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Course Outline for Data Science (Basic)

(Course duration 40 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 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

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

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