Data Science for Non-Programmers

Gain insights into conducting meaningful data analysis in Python without prior programming experience. Discover Python fundamentals, data preprocessing, EDA, and predictive analytics for solving business problems.

Beginner

70 Lessons

13h

Certificate of Completion

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

143 Playgrounds

8 Challenges

5 Quizzes

This course includes

143 Playgrounds

8 Challenges

5 Quizzes

Course Overview

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 ...Show More

Course Content

What is Data Science

Get familiar with the interdisciplinary study of data, data science lifecycle, and Python's key role.

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

Python Basics

Unpack the core of Python syntax, variables, operators, conditionals, functions, loops, and modules.

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

Handling Tabular Data in Python

Master the steps to handle, manipulate, and visualize tabular data using Pandas 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

Data Cleaning

Grasp the fundamentals of data cleaning, including handling missing values, duplicates, inconsistencies, and outliers.

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

Exploratory Data Analysis

Dig into understanding datasets, analyzing variables, and discovering trends and relationships.

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

Statistical Inference

6 Lessons

Simplify complex topics of statistical inference, including estimation, confidence intervals, and hypothesis testing.

Predictive Models

11 Lessons

Build on creating, optimizing, and evaluating predictive models using linear and logistic regression.

Machine Learning

11 Lessons

Sharpen your skills in machine learning algorithms, pipelines, and practical applications using Python.

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