Course Outline for Data Science (Advance)

(Course duration 60 hours)

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 ClusteringPrincipal Component Analysis (PCA), Factor analysis	15
Module 6: SQL	SQL and RDBMS, Advance SQL, NoSQL, HBase & MongoDB, JSON Data & CRUD, Programming with SQL	9
Module 7: Tableau	Introduction to Tableau, Visual Analytics, Dashboard and Stories, Hands-on: Connecting data source and data cleansing, Working with various charts, Deployment of Predictive model in visualization	5
Module 8: PowerBI	Getting Started with Power BI,Programming with Power BI	4
Module 9: Big Data & Spark Analytics	Introduction To Hadoop & Big Data, What is Spark, Getting to know PySpark	8
Module 10: Time Series	Introduction to Time Series Forecasting, Introduction to ARIMA Models, Performing Time Series Analysis on Stock Prices & Time series forecasting of sales data	3

Assignment on Module 3:

8+ Programs to be covered in the functions, Lambda, modules, Generators, and Packages class
10+ Programs to be covered in class from File IO, Reg-ex and exception handling
Case Study on Numpy, Pandas, Matplotlib and Plotly Express

Assignment on Module 4:

Total 4 practice sets and assignments from Statistics and EDA using Numpy, Pandas, and Matplotlib

Assignment on Module 5:

Total 10 practice sets and assignments from Supervised learning models
Total 6 practice sets and assignments from Unsupervised learning models

Assignment on Module 6:

Total 5 practice sets and assignments from SQL

Assignment on Module 9:

Hands-on: Map reduce Use Case: Youtube data analysis & Spark RDD programming

Assignment on Module 10:

1. Case Study on Time series classification of smartphone data to predict user behavior

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