Data Science with Python

COURSE DESCRIPTION

The Data Science with Python course spans twelve weeks, offering a comprehensive exploration of data science concepts and techniques using Python. Students begin by installing essential tools and libraries, then delve into Python programming basics, covering data types, control structures, functions, and object-oriented programming.

The course progresses to data manipulation and analysis, utilizing libraries such as NumPy and Pandas for handling arrays and data frames. Students learn to perform data cleaning, transformation, and exploratory data analysis, gaining insights through descriptive statistics and data visualization with Matplotlib and Seaborn.

Midway through the course, students focus on statistical analysis and hypothesis testing, learning to apply various statistical tests and interpret their results. The course then transitions to machine learning, where students explore supervised and unsupervised learning algorithms using the Scikit-Learn library. Topics include regression, classification, clustering, and model evaluation techniques.

In the later weeks, students dive into advanced machine learning concepts, such as ensemble methods, neural networks, and deep learning with TensorFlow and Keras. They also explore natural language processing and time series analysis, applying these techniques to real-world datasets.

The course emphasizes practical application, with students working on hands-on projects and assignments throughout. They learn to implement data pipelines, build and deploy machine learning models, and communicate their findings effectively through reports and presentations.

Towards the end, students cover essential topics in big data and cloud computing, understanding how to work with large datasets using tools like Apache Spark and deploying models in cloud environments like AWS and Azure. The course concludes with discussions on ethics in data science, including data privacy, bias, and fairness.

Through lectures, supervised labs, assignments, and quizzes, students develop the skills necessary for a career in data science, gaining proficiency in Python programming and applying data science techniques to solve complex problems.

CERTIFICATION

The Data Science with Python course lasts for twelve weeks and teaches you how to analyze data and build machine learning models using Python. At the beginning, you learn how to set up your computer with the necessary tools and understand Python programming basics, including data types, control structures, and functions.

As the course continues, you learn how to manipulate and analyze data using libraries like NumPy and Pandas. You perform data cleaning, transformation, and visualization to gain insights from datasets. You also learn about statistical analysis and hypothesis testing to interpret data effectively.

LEARNING OUTCOMES

– Over 40 lectures and 60 hours of content!
– Practical training included with hands-on projects and assignments.
– Learn data science and machine learning techniques from a professional trainer.
– Comprehensive coverage from basics to advanced techniques.
– Best suitable for beginners to advanced level users who learn faster with practical examples.
– Course content designed considering current industry trends and job market demands.
– Practical assignments at the end of every session.
– Practical learning experience with real-world data and project work.