Data analysis with Python

Categories: E-Learning, Python

About Course

Unlock the power of data with Python in this comprehensive online course designed for beginners and intermediate learners. Whether you’re looking to launch a career in data science, enhance your analytical skills, or automate reporting tasks, this course will equip you with the tools and techniques needed to extract insights from data using Python.

You’ll start with a foundation in Python programming and gradually move on to data manipulation, visualization, and statistical analysis using industry-standard libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn. Through real-world datasets and hands-on projects, you’ll learn how to clean, analyze, and visualize data to support decision-making and storytelling.

What You’ll Learn:

Python basics and data structures
Working with NumPy and Pandas for data manipulation
Data cleaning and preprocessing techniques
Data visualization with Matplotlib and Seaborn
Exploratory Data Analysis (EDA)
Introduction to statistical analysis
Basics of machine learning with Scikit-learn
Real-world projects and case studies

By the end of this course, you will be able to:
✅ Write Python code for data analysis tasks, even if you're a beginner
✅ Work confidently with NumPy and Pandas for data manipulation
✅ Clean and preprocess real-world datasets for analysis
✅ Visualize data effectively using Matplotlib and Seaborn
✅ Perform exploratory data analysis (EDA) to uncover insights
✅ Apply basic statistical techniques for data interpretation
✅ Build simple machine learning models using Scikit-learn
✅ Create end-to-end data analysis projects and share your findings

Course Content

Module 1: Introduction to Python for Data Analysis
Installing Python and Jupyter Notebook Python basics: variables, data types, functions Lists, tuples, dictionaries, and sets Control flow: if statements, loops Working with files

Module 2: Working with NumPy
Introduction to NumPy arrays Array operations and broadcasting Indexing, slicing, and reshaping Statistical operations and math functions

Module 3: Data Manipulation with Pandas
Introduction to Pandas Series and DataFrames Reading and writing data (CSV, Excel, JSON) Filtering, sorting, and selecting data Handling missing values and duplicates GroupBy operations and aggregation Merging, joining, and concatenating data

Module 4: Data Cleaning and Preprocessing
Identifying and handling missing or invalid data Data type conversions Encoding categorical variables Working with datetime objects Data normalization and scaling

Module 5: Data Visualization
Introduction to Matplotlib Creating line, bar, scatter, and histogram plots Customizing plots (titles, legends, labels) Advanced visualizations with Seaborn: Heatmaps, pairplots, boxplots, violin plots

Module 6: Exploratory Data Analysis (EDA)
Formulating questions and hypotheses Univariate and bivariate analysis Correlation and distribution analysis Feature engineering basics

Module 7: Introduction to Statistics for Data Analysis
Descriptive statistics: mean, median, mode, std dev Probability distributions Hypothesis testing basics (t-tests, chi-square tests) Confidence intervals and p-values

Module 8: Introduction to Machine Learning with Scikit-learn
What is machine learning? Supervised vs. unsupervised Preparing data for ML models Regression analysis (Linear Regression) Classification basics (Logistic Regression, Decision Trees) Model evaluation (accuracy, confusion matrix, cross-validation)

Module 9: Real-World Projects
Project 1: Customer segmentation using Pandas and Seaborn Project 2: Sales trend analysis from Excel datasets Project 3: Predicting house prices using Scikit-learn

Module 10: Final Assessment and Certification
Final project with full data analysis report Course recap and next steps Earning the certificate

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Data analysis with Python

About Course

What You’ll Learn:

What Will You Learn?

Course Content

Module 1: Introduction to Python for Data Analysis
Installing Python and Jupyter Notebook Python basics: variables, data types, functions Lists, tuples, dictionaries, and sets Control flow: if statements, loops Working with files

Module 2: Working with NumPy
Introduction to NumPy arrays Array operations and broadcasting Indexing, slicing, and reshaping Statistical operations and math functions

Module 3: Data Manipulation with Pandas
Introduction to Pandas Series and DataFrames Reading and writing data (CSV, Excel, JSON) Filtering, sorting, and selecting data Handling missing values and duplicates GroupBy operations and aggregation Merging, joining, and concatenating data

Module 4: Data Cleaning and Preprocessing
Identifying and handling missing or invalid data Data type conversions Encoding categorical variables Working with datetime objects Data normalization and scaling

Module 5: Data Visualization
Introduction to Matplotlib Creating line, bar, scatter, and histogram plots Customizing plots (titles, legends, labels) Advanced visualizations with Seaborn: Heatmaps, pairplots, boxplots, violin plots

Module 6: Exploratory Data Analysis (EDA)
Formulating questions and hypotheses Univariate and bivariate analysis Correlation and distribution analysis Feature engineering basics

Module 7: Introduction to Statistics for Data Analysis
Descriptive statistics: mean, median, mode, std dev Probability distributions Hypothesis testing basics (t-tests, chi-square tests) Confidence intervals and p-values

Module 9: Real-World Projects
Project 1: Customer segmentation using Pandas and Seaborn Project 2: Sales trend analysis from Excel datasets Project 3: Predicting house prices using Scikit-learn

Module 10: Final Assessment and Certification
Final project with full data analysis report Course recap and next steps Earning the certificate

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About Course

What You’ll Learn:

What Will You Learn?

Course Content

Module 1: Introduction to Python for Data Analysis Installing Python and Jupyter Notebook Python basics: variables, data types, functions Lists, tuples, dictionaries, and sets Control flow: if statements, loops Working with files

Module 2: Working with NumPy Introduction to NumPy arrays Array operations and broadcasting Indexing, slicing, and reshaping Statistical operations and math functions

Module 3: Data Manipulation with Pandas Introduction to Pandas Series and DataFrames Reading and writing data (CSV, Excel, JSON) Filtering, sorting, and selecting data Handling missing values and duplicates GroupBy operations and aggregation Merging, joining, and concatenating data

Module 4: Data Cleaning and Preprocessing Identifying and handling missing or invalid data Data type conversions Encoding categorical variables Working with datetime objects Data normalization and scaling

Module 5: Data Visualization Introduction to Matplotlib Creating line, bar, scatter, and histogram plots Customizing plots (titles, legends, labels) Advanced visualizations with Seaborn: Heatmaps, pairplots, boxplots, violin plots

Module 6: Exploratory Data Analysis (EDA) Formulating questions and hypotheses Univariate and bivariate analysis Correlation and distribution analysis Feature engineering basics

Module 7: Introduction to Statistics for Data Analysis Descriptive statistics: mean, median, mode, std dev Probability distributions Hypothesis testing basics (t-tests, chi-square tests) Confidence intervals and p-values

Module 9: Real-World Projects Project 1: Customer segmentation using Pandas and Seaborn Project 2: Sales trend analysis from Excel datasets Project 3: Predicting house prices using Scikit-learn

Module 10: Final Assessment and Certification Final project with full data analysis report Course recap and next steps Earning the certificate

Student Ratings & Reviews

Trending Courses

Trending College Workshops

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Categories

Module 1: Introduction to Python for Data Analysis
Installing Python and Jupyter Notebook Python basics: variables, data types, functions Lists, tuples, dictionaries, and sets Control flow: if statements, loops Working with files

Module 2: Working with NumPy
Introduction to NumPy arrays Array operations and broadcasting Indexing, slicing, and reshaping Statistical operations and math functions

Module 3: Data Manipulation with Pandas
Introduction to Pandas Series and DataFrames Reading and writing data (CSV, Excel, JSON) Filtering, sorting, and selecting data Handling missing values and duplicates GroupBy operations and aggregation Merging, joining, and concatenating data

Module 4: Data Cleaning and Preprocessing
Identifying and handling missing or invalid data Data type conversions Encoding categorical variables Working with datetime objects Data normalization and scaling

Module 5: Data Visualization
Introduction to Matplotlib Creating line, bar, scatter, and histogram plots Customizing plots (titles, legends, labels) Advanced visualizations with Seaborn: Heatmaps, pairplots, boxplots, violin plots

Module 6: Exploratory Data Analysis (EDA)
Formulating questions and hypotheses Univariate and bivariate analysis Correlation and distribution analysis Feature engineering basics

Module 7: Introduction to Statistics for Data Analysis
Descriptive statistics: mean, median, mode, std dev Probability distributions Hypothesis testing basics (t-tests, chi-square tests) Confidence intervals and p-values

Module 9: Real-World Projects
Project 1: Customer segmentation using Pandas and Seaborn Project 2: Sales trend analysis from Excel datasets Project 3: Predicting house prices using Scikit-learn

Module 10: Final Assessment and Certification
Final project with full data analysis report Course recap and next steps Earning the certificate