INTRODUCTION TO DATA SCIENCE (ML AND DL ) | IDS Online Course

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INTRODUCTION TO DATA SCIENCE (ML AND DL ) | IDS

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English

Course Description

Embark on a transformative journey into the world of data with our "Introduction to Data Science (ML & DL)" course. Designed for beginners, this comprehensive program covers essential topics such as Python programming, data manipulation, exploratory data analysis, and machine learning fundamentals. Participants will learn to acquire, clean, and analyze data, gaining practical skills in Python libraries like NumPy, Pandas, and Matplotlib. Advanced topics include feature engineering, deep learning basics, and model deployment using TensorFlow or PyTorch. The course culminates in a Capstone Project where learners apply their skills to solve a real-world problem, ensuring hands-on experience. With additional resources like recommended readings and industry case studies, graduates will be well-prepared for diverse career opportunities in machine learning engineering, data science, and research. Prerequisites include basic Excel, SQL, and Python skills, making it accessible to anyone eager to explore the exciting field of data science. TOOLS: • Python • Jupyter Notebooks • SQL Database • Tableau • Git • Machine Learning Libraries • Cloud Services • Kaggle • Browser CERTIFICATIONS: • Oracle Foundation Certificate – paid • Advance Excel Certificate • MySQL Certificate • Tableau certificate – paid • Data scientist Certificate • Data Analyst Certificate • Live Project Certificate

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What you’ll learn

Foundations of Data Science
Python Programming for Data Science
Data Acquisition and Cleaning
Exploratory Data Analysis (EDA)
Statistical Analysis with Python
Machine Learning Fundamentals
Supervised Learning Algorithms
Unsupervised Learning Algorithms
Advanced Topics
Capstone Project

Covering Topics

Module 1: Foundations of Data Science (2 weeks)

Module 2: Data Manipulation and Cleaning (3 weeks)

Module 3: Exploratory Data Analysis (2 weeks)

Module 4: Machine Learning Fundamentals (4 weeks)

Module 5: Advanced Topics (3 weeks)

Module 6: Capstone Project (4 weeks)

Live Projects

Curriculum

  Foundations of Data Science (2 weeks)

• Introduction to Data Science
•	Definition and scope
•	Applications in various industries

• Python for Data Science
•	Basics of Python programming
•	Libraries: NumPy, Pandas, Matplotlib

  Data Manipulation and Cleaning (3 weeks)

• Data Acquisition
•	Importing data from various sources
•	APIs and web scraping

• Data Cleaning and Preprocessing
•	Handling missing values
•	Data normalization and standardization

  Exploratory Data Analysis (2 weeks)

• Descriptive Statistics
•	Measures of central tendency and dispersion
•	Visualization techniques

• Statistical Analysis with Python
•	Hypothesis testing
•	Correlation and regression analysis

  Machine Learning Fundamentals (4 weeks)

• Introduction to Machine Learning
•	Supervised vs. unsupervised learning
•	Types of machine learning algorithms
•	Model Training and Evaluation
•	Splitting datasets
•	Cross-validation

• Supervised Learning Algorithms
•	Linear regression, logistic regression
•	Decision trees, random forests

• Unsupervised Learning Algorithms
•	Clustering (K-means, hierarchical)
•	Dimensionality reduction (PCA)

  Advanced Topics (3 weeks)

• Feature Engineering
•	Importance and techniques

• Deep Learning
•	Neural networks basics
•	Introduction to TensorFlow or PyTorch

• Model Deployment
•	Basics of deploying machine learning models
•	Basics of Cloud services (AWS, Azure)

  Capstone Project (4 weeks)

• Capstone Project
•	Apply learned skills to a real-world problem (Kaggle)
•	Presentation and documentation

Frequently Asked Questions

A: Yes, our courses are designed to be accessible both online and offline. You can choose your preferred mode of learning based on your convenience and availability of internet connectivity.

A: Basic Excel skills are necessary, including familiarity with fundamental functions for data manipulation and analysis.

A: Yes, understanding basic SQL queries for data extraction, joining, and aggregating datasets is essential for participants.

A: Participants should have a foundational understanding of Python programming, covering variables, data types, loops, and conditional statements.

A: No, the course is designed for beginners without any prior experience in data science. It is accessible to anyone eager to learn and dive into the world of data science. These prerequisites ensure that participants have the necessary skills to effectively engage with the course material and derive maximum benefit from the learning experience.