About course
Python has a big and active user and development community, as well as a plethora of tools and frameworks that are especially made for manipulating, analyzing, and visualizing data. These factors make it a popular programming language for data science. These packages include Matplotlib and Seaborn for data visualization, NumPy for numerical computing, and pandas for data cleaning and manipulation.
In addition to these packages, Python also provides a number of frameworks like scikit-learn and TensorFlow that are designed expressly for statistical modeling and machine learning. With the help of these tools, it is simple to create and train machine learning models as well as to carry out operations like clustering, classification, and regression.
Generally speaking, Python is a strong and versatile language that is ideal for data science projects.
Learning Outcome
- Understanding the basics of Python programming, including data types, variables, control structures, functions, and modules.
- Familiarity with Python libraries and tools commonly used in data science, such as NumPy, Pandas, and Matplotlib.
- Ability to manipulate and analyze data using Python, including cleaning and preprocessing data, performing statistical analysis, and visualizing data.
- Familiarity with machine learning concepts and the ability to apply machine learning algorithms using Python libraries such as scikit-learn.
- Ability to use Python for data exploration and visualization, including creating plots and charts to help understand and communicate insights from data.
- Understanding of best practices for working with large datasets and developing efficient data processing pipelines.
- Ability to work with real-world data and apply the skills and techniques learned to solve real-world problems.
Why Python With Data Science important
Python is a popular language for data science because it has a large and active community of users, as well as a wealth of libraries and frameworks that make it easy to perform data manipulation, visualization, and machine learning tasks. Some of the key reasons why Python is well-suited for data science include. It has a simple and easy-to-learn syntax, which makes it a great language for beginners. It has a large and comprehensive standard library, which includes modules for tasks such as connecting to web servers, reading and writing files, and working with data. It has a large number of third-party libraries and frameworks, such as NumPy, Pandas, and scikit-learn, which provide additional functionality for tasks such as numerical computation, data manipulation and analysis, and machine learning.
Key Highlight Of Course
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100% job opportunity -
Career Essentials Soft Skills modules -
Learner Support Available on All Days -
Data Extraction -
Time Series Data - CSV Module
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Python hashing -
1:1 with Industry Mentors - 100+ Learning Hours
- 43 Modules
- Career Essentials Soft Skills modules
- Career coaching (CV, motivational interviewing)
- Data Extraction
- Time Series Data
- CSV Module
- 1:1 with Industry Mentors
- 100+ Learning Hours
- 43 Modules
- Career Essentials Soft Skills modules
- Career coaching (CV, motivational interviewing)
Tools You’ll Learn
