Data Science for Non-Programmers

Ready to move past Excel for complex business analysis? Then you’ll find this course very helpful.

This hands-on introductory Data Science course is aimed at professionals and students who don't have any experience with programming.

It will help you advance your career by preparing you to conduct meaningful data analysis in Python on any dataset — large or small.

You’ll begin with the fundamentals of Python, with focus on CSV files in Python, covering concepts like data preprocessing and Exploratory Data Analysis (EDA).

In the second half, you'll focus on predictive and inferential analysis using statistical and machine learning techniques, and learn how these techniques can help solve business problems.

Contents

1. What is Data Science

The Buzzword "Data Science"
Data Science Lifecycle
Python for Data Science

2. Python Basics

Hello World
Variables and Data Types
Operators
Conditional Statements
Functions
Lists
Loops
Packages and Modules
Exercise: Average of a List
Solution Review: Average of a List
Exercise: Factorial of a Number
Solution Review: Factorial of a Number

3. Handling Tabular Data in Python

Importing Data in CSV Files with Pandas
Indexing and Selection
Filtering Data
Applying Functions to Data
Aggregating Data
Grouping Data
Pivot Tables
Plotting Data 1: Univariate Plots
Plotting Data 2: Bivariate Plots
Test Your Knowledge

4. Data Cleaning

Introduction to Data Cleaning
Data Types
Missing Values
Duplicates
Inconsistent Data
Outliers
Outlier Detection and Removal
Exercise: Cleaning NYC Property Sales
Solution Review: Cleaning NYC Property Sales

5. Exploratory Data Analysis

Introduction
Analyzing Individual Quantities
Exploring Categorical Quantities
Exploring Numerical Quantities
Correlation and Heatmaps
Exercise: Exploring E-Commerce
Solution Review: Exploring E-Commerce
Business Example: RFM Analysis in Python

6. Statistical Inference

The Basics of Statistical Inference
Confidence Intervals
Hypothesis Testing
One Sample t-Test
Two Sample t-Test
Paired t-Test

7. Predictive Models

A Simple Model
Model Fitting on a Loss Function
Gradient Descent
Optimization with Gradient Descent
Simple Linear Regression
Multiple Linear Regression
Evaluating Regression Models
Logistic Regression
Evaluating Logistic Regression Models
Exercise: Churn Prediction
Solution Review: Churn Prediction

8. Machine Learning

Why Machine Learning
Machine Learning Pipeline
Decision Trees
Random Forests
Support Vector Machines
Ensembles: Bagging vs Boosting
Clustering for Unsupervised Learning
K-Means on Two-Dimensional Data
K-Means on n-Dimensional Data
Test your Knowledge
Conclusion

Data Science for Non-Programmers