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Data Science using Python Training


New Batches Starts For Data Science using Python Training From November

New Batches Starts For Data Science using Python Training From November

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Data Science using Python

The Data Science with Python course teaches you to master the concepts of Python programming. Through this Python for Data Science training, you will learn Data Analysis, Machine Learning, Data Visualization, Web Scraping, & NLP. Upon course completion, you will master the essential Data Science tools using Python

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Weeks Duration
Hr/Week Therory
Hr/Week Lab
Students Per Batch
  • Introduction
  • Application of Data Science
    • The Machine Learning
      • Supervised, Unsupervised, & Reinforcement Learning
  • Essential Python for Data Science
    • Types of Data 
      • Numeric, Text, List, & Dictionary
    • The pandas 
      • DataFrame and Series
      • CSV Files
      • Excel Spreadsheets
      • JSON
  • Scikit-Learn package
    • Model
    • Model Hyperparameters
    • The scikit-learn API
  • Simple Linear Regression
    • The Method of Least Squares
  • Multiple Linear Regression
    • Estimating the Regression Coefficients (β₀, β₁, β₂ and β₃)
    • Logarithmic Transformations of Variables
    • Correlation Matrices
  • Conducting Regression Analysis Using Python  
    • The Correlation Coefficient  
    • The Statsmodels formula API
    • Analyzing the Model Summary  
    • The Model Formula Language  
    • Intercept Handling
  • Multiple Regression Analysis
  • Assumptions of Regression Analysis  
  • Explaining the Results of Regression Analysis  
    • Regression Analysis Checks and Balances
    • The F-test
    • The t-test
  • Introduction  
  • Understanding the Business Context  
    • Business Discovery
    • Testing Business Hypotheses Using Exploratory Data Analysis
    • Visualization for Exploratory Data Analysis
    • Intuitions from the Exploratory Analysis
  • Feature Engineering  
    • Business-Driven Feature Engineering
  • Data-Driven Feature Engineering  
    • A Quick Peek at Data Types and a Descriptive Summary  
  • Correlation Matrix and Visualization  
    • Skewness of Data  
    • Histograms  
    • Density Plots  
    • Other Feature Engineering Methods  
    • Summarizing Feature Engineering  
    • Building a Binary Classification Model Using the Logistic Regression Function  
    • Logistic Regression Demystified  
    • Metrics for Evaluating Model Performance
    • Confusion Matrix
    • Accuracy
    • Classification Report
    • Data Preprocessing
  • Introduction
  • Training a Random Forest Classifier
  • Evaluating the Model's Performance
    • Number of Trees Estimator  
  • Maximum Depth
  • Minimum Sample in Leaf
  • Maximum Features
  • Introduction
  • Clustering with k-means
  • Interpreting k-means Results
  • Choosing the Number of Clusters
  • Initializing Clusters  
  • Calculating the Distance to the Centroid  
  • Standardizing Data
  • Introduction
  • Splitting Data
  • Assessing Model Performance for Regression Models
    • Data Structures – Vectors and Matrices
      • Scalars
      • Vectors
      • Matrices
    • R² Score
    • Regression Model  
    • Mean Absolute Error
      • Other Evaluation Metrics
  • Assessing Model Performance for Classification Models
    • Computing Evaluation Metrics  
  • The Confusion Matrix  
    • More on the Confusion Matrix  
    • Precision  
    • Recall  
    • F1 Score
    • Accuracy  
    • Logarithmic Loss
  • Receiver Operating Characteristic Curve
  • Area Under the ROC Curve
  • Saving and Loading Models
  • Introduction
  • Overfitting
    • Training on Too Many Features  
    • Training for Too Long  
  • Underfitting  
  • Data
    • The Ratio for Dataset Splits  
    • Creating Dataset Splits  
  • Random State  
  • Cross-Validation  
    • KFold  
  • cross_val_score
    • Understanding Estimators That Implement CV
  • LogisticRegressionCV  
  • Hyperparameter Tuning with GridSearchCV  
    • Decision Trees  
  • Hyperparameter Tuning with RandomizedSearchCV  
  • Model Regularization with Lasso Regression  
  • Ridge Regression
  • Introduction  
  • What Are Hyperparameters?  
    • Difference between Hyperparameters and Statistical Model Parameters
    • Setting Hyperparameters  
    • A Note on Defaults  
  • Finding the Best Hyperparameterization
    • Advantages and Disadvantages of a Manual Search
  • Tuning Using Grid Search
    • Simple Demonstration of the Grid Search Strategy
  • GridSearchCV  
    • Tuning using GridSearchCV
      • Support Vector Machine (SVM) Classifiers
    • Advantages and Disadvantages of Grid Search
  • Random Search  
    • Random Variables and Their Distributions
    • Simple Demonstration of the Random Search Process
    • Tuning Using RandomizedSearchCV
    • Advantages and Disadvantages of a Random Search
  • Introduction  
  • Linear Model Coefficients  
  • RandomForest Variable Importance
  • Variable Importance via Permutation
  • Partial Dependence Plots
  • Local Interpretation with LIME
  • Introduction  
  • Exploring Your Data  
  • Analyzing Your Dataset  
  • Analyzing the Content of a Categorical Variable Summarizing Numerical Variables  
  • Visualizing Your Data  
    • Using the Altair API  
    • Histogram for Numerical Variables  
    • Bar Chart for Categorical Variables  
  • Boxplots
  • Introduction  
  • Handling Row Duplication
  • Converting Data Types  
  • Handling Incorrect Values  
  • Handling Missing Values
  • Introduction  
    • Merging Datasets  
      • The Left Join
      • The Right Join
    • Binning Variables  
    • Manipulating Dates  
    • Performing Data Aggregation
  • Introduction  
  • Understanding the Business Context  
    • Analysis of the Result  
  • Challenges of Imbalanced Datasets  
  • Strategies for Dealing with Imbalanced Datasets 
    • Collecting More Data  
    • Resampling Data  
    • Analysis  
  • Generating Synthetic Samples  
    • Implementation of SMOTE and MSMOTE  
    • Applying Balancing Techniques on a Telecom Dataset
  • Introduction
    • Business Context
  • Creating a High-Dimensional Dataset
  • Strategies for Addressing High-Dimensional Datasets
    • Backward Feature Elimination (Recursive Feature Elimination)
    • Forward Feature Selection
    • Principal Component Analysis (PCA)
    • Independent Component Analysis (ICA)  
    • Factor Analysis  
  • Comparing Different Dimensionality Reduction Techniques 
  • Introduction  
  • Ensemble Learning
    • Variance  
    • Bias  
    • Business Context  
  • Simple Methods for Ensemble Learning  
    • Averaging  
    • Weighted Averaging  
      • Iteration  with Different Weights
      • Max Voting
  • Advanced Techniques for Ensemble Learning
    • Bagging
    • Boosting
    • Stacking


N. Arnav

Ideal place for anyone willing to learn Data Science and Data Analytics. The teacher is very passionate about conveying the subject knowledge in a very understandable way for anyone. Covers basics to advanced concepts. Highly recommend Invictus !! "

M. Arun

The classes are interactive and the trainer has complete knowledge of the subject. The trainer had given us hands-on which helped us in clarifying the concepts. A detailed description of all the concepts related to deep learning was given like activation function, convolutional layer, Maxpooling layer, etc. were given.

R. Veer

All relevant topics were taught and understood well. Proper hands-on were given with encouragement of high level of participation. Teachings helped me in clearing all the related assessments

K. Pranav

Excellent teaching, The way of explanation is too good and easy to understand. The way the trainer teaches the theory along with practical sessions has helped me a lot in learning and understanding Data Science. The whole teaching is done practically. It's really helpful.

D. Aaradhya

Sessions were very informative and interactive. I got to learn about the in-depth working of the algorithms. I got to do a lot of Hands-on projects with the trainer's help.