Very good course! I liked it a lot. Thanks.
Practical Data Science Using Python
Apply Data Science using Python, Statistical Techniques, EDA, Numpy, Pandas, Scikit Learn, Statsmodel Libraries, etc.
Development ,Data Science,Python
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 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

Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction to Data Science
6 Lectures
-
Course Introduction 12:28 12:28
-
Data Science Introduction and Use Cases 19:34 19:34
-
Data Science Roles and Lifecycle 15:47 15:47
-
Data Science Stages and Technologies 11:20 11:20
-
Data Science Technologies and Analytics 18:30 18:30
-
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

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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