Data Science using python

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Data Science using Python

Data Science course teaches you to master the concepts of Python programming, Machine Learning, Deep Learning and Neural Network. Through this  Data Science training, you will gain knowledge in data analysis, machine learning, data visualization, web scraping, & natural language processing. 
 
Upon course completion, you will able a become Data Scientist.
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Course Fees: Rs.18000

  • Duration : 3 months
  • No Cost EMI : INR 6000 x 3 month

NEFT Payment :Bank account details: Account Name – KLMS Hands-On Systems Private Limited, Account No – 50200042627525, IFSC – HDFC000027

Support :9830247087

Course Description!

Data Science using Python Course Curriculum

The demand for Data Science professionals has surged, making this course well-suited for participants at all levels of experience. This Python for Data Science training is beneficial for analytics professionals willing to work with Python, Software, and IT professionals interested in the field of analytics, and anyone with a genuine interest in Data Science.

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Course content

  • What is Data Science?
  • Types of Data
  • The Data Science Lifecycle
  • Data Acquisition and Preparation
  • Data Modeling and Visualization
  • Data Science Roles
  • Benefits of Data Science
  • Challenges of Data Science
  • Business Use Cases for Data Science
  • Concept of Analytics and Statistics
  • Categories o Analytics
  • Properties of Measurement
  • Scales of Measurement
  • Types of Data
  • Concept of Data visualization
  • Measures of Central Tendency
  • Measures of Dispersion
  • Moments, Skewness and Kurtosis
  • Concept of Correlation and Covariance
  • Introduction to Probability Theory
  • Probability Distributions
  • Sampling and Estimation
  • Testing of Hypothesis
  • Introduction to python
  • History of Python
  • Internal & External IDLE
  • Installation of Python &Anaconda
  • Compiler & Interpreter
  • Write your first program
  • Data types, Input and output function
  • Types of Operators
  • Conditional Statement: if-else, if-elif-else, Nested if else
  • Loop: While loop, For loop
  • Nested while loop, Nested for loop Break, Continue and Pass
  • Basic Data Types- Numeric & String
  • Tuple and it’s operation
  • List and it’s operation
  • Dictionary and it’s operation
  • Sets and It’s operation
  • Basics Defining function
  • Function call Return statement
  • Function with parameter and without parameter
  • local and global variable
  • Recursion, Anonymous (lambda) function
  • User defined functions
  • OOPS concepts Defining
  • Class Creating object, Constructor
  • Method vs function Calling methods
  • Method Overriding, List of objects Inheritance
  • Defining a file, Types of file and it’s operations
  • Python read Files
  • Python Write/Create Files
  • Python Delete Files
  • Pickle Module
  • Introduction to Numpy, Pandas, Matplotlib
  • Array, Array indexing, Array operation
  • Data frame, series, Groupby
  • Missing values
  • Box plot, Scatter plot, Chart styling
  • Histogram, Bar chart etc.
  • Group by plotting
  • Concept of Supervised learning
  • Concept off Unsupervised learning
  • Concept of Reinforcement learning
  • Simple Linear Regression
  • Multiple Linear Regression
  • Implementation of Linear Regression
  • Advanced Topics: Normal Equation, Polynomial Regression, R-sq Score
  • Python Implementation
  • Concept and Theory
  • Sigmoid function
  • Mathematical Concepts of Logistic Regression
  • Binary and Multivariate Classification Problems
  • Implementation of Logistic Regression
  • K-Nearest Neighbors-Concept and Theory
  • Implementation of K-Nearest Neighbors
  • Support Vector Machine(SVM)-Concept and Theory
  • Implementation of Support Vector Machine
  • Naïve Bayes Classifier- Concept
  • Implementation of Naïve Bayes Classifier
  • Decision Tree Classifier-Concept
  • Implementation of Decision Tree Classifier
  • Random Forest Classifier-Concept
  • Implementation of Random Forest Classifier
  • Dimensionality Reduction Problem- Curse of Dimensionality
  • Principal Component Analysis(PCA)
  • Implementation of PCA
  • K-Means Clustering- Concept
  • Implementation of K-Means Clustering
  • Hierarchical Clustering- Concept
  • Implementation of Hierarchical Clustering
  • DBSCAN Clustering-Concept
  • Implementation of DBSCAN Clustering
  • Introduction of Deep Learning and Neural Network
  • Types and Applications of Neural Network
  • Skills required for Neural network
  • Why Python is best for Neural Network
  • Anaconda Installation: Spyder & Jupyter Notebook
  • Introduction to Keras & Tensor Flow
  • Installation of Keras & Tensor Flow
  • ANN and Neuron Structure
  • How does Neural Network Works?
  • Practical Implementation of ANN
  • Train-Test Splitting
  • ANN model Training
  • Activation Function
  • Fit all the Layers
  • Backpropogation
  • Fitting to the training Dataset and finding Accuracy
  • Image Reading and CNN Process
  • Steps of CNN
  • Conclusion of CNN Process
  • Importing Required libraries
  • Reading Cat & Dog Dataset
  • Applying CNN layers
  • Fitting the Dataset in Model
  • Visualization of Accuracy and Loss
  • Prediction with single image
  • Introduction and Application
  • Process of RNN, Types of RNN, Gradient Problem
  • LSTM & GRU Explanation
  • Steps of LSTM
  • Creation o Data Structure with Time Steps
  • LSTM layers
  • Google Stock market prediction
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Using a hands-on approach to training, participants will adopt the form of learning that typically benefit from the most. It allows people to learn by doing.

 

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