02
Introduction to statistics.
10:55
Statistical Analysis
13:59
Group by Function
05:50
Correlation Analysis
12:58
Section 7: Data Visualization
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Introduction to Visualization with Matplotlib and Seaborn
19:08
Basic plotting with Matplotlib.
19:18
Reading CSV Files
06:05
plotting (bar, Scatter, Pair, Count and Histogram Plots)
17:18
Heat Map
15:07
Python for Data Science
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