Data Scientist

Become a Data Scientist: The Complete Learning Path

Course Description:

This comprehensive course is designed to provide students with in-depth knowledge and practical skills in data science. Participants will learn to manipulate and analyze data using various tools and techniques, build predictive models, and communicate their findings effectively. The course covers essential topics such as statistics, programming in Python, machine learning, and data visualization, making it suitable for beginners and intermediate users aiming to enhance their data science proficiency.

Course Duration:

12 Weeks (3 hours per week)

Week 1: Introduction to Data Science

• Overview of Data Science and its applications
• The data science process
• Setting up the data science environment
• Introduction to Python programming

Week 2: Data Wrangling and Preprocessing

• Importing and exporting data
• Cleaning and preprocessing data
• Handling missing values
• Data transformation and normalization

Week 3: Exploratory Data Analysis (EDA)

• Understanding data distributions
• Summary statistics
• Data visualization techniques
• Identifying patterns and correlations

Week 4: Introduction to Statistics

• Descriptive statistics
• Probability theory and distributions
• Hypothesis testing
• Inferential statistics

Week 5: Python for Data Science

• Advanced Python programming
• Using libraries such as NumPy, Pandas, and SciPy
• Data manipulation and analysis
• Implementing functions and classes

Week 6: Data Visualization

• Principles of effective data visualization
• Using Matplotlib and Seaborn
• Creating various types of plots
• Interactive visualizations with Plotly

Week 7: Introduction to Machine Learning

• Supervised vs. unsupervised learning
• Key machine learning algorithms
• Training and evaluating models
• Cross-validation techniques

Week 8: Supervised Learning Algorithms

• Linear and logistic regression
• Decision trees and random forests
• Support vector machines
• Model evaluation metrics

Week 9: Unsupervised Learning Algorithms

• Clustering techniques (K-means, hierarchical clustering)
• Dimensionality reduction (PCA, t-SNE)
• Anomaly detection
• Association rule learning

Week 10: Advanced Machine Learning Techniques

• Ensemble methods (Bagging, Boosting)
• Neural networks and deep learning
• Natural Language Processing (NLP)
• Time series analysis

Week 11: Data Science Project Lifecycle

• Defining the problem and gathering data
• Data cleaning and preprocessing
• Model selection and training
• Model deployment and monitoring

Week 12: Capstone Project and Review

• Practical application: End-to-end data science project
• Peer review and feedback
• Final Q&A and course recap