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Program Overview

What you'll learn


Python is an interpreted, high-level, general-purpose programming language


Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series.


NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

Jupyter Notebook

Project Jupyter is a nonprofit organization created to "develop open-source software, open-standards, and services for interactive computing across dozens of programming languages"

Scikit learn

Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy


Best-in-class content by leading faculty and industry leaders in the form of videos, cases and projects

Introduction to Python
  • Concepts of Python Programming
  • Configuration of Development Environment
  • Using the Python Interpreter
  • Numbers and Strings
More on Python
  • Tuples and Lists
  • Functions
  • Control Flow and Loops
  • Dictionaries
Data Science Fundamentals
  • Introduction to Data Science
  • Real world Use-Cases of Data Science
  • Walkthrough of Data Types
  • Data Science Project Lifecycle
Introduction to NumPy
  • Basics of NumPy Arrays
  • Mathematical operations in NumPy
  • NumPy Array Manipulation
  • NumPy Array Broadcasting
Data Manipulation with Pandas
  • Data Structures in Pandas-Series and Data Frames
  • Data Cleaning in Pandas
  • Data Manipulation in Pandas
  • Handling Missing Values in Dataset
Data Visualization in Python
  • Plotting Basic Charts in Python
  • Data Visualization with Matplotlib
  • Statistical Data Visualization with Seaborn
  • Coding Sessions using Matplotlib, Seaborn
Exploratory Data Analysis
  • Introduction to Exploratory Data Analysis (EDA)
  • Plots to Explore Relationship between Two Variables
  • Histograms, Box Plots to Explore Variable
  • Heat maps, Pair Plots to Explore Correlations
  • Perform EDA to Explore Survival using Titanic Dataset
Introduction to Machine Learning
  • What is Machine Learning?
  • Use Cases of Machine Learning
  • Types of Machine Learning
  • Machine Learning Workflow
Linear Regression
  • Introduction to Linear Regression
  • Use Cases of Linear Regression
  • How to fit a Linear Regression Model?
  • Evaluating and Interpreting Results from Linear Regression models
  • Predict Bike sharing Demand
Logistic Regression
  • Introduction to Logistic Regression
  • Logistic Regression Use Cases
  • Understand use of odds & Logit function
  • Predicting Credit Card Default Cases
Decision Trees & Random Forest
  • Introduction to Decision Trees & Random Forest
  • Using Ensemble Methods in Decision Trees
  • Applications of Random Forest
  • Predict passenger Survival Using Titanic Dataset
Model Evaluation Techniques
  • Metrics and Model Selection in Machine
  • Matrix for Predictions
  • Measures of Model Evaluation-Sensitivity
  • Use AUC-ROC Curve to Decide Best Model
  • Applying Model Evaluation Techniques
Dimensionality Reduction using PCA
  • Introduction to Curse of Dimensionality
  • What is Dimensionality Reduction?
  • Applications of Principle Component Analysis
  • Optimize Model Using PCA
K-Nearest Neighbours
  • Introduction to KNN
  • Calculate Neighbours Using Distance Measures
  • Find Optimal Value of K in KNN Method
  • Advantage & Disadvantages of KNN
  • Classify Phishing Site Data Using Close Neighbour
Naive Bayes Classifier
  • Introduction to Naive Bayes Classification
  • Refresher on Probability Theory
  • Applications of Naive Bayes Algorithm
  • Classify Spam Emails based on Probability
K-Means Clustering
  • Introduction to K-means Clustering
  • Decide Clusters by Adjusting Centroids
  • Find Optimal 'K value' in K-means
  • Understand Applications of Clustering
  • Segment Hands in Pokerdata and Segment
Support Vector Machines
  • Introduction to SVM
  • Figure Decision Boundaries Using Support Vectors
  • Identify Hyperplane in SVM
  • Applications of SVM in Machine Learning
  • Predicting wine quality using SVM


Executive Program in Data Science Technology Certified By Microsoft


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Frequently Asked Questions

Data science is a broad field that refers to the collective processes, theories, concepts, tools and technologies that enable the review, analysis and extraction of valuable knowledge and information from raw data. It is geared toward helping individuals and organizations make better decisions from stored, consumed and managed data. Data science is formerly known as datalogy.
Supervised Machine learning: Supervised learning is the learning of the model where with input variable ( say, x) and an output variable (say, Y) and an algorithm to map the input to the output. That is, Y = f(X). Unsupervised Machine learning: Unsupervised learning is where only the input data (say, X) is present and no corresponding output variable is there.
When we remove sub-nodes of a decision node, this process is called pruning or opposite process of splitting.
Random forest is a versatile machine learning method capable of performing both regression and classification tasks. It is also used for dimentionality reduction, treats missing values, outlier values. It is a type of ensemble learning method, where a group of weak models combine to form a powerful model.
Deep learning is a machine learning technique. It teaches a computer to filter inputs through layers to learn how to predict and classify information. Observations can be in the form of images, text, or sound.
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