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, Introduction, Basic functions

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

  • Special functions, Integration, Optimization

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

  • Interpolation, 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