Data Science Training With Python In Jalandhar

Learn Data Science with Python Training in Jalandhar with Data Science Certified Experts using R, Python, Excel, SAS, and Tableau. For more information about Data Science Course Fees, Certification, Placements, and Real-Time Projects in Jalandhar City, call 9914077736. We are rated as the best Data Science with Python training provider in Jalandhar, with 100% job placement assistance. Statistics, hypothesis testing, Machine Learning, AI Concepts, Deep Learning, Data Science Algorithms, and Analytics Concepts are all covered in this course.

The Course By Itronix Solution Includes:

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