Data Science, Analytics & AI for Business & the Real World™
What you'll 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.
- 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
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:
Detecting Sentiment in text
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
Hi I'm Rajeev, a Data Scientist, and Computer Vision Engineer.
I have a BSc in Computer & Electrical Engineering and an MSc in Artificial Intelligence from the University of Edinburgh where I gained extensive knowledge of machine learning, computer vision, and intelligent robotics.
I have published research on using data-driven methods for Probabilistic Stochastic Modeling for Public Transport and even was part of a group that won a robotics competition at the University of Edinburgh.
I launched my own computer vision startup that was based on using deep learning in education since then I've been contributing to 2 more startups in computer vision domains and one multinational company in Data Science.
Previously, I worked for 8 years at two of the Caribbean’s largest telecommunication operators where he gained experience in managing technical staff and deploying complex telecommunications projects.
Nidia's specialities lie in war & conflict, data science and intelligence. She is a King's College Graduate and has a diverse background as her undergraduate studies include Computer Science and Civil & Environmental Engineering. She continued her postgraduate in Intelligence & Security. Her current research involves using NLP to analyse open-source data and opinion mining solutions. She is also a member of SHOC - the Strategic Hub for Organised Crime Research, as part of RUSI.