Data Science for Non-Programmers

Data Science for Non-Programmers

Data Science for Non-Programmers
Data Science for Non-Programmers

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.


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

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Santosh Kumar

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