Description

This course focuses on understanding all the basic theory and programming skills required as a data scientist, featuring 35+ practical case studies covering common business problems faced by them. This course seeks to fill all those gaps in knowledge that scare off beginners and simultaneously apply your knowledge of data science and deep learning to real-world business problems.

Right now, despite the Covid-19 economic contraction, traditional businesses are hiring data scientists in droves! Therefore, data scientist has become the top job in the U.S. for the last four years running. However, data science has a difficult learning curve. This course seeks to fill all those gaps and has a comprehensive syllabus that tackles all the major components of data science knowledge. You will be using data science to solve common business problems throughout this course. You will start with the basics of Python, Pandas, Scikit-learn, NumPy, Keras, Prophet, statsmod, SciPy, and more. You will learn statistics and probability for data science in detail. Then, you will learn visualization theory for data science and analytics using Seaborn, Matplotlib, and Plotly. You will look at dashboard design using Google Data Studio along with machine learning and deep learning theory/tools. Then, you will be solving problems using predictive modeling, classification, and deep learning. After this, you will move your focus to data analysis and statistical case studies, data science in marketing, and data science in retail. Finally, you will see deployment to the cloud using Heroku to build a machine learning API. By the end of this course, you will learn all the major components of data science and gain the confidence to enter the world of data science. All the code files and the resource files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Data-Science-Analytics-AI-for-Business-the-Real-World-

What You Will Learn

Look at machine learning algorithms with Scikit-learn
Create beautiful charts, graphs, and visualizations that tell a story with data
Understand common business problems and how to apply data science
Create data dashboards with Google Data Studio
Learn to apply data science in marketing and retail
Integrate big data analysis and machine learning with PySpark

Audience

This course is designed for beginners in data science; business analysts who wish to do more with their data; college graduates who lack real-world experience; business-oriented persons who would like to use data to enhance their business; software developers or engineers who would like to start learning data science. Anyone looking to become more employable as a data scientist and with an interest in using data to solve real-world problems will enjoy this course thoroughly.

No need to be a programming or math whiz; basic high school math will be sufficient.

Approach

This course has a comprehensive syllabus that tackles all the major components of data science. The course content is over 30 hours long and it also comes bundled with real-life 35+ practical case studies.

This is a highly practical course, and all programming is taught from scratch, making it beginner-friendly.

Key Features

Explore 16 statistical and data analysis, and six predictive modeling and classifiers case studies * Work on four: data science in marketing and retail, and two time-series forecasting case studies * Dive into three Natural Language Processing and one PySpark big data case studies, and a deployment project

Github Repo

https://github.com/PacktPublishing/Data-Science-Analytics-AI-for-Business-the-Real-World-

About the Author

Rajeev Ratan

Rajeev Ratan is a data scientist with an MSc in artificial intelligence from the University of Edinburgh and a BSc in electrical and computer engineering from the University of West Indies. He has worked in several London tech start-ups as a data scientist, mostly in computer vision. He was a member of Entrepreneur First, a London-based start-up incubator, where he co-founded an EdTech start-up. Later on, he worked in AI tech start-ups involved in the real estate and gambling sectors. Before venturing into data science, Rajeev worked as a radio frequency engineer for eight years. His research interests lie in deep learning and computer vision. He has created several online courses that are hosted on many global online portals.

Course Outline

1. Introduction to the Course

1. The Data Science Hype

This video demonstrates what hype and excitement are going on and around data science.

2. About Our Case Studies

This video talks about the case studies covered in this course.

3. Why Data is the New Oil

This video explains why data is the new aged oil and how it's really going to be the next big thing.

4. Defining Business Problems for Analytic Thinking and Data-Driven Decision Making

This video helps in defining business problems for analytic thinking and data-driven decision making.

5. 10 Data Science Projects Every Business Should Do!

This video talks about 10 data science projects every business should do.

6. How Deep Learning is Changing Everything

This video demonstrates how deep learning is changing everything.

7. The Career Paths of a Data Scientist

This video talks about the career paths of a data scientist.

8. The Data Science Approach to Problems

This video explains what the data science approach to problems is.

2. Set Up (Google Colab) and Download Code Files

1. Downloading and Running Your Code

This video shows downloading and running your code.

3. Introduction to Python

1. Why Use Python for Data Science?

This video explains Python for data science and its advantages.

2. Python Introduction - Part 1 - Variables

This video provides an introduction to variables in Python.

3. Python - Variables (Lists and Dictionaries)

This video explains lists and dictionaries variables in Python.

4. Python - Conditional Statements

This video explains conditional statements in Python.

5. Python - Loops

This video explains loops in Python.

6. Python - Functions

This video explains the functions in Python.

7. Python - Classes

This video explains classes in Python.

4. Pandas

1. Introduction to Pandas

This video provides an introduction to Pandas.

2. Pandas 1 - Data Series

This video talks about data series in Pandas.

3. Pandas 2A - DataFrames - Index, Slice, Stats, Finding Empty Cells

This video explains index, slice, stats, and finding empty cells from DataFrames in Pandas.

4. Pandas 2B - DataFrames - Index, Slice, Stats, Finding Empty Cells, and Filtering

This video explains index, slice, stats, finding empty cells, and filtering from DataFrames in Pandas.

5. Pandas 3A - Data Cleaning - Alter Columns/Rows, Missing Data, and String Operations

This video explains about alter columns/rows, missing data, and string operations, which are all part of data cleaning.

6. Pandas 3B - Data Cleaning - Alter Columns/Rows, Missing Data, and String Operations

This video explains about alter columns/rows, missing data, and string operations, which are all part of data cleaning.

7. Pandas 4 - Data Aggregation - GroupBy, Map, Pivot, Aggregate Functions

This video explains about GroupBy, Map, Pivot, and Aggregate functions, which are part of data aggregation.

8. Feature Engineer, Lambda, and Apply

This video explains feature engineer, lambda, and apply features.

9. Concatenating, Merging, and Joining

This video explains concatenating, merging, and joining features using Pandas.

10. Time Series Data

This video focuses on time series data.

11. Advanced Operations - Iterows, Vectorization, and NumPy

This video explores Iterows, vectorization, and NumPy; these are part of advanced operations.

12. Advanced Operations - Map, Filter, Apply

This video explains Map, Filter, and Apply features that are part of advanced operations.

13. Advanced Operations - Parallel Processing

This video explains about parallel processing, a part of advanced operations.

14. Map Visualizations with Plotly - Cloropeths from Scratch - USA and World

This video explores map visualizations with plotly - Cloropeths from scratch - USA and world.

15. Map Visualizations with Plotly - Heatmaps, Scatter Plots, and Lines

This video explains map visualizations with plotly - heatmaps, scatter plots, and lines.

5. Statistics and Visualizations

1. Introduction to Statistics

This video provides an introduction to statistics.

2. Descriptive Statistics - Why Statistical Knowledge is So Important

This video talks about descriptive statistics and helps you understand why statistical knowledge is so important.

3. Descriptive Statistics 1 - Exploratory Data Analysis (EDA) and Visualizations

This is the first of the two-part video on descriptive statistics that talk about Exploratory Data Analysis (EDA) and visualizations.

4. Descriptive Statistics 2 - Exploratory Data Analysis (EDA) and Visualizations

This is second of the two-part video on descriptive statistics that talk about Exploratory Data Analysis (EDA) and visualizations.

5. Sampling, Averages, and Variance, and How to Lie and Mislead with Statistics

This video explains sampling, averages, and variance. You will also look at how to lie and mislead with statistics.

6. Sampling - Sample Sizes and Confidence Intervals - What Can You Trust?

This video talks about sampling - sample sizes and confidence intervals. You will also understand what you can trust and discard the rest.

7. Types of Variables - Quantitative and Qualitative

This video demonstrates the types of variables, quantitative and qualitative, in detail.

8. Frequency Distributions

This video explains the concept of frequency distributions.

9. Frequency Distributions Shapes

This video explains the types of shapes in frequency distributions.

10. Analyzing Frequency Distributions - What is the Best Type of Wine? Red or White?

This video helps in analyzing frequency distributions. You will learn what is the best type of wine - red or white.

11. Mean, Mode, and Median - Not as Simple as You Think

This video explains the not-so-easy concepts at the implementation stage in statistics-mean, mode, and median.

12. Variance, Standard Deviation, and Bessel's Correction

This video explains variance, standard deviation, and Bessel's Correction concepts in statistics.

13. Covariance and Correlation - Do Amazon and Google Know You Better Than Anyone Else?

This video ponders upon the question of whether Amazon and Google know you better than anyone else, and this will be done by understanding the concepts of covariance and correlation in detail.

14. Lying with Correlations - Divorce Rates in Maine Caused by Margarine Consumption

This video demonstrates the concept of correlations with the case study of divorce rates in Maine caused by margarine consumption.

15. The Normal Distribution and the Central Limit Theorem

This video focuses on normal distribution and the central limit theorem.

16. Z-Scores

This video explains and demonstrates how to find the z-scores.

6. Probability Theory

1. Introduction to Probability

This video provides an introduction to probability.

2. Estimating Probability

This video talks about estimating probability.

3. Probability - Addition Rule

This video explains the addition rule in probability.

4. Probability - Permutations and Combinations

This video explains permutations and combinations in probability.

5. Bayes Theorem

This video explains the Bayes Theorem.

7. Hypothesis Testing

1. Introduction to Hypothesis Testing

This video provides an introduction to hypothesis testing.

2. Statistical Significance

This video talks about statistical significance.

3. Hypothesis Testing - P Value

This video explains P Value in hypothesis testing.

4. Hypothesis Testing - Pearson Correlation

This video explains the Pearson correlation in hypothesis testing.

8. A/B Testing - A Worked Example

1. Understanding the Problem + Exploratory Data Analysis and Visualizations

This video helps you with understanding the problem + exploratory data analysis and visualizations.

2. A/B Test Result Analysis

This video demonstrates A/B test result analysis.

3. A/B Testing a Worked Real-Life Example - Designing an A/B Test

This video is a worked real-life example of A/B testing. You will also be designing an A/B test.

4. Statistical Power and Significance

This video explains about the statistical power and significance.

5. Analysis of A/B Test Results

This video focuses on analysis of A/B test results.

9. Data Dashboards - Google Data Studio

1. Intro to Google Data Studio

This video provides an introduction to Google Data Studio.

2. Opening Google Data Studio and Uploading Data

This video shows how to open Google Data Studio and upload the data.

3. Your First Dashboard Part 1

This is the first of the two-part video that helps you create your first dashboard.

4. Your First Dashboard Part 2

This is the second of the two-part video that helps you create your first dashboard.

5. Creating New Fields to Our data

This video helps in creating new fields to our data.

6. Pivot Tables - Total Profit

This video talks about Pivot Tables - total profit.

7. Adding Filters to Tables

This video demonstrates adding filters to tables.

8. Scorecard KPI Visualizations

This video demonstrates scorecard KPI visualizations.

9. Scorecards with Time Comparison

This video talks about the scorecards with time comparison.

10. Bar Charts (Horizontal, Vertical, and Stacked)

This video explains the bar charts (horizontal, vertical, and stacked).

11. Line Charts

This video explains about the line charts.

12. Pie Charts, Donut Charts, and Tree Maps

This video explains about the pie charts, donut charts, and tree maps.

13. Time Series and Comparative Time Series Plots

This video explains the time series and comparative time series plots.

14. Scatter Plots

This video demonstrates and explains scatter plots.

15. Geographic Plots

This video demonstrates and explains the geographic plots.

16. Bullet and Line Area Plots

This video helps you understand the bullet and line area plots.

17. Sharing and Final Conclusions

This video helps you with sharing and final conclusions.

18. Our Executive Sales Dashboard

This video lets you go through the executive sales dashboard.

10. Machine Learning

1. Introduction to Machine Learning

This video provides an introduction to machine learning.

2. How Machine Learning enables Computers to Learn

This video explains how machine learning enables computers to learn.

3. What is a Machine Learning Model?

This video explains the machine learning model in detail.

4. Types of Machine Learning

This video explains the types of machine learning.

5. Linear Regression - Introduction to Cost Functions and Gradient Descent

This video talks about linear regression - introduction to cost functions and gradient descent.

6. Linear Regressions in Python from Scratch and Using Sklearn

This video explains linear regressions in Python from scratch and using Sklearn.

7. Polynomial and Multivariate Linear Regression

This video explains the polynomial and multivariate linear regression.

8. Logistic Regression

This video explains the logistic regression.

9. Support Vector Machines (SVMs)

This video talks about the Support Vector Machines (SVMs).

10. Decision Trees and Random Forests, and the Gini Index

This video explains the decision trees and random forests. Apart from this, you will also learn about the Gini Index.

11. K-Nearest Neighbors (KNN)

This video explains the concept of K-Nearest Neighbors (KNN).

12. Assessing Performance - Confusion Matrix, Precision, and Recall

This video focuses on assessing performance - confusion matrix, precision, and recall.

13. Understanding the ROC and AUC Curve

This video helps you with understanding the ROC and AUC Curve in detail.

14. What Makes a Good Model? Regularization, Overfitting, Generalization, and Outliers

This video explains what makes a good model-regularization, overfitting, generalization, and outliers.

15. Introduction to Neural Networks

This video provides an introduction to neural networks.

16. Types of Deep Learning Algorithms CNNs, RNNs, and LSTMs

This video talks about types of deep learning algorithms CNNs, RNNs and LSTMs.

11. Deep Learning

1. Neural Networks Chapter Overview

This video provides an overview of neural networks and its importance.

2. Machine Learning Overview

This video provides an overview of machine learning.

3. Neural Networks Explained

This video explains neural networks in detail.

4. Forward Propagation

This video talks about forward propagation.

5. Activation Functions

This video explains about the activation functions.

6. Training Part 1 - Loss Functions

This is the first of the two-part training video on loss functions.

7. Training Part 2 - Backpropagation and Gradient Descent

This is the first of the two-part training video on backpropagation and gradient descent.

8. Backpropagation and Learning Rates - A Worked Example

This video is a working example on backpropagation and learning rates.

9. Regularization, Overfitting, Generalization, and Test Datasets

This video explains the regularization, overfitting, generalization, and test datasets.

10. Epochs, Iterations, and Batch Sizes

This video explains the epochs, iterations, and batch sizes in detail.

11. Measuring Performance and the Confusion Matrix

This video helps in measuring performance and the confusion matrix.

12. Review and Best Practices

This video focuses on the review and best practices.

12. Unsupervised Learning - Clustering

1. Introduction to Unsupervised Learning

This video provides an introduction to unsupervised learning.

2. K-Means Clustering

This video talks about K-Means Clustering.

3. Choosing K

This video explains how to choose the value of K.

4. K-Means - Elbow and Silhouette Method

This video explains the K-Means - Elbow and Silhouette method.

5. Agglomerative Hierarchical Clustering

This video talks about agglomerative hierarchical clustering.

6. Mean Shift Clustering

This video explains mean shift clustering.

7. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

This video talks about DBSCAN (Density-Based Spatial Clustering of Applications with Noise).

8. DBSCAN in Python

This video focuses on DBSCAN in Python.

9. Expectation-Maximization (EM) Clustering Using Gaussian Mixture Models (GMM)

This video focuses on the Expectation-Maximization (EM) clustering using Gaussian Mixture Models (GMM).

13. Dimensionality Reduction

1. Principal Component Analysis

This video explains about Principal Component Analysis.

2. t-Distributed Stochastic Neighbor Embedding (t-SNE)

This video explains about t-Distributed Stochastic Neighbor Embedding (t-SNE).

3. PCA and t-SNE in Python with Visualization Comparisons

This video focuses on PCA and t-SNE in Python with visualization comparisons.

14. Recommendation Systems

1. Introduction to Recommendation Engines

This video provides an introduction to recommendation engines.

2. Before Recommending, How Do We Rate or Review Items?

This video explains how to rate or review items before recommending.

3. User Collaborative Filtering and Item/Content-Based Filtering

This video talks about user collaborative filtering and item/content-based filtering.

4. The Netflix Prize and Matrix Factorization and Deep Learning as Latent-Factor Me

This video talks about the Netflix Prize and Matrix Factorization and deep learning as Latent-Factor Me.

15. Natural Language Processing

1. Introduction to Natural Language Processing

This video provides an introduction to Natural Language Processing.

2. Modeling Language - The Bag of Words Model

This video explains about modeling language - the Bag of Words model.

3. Normalization, Stop Word Removal, Lemmatizing/Stemming

This video explains about normalization, stop word removal, lemmatizing/stemming.

4. TF-IDF Vectorizer (Term Frequency - Inverse Document Frequency)

This video explains about TF-IDF Vectorizer (term frequency - inverse document frequency).

5. Word2Vec - Efficient Estimation of Word Representations in Vector Space

This video talks about Word2Vec - efficient estimation of word representations in vector space.

16. Big Data

1. Introduction to Big Data

This video provides an introduction to Big Data.

2. Challenges in Big Data

This video talks about the challenges in Big Data.

3. Hadoop, MapReduce, and Spark

This video focuses on Hadoop, MapReduce, and Spark.

4. Introduction to PySpark

This video provides an introduction to PySpark.

5. RDDs, Transformations, Actions, Lineage Graphs, and Jobs

This video explains about RDDs, transformations, actions, lineage graphs, and jobs.

17. Predicting the US 2020 Election

1. Understanding Polling Data

This video helps in understanding polling data.

2. Cleaning and Exploring Our Dataset

This video demonstrates cleaning and exploring our dataset.

3. Data Wrangling Our Dataset

This video explains data wrangling our dataset.

4. Understanding the US Electoral System

This video helps in understanding the US electoral system.

5. Visualizing Our Polling Data

This video helps in visualizing our polling data.

6. Statistical Analysis of Polling Data

This video demonstrates statistical analysis of polling data.

7. Polling Simulations

This video demonstrates polling simulations.

8. Polling Simulation Result Analysis

This video shows polling simulation result analysis.

9. Visualizing Our results on a US Map

This video helps in visualizing our results on a US map.

18. Predicting Diabetes Cases

1. Understanding and Preparing Our Healthcare Data

This video helps in understanding and preparing our healthcare data.

2. First Attempt - Trying a Naive Model

This video is the first attempt on trying a Naive model.

3. Trying Different Models and Comparing the Results

This video helps in trying different models and comparing the results.

19. Market Basket Analysis

1. Understanding Our Dataset

This video helps in understanding our dataset.

2. Data Preparation

This video shows data preparation.

3. Visualizing Our Frequent Sets

This video helps in visualizing our frequent sets.

20. Predicting the World Cup Winner (Soccer/Football)

1. Understanding and Preparing Our Soccer Datasets - Part 1

This is the first of the two-part video that helps in Understanding and Preparing Our Soccer Datasets.

2. Understanding and Preparing Our Soccer Datasets - Part 2

This is the second of the two-part video that helps in understanding and preparing our soccer datasets.

3. Predicting Game Outcomes with Our Model

This video demonstrates predicting game outcomes with our model.

4. Simulating the World Cup Outcome with Our Model

This video demonstrates simulating the World Cup outcome with our model.

21. Covid-19 Data Analysis and Flourish Bar Chart Race Visualization

1. Understanding Our Covid-19 Data

This video helps in understanding our Covid-19 data.

2. Analysis of the Most Recent Data

This video talks about the analysis of the most recent data.

3. World Visualizations

This video focuses on world visualizations.

4. Analyzing Confirmed Cases in Each Country

This video helps in analyzing confirmed cases in each country.

5. Mapping Covid-19 Cases

This video demonstrates mapping Covid-19 cases.

6. Animating Our Maps

This video explains animating our maps.

7. Comparing Countries and Continents

This video demonstrates comparing countries and continents on the Covid crisis.

8. Flourish Bar Chart Race - 1

This is the first of the two-part video that helps in flourish bar chart race.

9. Flourish Bar Chart Race - 2

This is second of the two-part video that helps in flourish bar chart race.

22. Analyzing Olympic Winners

1. Understanding Our Olympic Dataset

This video helps in understanding our Olympic dataset.

2. Getting the Medals Per Country

This video shows the data where you can see medals won per country.

3. Analyzing the Winter Olympic Data and Viewing Medals Won Over Time

This video helps in analyzing the Winter Olympic data and viewing medals won over time.

23. Is Home Advantage Real in Soccer and Basketball

1. Understanding Our Dataset and EDA

This video helps in understanding our dataset and EDA.

2. Goal Difference Ratios Home Versus Away

This video demonstrates the goal difference ratios at home versus away.

3. How Home Advantage Have Evolved Over Time

In this video, you will see how home advantages have evolved over time.

24. IPL Cricket Data Analysis

1. Loading and Understanding Our Cricket Dataset

This video works on loading and understanding our cricket dataset.

2. Man of the Match and Stadium Analysis

This video focuses on the man of the match and stadium analysis.

3. Do Toss Winners Win More? And Team Versus Team Comparisons

This video reveals the answers to these questions: do toss winners win more, and team versus team comparisons.

25. Streaming Services (Netflix, Hulu, Disney Plus, and Amazon Prime)

1. Understanding Our Dataset

This video helps in understanding our dataset.

2. EDA and Visualizations

This video explains about EDA and visualizations.

3. Best Movies Per Genre Platform Comparisons

This video demonstrates the best movies per genre platform comparisons.

26. Micro Brewery and Pub Data Analysis

1. EDA, Visualizations, and Map

This video talks about EDA, visualizations, and map.

27. Pizza Restaurant Data Analysis

1. EDA and Visualizations

This video talks about EDA and visualizations.

2. Analysis Per State

This video explains analysis per state.

3. Pizza Maps

This video explains pizza maps.

28. Supply Chain Data Analysis

1. Understanding Our Dataset

This video helps in understanding our dataset.

2. Visualizations and EDA

This video focuses on visualizations and EDA.

3. More Visualizations

This video demonstrates more visualizations.

29. Indian Election Result Analysis

1. Introduction

This video provides an introduction to this section.

2. Visualizations of Election Results

This video demonstrates visualizations of election results.

3. Visualizing Gender Turnout

This video explains visualizing gender turnout.

30. Africa Economic Crisis Data Analysis

1. Economic Dataset Understanding

This video explains economic dataset in detail.

2. Visualizations and Correlations

This video helps in visualizations and correlations.

31. Predicting Which Employees May Quit

1. Figuring Out Which Employees May Quit - Understanding the Problem and EDA

This video helps in figuring out which employees may quit - understanding the problem and EDA.

2. Data Cleaning and Preparation

This video helps with data cleaning and preparation.

3. Machine Learning Modeling + Deep Learning

This video explains machine learning modeling and deep learning in detail.

32. Figuring Out Which Customers May Leave

1. Understanding the Problem

This video helps in understanding the problem.

2. Exploratory Data Analysis and Visualizations

This video focuses on exploratory data analysis and visualizations.

3. Data Pre-Processing

This video demonstrates about data pre-processing.

4. Machine Learning Modeling + Deep Learning

This video focuses on machine learning modeling and deep learning.

33. Who to Target for Donations?

1. Understanding the Problem

This video helps in understanding the problem.

2. Exploratory Data Analysis and Visualizations

This video explains exploratory data analysis and visualizations.

3. Preparing Our Dataset for Machine Learning

This video helps in preparing our dataset for machine learning.

4. Modeling Using Grid Search to Find the best parameters

This video explains modeling using grid search to find the best parameters.

34. Predicting Insurance Premiums

1. Understanding the Problem + Exploratory Data Analysis and Visualizations

This video helps in understanding the problem along with exploratory data analysis and visualizations.

2. Data Preparation and Machine Learning Modeling

This video demonstrates data preparation and machine learning modeling.

35. Predicting Airbnb Prices

1. Understanding the Problem + Exploratory Data Analysis and Visualizations

This video helps in understanding the problem and exploratory data analysis and visualizations.

2. Machine Learning Modeling

This video demonstrates machine learning modeling.

3. Using Our Model for Value Estimation for New Clients

This video explains how to use your model for value estimation for new clients.

36. Detecting Credit Card Fraud

1. Understanding Our Dataset

This video helps in understanding our dataset.

2. Exploratory Analysis

This video focuses on exploratory analysis.

3. Feature Extraction

This video explains how to do feature extraction.

4. Creating and Validating Our Model

This video explains creating and validating our model.

37. Analyzing Conversion Rates in Marketing Campaigns

1. Exploratory Analysis of Understanding Marketing Conversion Rates

This video is about exploratory analysis of understanding marketing conversion rates.

38. Predicting Advertising Engagement

1. Understanding the Problem + Exploratory Data Analysis and Visualizations

This video helps in understanding the problem along with exploratory data analysis and visualizations.

2. Data Preparation and Machine Learning Modeling

This video demonstrates data preparation and machine learning modeling.

39. Product Sales Analysis

1. Problem and Plan of Attack

This video talks about problems and plans of attack.

2. Sales and Revenue Analysis

This video demonstrates sales and revenue analysis.

3. Analysis Per Country, Repeat Customers, and Items

This video focuses on analysis per country, repeat customers and items.

40. Determining Your Most Valuable Customers

1. Understanding the Problem + Exploratory Data Analysis and Visualizations

This video helps in understanding the problem along with exploratory data analysis and visualizations.

2. Customer Lifetime Value Modeling

This video explains customer lifetime value modeling.

41. Customer Clustering (K-Means, Hierarchical) - Train Passenger

1. Data Exploration and Description

This video is about data exploration and description.

2. Simple Exploratory Data Analysis and Visualizations

This video demonstrates simple exploratory data analysis and visualizations.

3. Feature Engineering

This video talks about feature engineering.

4. K-Means Clustering of Customer Data

This video demonstrates K-Means clustering of customer data.

5. Cluster Analysis

This video explains cluster analysis.

42. Build a Product Recommendation System

1. Dataset Description and Data Cleaning

This video helps with dataset description and data cleaning.

2. Making a Customer-Item Matrix

This video helps in making a customer-item matrix.

3. User-User Matrix - Getting Recommended Items

This video demonstrates user-user matrix - getting recommended items.

4. Item-Item Collaborative Filtering - Finding the Most Similar Items

This video helps in item-item collaborative filtering and finding the most similar items.

43. Deep Learning Recommendation System

1. Understanding Our Wikipedia Movie Dataset

This video helps in understanding our Wikipedia movie dataset.

2. Creating Our Dataset

This video is about creating our dataset.

3. Deep Learning Embeddings and Training

This video explains deep learning embeddings and training.

4. Getting Recommendations Based on Movie Similarity

This video helps in getting recommendations based on movie similarity.

44. Predicting Brent Oil Prices

1. Understanding Our Dataset and Its Time Series Nature

This video helps in understanding our dataset and its time series nature.

2. Creating Our Prediction Model

This video is about creating our prediction model.

3. Making Future Predictions

This video helps in making future predictions.

45. Detecting Sentiment in Tweets

1. Understanding Our Dataset and Word Clouds

This video helps in understanding our dataset and word clouds.

2. Visualizations and Feature Extraction

This video is about visualizations and feature extraction.

3. Training Our Model

This video is about training our model.

46. Spam or Ham Detection

1. Loading and Understanding Our Spam/Ham Dataset

This video is about loading and understanding our Spam/Ham dataset.

2. Training Our Spam Detector

This video demonstrates training our spam detector.

47. Explore Data with PySpark and Titanic Survival Prediction

1. Exploratory Analysis of Our Titanic Dataset

This video talks about exploratory analysis of our Titanic dataset.

2. Transformation Operations

This video focuses on transformation operations.

3. Machine Learning with PySpark

This video talks about machine learning with PySpark.

48. Newspaper Headline Classification Using PySpark

1. Loading and Understanding Our Dataset

This video helps in loading and understanding our dataset.

2. Building Our Model with PySpark

This video is about building our model with PySpark.

49. Deployment into Production

1. Introduction to Production Deployment Systems

This video provides an introduction to production deployment systems.

2. Creating the Model

This video helps in creating the model.

3. Introduction to Flask

This video provides an introduction to Flask.

4. About Our WebApp

This video explains about your WebApp.

5. Deploying Our WebApp on Heroku

This video helps in deploying our WebApp on Heroku.

Course Images

By Packt

Booking options

Highlights

Description

What You Will Learn

Audience

Approach

Key Features

Github Repo

About the Author

Rajeev Ratan

Course Outline

1. Introduction to the Course

2. Set Up (Google Colab) and Download Code Files

3. Introduction to Python

4. Pandas

5. Statistics and Visualizations

6. Probability Theory

7. Hypothesis Testing

8. A/B Testing - A Worked Example

9. Data Dashboards - Google Data Studio

10. Machine Learning

11. Deep Learning

12. Unsupervised Learning - Clustering

13. Dimensionality Reduction

14. Recommendation Systems

15. Natural Language Processing

16. Big Data

17. Predicting the US 2020 Election

18. Predicting Diabetes Cases

19. Market Basket Analysis

20. Predicting the World Cup Winner (Soccer/Football)

21. Covid-19 Data Analysis and Flourish Bar Chart Race Visualization

22. Analyzing Olympic Winners

23. Is Home Advantage Real in Soccer and Basketball

24. IPL Cricket Data Analysis

25. Streaming Services (Netflix, Hulu, Disney Plus, and Amazon Prime)

26. Micro Brewery and Pub Data Analysis

27. Pizza Restaurant Data Analysis

28. Supply Chain Data Analysis

29. Indian Election Result Analysis

30. Africa Economic Crisis Data Analysis

31. Predicting Which Employees May Quit

32. Figuring Out Which Customers May Leave

33. Who to Target for Donations?

34. Predicting Insurance Premiums

35. Predicting Airbnb Prices

36. Detecting Credit Card Fraud

37. Analyzing Conversion Rates in Marketing Campaigns

38. Predicting Advertising Engagement

39. Product Sales Analysis

40. Determining Your Most Valuable Customers

41. Customer Clustering (K-Means, Hierarchical) - Train Passenger

42. Build a Product Recommendation System

43. Deep Learning Recommendation System

44. Predicting Brent Oil Prices

45. Detecting Sentiment in Tweets

46. Spam or Ham Detection

47. Explore Data with PySpark and Titanic Survival Prediction

48. Newspaper Headline Classification Using PySpark

49. Deployment into Production

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