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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
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
Last Updated 11/2020 |
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