Data Science and Machine Learning
Data Science and Machine Learning
Skills you will gain
Data Science Foundation Training is designed to provide participants with a comprehensive understanding of the fundamental concepts, tools, and techniques used in the field of data science. The curriculum covers a range of topics, including data analysis, statistics, machine learning, programming, and data visualization. Participants will gain practical skills through hands-on exercises and projects, enabling them to apply their knowledge to real-world data problems.
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By the end of the course, participants will be able to:
- Understand the role and importance of data science in various industries.
- Collect, clean, and preprocess data for analysis.
- Apply statistical techniques to analyze data and draw meaningful insights.
- Utilize machine learning algorithms to build predictive models.
- Implement data visualization techniques to effectively communicate findings.
- Write code in a programming language commonly used in data science, such as Python.
- Work with popular data science libraries and tools, such as Pandas, NumPy, and scikit-learn.
- Apply data science concepts to real-world case studies and projects.
Course Modules
- Definition
- Data Science, AI, ML and Deep Learning
- Data Analysis, Data Engineering
- Data Science and skills requirements
- Data science step and processes
- Story-telling
- Algorithms
- Cleaning and preparing the data
This module introduces Trainees to common machine learning algorithms. Topics covered include decision trees, k-nearest
neighbors, random forests, and support vector machines. Trainees will also understand the concepts of overfitting and
underfitting, and how to choose appropriate algorithms for specific data sets
• Basic Descriptive Statistics
• Basic Mathematical Models
• SQL Programming Basics
• Data Cleaning with SQL
• Descriptive Statistics with SQL
• Excel Data Science Tool
• Data Cleaning with Excel
• Descriptive Statistics with Excel
• Python Basics
• Python Data Structures
• Python Programming Statistics
• Working with Data in Python
• Working with Numpy Arrays
- Introduction to Visualization Tools
- Basic Visualization Tools
- Specialized Visualization Tools
• Applications of Machine Learning
• Supervised vs Unsupervised Learning
• Python libraries suitable for Machine Learning
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