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

  • 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

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
yasinuddowla.com