Data Analytics Fundamentals with Python
Course Desscription
This comprehensive 12-week course is designed for aspiring data analysts to master the foundational tools, techniques, and concepts of data analytics using Python. The curriculum covers everything from Python basics to advanced data manipulation and visualization, ensuring participants develop the skills needed to analyze, interpret, and present data effectively.
The course combines hands-on coding exercises, real-world case studies, and project-based learning to build proficiency in Python programming, data wrangling, exploratory data analysis (EDA), and feature engineering. Participants will also gain experience with industry-standard libraries like Numpy, Pandas, Matplotlib, and Seaborn, alongside techniques for handling missing data, detecting outliers, and reducing dimensionality with Principal Component Analysis (PCA).
Main Features
This hands-on course covers Python basics, data manipulation, visualization (Pandas, Numpy, Matplotlib, Seaborn), EDA, feature engineering, and PCA. Participants will apply skills to analyze real-world datasets and complete a capstone project.
Learning Outcomes
- Gain proficiency in Python programming and popular data analytics libraries.
- Manipulate, clean, and transform raw data into actionable insights.
- Create compelling visualizations to communicate data findings effectively.
- Understand the fundamentals of data science workflows, including EDA, feature engineering, and outlier detection.
- Apply dimensionality reduction techniques like PCA to streamline complex datasets.
- Complete a real-world data analytics project from start to finish.
The Data Analytics Fundamentals with Python course teaches data analysis using Python libraries like Pandas, NumPy, and Matplotlib. Topics include data cleaning, exploratory data analysis (EDA), visualization, handling missing data, and feature engineering. Students will gain practical skills to analyze real-world datasets and explore basic machine learning for predictive analytics.
List Of Weeks
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Class 1
Introduction to Python, installation, and IDE setup (Jupyter, VS Code)
Python syntax, variables, data types (integers, strings, floats)
Basic operators and expressionsDuration: 1 hour 30 min
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Class 2
Control structures: if, else, elif
Loops: for, while
Functions, defining, and calling functionsDuration: 1 hour 30 min
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Class 3
Lists, tuples, sets, and dictionaries
List comprehensionsDuration: 1 hour 30 min
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Class 4
File handling (reading/writing files)
Exception handling (try/except)Duration: 1 hour 30 min
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Class 5
Introduction to Numpy
Numpy arrays, array indexing, slicing
Basic operations with Numpy arraysDuration: 1 hour 30 min
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Class 6
Introduction to Pandas
Series and DataFrames
DataFrame operations: accessing rows/columns, filtering, and sortingDuration: 1 hour 30 min
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Class 7
Pandas: Merging, joining, and concatenating data
GroupBy operationsDuration: 1 hour 30 min
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Class 8
Aggregating data: sum(), mean(), count(), etc.
Pivot tablesDuration: 1 hour 30 min
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Class 9
Introduction to data visualization with Matplotlib
Basic plotting: line plots, bar charts, histogramsDuration: 1 hour 30 min
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Class 10
Introduction to Seaborn
Advanced visualizations: scatter plots, box plots, heatmapsDuration: 1 hour 30 min
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Class 11
Introduction to EDA: What is it and why it's important?
Descriptive statistics (mean, median, mode, std, variance)
Data distributions and relationshipsDuration: 1 hour 30 min
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Class 12
Using visualization for EDA
Pair plots, correlation matricesDuration: 1 hour 30 min
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Class 13
Data sources: APIs, web scraping, databases
Web scraping basics with BeautifulSoup and requestsDuration: 1 hour 30 min
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Class 14
Collecting data from APIs (using requests, JSON handling)
Storing data in Pandas DataFramesDuration: 1 hour 30 min
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Class 15
Introduction to feature engineering
Creating new features: transformations, combinations
Feature scaling: normalization and standardizationDuration: 1 hour 30 min
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Class 16
Handling categorical data: one-hot encoding, label encoding
Binning and discretizationDuration: 1 hour 30 min
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Class 17
Identifying missing data
Techniques to handle missing data: dropping, filling (imputation)
Advanced imputation strategies: mean, median, mode, KNN imputationDuration: 1 hour 30 min
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Class 18
Handling missing data with Pandas
Case study: Imputing missing data in a datasetDuration: 1 hour 30 min
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Class 19
Identifying outliers using box plots, Z-score, and IQR
Handling outliers: removal, transformationDuration: 1 hour 30 min
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Class 20
Case study: Detecting and handling outliers in real datasets
Duration: 1 hour 30 min
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Class 21
Introduction to PCA
The need for dimensionality reduction
Step-by-step PCA process: covariance matrix, eigenvalues, eigenvectorsDuration: 1 hour 30 min
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Class 22
Applying PCA using sklearn
Visualizing PCA resultsDuration: 1 hour 30 min
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Class 23
Project discussion: Overview of the final project
How to frame a data science project: Problem definition, data collection, and preparationDuration: 1 hour 30 min
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Class 24
Final project presentation or demo
Course review and final Q&ADuration: 1 hour 30 min
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