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Python Programming Foundations
6 Lessons-
StartSetting up the Python environment (VS Code)
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StartBasic syntax, data types, and variables
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StartConditional statements (if, else, elif) and loops (for, while)
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StartIntroduction to Integrated Development Environments (IDEs)
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StartBasic data structures (lists, dictionaries, sets, tuples)
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StartWriting simple functions, understanding built-in functions
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Data Manipulation with Python
8 Lessons-
StartIntroduction to Pandas for data manipulation
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StartUnderstanding Series and DataFrame structures
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StartImporting and exporting data (CSV, Excel)
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StartUntitled lesson
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StartImporting and exporting data (CSV, Excel, JSON)
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StartData Cleaning Techniques
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StartHandling missing data, duplicates, and outliers
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StartData transformation and normalization
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Data Visualization and Exploratory Data Analysis (EDA)
8 Lessons-
StartPrinciples of effective data visualization
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StartIntroduction to Matplotlib and Seaborn
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StartCreating Visual Representations
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StartPlot types: histograms, scatter plots, box plots, line charts, and bar charts
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StartCustomizing visualizations (themes, labels, legends, annotations)
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StartExploratory Data Analysis (EDA) Techniques
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StartSummary statistics and distribution analysis
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StartIdentifying patterns, correlations, and trends in data
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SQL for Data Analytics
9 Lessons-
StartUnderstanding Relational Databases
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StartIntroduction to databases and SQL
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StartOverview of MySQL, PostgreSQL, and SQLite
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StartWriting queries: SELECT, FROM, WHERE
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StartAggregation functions (GROUP BY, HAVING, ORDER BY)
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StartJoins (INNER, LEFT, RIGHT, FULL) and subqueries
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StartConnecting Python with SQL
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StartUsing SQLite3, SQLAlchemy, and Pandas’ read_sql function
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StartHands-on exercises with sample databases
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Statistical Analysis and Introduction to Machine Learning
10 Lessons-
StartStatistics for Data Analysis
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StartDescriptive statistics: mean, median, variance, standard deviation
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StartInferential statistics: probability distributions, hypothesis testing
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StartRegression and Correlation Analysis
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StartSimple and multiple linear regression
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StartUnderstanding correlation vs. causation
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StartIntroduction to Machine Learning with Python
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StartOverview of machine learning concepts
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StartBasic algorithms: linear regression, classification (logistic regression), clustering
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StartIntroduction to Scikit-learn (training models, cross-validation, model evaluation)
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Power BI for Data Analytics
7 Lessons -
Capstone Project and Advanced Topics
7 Lessons-
StartEnd-to-end project integrating Python, SQL, and visualization tools
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StartData sourcing, cleaning, analysis, and insights presentation
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StartCreating a report or dashboard
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StartAdvanced Topics
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StartAutomating data pipelines (Airflow, Python scripts)
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StartIntroduction to big data and cloud data services
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StartBest practices in reproducible research and version control
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Data Analytics
Python and Data Analysis Zero to Hero Course
Course Summary
This beginner-friendly Data Analytics with Python course is designed to take you from basic programming concepts to advanced data analysis skills. You will start with an introduction to Python, learning its syntax, data types, and basic programming structures. The course then covers data manipulation using Pandas, visualization with Matplotlib and Seaborn, and SQL for data querying. You'll dive into statistical analysis and machine learning basics, explore Power BI for data visualization, and work on a hands-on Capstone Project. Finally, the course includes job placement assistance, resume optimization, and mock interviews to help you launch your data analytics career.
Course Curriculum
AFRIUS TRAINING
Course Pricing
PAYMENT PLAN
4 payments of
$499.75 USD
every 2 weeks
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Payments will be deducted every 2 weeks
