Mastering Data Science with Python [2023]

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Prashant Mishra

17:27:07

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1. Fundamentals of python

1.1 1. Fundamentals of Python.zip

1. Getting Started With Python.mp4

01:04:43

2. String Basics.mp4

20:10

3. String Operations.mp4

44:18

4. Conditional Statements in Python.mp4

19:06

5. Loops in Python.mp4

14:32

6. Data Structures Basics.mp4

31:13

7. List.mp4

33:58

8. Tuple.mp4

08:01

9. Dictonary.mp4

22:19

10. Set.mp4

15:54

11. Functions Basics.mp4

34:07

12. Anonymous Function --Lambda Function.mp4

11:49

13. Special Function.mp4

14:12

14. Comprehensions.mp4

20:00

15. In-Built Functions.mp4

36:35

16. OOP --Basics.mp4

38:08

17. OOP --Inheritance, Encapsulation, Polymorphism.mp4

21:01

2. Data Science with Python

1.1 2. Python for Data Science.zip

1. Date Time Module.mp4

14:55

2. RegEx --Built In Functions.mp4

13:02

3. RegEx --Meta Characters.mp4

11:30

4. NumPy vs List.mp4

11:38

5. NumPy --Basics.mp4

27:01

6. NumPy --Operations.mp4

14:03

7. Pandas --Basics.mp4

28:32

8. Pandas --Header And Index.mp4

11:18

9. Pandas --Columns.mp4

23:32

10. Pandas --loc And iloc.mp4

11:24

11. Pandas --GroupBy, Sorting, Counts.mp4

15:13

12. Pandas --Merge And Concatenate.mp4

11:31

13. Pandas --Datetime.mp4

08:10

14. Pandas --Advanced (Seperators, Rename, String Functions).mp4

10:06

15. Matplotlib --Basics.mp4

22:25

16. Matplotlib --Types of Graphs.mp4

10:10

3. Data Cleaning

1.1 3. Data Cleaning.zip

1. Missing Values.mp4

49:30

2. Outliers --Basics.mp4

22:49

3. Outliers --Visualization.mp4

11:27

4. Data Cleaning on Naukri Dataset.mp4

24:56

4. Visualization

1.1 4. Visualization.zip

1. Data Visualization --Basics.mp4

35:11

2. Line and Area Plot.mp4

18:43

3. Scatter, Box and Violin Plot.mp4

31:40

4. Maps.mp4

09:29

5. Statistics for Data Science

1.1 5. Statistics for Data Science.zip

1. Descriptive and Inferential Statistics 1.mp4

40:48

2. Hypothesis Testing.mp4

39:37

6. Exploratory Data Analysis (EDA)

1.1 6. EDA.zip

1. Pandas Profiling.mp4

19:05

2. DABL and Sweetviz.mp4

08:54

7. Capstone Project

1.1 7. Capstone Project.zip

1. Capstone Project.mp4

40:22

Description

A Complete Python-Based Data Science Journey

What You'll Learn?

Data Manipulation: Learn how to effectively manipulate and transform data using Python libraries such as Pandas, NumPy, and SciPy.
Data Analysis: Develop the ability to explore and analyze datasets using Python's powerful data visualization libraries like Matplotlib and Seaborn.
Gain hands-on experience in conducting EDA, including using tools like Pandas Profiling, DABL, and Sweetviz to analyze and visualize datasets.
Capstone Project: Apply the knowledge and skills acquired throughout the course to tackle a real-world business problem, performing end-to-end analysis.

Who is this for?

This course is designed for individuals who are interested in learning and applying data science techniques using the Python programming language.

Aspiring Data Scientists: Individuals who want to pursue a career in data science and want to gain practical skills in using Python for data analysis, modeling, and visualization.

Python Programmers: Programmers who are already familiar with Python and want to expand their knowledge to the field of data science. This course will help them apply their programming skills to solve real-world data problems.

Data Analysts: Analysts who work with data and want to enhance their skills by incorporating Python into their data analysis workflows. This course will enable them to perform more advanced data manipulation, statistical analysis, and visualization using Python.

What You Need to Know?

Basic Programming Knowledge: A fundamental understanding of programming concepts and logic is necessary. Students should be familiar with variables, data types, control flow statements (if/else, loops), and functions.

Python Fundamentals: Proficiency in Python programming is essential. Students should have a solid understanding of variables, data types, operators, basic data structures (lists, tuples, dictionaries), and control flow.

Mathematics and Statistics Basics: A foundational understanding of mathematics and statistics is important for data analysis and modeling. Concepts such as algebra, calculus, probability, and descriptive statistics will be utilized.

More details

Description
Welcome to the "Data Science with Python" course, designed to equip you with essential skills and knowledge for a successful journey in the field of data science. In this comprehensive course, you will explore the latest capabilities of Python and its rich ecosystem of libraries to analyze, visualize, and derive meaningful insights from data.
Through a practical and hands-on approach, you will gain a deep understanding of key data science concepts and techniques, while mastering the tools and methodologies used by industry professionals. Starting with an introduction to Python programming, you will cover fundamental concepts, data structures, control flow, and functions. Progressing swiftly, you will delve into advanced topics like object-oriented programming (OOPs) and working with modules and packages.
Once you have established a solid Python foundation, the course will immerse you in the realm of data science. You will learn efficient data manipulation and cleaning techniques, handle missing values and outliers, and prepare data for analysis. Leveraging libraries such as NumPy and Pandas, you will perform exploratory data analysis, extract meaningful insights, and create visualizations that effectively communicate your findings.
The course also introduces you to statistical analysis, hypothesis testing, and advanced data visualization techniques using the latest libraries like Matplotlib, Seaborn, and Plotly. These skills will enable you to uncover patterns, identify relationships, and make data-driven decisions.
To solidify your understanding and apply the acquired knowledge, the course includes a capstone project. You will tackle a real-world business problem, conducting end-to-end analysis and creating a comprehensive report showcasing your data science skills.
Whether you are a beginner or have prior experience with Python, this course empowers you to confidently work with data, explore complex datasets, and extract valuable insights. By the course's end, you will possess the tools and expertise necessary to embark on data-driven endeavours, making a significant impact in your professional and academic pursuits.

Resource files are conveniently provided with the first lecture of each section, enhancing your learning experience throughout the course. Join us now and unlock the power of data science with Python in 2023, leveraging the latest techniques and technologies!
Who this course is for:
This course is designed for individuals who are interested in learning and applying data science techniques using the Python programming language.
Aspiring Data Scientists: Individuals who want to pursue a career in data science and want to gain practical skills in using Python for data analysis, modeling, and visualization.
Python Programmers: Programmers who are already familiar with Python and want to expand their knowledge to the field of data science. This course will help them apply their programming skills to solve real-world data problems.
Data Analysts: Analysts who work with data and want to enhance their skills by incorporating Python into their data analysis workflows. This course will enable them to perform more advanced data manipulation, statistical analysis, and visualization using Python.

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Python

Data Science

Prashant Mishra

Instructor's Courses

I am Computer Science Graduate in 2021 and with a passion for teaching, started back as a BDA in various Ed-tech companies, which increased a little more passion towards this industry to explore.Have trained more than 5000+ Individual students one-on-one and group-based, which not only found my classes very interesting but also developed a huge scope of job opportunities in the future.

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