Data Analysis with Python

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Data Analysis with Python

Master the art of Data Analysis with Python in this free, hands-on course! Learn to import datasets, clean and wrangle data, perform exploratory analysis, and build predictive models. Dive into model development, evaluation, and refinement to uncover data-driven insights and make impactful decisions. Perfect for beginners and aspiring data scientists!

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Admin NexSkillz
Users Enrolled 79

What you'll learn

  • Demonstrate the ability to import, clean, and preprocess data using Python libraries such as pandas and numpy.
  • Perform exploratory data analysis (EDA) to uncover insights and visualize data trends using tools like matplotlib and se...
  • Develop and evaluate predictive models for data-driven decision-making using machine learning techniques in Python.
  • Apply data wrangling and analysis skills to real-world problems, refining models to improve accuracy and relevance.

Requirements

  • Basic knowledge of Python programming and an interest in data analysis.

Description

Unlock the power of data with our free, comprehensive course, "Data Analysis with Python." This course is structured into 5 engaging modules, combining video tutorials, hands-on exercises, and practical projects to help you master the essential skills of data analysis. Whether you're a beginner or looking to expand your expertise, this course offers a step-by-step guide to uncovering insights from data using Python.

Course Modules:

  1. Importing Data Sets:
    Learn how to efficiently import and handle various types of datasets, including CSV files, databases, and web data, using Python libraries like pandas and numpy.

  2. Data Wrangling:
    Master the art of cleaning and transforming raw data into structured formats, ensuring accuracy and usability for analysis.

  3. Exploratory Data Analysis (EDA):
    Explore techniques to visualize, summarize, and uncover hidden patterns in your data using Python tools like matplotlib and seaborn.

  4. Model Development:
    Dive into the basics of machine learning to build predictive models. Learn how to create regression and classification models to address real-world challenges.

  5. Model Evaluation and Refinement:
    Fine-tune your models to improve their accuracy and performance. Understand evaluation metrics and techniques to validate your analysis effectively.

Advantages of This Course:
  • Practical Learning: Hands-on projects and real-world datasets to enhance your problem-solving skills.
  • Expert-Led Tutorials: Video tutorials by industry experts ensure high-quality guidance throughout the course.
  • Free of Cost: Learn data analysis without spending a dime, making it accessible to everyone.
  • Flexible Schedule: Learn at your own pace with lifetime access to the course material.
  • Career Boost: Equip yourself with in-demand skills to advance your career in data science, machine learning, and analytics.
Who Should Enroll?
  • Beginners interested in exploring the world of data analysis.
  • Python enthusiasts who want to apply programming skills to data-related challenges.
  • Students and professionals aiming to start a career in data science or business analytics.
  • Individuals keen on using data for decision-making in industries like healthcare, finance, or marketing.
Why This Course Increases Your Worth:

In today’s data-driven world, professionals with the ability to analyze and interpret data are in high demand. By completing this course, you will:

  • Gain a competitive edge in the job market.
  • Build a strong foundation in data analysis and Python programming.
  • Be better equipped to tackle data challenges in diverse industries.

Take the first step towards becoming a data-savvy professional. Enroll now and start your journey in Data Analysis with Python!

Course Content

5 sections • 27 lectures • 01h 44m total length
Module 1 Introduction
In this module, you will learn how to understand data and learn about how to use the libraries in Python to help you import data from multiple sources. You will then learn how to perform some basic tasks to start exploring and analyzing the imported data set. Learning Objectives ________________________________________ • Access databases using Python database APIs • Analyze Python data using a dataset • Identify three Python libraries and describe their uses • Read data using Python's Pandas package • Demonstrate how to import and export data in Python
mb
Python Packages for Data Science - Video Tutorial
03min
Understanding the Data - Video Tutorial
3min
Importing and Exporting Data in Python
4min
Accessing Databases - Video Tutorial
4min
Getting Started with Data Analysis
4min
Pre-Processing Data in Python - Video Tutorial
In this tutorial, we’ll explore the essential steps of data pre-processing in Python, a crucial phase in data science and machine learning workflows. From handling missing values to scaling and encoding data, we’ll cover techniques to prepare raw data for insightful analysis and model building.
2min
Data Normalization in Python - Video Tutorial
In this tutorial, we’ll dive into data normalization, a key technique for preparing data in Python. Learn how to scale your data to a consistent range, improving model performance and ensuring fair comparisons between features in machine learning.
4min
Dealing with Missing Values in Python - Video Tutorial
In this tutorial, we’ll tackle the challenge of dealing with missing values in datasets using Python. Learn practical techniques to identify, handle, and impute missing data to ensure your analysis and models remain accurate and reliable.
6min
Binning in Python - Video Tutorial
In this tutorial, we’ll explore binning in Python, a powerful technique for grouping data into intervals or categories. Discover how to simplify and analyze data effectively by transforming continuous variables into meaningful bins.
2min
Data Formatting in Python - Video Tutorial
In this tutorial, we’ll focus on data formatting in Python, an essential step in preparing datasets for analysis. Learn how to clean, structure, and standardize data to ensure compatibility and accuracy in your projects.
4min
Turning Categorical Variables into Quantitative Variables in Python - Video Tutorial
In this tutorial, we’ll learn how to transform categorical variables into quantitative formats in Python. Discover techniques like one-hot encoding and label encoding to make your data machine-learning ready.
2min
Exploratory Data Analysis - Video Tutorial
In this tutorial, we’ll dive into Exploratory Data Analysis (EDA), a vital step in understanding your data. Learn how to uncover patterns, detect anomalies, and summarize key insights using Python’s powerful libraries.
1min
Correlation Statistics - Video Tutorial
In this tutorial, we’ll explore correlation statistics, a fundamental concept for understanding relationships between variables. Learn how to calculate and interpret correlation coefficients using Python to uncover meaningful insights in your data.
3min
Correlation - Video Tutorial
In this tutorial, we’ll delve into correlation, a key statistical measure to understand the relationship between variables. Learn how to compute, visualize, and interpret correlation using Python for data-driven insights.
3min
Descriptive Statistics - Video Tutorial
In this tutorial, we’ll explore descriptive statistics, the foundation of data analysis. Learn how to summarize and describe key features of your dataset using Python, including measures of central tendency, dispersion, and distribution.
5min
GroupBy in Python - Video Tutorial
In this tutorial, we’ll uncover the power of the `GroupBy` function in Python for data aggregation and analysis. Learn how to efficiently group, summarize, and transform data to extract meaningful insights from complex datasets.
4min
Linear Regression and Multiple Linear Regression - Video tutorial
In this tutorial, we’ll explore Linear Regression and Multiple Linear Regression, essential techniques for modeling relationships between variables. Learn how to implement these methods in Python, interpret results, and make predictions based on data.
7min
Measures for In-Sample Evaluation - Video Tutorial
In this tutorial, we’ll discuss measures for in-sample evaluation, focusing on how to assess model performance using training data. Learn key metrics like Mean Absolute Error, Mean Squared Error, and R-squared to evaluate and refine your models in Python.
4min
Model Development - Video Tutorial
In this tutorial, we’ll dive into model development, the process of building predictive models using Python. Learn how to train, test, and optimize your models to turn raw data into actionable insights.
2min
Model Evaluation using Visualization - Video Tutorial
In this tutorial, we’ll explore model evaluation through visualization, a powerful way to assess and interpret model performance. Learn how to use Python to create insightful plots like residual plots and learning curves for better decision-making.
5min
Polynomial Regression and Pipelines - Video Tutorial
In this tutorial, we’ll explore Polynomial Regression and the use of Pipelines in Python. Learn how to model non-linear relationships and streamline your machine learning workflows for efficient and accurate predictions.
5min
Prediction and Decision Making - Video Tutorial
In this tutorial, we’ll focus on prediction and decision-making, key aspects of applied machine learning. Learn how to use Python to make accurate predictions and drive data-informed decisions with confidence.
5min
Grid Search - Video Tutorial
In this tutorial, we’ll delve into Grid Search, a systematic method for hyperparameter tuning in machine learning. Learn how to use Python to optimize your models and achieve the best possible performance.
5min
Model Evaluation and Refinement - Video Tutorial
In this tutorial, we’ll explore model evaluation and refinement, critical steps to improve the performance of your machine learning models. Learn how to assess, fine-tune, and optimize models using Python for better accuracy and reliability.
8min
Overfitting, Underfitting and Model Selection - Video Tutorial
In this tutorial, we’ll dive into the concepts of overfitting, underfitting, and model selection in machine learning. Learn how to balance model complexity and accuracy to build robust models that generalize well to unseen data.
4min
Ridge Regression - Video Tutorial
In this tutorial, we’ll explore Ridge Regression, a powerful technique to address multicollinearity and overfitting in linear models. Learn how to implement Ridge Regression in Python and improve your model’s performance.
5min

About Instructor

instructor
Admin NexSkillz
About Instructor

An LMS (Learning Management System) instructor is a person who is responsible for creating and delivering educational content to students through an LMS platform. They use the platform to create courses, assignments, quizzes, and other educational materials that are used to teach students. The instructor may also interact with students, grade assignments, and provide feedback on their progress. The goal of an LMS instructor is to provide an effective and efficient learning experience for students using the LMS platform.

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