Data Science
Data Science 3 Months Course Syllabus
Weeks 1 - 2: Introduction and Basics
Day 1-2: Introduction to Data Science
- Overview of Data Science and applications.
- Key tools and technologies (Python, R, SQL).
Day 3-4: Python Basics for Data Science
- Variables, data types, conditionals, loops, functions.
- Libraries: NumPy, Pandas.
Day 5-6: Data Manipulation with Pandas
- DataFrames, indexing, filtering.
- Aggregations, merging, reshaping.
Day 7-8: Exploratory Data Analysis (EDA)
- Data visualization (Matplotlib, Seaborn).
- Handling missing data, outliers, and data distributions.
Day 9-10: Statistics Fundamentals
- Descriptive statistics (mean, median, mode, variance, standard deviation).
- Probability concepts, distributions (Normal, Poisson, Binomial).
Day 11-12: Introduction to SQL
- SELECT, INSERT, UPDATE, DELETE queries.
- Joins, subqueries, and aggregations.
Day 13-14: Python for Data Cleaning
- Handling missing values, scaling, encoding categorical data.
- Regular expressions and string operations.
Weeks 3 - 4: Data Visualization and Basic Machine Learning
Day 15-16: Advanced Visualization
- Creating impactful visualizations with Seaborn and Plotly.
- Dashboard creation (Tableau/Power BI basics).
Day 17-18: Linear Algebra and Matrices
- Vectors, matrices, and matrix operations.
- Applications in machine learning.
Day 19-21: Introduction to Machine Learning
- Supervised vs. unsupervised learning.
- Scikit-learn basics.
Day 22-24: Linear Regression
- Simple and multiple regression.
- Metrics: RMSE, R².
Day 25-28: Classification Models
- Logistic Regression, Decision Trees.
- Model evaluation (confusion matrix, precision, recall, F1-score).
Weeks 5 - 6: Intermediate Machine Learning
Day 29-31: Feature Engineering
- Feature selection, extraction, and transformation.
- PCA, dimensionality reduction techniques.
Day 32-34: Ensemble Methods
- Random Forest, Gradient Boosting (XGBoost, LightGBM).
- Bagging vs. Boosting.
Day 35-37: Clustering
- K-means, Hierarchical Clustering.
- DBSCAN, evaluation metrics (silhouette score).
Day 7-8: Exploratory Data Analysis (EDA)
- Data visualization (Matplotlib, Seaborn).
- Handling missing data, outliers, and data distributions.
Day 38-40: Natural Language Processing (NLP) Basics
- Tokenization, stemming, lemmatization.
- Bag-of-words, TF-IDF.
Day 41-42: Advanced Regression and Classification
- Polynomial regression, Ridge, Lasso.
- SVMs and kernel tricks.
Weeks 7-8: Deep Learning and Time Series
Day 43-46: Introduction to Deep Learning
- Neural Networks, activation functions.
- Frameworks (TensorFlow, PyTorch).
Day 47-49: Convolutional Neural Networks (CNNs)
- Image processing and recognition tasks.
Day 50-52: Recurrent Neural Networks (RNNs)
- Sequence data, LSTMs, GRUs.
Day 53-54: Time Series Analysis
- ARIMA, SARIMA, seasonal decomposition.
- Forecasting with Prophet.
Day 55-56: Deployment Basics
- Flask/Django for deploying models.
- Introduction to cloud platforms (AWS, GCP, Azure).
Weeks 9-10: Advanced Topics and Projects
Day 57-60: Big Data
- Hadoop, Spark, and data pipelines.
- Working with large datasets.
Day 61-63: Advanced NLP
- Transformers, BERT, GPT.
- Sentiment analysis, text summarization.
Day 64-66: Reinforcement Learning
- Markov Decision Processes, Q-learning.
Day 67-70: Model Deployment and Optimization
- MLOps basics.
- Hyperparameter tuning with GridSearch, RandomizedSearch.
Weeks 11-12: Final Projects and Case Studies
Day 71-75: End-to-End Project (1)
- Data collection, cleaning, and exploratory analysis.
- Machine learning model building, evaluation, and deployment.
Day 76-80: End-to-End Project (2)
- Focus on time series, NLP, or advanced deep learning.
Day 81-85: Real-World Case Studies
- Kaggle competitions, publicly available datasets.
Day 86-90: Final Revision and Portfolio Building
- Prepare documentation, resumes, and LinkedIn profiles.
- Presentation of projects and interview preparation.
Note: Data Science Interviews Q & A’s will be provided.
5 Data Science Projects which are resumed based will be provided as Bonus for this Course.