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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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