FULL STACK DATA SCIENCE

What Will You Learn?

• Detailed Syllabus
• Week 1-2: Introduction to Data Science
• What is Data Science?
• The Data Science Process
• Key Roles in Data Science
• Overview of Tools and Technologies
• Setting Up Your Data Science Environment
• Week 3-4: Data Analysis and Visualization
• Introduction to Python for Data Science
• Data Manipulation with Pandas
• Data Cleaning Techniques
• Exploratory Data Analysis (EDA)
• Visualization with Matplotlib and Seaborn
• Week 5-6: Statistics and Probability for Data Science
• Descriptive Statistics (Mean, Median, Mode, Variance, etc.)
• Probability Distributions (Normal, Binomial, Poisson, etc.)
• Inferential Statistics (Confidence Intervals, p-values, etc.)
• Hypothesis Testing (t-tests, Chi-Square tests, etc.)
• Week 7-8: Machine Learning
• Introduction to Machine Learning
• Linear Regression and Logistic Regression
• Decision Trees and Random Forests
• Support Vector Machines (SVM)
• Clustering Algorithms (K-Means, Hierarchical Clustering)
• Dimensionality Reduction (PCA, LDA)
• Week 9-10: Deep Learning
• Introduction to Neural Networks
• Building Neural Networks with Keras
• Convolutional Neural Networks (CNNs) for Image Recognition
• Recurrent Neural Networks (RNNs) for Sequence Data
• Transfer Learning and Fine-Tuning
• Week 11-12: Natural Language Processing (NLP)
• Text Preprocessing (Tokenization, Lemmatization, etc.)
• Bag of Words and TF-IDF
• Sentiment Analysis with NLTK
• Named Entity Recognition (NER) with SpaCy
• Topic Modeling with Gensim
• Week 13-14: Data Engineering
• Introduction to Data Engineering
• Building Data Pipelines with Apache Airflow
• ETL Processes
• Working with Big Data (Hadoop, Spark)
• Data Warehousing Concepts
• Week 15-16: Data Visualization and Storytelling
• Advanced Visualization Techniques with Tableau
• Creating Interactive Dashboards with Power BI
• Principles of Data Storytelling
• Best Practices for Presenting Data Insights
• Week 17-20: Capstone Project
• Selecting a Project Topic
• Data Collection and Cleaning
• Exploratory Data Analysis (EDA)
• Model Development and Evaluation
• Creating a Project Report
• Presenting Your Project
• Week 21-24: Job Assistance
• Resume and Cover Letter Writing Workshop
• LinkedIn Profile Optimization Session
• Interview Preparation (Technical and Behavioral)
• Mock Interviews with Feedback
• Job Search Strategies and Networking Tips
• Career Counseling and Mentorship Sessions
• This syllabus provides a comprehensive overview of the Full Stack Data Science Job Assistance Program, ensuring that students gain the necessary skills and knowledge to excel in data science roles and receive support in securing a job in the industry.