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Data Science Training in Mumbai

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 skills and expertise in programming, databases or mathematics, you can step into this exciting area with highly transferable abilities. Even if you already use data on - the-job, you'll benefit from a formal certification in data science and the rigorous quality of our Data Science Graduate Program will ensure you gain the skills you need to differentiate yourself from your peer.

180 hours

Classroom Training



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.

  1. Basics of Python for Data Science

    Basic Concept
    Data Structures
    Control & Loop Statements
    Functions & Classes
    Working with Code & Data
    Opps Concept

  2. Data Frame Manipulation

    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

  3. Exploration of Data Analysis

    Visualization of the Data & EDA

  4. Time Series Forecasting

    Concept of time series & its visualization of components
    Exponential smoothing
    Holts model
    Holt-Winters model

  5. Unsupervised Learnings

    K- Clustering

  6. Dimensionality Reduction

    PCA - Principal Component Analysis
    Scree Plot
    One-Eigen Value Criterion
    Factor Analysis

  7. Introduction to Machine Learning

    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

  8. Supervised Learning

    Linear Regression
    Linear Regression with Stochastic Gradient Descent,Batch GD
    Optimizing Learning Rate

  9. Logistic Regression

    Logistic Regression with Stochastic Gradient Descent, Batch GD
    Optimizing Learning Rate

  10. K Nearest Neighbour

    Understanding KNN
    Voronoi Tessellation
    Choosing K
    Distance Metrics-Euclideam, Manhattan, Chebyshev

  11. Decision Tree & Random Forest

    Fundamental Concept of Ensemble

  12. Support Vector Machine

    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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