Video description
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.
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.
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.
Table of contents
-
Chapter 1 : Introduction to the Course
- The Data Science Hype
- About Our Case Studies
- Why Data is the New Oil
- Defining Business Problems for Analytic Thinking and Data-Driven Decision Making
- 10 Data Science Projects Every Business Should Do!
- How Deep Learning is Changing Everything
- The Career Paths of a Data Scientist
- The Data Science Approach to Problems
- Chapter 2 : Set Up (Google Colab) and Download Code Files
- Chapter 3 : Introduction to Python
-
Chapter 4 : Pandas
- Introduction to Pandas
- Pandas 1 - Data Series
- Pandas 2A - DataFrames - Index, Slice, Stats, Finding Empty Cells
- Pandas 2B - DataFrames - Index, Slice, Stats, Finding Empty Cells, and Filtering
- Pandas 3A - Data Cleaning - Alter Columns/Rows, Missing Data, and String Operations
- Pandas 3B - Data Cleaning - Alter Columns/Rows, Missing Data, and String Operations
- Pandas 4 - Data Aggregation - GroupBy, Map, Pivot, Aggregate Functions
- Feature Engineer, Lambda, and Apply
- Concatenating, Merging, and Joining
- Time Series Data
- Advanced Operations - Iterows, Vectorization, and NumPy
- Advanced Operations - Map, Filter, Apply
- Advanced Operations - Parallel Processing
- Map Visualizations with Plotly - Cloropeths from Scratch - USA and World
- Map Visualizations with Plotly - Heatmaps, Scatter Plots, and Lines
-
Chapter 5 : Statistics and Visualizations
- Introduction to Statistics
- Descriptive Statistics - Why Statistical Knowledge is So Important
- Descriptive Statistics 1 - Exploratory Data Analysis (EDA) and Visualizations
- Descriptive Statistics 2 - Exploratory Data Analysis (EDA) and Visualizations
- Sampling, Averages, and Variance, and How to Lie and Mislead with Statistics
- Sampling - Sample Sizes and Confidence Intervals - What Can You Trust?
- Types of Variables - Quantitative and Qualitative
- Frequency Distributions
- Frequency Distributions Shapes
- Analyzing Frequency Distributions - What is the Best Type of Wine? Red or White?
- Mean, Mode, and Median - Not as Simple as You Think
- Variance, Standard Deviation, and Bessel's Correction
- Covariance and Correlation - Do Amazon and Google Know You Better Than Anyone Else?
- Lying with Correlations - Divorce Rates in Maine Caused by Margarine Consumption
- The Normal Distribution and the Central Limit Theorem
- Z-Scores
- Chapter 6 : Probability Theory
- Chapter 7 : Hypothesis Testing
- Chapter 8 : A/B Testing - A Worked Example
-
Chapter 9 : Data Dashboards - Google Data Studio
- Intro to Google Data Studio
- Opening Google Data Studio and Uploading Data
- Your First Dashboard Part 1
- Your First Dashboard Part 2
- Creating New Fields to Our data
- Pivot Tables - Total Profit
- Adding Filters to Tables
- Scorecard KPI Visualizations
- Scorecards with Time Comparison
- Bar Charts (Horizontal, Vertical, and Stacked)
- Line Charts
- Pie Charts, Donut Charts, and Tree Maps
- Time Series and Comparative Time Series Plots
- Scatter Plots
- Geographic Plots
- Bullet and Line Area Plots
- Sharing and Final Conclusions
- Our Executive Sales Dashboard
-
Chapter 10 : Machine Learning
- Introduction to Machine Learning
- How Machine Learning enables Computers to Learn
- What is a Machine Learning Model?
- Types of Machine Learning
- Linear Regression - Introduction to Cost Functions and Gradient Descent
- Linear Regressions in Python from Scratch and Using Sklearn
- Polynomial and Multivariate Linear Regression
- Logistic Regression
- Support Vector Machines (SVMs)
- Decision Trees and Random Forests, and the Gini Index
- K-Nearest Neighbors (KNN)
- Assessing Performance - Confusion Matrix, Precision, and Recall
- Understanding the ROC and AUC Curve
- What Makes a Good Model? Regularization, Overfitting, Generalization, and Outliers
- Introduction to Neural Networks
- Types of Deep Learning Algorithms CNNs, RNNs, and LSTMs
-
Chapter 11 : Deep Learning
- Neural Networks Chapter Overview
- Machine Learning Overview
- Neural Networks Explained
- Forward Propagation
- Activation Functions
- Training Part 1 - Loss Functions
- Training Part 2 - Backpropagation and Gradient Descent
- Backpropagation and Learning Rates - A Worked Example
- Regularization, Overfitting, Generalization, and Test Datasets
- Epochs, Iterations, and Batch Sizes
- Measuring Performance and the Confusion Matrix
- Review and Best Practices
-
Chapter 12 : Unsupervised Learning - Clustering
- Introduction to Unsupervised Learning
- K-Means Clustering
- Choosing K
- K-Means - Elbow and Silhouette Method
- Agglomerative Hierarchical Clustering
- Mean Shift Clustering
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- DBSCAN in Python
- Expectation-Maximization (EM) Clustering Using Gaussian Mixture Models (GMM)
- Chapter 13 : Dimensionality Reduction
- Chapter 14 : Recommendation Systems
- Chapter 15 : Natural Language Processing
- Chapter 16 : Big Data
- Chapter 17 : Predicting the US 2020 Election
- Chapter 18 : Predicting Diabetes Cases
- Chapter 19 : Market Basket Analysis
- Chapter 20 : Predicting the World Cup Winner (Soccer/Football)
- Chapter 21 : Covid-19 Data Analysis and Flourish Bar Chart Race Visualization
- Chapter 22 : Analyzing Olympic Winners
- Chapter 23 : Is Home Advantage Real in Soccer and Basketball
- Chapter 24 : IPL Cricket Data Analysis
- Chapter 25 : Streaming Services (Netflix, Hulu, Disney Plus, and Amazon Prime)
- Chapter 26 : Micro Brewery and Pub Data Analysis
- Chapter 27 : Pizza Restaurant Data Analysis
- Chapter 28 : Supply Chain Data Analysis
- Chapter 29 : Indian Election Result Analysis
- Chapter 30 : Africa Economic Crisis Data Analysis
- Chapter 31 : Predicting Which Employees May Quit
- Chapter 32 : Figuring Out Which Customers May Leave
- Chapter 33 : Who to Target for Donations?
- Chapter 34 : Predicting Insurance Premiums
- Chapter 35 : Predicting Airbnb Prices
- Chapter 36 : Detecting Credit Card Fraud
- Chapter 37 : Analyzing Conversion Rates in Marketing Campaigns
- Chapter 38 : Predicting Advertising Engagement
- Chapter 39 : Product Sales Analysis
- Chapter 40 : Determining Your Most Valuable Customers
- Chapter 41 : Customer Clustering (K-Means, Hierarchical) - Train Passenger
- Chapter 42 : Build a Product Recommendation System
- Chapter 43 : Deep Learning Recommendation System
- Chapter 44 : Predicting Brent Oil Prices
- Chapter 45 : Detecting Sentiment in Tweets
- Chapter 46 : Spam or Ham Detection
- Chapter 47 : Explore Data with PySpark and Titanic Survival Prediction
- Chapter 48 : Newspaper Headline Classification Using PySpark
- Chapter 49 : Deployment into Production
Product information
- Title: Data Science, Analytics, and AI for Business and the Real World™
- Author(s):
- Release date: March 2022
- Publisher(s): Packt Publishing
- ISBN: 9781803240848
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