Data Science & Machine Learning Fundamentals
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Data Science & Machine Learning Fundamentals
This course is part of Practical Data Science for Data Analysts Specialization
2,631 already enrolled
Included with
Skills you'll gain
- Data Preprocessing
- Predictive Analytics
- Applied Machine Learning
- Regression Analysis
- Model Evaluation
- Data-Driven Decision-Making
- Statistical Analysis
- Data Processing
- Data Science
- Statistical Methods
- Machine Learning Methods
- Predictive Modeling
- Data Analysis
- Data Cleansing
- Data Literacy
- Exploratory Data Analysis
- Feature Engineering
- Business Intelligence
- Business Analytics
- Machine Learning
Details to know
1 assignment
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There are 9 modules in this course
Data science is about using statistics to draw insights from data to drive action and improve business performance.
This course will guide you through the world of data science and machine learning, using applied examples to demonstrate real-world applications. Whether you’re an aspiring data scientist or a c-level exec, this course will bring you up to speed on everything data science. You’ll be introduced to machine learning, classification, exploratory data analysis, feature selection, and feature engineering—what they mean and how they are relevant to your business. We’ll start by defining the skills, tools, and roles behind data science that work together to create insights. We’ll then walk through regression and classification—the most common predictive and statistical techniques. Finally, you will understand why having a basic understanding of data science outputs is essential to all business stakeholders and how we can use those outputs to make business decisions. Whether you are a business leader or an aspiring analyst exploring data science, this Data Science & Machine Learning Fundamentals course will serve as your comprehensive introduction to this fascinating subject. You’ll learn all the key terminology to allow you to talk data science with your teams, begin implementing analysis, and understand how data science can help your business.
We’ll start by defining the skills, tools, and roles behind data science that work together to create insights. We’ll then walk through regression and classification—the most common predictive and statistical techniques. Finally, we will understand why having a basic understanding of data science outputs is essential to all business stakeholders and how we can use those outputs to make business decisions.
What's included
1 video1 reading
1 video•Total 2 minutes
- Course Introduction•2 minutes
1 reading•Total 10 minutes
- Downloadable Learner Files•10 minutes
What's included
11 videos1 reading3 plugins
11 videos•Total 25 minutes
- Data Science - Definitions and Examples•4 minutes
- Types of Analysis•1 minute
- Data Science Skills•2 minutes
- Two Types of Computer Science•1 minute
- The Data Science Process•3 minutes
- Machine Learning Terminology•2 minutes
- Types of Data Science Models•3 minutes
- More Types of Data Science Models•2 minutes
- Data Science Tools•2 minutes
- Data Science Roles•3 minutes
- What about Artificial Intelligence•2 minutes
1 reading•Total 10 minutes
- Area of Interest Poll•10 minutes
3 plugins•Total 41 minutes
- Interactive Exercise 1•1 minute
- Interactive Exercise 2•20 minutes
- Interactive Exercise 3•20 minutes
What's included
10 videos1 plugin
10 videos•Total 21 minutes
- Regression - Theory & Business Objectives•2 minutes
- Simple & Multiple Linear Regression•3 minutes
- Ordinary Least Squares•2 minutes
- Interpreting Coefficients•2 minutes
- Training and Testing•1 minute
- Underfitting and Overfitting•1 minute
- Plotting a Line of Best Fit in Excel•3 minutes
- Calculating the Optimal Line•3 minutes
- Regression in Python•4 minutes
- Other Regression Techniques•2 minutes
1 plugin•Total 20 minutes
- Interactive Exercise 4•20 minutes
What's included
13 videos1 plugin
13 videos•Total 30 minutes
- Classification - Theory & Business Objectives•3 minutes
- Classification Models•1 minute
- Logistic Regression•2 minutes
- Decision Trees•2 minutes
- KNN - K Nearest Neighbors•3 minutes
- SVM - Support Vector Machines•2 minutes
- Naive Bayes•3 minutes
- Gaussian Naive Bayes•1 minute
- Confusion Matrix•4 minutes
- Evaluation Metrics•2 minutes
- Overfitting and Underfitting•1 minute
- Logistic Regression in Excel with RegressitLogistic•3 minutes
- Classification in Python•4 minutes
1 plugin•Total 10 minutes
- Interactive Exercise 5•10 minutes
What's included
4 videos
4 videos•Total 8 minutes
- Leadership is Essential•1 minute
- Model Objectives•3 minutes
- Model Limitations•2 minutes
- Evaluation Metrics•3 minutes
What's included
21 videos3 plugins
21 videos•Total 42 minutes
- Data Preparation & Terminology•2 minutes
- Data Cleansing Part 1•3 minutes
- Data Cleansing Part 2•2 minutes
- Data Cleansing - Solutions•3 minutes
- EDA Part 1 - Data Types•3 minutes
- EDA - Data Type Exceptions•1 minute
- EDA - Descriptive Stats - Binary Variables•2 minutes
- EDA - Descriptive Stats - Unordered Categories•2 minutes
- EDA - Descriptive Stats - Ordered Categories•2 minutes
- Descriptive Stats - Continuous Variables•1 minute
- EDA - Correlation & the Correlation Matrix•4 minutes
- EDA - Scatterplot Matrix•2 minutes
- Feature Selection•2 minutes
- Feature Engineering - Outliers•2 minutes
- Feature Engineering - Scaling Methods•2 minutes
- Feature Engineering - Grouping and Binning•2 minutes
- Training & Testing•2 minutes
- Data Prep in Python - Import & Explore•2 minutes
- Data Prep in Python - Correlation Matrix•1 minute
- Data Prep in Python - One Hot Encoding•1 minute
- Data Prep in Python - Train/Test Split•1 minute
3 plugins•Total 35 minutes
- Interactive Exercise 6•10 minutes
- Interactive Exercise 7•15 minutes
- Interactive Exercise 8•10 minutes
What's included
7 videos
7 videos•Total 12 minutes
- Ensemble Models•1 minute
- Unsupervised Learning•2 minutes
- Reinforcement Learning•2 minutes
- Neural Networks & Deep Learning•2 minutes
- Rule Based Models•1 minute
- Monte Carlo Simulation•2 minutes
- Other Statistical Models•2 minutes
What's included
2 videos
2 videos•Total 4 minutes
- Course Summary•3 minutes
- Data Science Fundamentals Conclusion•1 minute
What's included
1 assignment
1 assignment•Total 75 minutes
- Qualified Assessment•75 minutes
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