Online Learning Platform

EduSmart

Learn Anytime, Anywhere

Join our platform to access the best online courses tailored for your success.

Explore Courses

Our Features

Interactive Live Classes

Engage with experienced educators through live-streamed sessions.

Student Performance Insights

Track progress with detailed attendance and performance charts.

Parent Monitoring Tools

Allow parents to stay updated on their child’s academic journey.

Popular Courses

Web Development

Learn the basics of HTML, CSS, and JavaScript to build modern websites.

Data Science

Dive into data analysis, visualization, and machine learning techniques.

Digital Marketing

Master strategies to grow businesses using online marketing tools.

About Us

At EduSmart, we are dedicated to providing high-quality education that empowers learners to excel in their fields. Our platform bridges the gap between traditional and modern learning.

Home

Courses

/Data Scientist with Python

Data Scientist with Python

7 modules

English

Certificate of completion

Lifetime access

<p>"Unlock the Power of Data: Become a Master Data Scientist with Python's Robust Tools, Techniques, and Algorithms!"</p>

Overview

Description:

Learn the skills required to become a Data Scientist with Python. This course covers a comprehensive introduction to data science, focusing on Python programming language and its libraries for data handling, data visualization, and machine learning.

Key Highlights:

Understand the fundamentals of data science
Gain proficiency in Python programming
Learn data handling and analysis techniques
Master data visualization using Python libraries
Explore machine learning algorithms and techniques

What you will learn:

Data Manipulation and Analysis
Acquire skills for manipulating, cleaning, and processing data using Python libraries such as Pandas and NumPy.
Data Visualization
Learn how to create interactive and informative visualizations using libraries like Matplotlib and Seaborn.
Machine Learning
Discover machine learning algorithms, regression, classification, clustering, and predictive modeling with scikit-learn library.
Model Evaluation and Optimization
Gain insights into evaluating and optimizing machine learning models for better performance.

Note: The generated HTML is 1700 characters long (excluding HTML tags).

Modules

Topic 1: Introduction to Python

14 attachments

Chapter 1: Getting Started with Python

Unit 1.1: Setting up Python environment (Jupyter Notebook, Anaconda)

Unit 1.2: Python IDEs and their uses (Spyder, PyCharm, etc.)

Unit 1.3: Writing and running Python scripts

Chapter 2: Python Basics

Unit 2.1: Data types, variables, and operators

Question 1: Data Types and Variables

Unit 2.2: Basic input/output and string manipulation

Unit 2.3: Control flow (if-else, loops)

Question 2: Operators and Control Flow

Chapter 3: Python Functions and Modules

Unit 3.1: Defining and calling functions

Unit 3.2: Python libraries and importing modules

Unit 3.3: Writing reusable functions

Topic 2: Working with Data in Python

11 attachments

Chapter 4: Introduction to Data Structures

Unit 4.1: Lists, tuples, and sets

Unit 4.2: Dictionaries and nested data structures

Chapter 5: File Handling

Unit 5.1: Reading and writing text files

Unit 5.2: Working with CSV files using csv module

Unit 5.3: Introduction to JSON data

Chapter 6: NumPy for Numerical Computation

Unit 6.1: Introduction to NumPy arrays

Unit 6.2: Array operations and broadcasting

Unit 6.3: Linear algebra and mathematical functions with NumPy

Topic 3: Data Analysis and Visualization

8 attachments

Chapter 7: Data Analysis with Pandas

Unit 7.1: Introduction to Pandas: Series and DataFrames

Unit 7.2: Data manipulation (sorting, filtering, grouping)

Unit 7.3: Handling missing data and data cleaning

Chapter 8: Data Visualization

Unit 8.1: Introduction to Matplotlib

Unit 8.2: Plotting with Seaborn for statistical visualization

Unit 8.3: Customizing visualizations for presentation

Topic 4: Exploratory Data Analysis (EDA)

7 attachments

Chapter 9: Preparing Data for Analysis

Unit 9.1: Data transformation (scaling, normalization)

Unit 9.2: Feature engineering and feature selection

Chapter 10: Exploring and Summarizing Data

Unit 10.1: Descriptive statistics with Pandas

Unit 10.2: Data correlations and relationships

Unit 10.3: Visualizing data distributions

Topic 5: Introduction to Machine Learning

8 attachments

Chapter 11: Basics of Machine Learning

Unit 11.1: Overview of machine learning concepts

Unit 11.2: Introduction to scikit-learn

Unit 11.3: Splitting data into training and testing sets

Chapter 12: Building Simple Models

Unit 12.1: Linear regression

Unit 12.2: Logistic regression

Unit 12.3: Evaluating model performance (accuracy, precision, recall)

Topic 6: Advanced Python for Data Science

8 attachments

Chapter 13: Advanced Pandas Techniques

Unit 13.1: Merging and joining datasets

Unit 13.2: Pivot tables and cross-tabulations

Unit 13.3: Time-series analysis with Pandas

Chapter 14: Data Handling with SQL and Python

Unit 14.1: Introduction to SQL for data science

Unit 14.2: Connecting Python to databases (using SQLite)

Unit 14.3: Querying data and integrating results into Pandas

Topic 7: Capstone Project

4 attachments

Chapter 15: Building a Data Science Project

Unit 15.1: Identifying the problem and dataset

Unit 15.2: Performing EDA and data cleaning

Unit 15.3: Building models and presenting insights

Certification

When you complete this course you receive a ‘Certificate of Completion’ signed and addressed personally by me.

Data Scientist with Python

7 modules

English

Certificate of completion

Lifetime access

R 300.00