Mastering Python : From Basic To Advance Bootcamp
Section 1: Getting Started With Python
Lecture 1: Data Types In Python
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Overview of different data types: integers, floats, strings, lists, tuples, sets, dictionaries.
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Practical examples and exercises to illustrate each data type.
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Common operations and methods for each data type.
Section 2: Python Basic Constructs
Lecture 2: Functions
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Definition and syntax of functions in Python.
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Writing simple functions and understanding function parameters.
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The concept of return values and scope.
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Practical examples and exercises.
Section 3: Introduction To NumPy
Lecture 3: Performing Mathematical Functions Using NumPy
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Overview of NumPy and its importance in scientific computing.
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Basic operations using NumPy arrays.
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Mathematical functions and operations with NumPy.
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Examples and exercises demonstrating these functions.
Section 4: NumPy Advanced
Lecture 4: NumPy Vs List
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Differences between NumPy arrays and Python lists.
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Performance comparison and use cases.
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Practical examples to illustrate the differences.
Lecture 5: SciPy Introduction
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Introduction to SciPy and its ecosystem.
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Key modules and functionalities in SciPy.
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Examples of using SciPy for scientific computations.
Lecture 6: Sub-Package Cluster
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Detailed look into the sub-packages within SciPy.
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Focus on the cluster sub-package for clustering data.
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Practical examples and exercises.
Section 5: Data Manipulation Using Pandas
Lecture 7: Introduction To Pandas
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Overview of the Pandas library.
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Importance of data manipulation in data science.
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Basic data structures in Pandas: Series and DataFrame.
Lecture 8: DataFrame In Pandas
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Creating and manipulating DataFrames.
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Indexing, selecting, and filtering data.
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Practical exercises to create and manipulate DataFrames.
Lecture 9: Merge, Join And Concatenate
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Techniques to combine data in Pandas.
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Using merge, join, and concatenate functions.
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Practical examples and exercises.
Lecture 10: Importing And Analyzing Data Set
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Methods to import data from different sources.
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Initial analysis and exploration of data.
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Practical exercises on importing and analyzing datasets.
Lecture 11: Cleaning The Data Set
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Importance of data cleaning.
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Techniques for handling missing data, duplicates, and outliers.
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Practical examples and exercises.
Lecture 12: Manipulating The Data Set
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Advanced data manipulation techniques.
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Using apply, map, and groupby functions.
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Practical exercises to manipulate datasets.
Lecture 13: Visualizing The Data Set
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Basic principles of data visualization.
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Creating visualizations using Pandas built-in functions.
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Practical exercises on visualizing datasets.
Section 6: Data Visualization Using Matplotlib
Lecture 14: What Is Data Visualization?
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Definition and importance of data visualization.
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Different types of visualizations and their use cases.
Lecture 15: Introduction To Matplotlib
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Overview of Matplotlib library.
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Basic plotting functions and customization options.
Lecture 16: How To Create A Line Plot?
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Step-by-step guide to creating line plots.
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Customization options for line plots.
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Practical examples and exercises.
Lecture 17: How To Create A Bar Plot?
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Step-by-step guide to creating bar plots.
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Customization options for bar plots.
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Practical examples and exercises.
Lecture 18: How To Create A Scatter Plot?
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Step-by-step guide to creating scatter plots.
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Customization options for scatter plots.
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Practical examples and exercises.
Lecture 19: How To Create A Histogram?
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Step-by-step guide to creating histograms.
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Customization options for histograms.
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Practical examples and exercises.
Lecture 20: How To Create A Box And Violin Plot?
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Step-by-step guide to creating box and violin plots.
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Customization options for these plots.
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Practical examples and exercises.
Lecture 21: How To Create A Pie Chart And Doughnut Chart?
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Step-by-step guide to creating pie and doughnut charts.
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Customization options for these charts.
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Practical examples and exercises.
Lecture 22: How To Create An Area Chart?
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Step-by-step guide to creating area charts.
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Customization options for area charts.
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Practical examples and exercises.
Section 7: Statistics
Lecture 23: What Is Data?
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Definition and types of data.
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Data collection methods and sources.
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Practical examples to illustrate different types of data.
Lecture 24: Introduction To Statistics
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Basic concepts of statistics.
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Descriptive vs. inferential statistics.
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Practical examples and exercises.
Lecture 25: Sampling
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Importance of sampling in statistics.
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Different sampling methods.
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Practical examples and exercises.
Lecture 26: Probability
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Basic concepts of probability.
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Probability rules and theorems.
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Practical examples and exercises.
Lecture 27: Probability Distribution
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Types of probability distributions.
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Characteristics and applications of different distributions.
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Practical examples and exercises.
Lecture 28: Inferential Statistics
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Concepts of hypothesis testing and confidence intervals.
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Techniques for making inferences about a population.
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Practical examples and exercises.
Section 8: Machine Learning Using Python
Lecture 29: Types Of Machine Learning
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Overview of supervised, unsupervised, and reinforcement learning.
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Practical examples of each type.
Lecture 30: What Can You Do With Machine Learning?
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Applications of machine learning in various industries.
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Practical examples and case studies.
Lecture 31: Machine Learning Demo
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Demonstration of a simple machine learning project.
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Step-by-step guide to implementing a machine learning model.
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Practical exercises to build and evaluate a model.
Course Details
- Language: #English
- Students: 61
- Rating: 0 / 5.0
- Reviews: 0
- Category: #IT_and_Software
- Published: 2024-05-28 06:26:28 UTC
- Price: €94.99
- Instructor: | | Anand Mishra | |
- Content: 5 total hours
- Articles: 0
- Downloadable Resources: 0
Coupon Details
- Coupon Code: D5EC17F2D4AD4E5E3311
- Expire Time: 2024-06-03 07:07:00 UTC
