## 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 MNCâ€™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