DataScience with Python Training in Chennai

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DataScience with Python Training in Chennai

Training in Chennai offers best DataScience with Python Training in Chennai with most experienced professionals. We aware of industry needs and we are offering DataScience with Python Training in Chennai in more practical way. Our team of DataScience with Python trainers offers DataScience with Python in Classroom training, DataScience with Python Online Training and DataScience with Python Corporate Training services. We framed our syllabus to match with the real world requirements for both beginner level to advanced level. Our training will be handled in either weekday or weekend’s program depends on participant’s requirement.

We are the best Training Institute offers certification oriented DataScience with Python Training in Chennai. Our participants will be eligible to clear all type of interviews at end of our sessions.

DataScience with Python Training Syllabus:


  • Interaction with Numpy
  • Index Tricks
  • Shape manipulation
  • Polynomials
  • Vectorizing functions
  • Type handling
  • Other useful functions
  • Special functions
  • Integration
  • Interpolation
  • 1-D interpolation


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

Spline interpolation

  • Multivariate data interpolation (griddata)
  • 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)
  • Using radial basis functions for smoothing/interpolation 1-d Example
  • Using radial basis functions for smoothing/interpolation 2-d Example

Fast Fourier transforms

  • Fourier Transforms
  • Type I DCT
  • Type II DCT
  • Type III DCT
  • DCT and IDCT

Discrete Sine Transforms

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

Basic Routines

  • Cache Destruction
  • Signal Processing
  • Linear Algebra
  • Finding determinant ( matrix )
  • Computing norms
  • Solving least squares problems and pseudo inverses
  • Decompositions

Sparse Eigenvalue Problems with ARPACK
Compressed Sparse Graph Routines
Spatial data structures and algorithms

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

Statistics Random Variables

  • Shifting and Scaling
  • Shape parameters
  • Freezing and Distribution
  • Fitting distributions
  • Building specific distributionS
  • Analysing one sample
  • Kernel Density estimation
  • Multidimensional image processing
  • File IO
  • Matlab
  • Weave

Course Duration:  2  to  3 Month, 2 hpd

Contact:  +91  9080334727


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