$ 19.99

Lifetime access

Abdikadir Hussein Elmi

The course starts on TODAYNovember 23, 2024

Course will end on January 22, 2025

7 Weeks (8-10 hours per week)

Last update on November 20, 2024

Data Science with Python

somX

Beginner Data Science course with Python: Learn programming, statistics, and visualization online with expert Abdikadir Hussein Elmi.

Somali

Self Paced

Verified certificate

ABOUT THIS COURSE

"Introduction to Data Science with Python" is a beginner-level course designed to introduce you to the fundamental concepts and techniques of data science using Python, one of the most in-demand progr

What you'll learn

1. Data Science Basics: Understand the scope and impact of data science in various sectors.

2. Python Programming: Learn Python syntax, data types, and basic scripting for data science.

3. Essential Python Libraries: Get hands-on with libraries like Pandas, NumPy, and Matplotlib for data analysis and visualization.

4. Data Handling: Master the techniques of data cleaning, manipulation, and preprocessing.

5. Statistical Analysis: Dive into descriptive and inferential statistics essential for data interpretation.

6. Data Visualization: Learn to create insightful visual representations of data.

7. Introduction to Machine Learning: Get acquainted with basic machine learning concepts and algorithms.

8. Real-world Projects: Apply your learning to practical projects for a solid understanding of data science applications.

Course content

Workshop 1: Introduction to Data Science and Python

Lesson 1: Introduction

1 lectures

Lesson 2: Setup Environment

1 lectures

Lesson 3: Python basics

1 lectures

Lesson 4: Variables I

1 lectures

Lesson 5: Variables II

1 lectures

Lesson 6: Variables III

1 lectures

Lesson 7: Basic Calculations

1 lectures

Lesson 8 Concatenations

1 lectures

Assignment I: Python Programming Fundamentals

1 lectures

Workshop 2: Python Basics for Data Science

Lesson 1: Python Basics for Data Science

1 lectures

Lesson 2: Numeric and Text Types

1 lectures

Lesson 3: Sequencial Types

1 lectures

Lesson 4: Lists

1 lectures

Lesson 5: Tuples

1 lectures

Lesson 6: DataFrame

1 lectures

Lesson 7: Accessing Dictionary Key, Values and Items

1 lectures

Lesson 8: Adding and Deleting columns

1 lectures

Assignment II: Data Types, Control Structures and Dictionary

1 lectures

WorkShop 3: Python Libraries for Data Science and Descriptive Statistics

Lesson 1 Introduction Python Libraries

1 lectures

Lesson 2 Basic Numpy Operations

1 lectures

Lesson 3 Basic Pandas Operations and Accessing columns and Rows

1 lectures

Lesson 4 Basic Operation on Columns

1 lectures

Lesson 5 Updating Data Values based on Conditions

1 lectures

Lesson 6 Filtering Data

1 lectures

Lesson 7 Basic Statistics and Data Analysis

1 lectures

Lesson 8 Mean, Median, Mode and Standard Deviation

1 lectures

Lesson 9 Data Distributions

1 lectures

Lesson 10 Basic Data Processing

1 lectures

Lesson 11 Basic Data Processing 2

1 lectures

Lesson 12 Some Visualization and Specifications

1 lectures

Assignments III: Numpy array manipulation, Pandas Data Handling, and Descriptive Statistics.

1 lectures

Workshop 4: Data Preprocessing and analysis with Python

Lesson 1 Basic Data Preprocessing

1 lectures

Lesson 2 Accessing the dataset based on conditions

1 lectures

Lesson 3 Using Function to fix Density_status

1 lectures

Lesson 4 Apply Function

1 lectures

Lesson 5 Data Visualizations

1 lectures

Lesson 6 Data Visualizations 2

1 lectures

Lesson 7 Working with Outliers

1 lectures

Lesson 8 Checking Outliers

1 lectures

Lesson 9 Detecting Lower Outliers

1 lectures

Lesson 10 Detecting and Removing Upper Outliers

1 lectures

Assignments IV: Import Data, Handling Missing values and Duplicate Data Management

1 lectures

Workshop 5: Data Manipulation, Data Exploration and Data Visualization

Lesson 1 Loading CSV Dataset

1 lectures

Lesson 2 Some basic calculations about dataset

1 lectures

Lesson 3 Data Visualizations

1 lectures

Lesson 4 Loading another dataset

1 lectures

Lesson 5 Reading Some columns and rows

1 lectures

Lesson 6 Countries Data Visualizations

1 lectures

Lesson 7 Manual assigning values visualizations

1 lectures

Lesson 8 Using Subplot

1 lectures

Lesson 9 Working with missing values

1 lectures

Lesson 10 Methods for handling missing values

1 lectures

Lesson 11 Interpolation, Forward Filling and Backward Filling

1 lectures

Lesson 12 Working with duplicates

1 lectures

Assignments V: Data Visualizations Techniques, Advanced Data Manipulation, and Statistical Analysis and Interpretation

1 lectures

Workshop 6: Introduction to Machine Learning

Lesson 1 Introduction to Machine Learning

1 lectures

Lesson 2 Real word project overview and Importing libraries

1 lectures

Lesson 3 Loading Real World Dataset

1 lectures

Lesson 4 Data Preprocessing

1 lectures

Lesson 5 Data Visualizations

1 lectures

Lesson 6 Data Splitting

1 lectures

Lesson 7 Machine Learning Algorithm

1 lectures

Lesson 8 Future Prediction

1 lectures

Project: Linear Regression

1 lectures

Workshop 7: Applying Real World Project

Lesson 1 Importing necessary libraries and loading dataset

1 lectures

Lesson 2 Data Preprocessing

1 lectures

Lesson 3 Visualizations

1 lectures

Lesson 4 Splitting the Dataset

1 lectures

Lesson 5 Model Selection and Training

1 lectures

Final Course Project

1 lectures

Instructors

Abdikadir Hussein Elmi

55 Learners

1 Course