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DataScience with Python Training in BTM Layout

Home/DataScience with Python Training in BTM Layout
DataScience with Python Training in BTM Layout 2018-04-25T16:47:29+00:00

Session : 1 – Introduction

Session : 2 – Basic functions

  • Interaction with Numpy
  • Index Tricks
  • Shape manipulation
  • Polynomials
  • Vectorizing functions
  • Type handling
  • Other useful functions

Session : 3 – Special functions

Session : 4 – Integration

Session : 5 – Optimization

  • Nelder-Mead Simplex algorithm
  • Broyden-Fletcher-Goldfarb-Shanno Algorithm
  • Newton Conjugate Gradient Algorithm
  • Least Squares minimization
  • Root Finding

Session : 6 – Interpolation

Session : 7 – 1-D interpolation

Session : 8 – Multivariate data interpolation (griddata)

Session : 9 – Spline interpolation

  • Spline interpolation in 1-d: Procedural (interpolate.splXXX)
  • Spline interpolation in 1-d: Object-oriented (UnivariateSpline)
  • Two-dimensional spline representation: Procedural (bisplrep)
  • Two-dimensional spline representation: Object-oriented (BivariateSpline)

Session : 10 – Using radial basis functions for smoothing/interpolation

  • 1-d Example
  • 2-d Example

Session : 11 – Fourier Transforms

Session : 12 – Fast Fourier transforms

  • One-dimensional discrete Fourier transforms
  • Two and n-dimensional discrete Fourier transforms
  • FFT convolution

Session : 13 – Discrete Cosine Transforms

  • Type I DCT
  • Type II DCT
  • Type III DCT
  • DCT and IDCT
  • Example

Session : 14 – Discrete Sine Transforms

  • Type I DST
  • Type II DST
  • Type III DST
  • DST and IDST

Session : 15 – Cache Destruction

Session : 16 – Signal Processing

Session : 17 – Linear Algebra

Session : 18 – Basic Routines

  • Finding determinant ( matrix )
  • Computing norms
  • Solving least squares problems and pseudo inverses
  • Decompositions

Session : 19 – Sparse Eigenvalue Problems with ARPACK

Session : 20 – Compressed Sparse Graph Routines

Session : 21 – Spatial data structures and algorithms

  • Delaunay trangulations
  • Coplanar points
  • Convex hulls
  • Voronoi diagrams

Session : 22 – Statistics Random Variables

  • Shifting and Scaling
  • Shape parameters
  • Freezing and Distribution
  • Fitting distributions
  • Building specific distributions
  • Analysing one sample
  • Kernel Density estimation

Session : 23 – Multidimensional image processing

Session : 24 File IO

Session : 25 Matlab

Session : 26 – Weave

Thanks for the best ever Training. I did my Android Course here. Trainer is very good in knowledge. They provides the best ever Support to me.

Kaviya

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