This article helps you a lot to know more about the various programming languages. This article includes 4 courses such as Data Science, Data Visualization, Data Analysis, and Machine Learning using Python.
Details about Applied Data Science with Python
This course helps you to know about how to use Python for cleaning, analyzing, and visualizing data. It helps you to tackle the problems related to data through brilliant lectures and their guides.
You will get hands-on projects through this learning Data Science course that directly work with real-world problems using Python.
Definitely learn this course to make your learning skills stronger and solidify and follow his path to becoming experienced in Python.
This article completes popular topics like Machine Learning, Data Science, Data Visualization, Data Analysis, and many more.
- This course gets a 4.6 rating out of 5
- This course is absolutely for beginners
Let’s see the small introduction to the different courses covered in this article.
Introduction-
Here, you will get an introduction to 4 different courses that help you to understand more details about it.
Data Science with Python- Data science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines techniques from statistics, mathematics, computer science, and domain-specific knowledge to analyze and interpret complex data sets.
Data Visualization with Python– Data visualization is a crucial aspect of data science and analysis. It involves representing data graphically to extract meaningful insights, patterns, and trends. Effective data visualization not only makes complex datasets more understandable but also aids in decision-making and communication of findings to a broader audience.
Machine Learning with Python– Machine learning is a subfield of artificial intelligence (AI) that focuses on developing algorithms and statistical models that enable computers to perform tasks without explicit programming. The primary goal of machine learning is to allow computers to learn from data and make predictions or decisions based on that learning.
Data Analysis with Python– Data analysis is the process of inspecting, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. It involves a variety of techniques and methods to uncover patterns, relationships, and trends within datasets.
Details about 4 courses in brief-
->Data Science with Python-
this helps you to start your learning in Data Science as well as programming. Beginners can easily learn this course because it provides full information in detail from zero to hero in a couple of hours.
After completing the course you will be able to write your own projects in Data Science using Python and also able to perform data analysis using Jupyter. This course is for you if you want to learn Scratch in Python.
->This course is self-paced so, that you can easily learn this course according to your comfort
->You can learn this course at any time
->You can audit this course as many times as you wish
->You can attempt this course many times but can pass the course only one time
->It takes 20 hours to complete learning
Syllabus of Data Science with Python-
It has 5 Modules, where each Module has a separate topic.
Module 1- basics of Python
Python Data Structures
Python programming fundamentals
Working with Data in Python
Working with Numpy arrays and simple APIs
->Data Visualization with Python-
Python offers several powerful libraries for data visualization, making it a popular choice for creating insightful and compelling visualizations. The choice of visualization depends on the nature of the data and the specific insights you want to convey. Python libraries such as Matplotlib, Seaborn, Plotly, and others can be employed to create these visualizations efficiently.
->This course is self-paced
->Any time you learn this course of Data Visualization
->It takes only 3-4 to complete
Syllabus of Data Visualization with Python-
It has 5 Modules that help you to better understand Data Visualization in details
Module 1- Introduction to Data Visualization Tools
Module 2- Basic Visualization Tools
Module 3- Specialized Visualization Tools
Module 4- Advanced Visualization Tools
Module 5- Creating Maps and Visualizing Geospatial Data
->Machine Learning with Python-
Machine learning is a rapidly evolving field with applications in various industries, including healthcare, finance, natural language processing, computer vision, and more. The success of a machine learning project often depends on the quality of data, the choice of the appropriate algorithm, and effective model training and evaluation.
->Popular algorithm can use this course for making more effective programming and projects
->You can also learn classification, Regression, Clustering, and Dimensional reduction
->Here, popular models also use this course for better working
->Here, are Train/test split, root mean squared error, and random forests.
->It takes 3 hours to complete the course
Syllabus of Machine Learning with Python-
It has total 5 Modules that help you to learn Machine Learning in detail
Module 1- Supervised vs Unsupervised Learning
Module 2- Supervised Learning I
Module 3- Supervised Learning II
Module 4- Unsupervised Learning
Module 5- Dimensionally Reduction and Collaborative Filtering
->Data Analysis with Python-
Common tools for data analysis include programming languages like Python and R, along with libraries such as Pandas, NumPy, and sci-kit-learn in Python, and dplyr and ggplot2 in R. Additionally, data analysis can be performed using spreadsheet software like Microsoft Excel or specialized data analysis software like Tableau.
->You can also learn libraries of data analysis
->You can explore many different types of data
->How to import data sets
->How to clean and prepare data for analysis
->You can manipulate a panda’s data frame
->You can summarize data
->You can build machine learning models using Scikit-learn
->Easily you can build data pipelines
->Learn Python programming and its Statistics
Syllabus of Data Analysis with Python-
It has a total 5 Modules to learn Data Analysis with Python
Module 1- Importing Datasets
Module 2- Cleaning and Preparing the Data
Module 3- Summarizing the Data Frame
Module 4- Model Development
Module 5- Model Evaluation
Uses of Data analysis, data visualization, machine learning, and data science in Python-
Python is a versatile programming language with a rich ecosystem of libraries and tools that make it particularly well-suited for data analysis, data visualization, machine learning, and data science.
Here are some common uses of these techniques in Python:
->Data Analysis with Python:
Pandas Library: Python’s Pandas library is widely used for data manipulation and analysis.
NumPy Library: NumPy is used for numerical computing in Python, providing support for large, multi-dimensional arrays and matrices.
Statistical Analysis: Python’s SciPy library, along with Pandas, is commonly used for statistical analysis.
->Data Visualization with Python:
Matplotlib: A popular 2D plotting library for creating static, animated, and interactive visualizations.
Seaborn: Built on top of Matplotlib, Seaborn provides a high-level interface for creating attractive and informative statistical graphics.
Plotly: Enables the creation of interactive and dynamic visualizations, suitable for dashboards and web applications.
->Machine Learning with Python:
Scikit-Learn: A simple and efficient tool for data analysis and modeling.
TensorFlow and PyTorch: Deep learning frameworks for building and training neural networks.
Scikit-Optimize: A library for hyperparameter optimization, which is crucial for improving the performance of machine learning models.
->Data Science with Python:
Jupyter Notebooks: An interactive computing environment widely used in data science for creating and sharing documents.
Scrapy: A web scraping framework used for extracting data from websites.
Statsmodels: A library for estimating and testing statistical models, providing classes and functions for various statistical models.
