Data Science, Analytics & AI for Business & the Real World™

Data Science, Analytics & AI for Business & the Real World™, available at $69.99, has an average rating of 4.56, with 248 lectures, based on 498 reviews, and has 4763 subscribers.

You will learn about Pandas to become a Data Analytics & Data Wrangling Whiz ensuring Data Quality The most useful Machine Learning Algorithms with Scikit-learn Statistics and Probability Hypothesis Testing & A/B Testing To create beautiful charts, graphs and Visualisations that tell a Story with Data Understand common business problems and how to apply Data Science in solving them Data Dashboards with Google Data Studio 36 Real World Business Problems and Case Studies Recommendation Engines – Collaborative Filtering, LiteFM and Deep Learning methods Natural Language Processing (NLP) using NLTK and Deep Learning Time Series Forecasting with Facebook's Prophet Data Science in Marketing (Ad engagemnt & Performance) Consumer Analytics and Clustering Social Media Sentiment Analysis Understand Deep Learning (Keras, Tensorflow) and how to use it in several real world case studies Deployment of Machine Learning Models in Production using Heroku and Flask (CI/CD) Perform Sports, Healthcare, Resturant and Economic Analaytics Big Data Analysis and Machine Learning with PySpark How to use Data Science in Retail (Market Basket Analysis, Sales Analytics and Demand forecasting) You'll be using pre-configured Jupyter Notebooks in Google Colab (no hassle or setup, extremely simple to get started) All code examples run in your web browser regardless if you're running Windows, macOS, Linux or Android. This course is ideal for individuals who are Beginners to Data Science or Business Analysts who wish to do more with their data or College graduates who lack real world experience or Business oriented persons (Management or MBAs) who'd like to use data to enhance their business or Software Developers or Engineers who'd like to start learning Data Science or Anyone looking to become more employable as a Data Scientist or Anyone with an interest in using Data to Solve Real World Problems It is particularly useful for Beginners to Data Science or Business Analysts who wish to do more with their data or College graduates who lack real world experience or Business oriented persons (Management or MBAs) who'd like to use data to enhance their business or Software Developers or Engineers who'd like to start learning Data Science or Anyone looking to become more employable as a Data Scientist or Anyone with an interest in using Data to Solve Real World Problems.

Enroll now: Data Science, Analytics & AI for Business & the Real World™

Summary

Title: Data Science, Analytics & AI for Business & the Real World™

Price: $69.99

Average Rating: 4.56

Number of Lectures: 248

Number of Published Lectures: 248

Number of Curriculum Items: 248

Number of Published Curriculum Objects: 248

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Pandas to become a Data Analytics & Data Wrangling Whiz ensuring Data Quality
The most useful Machine Learning Algorithms with Scikit-learn
Statistics and Probability
Hypothesis Testing & A/B Testing
To create beautiful charts, graphs and Visualisations that tell a Story with Data
Understand common business problems and how to apply Data Science in solving them
Data Dashboards with Google Data Studio
36 Real World Business Problems and Case Studies
Recommendation Engines – Collaborative Filtering, LiteFM and Deep Learning methods
Natural Language Processing (NLP) using NLTK and Deep Learning
Time Series Forecasting with Facebook's Prophet
Data Science in Marketing (Ad engagemnt & Performance)
Consumer Analytics and Clustering
Social Media Sentiment Analysis
Understand Deep Learning (Keras, Tensorflow) and how to use it in several real world case studies
Deployment of Machine Learning Models in Production using Heroku and Flask (CI/CD)
Perform Sports, Healthcare, Resturant and Economic Analaytics
Big Data Analysis and Machine Learning with PySpark
How to use Data Science in Retail (Market Basket Analysis, Sales Analytics and Demand forecasting)
You'll be using pre-configured Jupyter Notebooks in Google Colab (no hassle or setup, extremely simple to get started)
All code examples run in your web browser regardless if you're running Windows, macOS, Linux or Android.

Who Should Attend

Beginners to Data Science
Business Analysts who wish to do more with their data
College graduates who lack real world experience
Business oriented persons (Management or MBAs) who'd like to use data to enhance their business
Software Developers or Engineers who'd like to start learning Data Science
Anyone looking to become more employable as a Data Scientist
Anyone with an interest in using Data to Solve Real World Problems

Target Audiences

Beginners to Data Science
Business Analysts who wish to do more with their data
College graduates who lack real world experience
Business oriented persons (Management or MBAs) who'd like to use data to enhance their business
Software Developers or Engineers who'd like to start learning Data Science
Anyone looking to become more employable as a Data Scientist
Anyone with an interest in using Data to Solve Real World Problems

Data Science, Analytics & AI for Business & the Real World™ 2020

This is a practical course, the course I wish I had when I first started learning Data Science.

It focuses on understanding all the basic theory and programming skills required as a Data Scientist, but the best part is that it features 35+ Practical Case Studies covering so many common business problems faced by Data Scientists in the real world.

Right now, even in spite of the Covid-19 economic contraction, traditional businesses are hiring Data Scientists in droves!

And they expect new hires to have the ability to apply Data Science solutions to solve their problems. Data Scientists who can do this will prove to be one of the most valuable assets in business over the next few decades!

“Data Scientist has become the top job in the US for the last 4 years running!” according to Harvard Business Review & Glassdoor.

However, Data Science has a difficult learning curve – How does one even get started in this industry awash with mystique, confusion, impossible-looking mathematics, and code? Even if you get your feet wet, applying your newfound Data Science knowledge to a real-world problem is even more confusing.

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.

This course has a comprehensive syllabus that tackles all the major components of Data Science knowledge.

Our Complete 2020 Data Science Learning path includes:

Using Data Science to Solve Common Business Problems
The Modern Tools of a Data Scientist– Python, Pandas, Scikit-learn, NumPy, Keras, prophet, statsmod, scipy and more!
Statistics for Data Science in Detail– Sampling, Distributions, Normal Distribution, Descriptive Statistics, Correlation and Covariance, Probability Significance Testing, and Hypothesis Testing.
Visualization Theory for Data Scienceand Analytics using Seaborn, Matplotlib & Plotly (Manipulate Data and Create Information Captivating Visualizations and Plots).
Dashboard Design using Google Data Studio
Machine Learning Theory– Linear Regressions, Logistic Regressions, Decision Trees, Random Forests, KNN, SVMs, Model Assessment, Outlier Detection, ROC & AUC and Regularization
Deep Learning Theory and Tools – TensorFlow 2.0 and Keras (Neural Nets, CNNs, RNNs & LSTMs)
Solving problems using Predictive Modeling, Classification, and Deep Learning
Data Analysis and Statistical Case Studies – Solve and analyze real-world problems and datasets.
Data Science in Marketing – Modeling Engagement Rates and perform A/B Testing
Data Science in Retail– Customer Segmentation, Lifetime Value, and Customer/Product Analytics
Unsupervised Learning – K-Means Clustering, PCA, t-SNE, Agglomerative Hierarchical, Mean Shift, DBSCAN and E-M GMM Clustering
Recommendation Systems – Collaborative Filtering and Content-based filtering + Learn to use LiteFM + Deep Learning Recommendation Systems
Natural Language Processing –Bag of Words, Lemmatizing/Stemming, TF-IDF Vectorizer, and Word2Vec
Big Data with PySpark –Challenges in Big Data, Hadoop, MapReduce, Spark, PySpark, RDD, Transformations, Actions, Lineage Graphs & Jobs, Data Cleaning and Manipulation, Machine Learning in PySpark (MLLib)
Deployment to the Cloud using Heroku to build a Machine Learning API

Our fun and engaging Case Studies include:

Sixteen (16) Statistical and Data Analysis Case Studies:

Predicting the US 2020 Election using multiple Polling Datasets
Predicting Diabetes Cases from Health Data
Market Basket Analysis using the Apriori Algorithm
Predicting the Football/Soccer World Cup
Covid Analysis and Creating Amazing Flourish Visualisations (Barchart Race)
Analyzing Olympic Data
Is Home Advantage Real in Soccer or Basketball?
IPL Cricket Data Analysis
Streaming Services (Netflix, Hulu, Disney Plus and Amazon Prime) – Movie Analysis
Pizza Restaurant Analysis – Most Popular Pizzas across the US
Micro Brewery and Pub Analysis
Supply Chain Analysis
Indian Election Analysis
Africa Economic Crisis Analysis

Six (6) Predictive Modeling & Classifiers Case Studies:

Figuring Out Which Employees May Quit (Retention Analysis)
Figuring Out Which Customers May Leave (Churn Analysis)
Who do we target for Donations?
Predicting Insurance Premiums
Predicting Airbnb Prices
Detecting Credit Card Fraud

Four (4) Data Science in Marketing Case Studies:

Analyzing Conversion Rates of Marketing Campaigns
Predicting Engagement – What drives ad performance?
A/B Testing (Optimizing Ads)
Who are Your Best Customers? & Customer Lifetime Values (CLV)

Four (4) Retail Data Science Case Studies:

Product Analytics (Exploratory Data Analysis Techniques
Clustering Customer Data from Travel Agency
Product Recommendation Systems – Ecommerce Store Items
Movie Recommendation System using LiteFM

Two (2) Time-Series Forecasting Case Studies:

Sales Forecasting for a Store
Stock Trading using Re-Enforcement Learning
Brent Oil Price Forecasting

Three (3) Natural Langauge Processing (NLP) Case Studies:

Summarizing Reviews
Detecting Sentiment in text
Spam Detection

One (1) PySpark Big Data Case Studies:

News Headline Classification

One (1) Deployment Project:

Deploying your Machine Learning Model to the Cloud using Flask & Heroku

Course Curriculum

Chapter 1: Introduction

Lecture 1: The Data Science Hype

Lecture 2: About Our Case Studies

Lecture 3: Why Data is the new Oil

Lecture 4: Defining Business Problems for Analytic Thinking & Data Driven Decision making

Lecture 5: 10 Data Science Projects every Business should do!

Lecture 6: How Deep Learning is Changing Everything

Lecture 7: The Career paths of a Data Scientist

Lecture 8: The Data Science Approach to Problems

Chapter 2: Setup (Google Colab) & Download Code

Lecture 1: Downloading and Running Your Code

Lecture 2: Colab Setup

Chapter 3: Introduction to Python

Lecture 1: Why use Python for Data Science?

Lecture 2: Python Introduction – Part 1 – Variables

Lecture 3: Python – Variables (Lists and Dictionaries)

Lecture 4: More information on elif

Lecture 5: Python – Conditional Statements

Lecture 6: Python – Loops

Lecture 7: Python – Functions

Lecture 8: Python – Classes

Chapter 4: Pandas

Lecture 1: Pandas Introduction

Lecture 2: Pandas 1 – Data Series

Lecture 3: Pandas 2A – DataFrames – Index, Slice, Stats, Finding Empty cells

Lecture 4: Pandas 2B – DataFrames – Index, Slice, Stats, Finding Empty cells & Filtering

Lecture 5: Pandas 3A – Data Cleaning – Alter Colomns/Rows, Missing Data & String Operations

Lecture 6: Pandas 3B – Data Cleaning – Alter Colomns/Rows, Missing Data & String Operations

Lecture 7: Pandas 4 – Data Aggregation – GroupBy, Map, Pivot, Aggreate Functions

Lecture 8: Pandas 5 – Feature Engineer, Lambda and Apply

Lecture 9: Pandas 6 – Concatenating, Merging and Joinining

Lecture 10: Pandas 7 – Time Series Data

Lecture 11: Pandas 8 – ADVANCED Operations – Iterows, Vectorization and Numpy

Lecture 12: Pandas 9 – ADVANCED Operations – Map, Filter, Apply

Lecture 13: Pandas 10 – ADVANCED Operations – Parallel Processing

Lecture 14: Map Visualizations with Plotly – Cloropeths from Scratch – USA and World

Lecture 15: Map Visualizations with Plotly – Heatmaps, Scatter Plots and Lines

Chapter 5: Statistics & Visualizations

Lecture 1: Introduction to Statistics

Lecture 2: Descriptive Statistics – Why Statistical Knowledge is so Important

Lecture 3: Descriptive Statistics 1 – Exploratory Data Analysis (EDA) & Visualizations

Lecture 4: Descriptive Statistics 2 – Exploratory Data Analysis (EDA) & Visualizations

Lecture 5: Sampling, Averages & Variance And How to lie and Mislead with Statistics

Lecture 6: Sampling – Sample Sizes & Confidence Intervals – What Can You Trust?

Lecture 7: Types of Variables – Quantitive and Qualitative

Lecture 8: Frequency Distributions

Lecture 9: Frequency Distributions Shapes

Lecture 10: Analyzing Frequency Distributions – What is the Best Type of WIne? Red or White?

Lecture 11: Mean, Mode and Median – Not as Simple As You'd Think

Lecture 12: Variance, Standard Deviation and Bessel’s Correction

Lecture 13: Covariance & Correlation – Do Amazon & Google know you better than anyone else?

Lecture 14: Lying with Correlations – Divorce Rates in Maine caused by Margarine Consumption

Lecture 15: The Normal Distribution & the Central Limit Theorem

Lecture 16: Z-Scores

Chapter 6: Probability Theory

Lecture 1: Introduction to Probability

Lecture 2: Estimating Probability

Lecture 3: Addition Rule

Lecture 4: Bayes Theorem

Chapter 7: Hypothesis Testing

Lecture 1: Introduction to Hypothesis Testing

Lecture 2: Statistical Significance

Lecture 3: Hypothesis Testing – P Value

Lecture 4: Hypothesis Testing – Pearson Correlation

Chapter 8: A/B Testing – A Worked Example

Lecture 1: Understanding the Problem + Exploratory Data Analysis and Visualizations

Lecture 2: A/B Test Result Analysis

Lecture 3: A/B Testing a Worked Real Life Example – Designing an A/B Test

Lecture 4: Statistical Power and Significance

Lecture 5: Analysis of A/B Test Resutls

Chapter 9: Data Dashboards – Google Data Studio

Lecture 1: Intro to Google Data Studio

Lecture 2: Opening Google Data Studio and Uploading Data

Lecture 3: Your First Dashboard Part 1

Lecture 4: Your First Dashboard Part 2

Lecture 5: Creating New Fields

Lecture 6: Adding Filters to Tables

Lecture 7: Scorecard KPI Visalizations

Lecture 8: Scorecards with Time Comparison

Lecture 9: Bar Charts (Horizontal, Vertical & Stacked)

Lecture 10: Line Charts

Lecture 11: Pie Charts, Donut Charts and Tree Maps

Lecture 12: Time Series and Comparitive Time Series Plots

Lecture 13: Scatter Plots

Lecture 14: Geographic Plots

Lecture 15: Bullet and Line Area Plots

Lecture 16: Sharing and Final Conclusions

Lecture 17: Our Executive Sales Dashboard

Chapter 10: Machine Learning

Lecture 1: Introduction to Machine Learning

Lecture 2: How Machine Learning enables Computers to Learn

Lecture 3: What is a Machine Learning Model?

Lecture 4: Types of Machine Learning

Lecture 5: Linear Regression – Introduction to Cost Functions and Gradient Descent

Lecture 6: Linear Regressions in Python from Scratch and using Sklearn

Lecture 7: Polynomial and Multivariate Linear Regression

Lecture 8: Logistic Regression

Lecture 9: Support Vector Machines (SVMs)

Lecture 10: Decision Trees and Random Forests & the Gini Index

Lecture 11: K-Nearest Neighbors (KNN)

Instructors

Rajeev D. Ratan
Data Scientist, Computer Vision Expert & Electrical Engineer
Nidia Sahjara
NLP Engineer & Researcher

Rating Distribution

1 stars: 15 votes
2 stars: 8 votes
3 stars: 52 votes
4 stars: 172 votes
5 stars: 251 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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