Dsba Data Science

Data Science Topics covered

Plotting for Exploratory Data Analysis[Duration : 10 Hours ]

  1. 2D,3D Scatter Plots
  2. Pair Plots
  3. Limitation of Pair Plots
  4. Histograms(Probability Density Functions)
  5. Univariate Analysis
  6. Cumulative Distribution Function
  7. Variance,SD,Range
  8. Percentiles and Quantiles
  9. Box-Plot with Whiskers
  10. Violin Plots
  11. Summary of Univariate,Bivariate and Multivariate Analysis
  12. Multivariate Probability Denisty Contour Plot
  13. Perform EDA

Linear Algebra[Duration : 10 Hours ]

  1. Introduction to Vectors,Row Vectors and Column Vectors
  2. Angle between two vectors
  3. Projection and Unit Vector
  4. Equation of Line , Plane and Hyperplane
  5. Distance of a point from Plane/Hyperplance and Half spaces
  6. Equation of a circle,Sphere and Hemisphere
  7. Equation of a Ellipse,Ellipsoid and Hyperellipsoid
  8. Square,Rectangle
  9. Hypercube ,Hyper Cuboid

Probability and Statistics[Duration : 10 Hours ]

  1. Introduction to Probability and Statistics
  2. Population and Sample
  3. Gaussian/ Normal Distribution and its PDF
  4. Cumulative Distribution Fuction of Guassian/Normal Distribution
  5. Symmetric Distribution,Skewness and Kurtosis
  6. Standard Normal Variate and Standardization
  7. Kernel Density Estimation
  8. Sampling Distribution and Central Limit theorm
  9. QQ plot – How to test if a random variable is normally distributed or not
  10. Discrete and Continuous Uniform Distributions
  11. How to randomly sample data points(Uniform Distribution)
  12. Bernouli and Binomial Distribution
  13. Log Normal Distribution
  14. Power Law Distribution
  15. Box Cox Transform
  16. Co-Variance
  17. Pearson Correlation Coefficient
  18. Chi-Square test
  19. Correlation Vs Causation
  20. Confidence Level Introduction
  21. Computing Confidence-Interval given distribution
  22. CI for mean of a normal random variable
  23. Confidence interval using bootstrapping
  24. Hypothesis testing methodology ,Null Hypothesis ,P-Value
  25. Re-Sampling and permutation test
  26. K-S test and K-S test for similarity for two distributions

Dimensionality Reduction and Visualization[Duration : 10 Hours ]

  1. What is Dimensionality reduction
  2. Row Vector and Column Vector
  3. How to represent a data set
  4. How to represent a data set as a matrix
  5. Data preprocessing – Column Normalization
  6. Mean of data matrix
  7. Data preprocessing :Column Standardization
  8. Co-Variance of a Data matrix

Principal Component Analysis[Duration : 10 Hours ]

  1. What is PCA
  2. Geometric Intuition of PCA
  3. Mathematical Objective Function of PCA
  4. Alternative Formulation of PCA :Distance minimization
  5. Eigen Values and Eigen Vectors
  6. PCA for Dimensionality Reduction and Visualization
  7. Limitation of PCA

What is Machine Learning[Duration : 10 Hours ]

  1. Machine Learning: What's the challenge?
  2. Acquainting yourself with the data
  3. What is, what isn't?
  4. Basic prediction model
  5. Classification, Regression, Clustering
  6. Classification, regression or clustering?
  7. Classification: Filtering spam
  8. Regression: LinkedIn views
  9. Clustering: Separating the iris species
  10. Supervised vs. Unsupervised
  11. Getting practical with supervised learning
  12. How to do unsupervised learning
  13. Tell the difference

Performance menasures [Duration : 10 Hours ]

  1. Measuring model performance or error
  2. The Confusion Matrix
  3. Deriving ratios from the Confusion Matrix
  4. The quality of a regression
  5. Adding complexity to increase quality
  6. Let's do some clustering!
  7. What to do with all these performance measures?
  8. Training set and test set
  9. Split the sets
  10. First you train, then you test
  11. Using Cross Validation
  12. How many folds?
  13. Bias and Variance
  14. Overfitting the spam!
  15. Increasing the bias
  16. Interpretability

Classification[Duration : 10 Hours ]

  1. Decision trees
  2. Learn a decision tree
  3. Understanding the tree plot
  4. Classify with the decision tree
  5. Pruning the tree
  6. Interpreting the tree
  7. Splitting criterion
  8. k-Nearest Neighbours
  9. Preprocess the data
  10. The knn() function
  11. K's choice
  12. Interpreting a Voronoi diagram
  13. The ROC curve
  14. Creating the ROC curve
  15. The area under the curve
  16. Interpreting the curves
  17. Comparing the methods

Regression[Duration : 10 Hours ]

  1. Regression: simple and linear!
  2. Simple linear regression: your first step!
  3. Performance measure: RMSE
  4. Performance measures: R-squared
  5. Another take at regression: be critical
  6. Non-linear, but still linear?
  7. Interpreting R-squared
  8. Multivariable Linear Regression
  9. Going all-in with predictors!
  10. Are all predictors relevant?
  11. Interpreting the residuals and p-values
  12. k-Nearest Neighbors and Generalisation
  13. Does your model generalize?
  14. Your own k-NN algorithm!
  15. Parametric vs non-parametric!

Clustering[Duration : 10 Hours ]

  1. Clustering with k-means
  2. k-means: how well did you do earlier?
  3. The influence of starting centroids
  4. Making a scree plot!
  5. What is your optimal k?
  6. Performance and scaling issues
  7. When to standardize your data?
  8. Standardized vs non-standardized clustering
  9. Hierarchical Clustering
  10. Did you get it all?
  11. Single Hierarchical Clustering
  12. Complete Hierarchical Clustering
  13. Hierarchical vs k-means
  14. Interpreting Dunn's Index
  15. Clustering US states based on criminal activity

Association Rule Learning[Duration : 2 Hours ]

  1. Apriori
  2. Eclat

Reinforcement Learning[Duration : 2 Hours ]

  1. Upper Confidence Bound (UCB)
  2. Thompson Sampling

Natural Language Processing[Duration : 2 Hours ]

Deep Learning[Duration : 12 Hours ]

  1. Artificial Neural Networks
  2. Convolutional Neural Networks

Model Selection and Boosting[Duration : 2 Hours ]

  1. Model Selection
  2. XGBoost

Preferred Programming Languages Needed : R , Python

Preferred Tools Needed : Tableau

Total Duration : 120 Hours

Contact Details:

Mr. Vijay

Mobile: 0091 94400 89341

Phone: 0091 040-6456 8797

Mexico/Canada/USA phone : 001 (650) 550-9178

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