Data Analytics Using Python (Beginner)

Instructor: Mohammad Yasin Ud Dowla

yasinuddowla.com

Course Duration: 8-10 weeks (2 hours per week)

Prerequisites: Basic computer skills, familiarity with Python is helpful but not required.

Tools: Python, Jupyter Notebook/Google Colab, Pandas, Matplotlib, Seaborn, Scikit-learn

Course Objectives:

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• Gain a strong foundation in Python programming for data analytics.
• Understand key data structures and control flows in Python.
• Learn to manipulate, clean, and process data using the Pandas library.
• Develop skills to visualize data using Matplotlib and Seaborn.
• Apply basic descriptive statistics to summarize and interpret data.
• Conduct exploratory data analysis (EDA) to detect patterns and trends.
• Work with time series data and perform basic time series operations.
• Gain an introduction to machine learning with Scikit-learn, focusing on regression and classification.
• Build real-world data-driven projects and derive actionable insights from data.

Course Outline:

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Module 1: Introduction to Python for Data Analytics

• Introduction to Data Analytics
  • Definition and scope of data analytics
  • Applications in various industries
• Getting Started with Python
  • Python installation (Anaconda, Jupyter, etc.)
  • Basic Python syntax, data types, and structures
• Python IDEs and Jupyter Notebook/Google Colab
  • Introduction to Jupyter Notebook
  • Writing Python scripts in IDEs (VSCode/PyCharm)

Module 2: Python Essentials for Data Analytics

• Data Structures in Python
  • Lists, Tuples, Sets, and Dictionaries
• Control Structures
  • Conditionals (if, else, elif)
  • Loops (for, while)
• Functions and Modules
  • Defining functions
  • Importing and using libraries

Module 3: Data Handling and Processing

• Introduction to Pandas
  • Series and DataFrames
  • Loading datasets (CSV, Excel, JSON)
• Data Cleaning Techniques
  • Handling missing data
  • Removing duplicates, handling outliers
  • Data transformation and formatting
• Data Manipulation with Pandas
  • Filtering, sorting, and grouping data
  • Merging and joining DataFrames
  • Aggregating data

Module 4: Data Visualization

• Introduction to Matplotlib
  • Basic plotting (line, bar, scatter plots)
  • Customizing plots (titles, labels, legends)
• Data Visualization with Seaborn
  • Advanced visualizations (heatmaps, pair plots)
  • Plotting categorical and continuous data
• Saving and Exporting Plots

Module 5: Introduction to Descriptive Statistics

• Basic Statistical Concepts
  • Mean, Median, Mode, Variance, and Standard Deviation
• Data Distributions
  • Normal distribution, skewness, and kurtosis
• Correlation and Covariance

Module 6: Exploratory Data Analysis (EDA)

• Understanding EDA
  • Purpose and importance of EDA in analytics
• EDA Techniques
  • Univariate, Bivariate, and Multivariate analysis
  • Visual and statistical summaries
• Detecting Patterns and Trends

Module 7: Working with Time Series Data

• Introduction to Time Series Data
  • Time series basics and use cases
• Time Series Visualization
  • Plotting time series data
  • Trend detection
• Basic Time Series Operations
  • Shifting, lagging, rolling window operations

Module 8: Introduction to Machine Learning with Scikit-learn

• Introduction to Machine Learning
  • Supervised vs. unsupervised learning
  • Key concepts: training and testing, overfitting
• Simple Regression Models
  • Linear Regression with Scikit-learn
  • Model evaluation (R-squared, MSE)
• Classification Techniques
  • K-Nearest Neighbors (KNN) classifier

Module 9: Case Studies and Projects

• Case Study 1: Analyzing Sales Data
  • Dataset overview and data cleaning
  • Sales trend analysis and visualization
• Case Study 2: Customer Segmentation
  • Clustering customers using KMeans
  • Interpreting clusters and business insights
• Final Project: Build Your Own Data Analytics Project
  • From data acquisition to reporting

Module 10: Final Assessment and Certification

• Final Quiz
• Assignment

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