Course Curriculum for Data Science Training at Infinity Net Solution, Ludhiana
Module 1: Introduction to Data Science
- What is Data Science?
- What is Machine Learning?
- What is Deep Learning?
- What is AI?
- Data Analytics & it’s Types
Module 2: Introduction to Python
- What is Python?
- Why Python?
- Installing Python
- Python IDEs
- Jupyter Notebook Overview
- Installing Jupyter Notebooks
- Python 2.7 vs Python 3
- Python Identifiers
- Various Operators and Operators Precedence
- Getting input from User, Comments, Multi line Comments.
Module 3: Making Decisions and Loop Control
- Simple if Statement, if-else Statement
- if-elif Statement.
- Introduction to while Loops.
- Introduction to for Loops, Using continue and break
Module 4: Python Data Types: List, Tuples, And Dictionaries
- Python Lists, Tuples, Dictionaries
- Accessing Values
- Basic Operations
- Indexing, Slicing, and Matrixes
- Built-in Functions & Methods
- Exercises on List, Tuples And Dictionary
Module 5: Functions and Modules
- Introduction To Functions – Why
- Defining Functions
- Calling Functions
- Functions with Multiple Arguments.
- Anonymous Functions – Lambda
- Using Built-In Modules, User-Defined Modules, Module Namespaces,
- Iterators And Generators
Module 6: File I/O and Exceptional Handling
- Opening and Closing Files
- open Function, file Object Attributes
- Close() Method , Read, write, seek. Exception handling, the try-finally Clause
- Raising an Exceptions, User-Defined Exceptions
- Regular Expression- Search and Replace
- Regular Expression Modifiers
- Regular Expression patterns, re module
Module 7: Numpy
- NumPy – Introduction
- NumPy – Environment
- NumPy – Ndarray Object
- NumPy – Data Types
- NumPy – Array Attributes
- NumPy – Array Creation Routines
- NumPy – Array from Existing Data
- Numpy – Array From Numerical Ranges
- NumPy – Indexing & Slicing
- NumPy – Advanced Indexing
- NumPy – Broadcasting
- NumPy – Iterating Over Array
- NumPy – Array Manipulation
- NumPy – Binary Operators
- NumPy – String Functions
- NumPy – Mathematical Functions
- NumPy – Arithmetic Operations
- NumPy – Statistical Functions,Sort, Search & Counting Functions
- NumPy – Byte Swapping
- NumPy – Copies & Views
- NumPy – Matrix Library
- NumPy – Linear Algebra
Module 8: Pandas
- Pandas – Introduction
- Pandas – Environment Setup
- Pandas – Introduction to Data Structures
- Pandas – Series
- Pandas – DataFrame
- Pandas – Panel
- Pandas – Basic Functionality
- Pandas – Descriptive Statistics
- Pandas – Function Application
- Pandas – Reindexing
- Pandas – Iteration
- Pandas – Sorting
- Pandas – Working with Text Data
- Pandas – Options & Customization
- Pandas – Indexing & Selecting Data
- Pandas – Statistical Functions
- Pandas – Window Functions
- Pandas – Aggregations
- Pandas – Missing Data
- Pandas – GroupBy
- Pandas – Merging/Joining
- Pandas – Concatenation
- Pandas – Date Functionality
- Pandas – Timedelta
- Pandas – Categorical Data
- Pandas – Visualization
- Pandas – IO Tools
- Pandas – Sparse Data
- Pandas – Caveats & Gotchas
- Pandas – Comparison with SQL
Module 9: Matplotlib
- What Is Python Matplotlib?
- Line Plot
- Bar Graph
- Histogram
- Scatter Plot
- Area Plot
- Pie Chart
- Working With Multiple Plots
Module 10: Importing data
- Reading CSV files
- Saving in Python data
- Loading Python data objects
- Writing data to csv file
Module 11: Manipulating Data
- Selecting rows/observations
- Rounding Number
- Selecting columns/fields
- Merging data
- Data aggregation
- Data munging techniques
Module 12: Statistics Basics
- Central Tendency
- Mean
- Median
- Mode
- Skewness
- Normal Distribution
- Probability Basics
- What does mean by probability?
- Types of Probability
- ODDS Ratio?
- Standard Deviation
- Data deviation & distribution
- Variance
- Bias variance Trade off
- Underfitting
- Overfitting
- Distance metrics
- Euclidean Distance
- Manhattan Distance
- Outlier analysis
- What is an Outlier?
- Inter Quartile Range
- Box & whisker plot
- Upper Whisker
- Lower Whisker
- Scatter plot
- Cook’s Distance
- Missing Value treatments
- What is a NA?
- Central Imputation
- KNN imputation
- Dummification
- Correlation
- Pearson correlation
- Positive & Negative correlation
Module 13: Error Metrics
- Classification
- Confusion Matrix
- Precision
- Recall
- Specificity
- F1 Score
- Regression
- MSE
- RMSE
- MAPE