Data Science is a highly in demand but there is currently a shortage of trained, professional data scientists. Research estimates there will be more than 2.7 million new jobs in data science in the next few years and the need for data education is becoming increasingly apparent.If you already have s...
During the course you will learn
Data acquisition, cleaning and aggregation
Exploratory data analysis and visualisation
Feature engineering
Model creation and validation
Basic statistical and mathematical foundations for data science
The Outcome of the Course
A understanding of data science solvable problems, and the capacity to approach them from a mathematical perspective.
An understanding of when to use supervised and unattended
methods of statistical learning on labeled and unlabeled data-rich problems
The ability to build pipelines and applications for analytical data in Python.
Familiarity with the environment of Python's data science and the various resources required to continue evolving as a data scientist.
Basic Concept
Data Structures
Control & Loop Statements
Functions & Classes
Working with Code & Data
Opps Concept
Data collection (Import & Export)
Sorting & Description selection and fitting
Concise statistics
Combination and combining/merging of data frames
Deleting duplicates
Discretization and binning
Manipulation of strings
Indexation
Visualization of the Data & EDA
Concept of time series & its visualization of components
Exponential smoothing
Holts model
Holt-Winters model
ARIMA
ARCH & GARCH
K- Clustering
PCA - Principal Component Analysis
Scree Plot
One-Eigen Value Criterion
Factor Analysis
Machine Learning Modelling Flow
How to treat Data in ML
Parametric & Non-Parametric ML Algorithm
Types of Machine Learning
Bias-Variance Trade-off
Overfitting & Underfitting
Optimization Techniques
Scikit-Learn Library
Linear Regression
Linear Regression with Stochastic Gradient Descent,Batch GD
Optimizing Learning Rate
Momentum
Logistic Regression with Stochastic Gradient Descent, Batch GD
Optimizing Learning Rate
Momentum
Understanding KNN
Voronoi Tessellation
Choosing K
Distance Metrics-Euclideam, Manhattan, Chebyshev
Fundamental Concept of Ensemble
Hyper-Parameters
What is SVM?
When do you use SVM?
Understanding Hyperplane
What is Support Vector?
Understanding Langragian Multiplier, Karush Kuhn Tucker Conditions
SVM Kernels-Radial Basis Function, Gaussian Kernel, Linear Kernel
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