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Data Science with Python

We offer best Data Science with Python Training with most experienced professionals. Our Instructors are working in Data Science with Python and related technologies for more years in MNC’s. We aware of industry needs and we are offering Data Science with Python Training in Bangalore in more practical way. Our team of Data Science with Python trainers offers Data Science with Python in Classroom training, Data Science with Python Online Training and Data Science with Python Corporate Training services. We framed our syllabus to match with the real world requirements for both beginner level to advanced level.

Data Science with Python Training Syllabus

Course Syllabus


Basic functions

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

Special functions



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


1-D interpolation

Multivariate data interpolation (griddata)

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)

Using radial basis functions for smoothing/interpolation

  • 1-d Example
  • 2-d Example

Fourier Transforms

Fast Fourier transforms

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

Discrete Cosine Transforms

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

Discrete Sine Transforms

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

Cache Destruction

Signal Processing

Linear Algebra

Basic Routines

  • 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