Foundational Courses
Data Science
Essentials
To equip participants with fundamental skills in data analysis
5
31 enrolled students
Objective
To equip participants with fundamental skills in data analysis, statistical modeling, and machine learning, enabling them to extract actionable insights from data and apply data-driven decision-making in real-world scenarios
Basic To Advance
You will progress through this course from basics to advanced level.
Duration
3 Months
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Modules
Module1: Introduction to Data Science and Python for Data Science
Topics:
- What is Data Science?
- The Data Science lifecycle: Data Collection, Preparation, Exploration, Modeling,
- Interpretation
- Python programming basics
- Overview of Python libraries: NumPy, Pandas, Matplotlib, Seaborn
Hands on exercises:
- Install Python and set up Jupyter Notebooks.
- Python exercises (variables, data types, control flow).
- Introduction to NumPy and Pandas: working with arrays and dataframes.
- Basic data visualization using Matplotlib and Seaborn.
Module 2: Data Exploration and Preprocessing
Topics:
- Data Exploration: Descriptive statistics and visualizing data distributions
- Data Cleaning: Handling missing data, outliers, and duplicates
- Feature engineering and scaling
- Introduction to Exploratory Data Analysis (EDA)
Hands on exercises:
- Perform descriptive analysis on a sample dataset (e.g., Titanic dataset).
- Clean a messy dataset: filling missing values, handling outliers, normalizing data.
- Visualize key insights from the dataset using histograms, box plots, and scatter plots.
Module 3: Probability, Statistics, and Introduction to Linear Regression
Topics:
- Basic statistics: mean, median, mode, variance, standard deviation
- Probability theory basics
- Introduction to probability distributions: Normal, Binomial, Poisson
- Linear Regression:
- Simple Linear Regression and multiple regression
- Understanding residuals, RMSE, and R-squared
- Assumptions of Linear Regression (linearity, homoscedasticity, independence, normality)
Hands on exercises:
- Build a Linear Regression model in Python using Scikit-learn.
- Interpret coefficients and evaluate the model’s performance (R-squared, RMSE).
- Perform exploratory analysis to verify assumptions of linear regression (e.g., linearity and normality).
Module 4: Regularization Techniques – Ridge and Lasso Regression
Topics:
- Ridge Regression
- Understanding L2 regularization
- How Ridge helps reduce overfitting by shrinking coefficients
- Lasso Regression:
- L1 regularization
- Feature selection properties of Lasso (shrinking coefficients to zero)
- Comparing Ridge and Lasso and their trade-offs
Hands on exercises:
- Implement Ridge and Lasso regression models.
- Use cross-validation to tune the regularization parameter (alpha).
- Compare the performance of Ridge, Lasso, and Linear Regression on a dataset.
- Interpret the coefficients to understand the impact of regularization.
Module 5: Decision Trees and Random Forests
Topics:
- Decision Trees:
- How Decision Trees work (splitting criteria, Gini Index, Entropy)
- Pruning and avoiding overfitting
- Random Forests:
- The concept of bagging (Bootstrap Aggregation)
- How Random Forests reduce overfitting and improve prediction accuracy
- Feature importance in Random Forests
- Model evaluation: accuracy, precision, recall, and confusion matrix
Hands on exercises:
- Build and visualize a Decision Tree using the Scikit-learn library.
- Implement a Random Forest classifier and regressor.
- Analyze feature importance from the Random Forest model.
- Compare Decision Tree vs. Random Forest performance on a classification task.
Module 6: Advanced Machine Learning Concepts and Boosting Techniques
Topics:
- Boosting Techniques:
- Introduction to Boosting (AdaBoost, Gradient Boosting)
- XGBoost and LightGBM: How they work, tuning hyperparameters.
- Model interpretability: SHAP, feature importance
Hands on exercises:
- Apply XGBoost or LightGBM to a classification or regression problem.
- Hyperparameter tuning using GridSearchCV or RandomizedSearchCV.
- Compare the performance of Boosting techniqueswith Random Forest.
- Interpret model outputs using SHAP values or feature importance plots.
Module 7: Unsupervised Learning and Clustering Techniques
Topics:
- Introduction to Unsupervised Learning
- Clustering Algorithms:
- K-means Clustering
- Hierarchical Clustering
- DBSCAN (Density-Based Spatial Clustering)
- Dimensionality Reduction: PCA (Principal Component Analysis), t-SNE
Hands on exercises:
- Perform K-means clustering on a dataset (e.g., customer segmentation).
- Implement hierarchical clustering and visualize dendrograms.
- Apply DBSCAN for anomaly detection in a dataset.
- Use PCA to reduce dimensions of a high-dimensional dataset and visualize the results using t-SNE.
Module 8: Capstone Project & Model Deployment
Topics:
- Solving a real-world data science problem using a dataset (students choose a problem related to finance, healthcare, etc.)
- Model deployment concepts: saving models, APIs for inference.
- Introduction to cloud platforms (AWS, GCP) for model deployment (optional)
Hands on exercises:
- End-to-end project: clean a dataset, explore, model, and make predictions.
- Evaluate and interpret the results.
- Optional: Deploy the model using Flask/Django or cloud-based tools.
Frequently Asked Questions
1. What is the Data Scientist Essentials course about?
This course is designed to provide foundational knowledge and practical skills for aspiring data scientists. It covers the key concepts and techniques in data analysis, statistical modeling, machine learning, and data visualization, using Python and popular data science libraries.
2. Who should take this course?
This course is ideal for beginners who are looking to break into data science, as well as professionals seeking to enhance their data analysis and machine learning skills. No prior experience in data science is required, although familiarity with basic programming concepts is beneficial.
3. What topics will be covered in this course?
opics include data exploration, data cleaning, statistical analysis, machine learning algorithms (supervised and unsupervised), data visualization using libraries like Matplotlib and Seaborn, and real-world applications of data science techniques.
5. How long does the course take to complete?
The course is designed to be completed in approximately 8-10 weeks, with an expected commitment of 6-8 hours per week. The course is self-paced, and you can adjust the schedule based on your availability.
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