Data Science and Data Visualization with Python
Data Science and Data Visualization with Python, available at $54.99, has an average rating of 4.89, with 78 lectures, based on 30 reviews, and has 197 subscribers.
You will learn about Bar, Line, Stacked Charts Mathematical Plots Histograms, Pie Charts Grids, Subplots 3D Histograms, 3D Bars PIL, Images, Basemap CAPTCHA Images Logarithmic Plots, Spectrograms Gantt Charts, Error Bars Trefoil Knot This course is ideal for individuals who are People who want to explore Data Science or People who want to explore Data Visualization It is particularly useful for People who want to explore Data Science or People who want to explore Data Visualization.
Enroll now: Data Science and Data Visualization with Python
Summary
Title: Data Science and Data Visualization with Python
Price: $54.99
Average Rating: 4.89
Number of Lectures: 78
Number of Published Lectures: 78
Number of Curriculum Items: 78
Number of Published Curriculum Objects: 78
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will Learn
- Bar, Line, Stacked Charts
- Mathematical Plots
- Histograms, Pie Charts
- Grids, Subplots
- 3D Histograms, 3D Bars
- PIL, Images, Basemap
- CAPTCHA Images
- Logarithmic Plots, Spectrograms
- Gantt Charts, Error Bars
- Trefoil Knot
Who Should Attend
- People who want to explore Data Science
- People who want to explore Data Visualization
Target Audiences
- People who want to explore Data Science
- People who want to explore Data Visualization
Course Overview:
The course begins with a solid foundation in Python programming, ensuring that participants, regardless of their prior experience, can comfortably navigate the language. From there, we delve into the core concepts of data science, covering topics such as data manipulation, cleaning, and exploratory data analysis. Participants will gain hands-on experience using popular Python libraries like NumPy, Pandas, and Matplotlib.
Key Learning Objectives:
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Python Fundamentals: Develop a strong command of Python programming, enabling participants to efficiently manipulate data and perform complex analyses.
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Data Manipulation and Analysis: Learn to clean and preprocess data effectively using Pandas, and perform advanced data manipulation tasks to extract meaningful insights.
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Exploratory Data Analysis (EDA): Master the art of exploring and summarizing data using statistical and visual methods, laying the groundwork for informed decision-making.
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Data Visualization: Dive into the world of data visualization with Matplotlib and Seaborn. Create stunning and informative visualizations that effectively communicate complex insights to diverse audiences.
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Machine Learning Foundations: Gain an understanding of the fundamentals of machine learning and explore how Python can be used to implement and deploy basic machine learning models.
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Real-world Applications: Apply acquired skills to real-world scenarios and datasets, ensuring that participants can tackle data-driven challenges in their professional environments.
Why Python for Data Science and Visualization?
Python has emerged as the language of choice for data scientists due to its versatility, ease of learning, and a rich ecosystem of libraries. Our course emphasizes Python’s role in the entire data science workflow, from data cleaning to visualization, and provides participants with a holistic understanding of its capabilities.
Who Should Enroll:
This course is ideal for aspiring data scientists, analysts, researchers, and anyone eager to harness the power of Python for effective data analysis and visualization. Whether you are a beginner or have some experience in data science, this course will elevate your skills and empower you to make data-driven decisions confidently.
Conclusion:
Equip yourself with the essential skills in data science and data visualization that are in high demand across industries. Enroll in “Mastering Data Science and Data Visualization with Python” and embark on a journey towards becoming a proficient data practitioner, ready to tackle the challenges of the modern data landscape. Elevate your career prospects and unlock new opportunities with the knowledge and expertise gained in this comprehensive and practical course.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: Plot Types
Lecture 3: Plot Types – 2
Lecture 4: Sine and Cosine Plots
Lecture 5: Sine and Cosine Plots – 2
Lecture 6: Sine and Cosine Plots – 3
Lecture 7: Styles, Properties and Format Strings
Lecture 8: Ticks, Labels and Grids
Lecture 9: Adding Legends and Annotations
Lecture 10: Spines
Lecture 11: Histograms
Lecture 12: Bar Charts with Error Bars
Lecture 13: Pie Charts
Lecture 14: Filled Areas
Lecture 15: Stacked Plots
Lecture 16: Scatter Plots with Colored Markers
Chapter 2: More of it… The Plots Stuff
Lecture 1: Introduction
Lecture 2: Transparency and Size of Axis Labels
Lecture 3: Shadow to Chart Line
Lecture 4: Adding a Data Table
Lecture 5: Subplots
Lecture 6: Subplots – 2
Lecture 7: Customizing Grid
Lecture 8: Customizing Grid – 2
Lecture 9: Creating Contour Plots
Lecture 10: Under-Plot Area
Lecture 11: Under-Plot Area – 2
Lecture 12: Polar Plots
Lecture 13: Matplotlib with Style
Chapter 3: 3D Visualizations
Lecture 1: 3D Visualizations
Lecture 2: 3D Visualizations – 2
Lecture 3: 3D Visualizations – 3
Lecture 4: 3D Histograms – 4
Lecture 5: Animating
Lecture 6: Animating – 2
Lecture 7: Animating – 3
Chapter 4: Chart Plotting
Lecture 1: Introduction
Lecture 2: Introduction – 2
Lecture 3: Introduction – 3
Lecture 4: Plotting with Images
Lecture 5: Plotting with Images – 2
Lecture 6: Plotting with Images – 3
Lecture 7: Displaying Images with Other Plots
Lecture 8: Basemap
Lecture 9: Basemap – 2
Lecture 10: Plotting Data on Map using Google Map API
Lecture 11: Generating CAPTCHA Images
Lecture 12: Generating CAPTCHA Images – 2
Chapter 5: Plots and Data
Lecture 1: Plots and Data, Logarithmic Plots
Lecture 2: Logarithmic Plots
Lecture 3: Spectrograms
Lecture 4: Spectrograms – 2
Lecture 5: Stem Plot
Lecture 6: Vector Flow
Lecture 7: Colormaps
Lecture 8: Colormaps – 2
Lecture 9: Scatter Plots and Histograms
Lecture 10: Scatter Plots and Histograms – 2
Lecture 11: Scatter Plots and Histograms – 3
Lecture 12: Scatter Plots and Histograms – 4
Lecture 13: Cross Correlation between Two Variables
Lecture 14: Autocorrelation
Chapter 6: More on Matplotlib
Lecture 1: More on Matplotlib
Lecture 2: Barb
Lecture 3: Barb – 2
Lecture 4: Box and Whisker Plot
Lecture 5: Gantt Charts
Lecture 6: Gantt Charts – 2
Lecture 7: Error Bars
Lecture 8: Text and Font Properties
Lecture 9: Text and Font Properties – 2
Lecture 10: LaTeX
Lecture 11: LaTeX – 2
Lecture 12: LaTeX – 3
Lecture 13: LaTeX – 4
Lecture 14: Multiple Statements on a Single Line
Lecture 15: Multiple Statement Groups as Suites and Header Line
Lecture 16: Values Assigning to Variables
Instructors
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Tech Career World
Udemy Instructor
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 0 votes
- 3 stars: 1 votes
- 4 stars: 1 votes
- 5 stars: 27 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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