Python for Data Science Training for Beginners is a hands-on course designed to build a strong foundation in data science using Python. The course introduces core data science concepts and guides learners through Python programming, data manipulation, statistical analysis, visualization, and introductory machine learning. 

Learners work with industry-standard tools such as Jupyter Notebook, Google Colab, and Python libraries including NumPy, Pandas, Matplotlib, and Scikit-learn to analyze and visualize real-world datasets. The course also covers essential statistics, probability, and data preprocessing techniques to prepare data for analysis and modeling. 

By the end of the course, learners gain practical experience applying Python to end-to-end data science workflows and are well prepared to advance into machine learning, analytics, or cloud-based data science training. 

To successfully complete Python for Data Science Training for Beginners, learners should have: 

• Basic computer literacy and comfort using a computer 

• Interest in working with data, analysis, and problem-solving 

• Willingness to learn Python programming from the ground up 

• No prior programming experience required 

• Willingness to work with hands-on labs, assignments, and real-world datasets 

By the end of this course, you will be able to:

• Understand core data science concepts and how Python is used in data-driven workflows

• Write and execute Python programs using fundamental programming constructs

• Work with NumPy and Pandas to manipulate, clean, and analyze datasets

• Perform data preprocessing tasks including handling missing values, normalization, and encoding

• Apply statistical and probability concepts to analyze and interpret data

• Create meaningful data visualizations using Matplotlib and Pandas

• Perform exploratory data analysis (EDA) to identify patterns and insights

• Build and evaluate basic machine learning models using Scikit-learn

• Apply regression techniques for prediction and decision-making

• Understand supervised and unsupervised machine learning concepts

• Work with real-world datasets through hands-on labs and assignments

• Build a strong foundation to progress into advanced data science or Azure ML training

This course prepares learners for entry-level and foundational roles in data science and analytics. After completing the training, learners will be better prepared for positions such as:

• Data Analyst

• Junior Data Scientist

• Business Analyst (Data & Analytics)

• Data Science Associate

• Analytics Associate

• Reporting Analyst

• Python Data Analyst

• Entry-Level Machine Learning Analyst

• Data Operations Analyst

• Data Visualization Analyst

Module 1: Introduction to Data Science

• What is Data Science and why it matters

• Data science lifecycle and real-world applications

• Collecting, storing, and processing data

• Describing data and basic analytical thinking

• Statistical modeling vs algorithmic modeling

• Relationship between Data Science, Machine Learning, and AI

Module 2: Introduction to Python

• Why Python for data science

• Key features of Python

• Installing Python and setting up the environment

• Python IDEs overview

• Working with Jupyter Notebook

• Using Google Colab for cloud-based development

Module 3: Python Refresher

• Basic data types

• Operators and expressions

• Input and output statements

• Control statements (if, loops)

• Functions and modular code

• Working with strings

• Lists and tuples

• Dictionaries

• File handling

• Exception handling

• Introduction to Object-Oriented Programming (OOP)

Module 4: Python Libraries for Data Science

• Introduction to data science libraries

• NumPy for numerical computing

• Pandas for data manipulation and analysis

• DataFrames and Series operations

• Matplotlib for basic data visualization

• Introduction to Scikit-learn

Module 5: Introduction to Statistics

• What is statistics and why it matters in data science

• Quantitative vs categorical data

• Grouping and displaying data

• Histograms, stem plots, and time plots

• Summary statistics

• Measures of center (mean, median, mode)

• Measures of spread (range, variance, standard deviation)

• Identifying outliers

• Interquartile range (IQR)

• Percentiles

• Box and whisker plots

Module 6: Probability

• Introduction to probability

• Random variables

• Probability distributions

• Binomial distribution

• Poisson distribution

• Normal distribution

• Density curves

• Symmetry and skewness

• 68–95–99.7 rule

• Z-scores

Module 7: Data Preprocessing with Python

• Importance of data preprocessing

• Python libraries for preprocessing

• Data cleaning and data wrangling

• Handling missing values

• Data formatting and transformation

• Data normalization and scaling

• Binning techniques

• One-hot encoding and categorical data handling

Module 8: Data Visualization with Python

• Introduction to data visualization principles

• Matplotlib and Pandas integration

• Creating histograms

• Line plots

• Bar charts

• Area plots

• Pie charts

• Box plots

• Scatter plots

• Visualizing patterns and relationships in data

Module 9: Data Analysis with Python

• Exploratory Data Analysis (EDA)

• Understanding data patterns and trends

• Analysis of variance (ANOVA)

• Positive and negative correlation

• Pearson correlation coefficient

• Introduction to regression analysis

Module 10: Model Development and Evaluation

• Introduction to the Scikit-learn library

• Simple linear regression

• Multiple linear regression

• Model training workflows

• Model evaluation concepts

• Prediction and decision-making

Module 11: Introduction to Machine Learning

• What is machine learning

• Supervised machine learning

• Unsupervised machine learning

• Introduction to recommender systems

• Overview of deep learning concept

Module 12: Assignments and Hands-On Practice

• Guided hands-on exercises for each module

• Real-world datasets and scenarios

• End-to-end data analysis mini projects

• Reinforcement through practice and review

• Preparation for next-level data science and Azure ML courses