Data Science Tools: Python, Pandas, Machine Learning, EDA

Data Science Tools: Python, Pandas, Machine Learning, EDA, available at $54.99, has an average rating of 5, with 122 lectures, based on 3 reviews, and has 27 subscribers.

You will learn about Utilize essential data science libraries such as Pandas, NumPy, Matplotlib, and Seaborn. Differentiate between structured and unstructured data. Gain proficiency in Python programming language for data analysis. Understand the fundamental concepts of data science. Differentiate between data science, data engineering, and data analysis. Recognize the applications and industry impact of data science. 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. Transform and analyze data using Pandas methods. Visualize data using Matplotlib and Seaborn for insights and reporting. Utilize statistical techniques for data exploration and hypothesis testing. Define machine learning and its application in data science. 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. This course is ideal for individuals who are Data Analysts and Engineers or Aspiring Data Scientists or Students and Graduates or Professionals Transitioning Careers or Entrepreneurs and Business Owners or Anyone Curious About Data Science It is particularly useful for Data Analysts and Engineers or Aspiring Data Scientists or Students and Graduates or Professionals Transitioning Careers or Entrepreneurs and Business Owners or Anyone Curious About Data Science.

Enroll now: Data Science Tools: Python, Pandas, Machine Learning, EDA

Summary

Title: Data Science Tools: Python, Pandas, Machine Learning, EDA

Price: $54.99

Average Rating: 5

Number of Lectures: 122

Number of Published Lectures: 122

Number of Curriculum Items: 122

Number of Published Curriculum Objects: 122

Original Price: $69.99

Quality Status: approved

Status: Live

What You Will Learn

Utilize essential data science libraries such as Pandas, NumPy, Matplotlib, and Seaborn.
Differentiate between structured and unstructured data.
Gain proficiency in Python programming language for data analysis.
Understand the fundamental concepts of data science.
Differentiate between data science, data engineering, and data analysis.
Recognize the applications and industry impact of data science.
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.
Transform and analyze data using Pandas methods.
Visualize data using Matplotlib and Seaborn for insights and reporting.
Utilize statistical techniques for data exploration and hypothesis testing.
Define machine learning and its application in data science.
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.

Who Should Attend

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

Target Audiences

Data Analysts and Engineers
Aspiring Data Scientists
Students and Graduates
Professionals Transitioning Careers
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 and R. 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. From introductory SQL for data querying to advanced techniques in web scraping for data retrieval, 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.

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 mastering data science—one insightful discovery at a time.

Course Curriculum

Chapter 1: Introduction to Data Science

Lecture 1: Introduction

Lecture 2: What is Data Science

Lecture 3: Data Science vs. Data Engineering vs. Data Analysis

Lecture 4: Applications of Data Science

Lecture 5: Overview of tools and technologies used in data science.

Lecture 6: Basics of statistics for data analysis

Lecture 7: Introduction to Python for Data Science

Lecture 8: Structured vs. Unstructured Data

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: Python Fundamentals

Lecture 1: Python Expressions

Lecture 2: Python Statements

Lecture 3: Python Code Comments

Lecture 4: Python Data Types

Lecture 5: Casting Data Types

Lecture 6: Python Variables

Lecture 7: Python List

Lecture 8: Python Tuple

Lecture 9: Python Dictionaries

Lecture 10: Python Operators

Lecture 11: Python Conditional Statements

Lecture 12: Python Loops

Lecture 13: Python Functions

Chapter 4: 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: Download Dataset

Lecture 12: Loading Dataset into a DataFrame

Lecture 13: Inspecting the data

Lecture 14: Data Cleaning

Lecture 15: Data transformation and analysis

Lecture 16: Visualizing data

Chapter 5: Machine Learning Essentials: Build and Train a Machine Learning Model

Lecture 1: What is Machine Learning?

Lecture 2: Installing and importing libraries

Lecture 3: Introduction to Data Preprocessing

Lecture 4: What is a Dataset

Lecture 5: Downloading dataset

Lecture 6: Exploring the Dataset

Lecture 7: Handle missing values and drop unnecessary columns.

Lecture 8: Encode categorical variables.

Lecture 9: What is Feature Engineering

Lecture 10: Create new features.

Lecture 11: Dropping unnecessary columns

Lecture 12: Visualize survival rate by gender

Lecture 13: Visualize survival rate by class

Lecture 14: Visualize numerical features

Lecture 15: Visualize the distribution of Age

Lecture 16: Visualize number of passengers in each passenger class

Lecture 17: Visualize number of passengers that survived

Lecture 18: Visualize the correlation matrix of numerical variables

Lecture 19: Visualize the distribution of Fare.

Lecture 20: Data Preparation and Training Model

Lecture 21: What is a Model

Lecture 22: Define features and target variable.

Lecture 23: Split data into training and testing sets.

Lecture 24: Standardize features.

Lecture 25: What is a logistic regression model.

Lecture 26: Train logistic regression model.

Lecture 27: Making Predictions

Lecture 28: What is accuracy in machine learning

Lecture 29: What is confusion matrix.

Lecture 30: What is is classification report.

Lecture 31: What is a Heatmap

Lecture 32: Evaluate the model using accuracy, confusion matrix, and classification report.

Lecture 33: Visualize the confusion matrix.

Lecture 34: Saving the Model

Lecture 35: Loading the model

Lecture 36: Improving Understanding of the model's prediction

Lecture 37: Building a decision tree

Lecture 38: Building a random forest

Chapter 6: Real World 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: Initialize and train the regression model

Lecture 7: Make predictions on the test set.

Instructors

Bluelime Learning Solutions
Making Learning Simple

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Frequently Asked Questions

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