Practical Data Science Using Python

Practical Data Science Using Python

by Manas Dasgupta
Released August 2022
Publisher(s): Packt Publishing
ISBN: 9781804611814

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Video description

In this course, you will learn about core concepts of data science, exploratory data analysis, statistical methods, 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, and more.

You will learn how to perform detailed data analysis using Python, statistical techniques, and exploratory data analysis, using various predictive modeling 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.

This course also covers classification using decision trees, which include the Gini index and entropy measures and hyperparameter tuning. It covers the use of NumPy and Pandas libraries extensively for teaching exploratory data analysis. In addition, you will also explore advanced classification techniques and support vector machine predictions. There is also an introductory lesson included on Deep Neural Networks with a worked-out example on image classification using TensorFlow and Keras.

By the end of the course, you will learn some basic foundations of data science using Python.

What You Will Learn

  • Learn all about exploratory data analysis (EDA)
  • Explore various statistical techniques
  • Understand Dimensionality Reduction Techniques (PCA)
  • Learn about feature engineering techniques
  • Learn about data science use cases, life cycle and methodologies
  • Learn about Deep Neural Networks

Audience

This course is for Python, machine learning developers, data scientists, data analysts, and business analysts. This course will also be beneficial for aspiring data science professionals and machine learning engineers.

Exposure to programming languages will be useful.

About The Author

Manas Dasgupta: Manas Dasgupta holds a master’s degree (MSc) from the Liverpool John Moore’s University (LJMU), the UK in Artificial Intelligence and Machine Learning (AI/ML). My specialization and research areas are Natural Language Processing (NLP) using Deep Learning Methods such as Siamese Networks, Encoder-Decoder techniques, various Language Embedding methods such as BERT, and areas such as Supervised Learning on Semantic Similarity and so on.

His expertise area also encompasses an array of Machine Learning and Data Science / Predictive Analytics areas including various Supervised, Unsupervised, and Clustering methods.

He has almost 20 Years of experience in the IT Industry, mostly in the Financial Services domain. Starting as a Developer to being an Architect for several years to a leadership position. His key focus and passion are to increase technical breadth and innovation.

Table of contents

  1. Chapter 1 : Introduction to Data Science
    1. Data Science Introduction and Use Cases
    2. Data Science Roles and Lifecycle
    3. Data Science Stages and Technologies
    4. Data Science Technologies and Analytics
    5. ML-Data and CRISP-DM
  2. Chapter 2 : Statistical Techniques
    1. Statistics and Experiments
    2. Types of Data and Descriptive Statistics
    3. Random Variables and Normal Distribution
    4. Histograms and Normal Approximation
    5. Central Limit Theorem
    6. Probability Theory
    7. Binomial Theory - Expected Value and Standard Error
    8. Hypothesis Testing
  3. Chapter 3 : Python for Data Science
    1. Introduction to Python
    2. Starting with Python with Jupyter Notebook
    3. Python Variables and Conditions
    4. Python Iterations 1
    5. Python Iterations 2
    6. Python Lists
    7. Python Tuples
    8. Python Dictionaries 1
    9. Python Dictionaries 2
    10. Python Sets 1
    11. Python Sets 2
    12. NumPy Arrays 1
    13. NumPy Arrays 2
    14. NumPy Arrays 3
    15. Pandas Series 1
    16. Pandas Series 2
    17. Pandas Series 3
    18. Pandas Series 4
    19. Pandas DataFrame 1
    20. Pandas DataFrame 2
    21. Pandas DataFrame 3
    22. Pandas DataFrame 4
    23. Pandas DataFrame 5
    24. Pandas DataFrame 6
    25. Python User-Defined Functions
    26. Python Lambda Functions
    27. Python Lambda Functions and Date-Time Operations
    28. Python String Operations
  4. Chapter 4 : Exploratory Data Analysis (EDA)
    1. Introduction to EDA
    2. EDA Tools and Processes
    3. EDA Project - 1
    4. EDA Project - 2
    5. EDA Project - 3
    6. EDA Project - 4
    7. EDA Project - 5
    8. EDA Project - 6
    9. EDA Project - 7
  5. Chapter 5 : Machine Learning
    1. Introduction to Machine Learning
    2. Machine Learning Terminology
    3. History of Machine Learning
    4. Machine Learning Use Cases and Types
    5. Role of Data in Machine Learning
    6. Challenges in Machine Learning
    7. Machine Learning Lifecycle and Pipelines
    8. Regression Problems
    9. Regression Models and Performance Metrics
    10. Classification Problems and Performance Metrics
    11. Optimizing Classification Metrics
    12. Bias and Variance
  6. Chapter 6 : Linear Regression
    1. Linear Regression Introduction
    2. Linear Regression - Training and Cost Function
    3. Linear Regression - Cost Functions and Gradient Descent
    4. Linear Regression - Practical Approach
    5. Linear Regression - Feature Scaling and Cost Functions
    6. Linear Regression OLS Assumptions and Testing
    7. Linear Regression Car Price Prediction
    8. Linear Regression Data Preparation and Analysis 1
    9. Linear Regression Data Preparation and Analysis 2
    10. Linear Regression Data Preparation and Analysis 3
    11. Linear Regression Model Building
    12. Linear Regression Model Evaluation and Optimization
    13. Linear Regression Model Optimization
  7. Chapter 7 : Logistic Regression
    1. Logistic Regression Introduction
    2. Logistic Regression - Logit Model
    3. Logistic Regression - Telecom Churn Case Study
    4. Logistic Regression - Data Analysis and Feature Engineering
    5. Logistic Regression - Build the Logistic Model
    6. Logistic Regression - Model Evaluation - AUC-ROC
    7. Logistic Regression - Model Optimization
    8. Logistic Regression - Model Optimization 2
  8. Chapter 8 : Unsupervised Learning - K-Means Clustering
    1. Unsupervised Learning - K-Means Clustering
    2. K-Means Clustering Computation
    3. K-Means Clustering Optimization
    4. K-Means - Data Preparation and Modelling
    5. K-Means - Model Optimization
  9. Chapter 9 : Naive Bayes Probability Model
    1. Naive Bayes Probability Model - Introduction
    2. Naive Bayes Probability Computation
    3. Naive Bayes - Employee Attrition Case Study
    4. Naive Bayes - Model Building and Optimization
  10. Chapter 10 : Classification using decision trees
    1. Decision Tree - Model Concept
    2. Decision Tree - Learning Steps
    3. Decision Tree - Gini Index and Entropy Measures
    4. Decision Tree - Hyperparameter Tuning
    5. Decision Tree - Iris Dataset Case Study
    6. Decision Tree - Model Optimization using Grid Search Cross Validation
  11. Chapter 11 : Ensemble Methods – Random Forest
    1. Random Forest - Ensemble Techniques Bagging and Random Forest
    2. Random Forest Steps Pruning and Optimization
    3. Random Forest - Model Building and Hyperparameter Tuning using Grid Search CV
    4. Random Forest - Optimization Continued
  12. Chapter 12 : Advanced Classification Techniques – Support Vector Machine
    1. Support Vector Machine Concepts
    2. Support Vector Machine Metrics and Polynomial SVM
    3. Support Vector Machine Project 1
    4. Support Vector Machine Predictions
    5. Support Vector Machine - Classifying Polynomial Data
  13. Chapter 13 : Dimensionality Reduction Using PCA
    1. Principal Component Analysis - Concepts
    2. Principal Component Analysis - Computations 1
    3. Principal Component Analysis - Computations 2
    4. Principal Component Analysis Practical
  14. Chapter 14 : Introduction to Deep Learning
    1. Introduction to Deep Learning

Product information

  • Title: Practical Data Science Using Python
  • Author(s): Manas Dasgupta
  • Release date: August 2022
  • Publisher(s): Packt Publishing
  • ISBN: 9781804611814