Accelerate your Career
This comprehensive Data Science course offered by Skildgrad is designed to equip you with the essential skills and knowledge to excel in the world of data-driven decision-making. From data collection and analysis to machine learning and predictive modeling, this course covers the entire data science lifecycle. Whether you’re a beginner or an experienced professional, this course will empower you to harness the power of data for insightful business solutions.
Course Overview
The Data Science course at Skildgrad is designed for individuals who want to harness the power of data to make informed decisions. Through a combination of theoretical concepts and hands-on exercises, you’ll learn how to collect, clean, analyze, and interpret data. The course covers a wide range of topics, including statistics, machine learning, neural networks, and natural language processing. By the end of the course, you’ll have the skills to undertake data-driven projects and contribute meaningfully to the field of data science.
Key Features
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Full lifetime access to all content
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Real-life Case Studies
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Industry Recognised Certification at affordable fee
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100% online and Self-paced learning
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Assignments
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Dedicated Forum Support to clear all your doubts
Introduction to Data Science
- Understanding the Data Science landscape
- Importance of data in modern decision-making
Data Wrangling and Exploration
- Data collection and cleaning
- Exploratory data analysis techniques
Fundamentals of Statistics
- Descriptive and inferential statistics
- Probability distributions and hypothesis testing
Machine Learning Foundations
- Supervised vs. unsupervised learning
- Feature engineering and selection
Regression and Classification
- Linear regression
- Logistic regression
Clustering and Dimensionality Reduction
- K-means clustering
- Principal Component Analysis (PCA)
Introduction to Neural Networks
- Basics of artificial neural networks
- Activation functions and architecture
Natural Language Processing (NLP)
- Text preprocessing and sentiment analysis
- Introduction to tokenization and word embeddings
Time Series Analysis
- Forecasting techniques
- ARIMA and Exponential Smoothing
Capstone Project
- Applying data science techniques to solve a real-world problem
FAQs
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