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

Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: aac, 44100 Hz
Language: English | VTT | Size: 12.8 GB | Duration: 29h 54m

What you’ll learn
Pandas to become a Data Analytics & Data Wrangling Whiz
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.
Requirements
No need to be a programming or math whiz, basic highschool math would be sufficient
All programming is taught in this course making it beginner friendly
Description
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 Science and 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

Who this course is for:
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

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