| Topic No | Topic Name | Sub Topics |
|---|---|---|
| 1 | Introduction to Data Science | Introduction to Data Science What is Data Science Types of Data in Statistics (Numerical & Categorical) Overview of Python Concepts What is Machine Learning Machine Learning Classification Types of Algorithms |
| 2 | Data Manipulation with NumPy | Introduction NumPy Arrays NumPy Basics Math Indexing Random Filtering Statistics Aggregation Saving Data |
| 3 | Data Analysis with Pandas | Introduction to Data Analysis using Pandas Pandas Series Pandas DataFrame Combining Indexing File I/O Grouping Features Filtering Sorting Stastical Plotting |
| 4 | Data Cleaning with Pandas and Data Preprocessing with Scikit Learn |
Introduction to Data Preprocessing and Scikit-Learn Standardizing of Data Robust Scaling Data Range Normalizing Data Label Encoder and One Hot Encoding Polynomial Features Working with Duplicates and Missing Values Which values should be replace with missing values based on type of data Identifying and Eliminating of Outliers Filling missing data using Data Imputation |
| 5 | Introduction to Data Visualization with Matplotlib | Introduction to Visualization and Python packages Matplotlib history and Architecture Introduction to plotting Line Plot Scatter Plot Bar Graph Histogram Pie Chart Box Plot |
| 6 | Data Visualization With Seaborn | Using Seaborn Styles Setting the default style Color Palettes Creating Custom Palettes stripplot() and swarmplot() boxplots, violinplots barplots, pointplots and countplots Regression Plots Binning data Creating heatmaps Applying on raw dataset and introduction to Kaggle and other data sources |
| 7 | Regression Models | Linear Regression with One variable Evaluation Metrics in Regression Models Train/Test splitting of data & Cross Validation Linear Regression with Multiple Variables Polynomial Features Non-Linear Regression with One variable Non-Linear Regression with Multiple variable |
| 8 | Regularization Models | Under fitting Overfitting Best fit Applying Ridge Regression Lasso Regression Algorithms |
| 9 | Classification models - 1 | Introduction to categorical types of data Types of classification K-Nearest Neighbors Classifier Evaluation Metrics for classification Models Logistic regression Support Vector Machines |
| 10 | Classification Models - 2 | Introduction to Decision Tree Terminology related to Decision Trees Types of Decision Trees Decision Trees Classifier Decision Tree Regressor Random Forest Algorithm |
| 11 | Unsupervised Machine Learning | Introduction to Unsupervised Learning Types of Unsupervised Learning |
| 12 | Clustering | Introduction to clustering Types of Clustering Methods KMeans Clustering Applications |
| 13 | Dimensionality Reduction: | Dimensionality Reduction: Principal Component Analysis (PCA) |
- i3 or above Processor is required
- 4 GB or above RAM is recommended
- Good Internet Connectivity
- OS-Windows 10 is Preferable
60 Hours (6 hours each day X 10 days)
- To introduce students/Faculty to the basic concepts and techniques of Data Science and Machine Learning.
- To develop skills of using recent machine learning software for solving practical problems.
- To gain experience in doing independent study and research.
- Students must have Knowledge of Python Programming.
- Statistics and Algebra, Maths.