Data Science Training

4.00(2 Ratings)

Course Duration: 70h

About Course

DevOps Training provides an immersive and holistic educational journey tailored for individuals aspiring to master the intricacies of DevOps methodologies. This comprehensive program meticulously blends foundational principles with advanced techniques, ensuring participants gain a deep understanding of automation, collaboration, and the pivotal principles underpinning continuous integration and delivery (CI/CD). Through an engaging curriculum, learners delve into cutting-edge practices, equipping them with the skills and insights needed to seamlessly implement DevOps strategies within their organizational frameworks. With a focus on hands-on learning, real-world case studies, and interactive sessions, participants emerge equipped to drive transformative change, fostering agility, efficiency, and innovation across software development and operations domains.

* Understand the fundamentals of data science and its applications.
* Learn to collect, clean, preprocess, and analyze data using statistical and machine learning techniques.
* Gain proficiency in programming languages such as Python or R for data manipulation and analysis.
* Develop skills in data visualization to communicate insights effectively.
* Master advanced machine learning algorithms and techniques for predictive modeling and pattern recognition.
* Learn to evaluate, validate, and optimize machine learning models.
* Gain hands-on experience through projects and case studies to apply data science concepts in real-world scenarios.
* Prepare for data science certification exams such as Certified Data Scientist (CDS) or AWS Certified Data Analytics – Specialty.

Course Curriculum

Basic Concepts
1. Introduction to Data Science 2. Overview of Data Science Lifecycle 3. Data Collection and Data Types 4. Data Cleaning and Preprocessing 5. Exploratory Data Analysis (EDA) 6. Introduction to Statistics for Data Science 7. Data Visualization Techniques 8. Introduction to Machine Learning 9. Linear Algebra and Calculus Basics for Data Science 10. Introduction to Python or R Programming for Data Science 11. Hands-on Projects and Exercises

Lesson Plan – Basic Concepts

Advanced Concepts
1. Advanced Statistical Analysis 2. Feature Engineering and Selection 3. Dimensionality Reduction Techniques 4. Advanced Machine Learning Algorithms: - Ensemble Learning - Support Vector Machines (SVM) - Neural Networks and Deep Learning - Time Series Analysis - Natural Language Processing (NLP) - Recommender Systems 5. Model Evaluation and Validation 6. Hyperparameter Tuning and Optimization 7. Big Data Technologies (e.g., Apache Spark) 8. Deployment of Machine Learning Models 9. Advanced Data Visualization Libraries and Techniques 10. Advanced Python or R Programming for Data Science 11. Ethical Considerations and Bias in Data Science 12. Hands-on Projects and Case Studies