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Practical Data Science Using Python

person icon MANAS DASGUPTA

4.2

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 Jun, 2025

language icon Language - English

person icon MANAS DASGUPTA

English [CC]

category icon Development ,Data Science,Python

Lectures -120

Resources -2

Duration -30.5 hours

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4.2

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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 beginners in 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, with completely worked-out projects and examples taking you through 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 Matplotlib 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 and 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's 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

Goals

  • 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

Prerequisites

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

Curriculum

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
Tutorialspoint

Exploratory Data Analysis (EDA)

9 Lectures
Tutorialspoint

Python for Data Science

27 Lectures
Tutorialspoint

Machine Learning

12 Lectures
Tutorialspoint

Linear Regression

13 Lectures
Tutorialspoint

Logistic Regression

8 Lectures
Tutorialspoint

Unspervised Learning - K-Means Clustering

5 Lectures
Tutorialspoint

Naive Bayes Probability Model

4 Lectures
Tutorialspoint

Decision Tree

6 Lectures
Tutorialspoint

Random Forest

4 Lectures
Tutorialspoint

Advanced Classification Techniques - Support Vector Machines

5 Lectures
Tutorialspoint

Dimensionality Reduction - Principal Component Analysis

4 Lectures
Tutorialspoint

Introduction to Deep Learning

1 Lectures
Tutorialspoint

Time Series Analysis with ARIMA

7 Lectures
Tutorialspoint

Codes and Data Files

1 Lectures
Tutorialspoint

Instructor Details

MANAS DASGUPTA

MANAS DASGUPTA

Hi there, I am Manas Dasgupta, from Bangalore, the Silicon Valley of India.

By qualification, I hold a Master's Degree (MSc) in AI from the Liverpool John Moores University (LJMU), UK.

My expertise area encompass Generative AI - RAG Application Development using Frameworks like LangChain and LlamaIndex, Machine Learning and Data Science / Predictive Analytics areas including various Supervised, Unsupervised, Deep Neural Networks, Clustering Techniques, etc. 

My research areas in Masters were Natural Language Processing (NLP) using Deep Learning Methods such as Siamese Networks, Encoder-Decoder techniques, various Language Embedding methods such as BERT, areas such as Supervised Learning on Semantic Similarity and so on.

I have > 20 Years of experience in the IT Development mostly in the Financial Services domain, developing products and solutions. I am also the Founder of Teksands where me and my team develop Gen AI rich applications in the Talent space.

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Orlando O. S.

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

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