Syllabus

  • What is Data Science
  • Introduction to Business analysis
  • Application of Data science
  • Learning path of Data Science
  • Introduction to python
  • Why python for Data Science
  • How to run python for different OS
  • Basics for python for Data Science
  • Arithmetic operators
  • Variables and assignment operators
  • Data Structures of python
  • Looping, conditions, functions
  • Numpy (Numerical Python)
  • Pandas
  • Introduction to Probability and Distributions
  • Random variables
  • Different discrete probability distributions
  • Different continuous probability distributions
  • Confidence Interval and its importance
  • Hypothesis testing
  • Anova
  • Ordinary Least square methods
  • Multiple Linear regression
  • advanced regression techniques
  • R^2 value and Adjusted R^2 value
  • Data manipulations / munging
  • Data Visualizations
  • Data understanding
  • Missing value techniques
  • Outlier detection and treatment
  • Subset selection
  • Correlation
  • Autocorrelation and Multicollinearity
  • Variance Inflation Factor(VIF)
  • Dummy variable Implementation
  • All about machine learning
  • Matrices:

    RMSE, Confusion Matrix, Accuracy and Misclassification error, Sensitivity and Specificity, Precision and Recall, F1-score, ROC-AUC curve, Cohen's kappa, Weights of evidence, Lift and Gain

  • Model improvement:

    Overfitting, Bias-variance tradeoff, Imbalanced Dataset problem, L1 and l2 regularization, Hyper parameter tuning

  • Logistic Regression:

    Concepts of MLE, Drawback of Linear Regression fails, Sigmoid function, log odds ratio, Cost Function, real-life case study with python

  • k-Nearest Neighbors and Naive Bayes:

    KNN detailed algorithm, bayes theorem

  • Decision Tree and Random Forest:

    Construction of tree, terminologies, gini index, information gain, optimising performance, variable importance

  • SVM:

    Rewind of basic calculus, LaGrange’s Theorem, SVM hyperplane, Hinge loss

  • Bagging & Boosting
  • Introduction to Unsupervised Learning
  • Distance Metrics
  • Clustering and its types:
    • Hierarchical Clustering
    • K-means Clustering
    • Density Based Clustering
    • DBSCAN
  • Dimensionality Reduction:
    • PCA
    • Non Negative Matrix Factorization
  • Introduction to NLP
  • NLTK, regex, regular expressions
  • Data Preparation: punctuation removal, stop-words removal, numeric value removal, frequent words removal, rare words removal, spelling correction
  • tokenization, stemming, lemmatization
  • Feature Engineering: count vectors as features
  • Term Frequency-Inverse Document Frequency (TF-IDF), word embedding as features, word2vec
  • Sentiment Analysis

Key Features

End to end 10 case studies
Build your portfolio
Mock interview
Q & A Session
Assignments
Certification and Job assistance

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