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Practical Data Science Using Python

person icon MANAS DASGUPTA


Practical Data Science Using Python

Apply Data Science using Python, Statistical Techniques, EDA, Numpy, Pandas, Scikit Learn, Statsmodel Libraries, etc.

updated on icon Updated on May, 2024

language icon Language - English

person icon MANAS DASGUPTA

English [CC]

category icon Development,Python,Data Science,Machine Learning

Lectures -120

Resources -2

Duration -30.5 hours



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Course Description

Practical Data Science Using Python course is your sure guide if you are aspiring to become a Data Scientist or Machine Learning Engineer.

This course covers Python for Data Science and Machine Learning in great detail and is absolutely essential for the beginner in Python.

Practical Data Science Using Python Course Overview

In this course, you will learn about core concepts of Data Science, Exploratory Data Analysis, Statistical Methods, the role of Data, Python Language, challenges of Bias, Variance, and Overfitting, choosing the right Performance Metrics, Model Evaluation Techniques, Model Optimization using Hyperparameter Tuning and Grid Search Cross Validation techniques, etc.

You will learn how to perform detailed Data Analysis using Python, Statistical Techniques, Exploratory Data Analysis, using various Predictive Modelling Techniques such as a range of Classification Algorithms, Regression Models, and Clustering Models. You will learn the scenarios and use cases of deploying Predictive models.

Most of this course is hands-on, through completely worked-out projects and examples taking you through the Exploratory Data Analysis, Model development, Model Optimization, and Model Evaluation techniques.

This course covers the use of Numpy and Pandas Libraries extensively for teaching Exploratory Data Analysis. In addition, it also covers Marplotlib and Seaborn Libraries for creating Visualizations.

There is also an introductory lesson included on Deep Neural Networks with a worked-out example of Image Classification using TensorFlow and Keras.

Course Sections:

  • Introduction to Data Science
  • Use Cases, Methodologies
  • Role of Data in Data Science
  • Statistical Methods
  • Exploratory Data Analysis
  • Understanding the process of Training or Learning
  • Understanding Validation and Testing
  • Python Language in Detail
  • Setting up your DS/ML Development Environment
  • Python internal Data Structures
  • Python Language Elements
  • Pandas Data Structure – Series and DataFrames
  • Exploratory Data Analysis (EDA)
  • Learning Linear Regression Model using the House Price Prediction Case Study
  • Learning Logistic Model using the Credit Card Fraud Detection case study
  • Evaluating your model performance
  • Fine-tuning your model
  • Hyperparameter Tuning
  • Cross Validation
  • Learning SVM through an Image Classification Project
  • Understanding Decision Trees
  • Understanding Ensemble techniques using Random Forest
  • Dimensionality Reduction using PCA
  • K-Means Clustering with Customer Segmentation Project
  • Introduction to Deep Learning

Who this course is for:

  • Aspiring Data Science Professionals
  • Aspiring Machine Learning Engineers


What will you learn in this course:

  • Data Science Core Concepts in Detail
  • Data Science Use Cases, Life Cycle and Methodologies
  • Exploratory Data Analysis (EDA)
  • Statistical Techniques
  • Detailed coverage of Python for Data Science and Machine Learning
  • Regression Algorithm - Linear Regression
  • Classification Problems and Classification Algorithms
  • Unsupervised Learning using K-Means Clustering
  • Dimensionality Reduction Techniques (PCA)
  • Feature Engineering Techniques
  • Model Optimization using Hyperparameter Tuning
  • Model Optimization using Grid-Search Cross Validation
  • Introduction to Deep Neural Networks


What are the prerequisites for this course?

  • Some exposure to Programming Languages will be useful
Practical Data Science Using Python


Check out the detailed breakdown of what’s inside the course

Introduction to Data Science
6 Lectures
  • play icon Course Introduction 12:28 12:28
  • play icon Data Science Introduction and Use Cases 19:34 19:34
  • play icon Data Science Roles and Lifecycle 15:47 15:47
  • play icon Data Science Stages and Technologies 11:20 11:20
  • play icon Data Science Technologies and Analytics 18:30 18:30
  • play icon ML-Data and CRISP-DM 15:13 15:13
Statistical Techniques
8 Lectures
Exploratory Data Analysis (EDA)
9 Lectures
Python for Data Science
27 Lectures
Machine Learning
12 Lectures
Linear Regression
13 Lectures
Logistic Regression
8 Lectures
Unspervised Learning - K-Means Clustering
5 Lectures
Naive Bayes Probability Model
4 Lectures
Decision Tree
6 Lectures
Random Forest
4 Lectures
Advanced Classification Techniques - Support Vector Machines
5 Lectures
Dimensionality Reduction - Principal Component Analysis
4 Lectures
Introduction to Deep Learning
1 Lectures
Time Series Analysis with ARIMA
7 Lectures
Codes and Data Files
1 Lectures

Instructor Details



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Orlando O. S.


Very good course! I liked it a lot. Thanks.

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