Data Science With Python Training

Data Science with Python training offers a comprehensive and practical exploration of the multifaceted field of data science using the Python programming language. Participants in this training typically gain proficiency in various Python libraries and tools essential for data manipulation, analysis, and visualization, such as NumPy, Pandas, and Matplotlib.

The course often covers statistical concepts and machine learning algorithms, enabling learners to extract meaningful insights and patterns from large datasets.

Practical application of data science techniques through hands-on projects is a common feature, allowing participants to hone their skills in real-world scenarios. Topics often include data cleaning and preprocessing, exploratory data analysis, feature engineering, and model evaluation.

Additionally, training may delve into data ethics, best practices, and communication of findings to diverse audiences. As the field of data science continues to evolve, this training equips individuals with the foundational skills and Python expertise needed to navigate the complex landscape of data-driven decision-making and analytics.

Syllabus

• → Introduction to Data Science
• • Overview of data science and its applications
• • Understanding the data science workflow
• • Role of Python in data science
• → Python Basics
• → Data Manipulation with Pandas
• → Numerical Computing with NumPy
• → Data Visualization with Matplotlib and Seaborn
• → Exploratory Data Analysis (EDA)
• → Introduction to Machine Learning
• → Supervised Learning Algorithms
• → Unsupervised Learning Algorithms
• → Model Evaluation and Validation
• → Feature Engineering
• → Introduction to Deep Learning
• → Big Data and Spark
• → Capstone Project
• → Ethics in Data Science