| Module |
Syllabus in detail |
Contact hour |
| Module 1: Fundamental of Programming |
Introduction to Anaconda & Jupyter notebook, Flavors of python Introduction to Git, GitHub Python Fundamentals |
2 |
| Module 2: Fundamentals of Statistics |
Basic statistics, Populations, and sampling Mean, Median, Mode Standard Deviation Probability, Permutations, and Combinations
|
2 |
| Module 3: Python for Data Science |
Programming Basics & Environment Setup,Python Programming Overview, Strings, Decisions & Loop Control, Python Data Types, Functions And Modules, File I/O and Exceptional Handling and Regular Expression, OOPs: Class and Object, Data Analysis using Numpy, Data Analysis using Pandas, Data visualization using Matplotlib, Data Visualization using Plotly Express
|
6 |
| Module 4: Statistics for Machine Learning |
Fundamentals of Probability, Distributions, Introduction to Statistics, Statistical Thinking, Descriptive Statistics, Inferential Statistics, Hypothesis Testing, Linear Algebra, Data Processing & Exploratory Data Analysis (EDA) |
6 |
| Module 5: Machine Learning Fundamental |
Introduction to Machine Learning, supervised vs unsupervised learning, Regression and Classification Models, Data Preprocessing, Encoding the Data
K Nearest Neighbours Model, Decision Tree Model, Random Forest Model, Evaluation Metrics for Classification model, Hyperparameter Tuning, Naive Baye’s Model, Support Vector Machine (SVM), Neural Network,
Linear Regression Model, Logistic Regression Model, K Means and Hierarchical Clustering, Hierarchical Clustering
Principal Component Analysis (PCA) |
14 |
| Module 6: SQL
|
SQL and RDBMS, Advance SQL, NoSQL, HBase & MongoDB, JSON Data, Programming with SQL
|
6 |
| Module 7: PowerBI |
Getting Started with Power BI,
Programming with Power BI
|
4 |
