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Data Science & Analysis - Course Outline
1. Introduction to Data Science
- Definition and Importance of Data Science
- Overview of the Data Science Process
- Key Concepts and Terminology in Data Science
2. Understanding Data Types and Sources
- Types of Data (Structured, Unstructured, Semi-structured)
- Common Data Sources (Databases, APIs, Web Scraping)
- Data Collection Methods
3. Data Preparation and Cleaning
- Importance of Data Quality
- Techniques for Data Cleaning and Preprocessing
- Handling Missing Values and Outliers
- Data Transformation and Normalization
4. Exploratory Data Analysis (EDA)
- Overview of EDA Techniques
- Using Visualization Tools (e.g., Matplotlib, Seaborn)
- Descriptive Statistics and Summarizing Data
- Identifying Patterns and Trends in Data
5. Statistical Analysis for Data Science
- Introduction to Statistics in Data Science
- Inferential Statistics and Hypothesis Testing
- Correlation and Regression Analysis
- ANOVA and Chi-Square Tests
6. Introduction to Programming for Data Science
- Overview of Programming Languages (Python and R)
- Basic Programming Concepts (Variables, Data Structures, Control Flow)
- Libraries and Frameworks for Data Science (Pandas, NumPy, Scikit-learn)
7. Data Visualization Techniques
- Importance of Data Visualization
- Creating Effective Visualizations (Bar Charts, Line Graphs, Heatmaps)
- Tools for Data Visualization (Tableau, Power BI)
8. Machine Learning Fundamentals
- Introduction to Machine Learning Concepts
- Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
- Common Algorithms (Linear Regression, Decision Trees, Clustering)
9. Building Machine Learning Models
- Data Splitting (Training, Validation, Test Sets)
- Model Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)
- Model Optimization and Tuning (Cross-Validation, Grid Search)
10. Big Data Technologies
- Introduction to Big Data Concepts
- Overview of Big Data Tools (Hadoop, Spark)
- Working with Distributed Data Systems
11. Ethics in Data Science
- Understanding Data Privacy and Security
- Ethical Considerations in Data Analysis
- Fairness and Bias in Data Science
12. Capstone Project: Data Science Application
- Defining the Project Scope and Objectives
- Applying Data Science Techniques to Real-world Problems
- Presenting Findings and Insights
- Reflection on Learning and Challenges
Duration
- 3 - 4 Months
Benefits
- Assigned Live Tutor to take you from start to finish.
- Hands-on Practical Training and Assignments.
- Accredited Examination and Certification.
- Paid Internships and Industrial Training.
- Career Guidance and Mentorship.
