Data Science Skills: Python ,Pandas ,Machine Learning, etc

Data Science Skills: Python ,Pandas ,Machine Learning, etc, available at $54.99, with 111 lectures, and has 2 subscribers.

You will learn about Understand the fundamental concepts of data science. Recognize the applications and industry impact of data science. Utilize essential data science libraries such as Pandas, NumPy, Matplotlib, and Seaborn. Install Python and set up a development environment on Windows and macOS. Familiarize with Jupyter Notebook and use it for interactive data analysis Explore and manipulate data using Pandas DataFrames. Create and manipulate Pandas Series for efficient data handling. Load datasets into Pandas and perform initial data inspection and cleaning Load datasets into Pandas and perform initial data inspection and cleaning Visualize data using Matplotlib and Seaborn for insights and reporting. Understand supervised, unsupervised, and reinforcement learning techniques. Preprocess data for machine learning models, including handling missing values and encoding categorical variables. Build, train, and evaluate machine learning models using scikit-learn. Measure model performance using metrics like accuracy, confusion matrix, and classification report. Deploy a machine learning model for real-time predictions and understand model interpretability techniques. Understanding Data Privacy and Security This course is ideal for individuals who are Aspiring Data Scientists or Aspiring Data Scientists or Professionals Transitioning Careers or Data Analysts and Engineers or Entrepreneurs and Business Owners or Anyone Curious About Data Science It is particularly useful for Aspiring Data Scientists or Aspiring Data Scientists or Professionals Transitioning Careers or Data Analysts and Engineers or Entrepreneurs and Business Owners or Anyone Curious About Data Science.

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Summary

Title: Data Science Skills: Python ,Pandas ,Machine Learning, etc

Price: $54.99

Number of Lectures: 111

Number of Published Lectures: 109

Number of Curriculum Items: 111

Number of Published Curriculum Objects: 109

Original Price: $49.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the fundamental concepts of data science.
Recognize the applications and industry impact of data science.
Utilize essential data science libraries such as Pandas, NumPy, Matplotlib, and Seaborn.
Install Python and set up a development environment on Windows and macOS.
Familiarize with Jupyter Notebook and use it for interactive data analysis
Explore and manipulate data using Pandas DataFrames.
Create and manipulate Pandas Series for efficient data handling.
Load datasets into Pandas and perform initial data inspection and cleaning
Load datasets into Pandas and perform initial data inspection and cleaning
Visualize data using Matplotlib and Seaborn for insights and reporting.
Understand supervised, unsupervised, and reinforcement learning techniques.
Preprocess data for machine learning models, including handling missing values and encoding categorical variables.
Build, train, and evaluate machine learning models using scikit-learn.
Measure model performance using metrics like accuracy, confusion matrix, and classification report.
Deploy a machine learning model for real-time predictions and understand model interpretability techniques.
Understanding Data Privacy and Security

Who Should Attend

Aspiring Data Scientists
Aspiring Data Scientists
Professionals Transitioning Careers
Data Analysts and Engineers
Entrepreneurs and Business Owners
Anyone Curious About Data Science

Target Audiences

Aspiring Data Scientists
Aspiring Data Scientists
Professionals Transitioning Careers
Data Analysts and Engineers
Entrepreneurs and Business Owners
Anyone Curious About Data Science

In today’s data-driven world, the ability to harness and interpret data is not just a valuable skill but a crucial advantage. Whether you’re an aspiring data scientist, a seasoned professional looking to expand your skill set, or an entrepreneur aiming to leverage data for strategic decisions, our comprehensive course on data science offers a transformative learning experience.

Course Overview

Our course begins with a foundational exploration of data science, introducing you to its principles and importance in various industries. You’ll delve into the distinctions between data science, data engineering, and data analysis, gaining a clear understanding of their respective roles and applications. Through real-world case studies and examples, you’ll discover how data science drives innovation and impacts decision-making processes across different sectors.

Essential Tools and Technologies

To equip you with the tools needed for effective data analysis, the course covers essential programming languages such as Python . Whether you’re manipulating data with Pandas, performing numerical operations with NumPy, or creating insightful visualizations with Matplotlib and Seaborn, you’ll develop a versatile skill set that forms the backbone of data science projects.

Practical Skills Development

A significant focus of the course is hands-on learning. You’ll gain practical experience in gathering, cleaning, and analyzing data from diverse sources. Through interactive exercises and projects, you’ll hone your ability to transform raw data into actionable insights that drive business decisions.

Environment Setup and Best Practices

Navigating the data science environment can be daunting, especially for beginners. That’s why we guide you through the setup of Python and Jupyter Notebook on both Windows and macOS, ensuring you’re equipped with the right tools from the start. You’ll learn to create and manage virtual environments, enhancing your ability to work efficiently and maintain project dependencies.

Data Manipulation and Visualization Mastery

Central to effective data science is the ability to manipulate and visualize data effectively. Our course provides in-depth training in Pandas, where you’ll learn to handle complex datasets, perform data transformations, and conduct exploratory data analysis. Through immersive visualization exercises, you’ll discover how to communicate insights visually, making complex data accessible and actionable.

Machine Learning Fundamentals

Understanding machine learning is essential for any aspiring data scientist. You’ll explore supervised, unsupervised, and reinforcement learning techniques, applying them to real-world datasets. From preprocessing data to training and evaluating machine learning models, you’ll develop the skills needed to predict outcomes and optimize performance in various scenarios.

Real-world Applications and Projects

Throughout the course, you’ll apply your newfound knowledge to practical projects that simulate real-world challenges. Whether it’s predicting house prices using regression models or building a web app for interactive data analysis, these projects provide a platform to showcase your skills and build a professional portfolio.

Career Readiness and Support

Beyond technical skills, we prepare you for success in the competitive field of data science. You’ll learn to interpret model performance metrics like accuracy and precision, communicate findings effectively through tools like the confusion matrix and classification reports, and understand the ethical implications of data-driven decisions.

Who Should Enroll?

This course is designed for anyone eager to embark on a journey into data science or enhance their existing skills:

Aspiring Data Scientists: Individuals looking to break into the field and build a strong foundation in data analysis and machine learning.
Professionals Seeking Career Advancement: Data analysts, engineers, and professionals from diverse industries seeking to expand their skill set and transition into data-driven roles.
Entrepreneurs and Business Owners: Leaders interested in leveraging data science to drive strategic decisions and gain a competitive edge in their industry.
Curious Learners: Enthusiasts with a passion for data-driven insights and a desire to understand the transformative potential of data science in today’s world.

Prerequisites

No prior experience in data science or programming is required to enroll in this course. However, a basic understanding of mathematics and a willingness to learn new concepts and tools are beneficial. The course is structured to accommodate learners from diverse backgrounds and industries, making it accessible to anyone interested in harnessing the power of data.

Career Opportunities

The demand for skilled data scientists continues to grow across industries such as healthcare, finance, retail, and technology. Upon completing this course, you’ll be equipped with the foundational skills needed to pursue various career paths, including:

Data Analyst: Analyze data to extract insights and inform business decisions.
Machine Learning Engineer: Develop and deploy machine learning models to solve complex problems.
Business Intelligence Analyst: Transform data into actionable insights to drive organizational growth.
Data Scientist: Utilize statistical analysis and machine learning techniques to extract knowledge and insights from data.

Conclusion

By the end of this course, you’ll have gained the confidence and skills needed to tackle complex data challenges with proficiency and precision. Whether you’re looking to pivot your career, enhance your business acumen, or simply satisfy your curiosity about data science, our comprehensive curriculum and hands-on approach will empower you to unlock the power of data and chart your path to success.

Enroll today and embark on your journey to acquiring essential data science skills .

Course Curriculum

Chapter 1: Introduction to Data Science

Lecture 1: Introduction

Lecture 2: What is data science?

Lecture 3: Importance and Applications of Data Science

Lecture 4: Overview of the Data Science Process

Lecture 5: Introduction to Python for Data Science

Lecture 6: Understanding Different Data Types

Chapter 2: Environment Setup

Lecture 1: Python Installation on Windows

Lecture 2: What are virtual environments

Lecture 3: Creating and activating a virtual environment on Windows

Lecture 4: Python Installation on macOS

Lecture 5: Creating and activating a virtual environment on macOS

Lecture 6: What is Jupyter Notebook

Lecture 7: Installing Pandas and Jupyter Notebook in the Virtual Environment

Lecture 8: Starting Jupyter Notebook

Lecture 9: Exploring Jupyter Notebook Server Dashboard Interface

Lecture 10: Creating a new Notebook

Lecture 11: Exploring Jupyter Notebook Source and Folder Files

Lecture 12: Exploring the Notebook Interface

Chapter 3: Data Manipulation and visualization with pandas

Lecture 1: Overview of Pandas

Lecture 2: Creating a Pandas Series from a List

Lecture 3: Creating a Pandas Series from a List with Custom Index

Lecture 4: Creating a pandas series from a Python Dictionary

Lecture 5: Accessing Data in a Series using the index by label

Lecture 6: Accessing Data in a Series By position

Lecture 7: Slicing a Series by Label

Lecture 8: Creating a DataFrame from a dictionary of lists

Lecture 9: Creating a DataFrame From a list of dictionaries

Lecture 10: Accessing data in a DataFrame

Lecture 11: Manipulating Data in a dataframe

Lecture 12: Download Dataset

Lecture 13: Loading Dataset into a DataFrame

Lecture 14: Inspecting the data

Lecture 15: Data Cleaning

Lecture 16: Data transformation and analysis

Lecture 17: Visualizing data

Chapter 4: Machine Learning: Build and train a machine learning model

Lecture 1: What is Machine Learning?

Lecture 2: What is an Algorithm

Lecture 3: Installing and importing libraries

Lecture 4: Data Preprocessing

Lecture 5: What is a Dataset

Lecture 6: Downloading dataset

Lecture 7: Exploring the Dataset

Lecture 8: Handle missing values and drop unnecessary columns.

Lecture 9: Encode categorical variables.

Lecture 10: What is Feature Engineering

Lecture 11: Create new features.

Lecture 12: Dropping unnecessary columns

Lecture 13: Visualize survival rate by gender

Lecture 14: Visualize survival rate by class

Lecture 15: Visualize numerical features

Lecture 16: Visualize the distribution of Age

Lecture 17: Visualize number of passengers in each passenger class

Lecture 18: Visualize number of passengers that survived

Lecture 19: Visualize the correlation matrix of numerical variables

Lecture 20: Visualize the distribution of Fare.

Lecture 21: Data Preparation and Training Model

Lecture 22: What is a Model

Lecture 23: Define features and target variable.

Lecture 24: Split data into training and testing sets.

Lecture 25: Standardize features.

Lecture 26: What is a logistic regression model.

Lecture 27: Train logistic regression model.

Lecture 28: Making Predictions

Lecture 29: What is accuracy in machine learning

Lecture 30: What is confusion matrix.

Lecture 31: What is is classification report.

Lecture 32: What is a Heatmap

Lecture 33: Evaluate the model using accuracy, confusion matrix, and classification report.

Lecture 34: Visualize the confusion matrix.

Lecture 35: Saving the Model

Lecture 36: Loading the model

Lecture 37: Improving Understanding of the model's prediction

Lecture 38: Building a decision tree

Lecture 39: Building a random forest

Chapter 5: Hands-on Project: Predicting real house prices using machine learning

Lecture 1: Importing Libraries and modules

Lecture 2: Loading dataset and creating a dataframe

Lecture 3: Checking for missing values

Lecture 4: Dropping column and splitting data

Lecture 5: Standardize the features for housing dataframe

Lecture 6: Make predictions on the test set.

Lecture 7: Evaluating the model for the housing dataset.

Lecture 8: Creating scatter plot

Lecture 9: Creating a bar plot

Lecture 10: Saving the housing model

Lecture 11: Loading the housing model

Chapter 6: Build a Web App House Price Prediction Tool

Lecture 1: What is Flask

Lecture 2: Installing Flask

Lecture 3: Installing Visual Studio Code

Lecture 4: Creating a minimal flask app

Lecture 5: How to run a flask app

Lecture 6: Http and Https Methods

Lecture 7: Loading the saved model and scaler into Python file

Lecture 8: Define the home route

Lecture 9: Define the prediction route

Instructors

Digital Learning Academy
Digital learning 24 /7

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