Data Analytics Using Python (Beginner)
Instructor: Mohammad Yasin Ud Dowla
yasinuddowla.com
Course Duration: 810 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
,
Scikitlearn
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
Scikitlearn
, focusing on regression and
classification.
 Build realworld datadriven 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 Scikitlearn

Introduction to Machine Learning
 Supervised vs. unsupervised learning
 Key concepts: training and testing, overfitting

Simple Regression Models
 Linear Regression with
Scikitlearn
 Model evaluation (Rsquared, MSE)

Classification Techniques
 KNearest 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
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