Who should learn Data Science with Python?

Aspiring data scientists, data analysts, and software professionals should learn Data Science with Python.

What libraries are commonly used in Data Science with Python?

Common libraries include NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, and TensorFlow.

Is Data Science with Python suitable for beginners?

Yes, basic Python and statistics knowledge is enough to start learning Data Science.

What skills are gained from Data Science with Python?

Skills include data analysis, visualization, machine learning, and predictive modeling.

Does Data Science with Python include machine learning?

Yes, machine learning is a core part of Data Science with Python.

Which industries use Data Science with Python?

Industries include finance, healthcare, retail, IT, marketing, and e-commerce.

Can Data Science with Python support real-world projects?

Yes, it is widely used for real-world analytics and AI projects.

What career opportunities are available after learning Data Science with Python?

Career roles include data scientist, data analyst, ML engineer, and AI specialist.

How can I enroll in Data Science with Python training at ZebLearn?

You can enroll by visiting the ZebLearn website and registering for the Data Science with Python course online.

Data Science with Python Training Program

Enroll in Zeblearns Data Science with Python Training Program to master Python for data analysis, machine learning, and data visualization. Learn about Python programming fundamentals, data manipulation with libraries like Pandas, data visualization with Matplotlib and Seaborn, and machine learning with scikit-learn. Gain hands-on experience through practical exercises and real-world projects. Prepare for industry-recognized certifications in Python. Equip yourself with the skills needed to excel in data science with Python. Register now for comprehensive training with Zeblearn.

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KEY POINTS

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Outcomes

Skills You'll Gain

From cleaning messy data to deploying production AI models — the full data science skillset employers ask for.

Data Wrangling

Clean, transform & engineer features from real-world messy datasets.

Statistics & Probability

Hypothesis testing, distributions & the math behind every model.

Machine Learning

Regression, classification, clustering, ensembles & model tuning.

Deep Learning

Neural networks, CNNs & RNNs with TensorFlow and PyTorch.

NLP

Text processing, embeddings, sentiment analysis & transformers.

Computer Vision

Image classification, object detection & OpenCV pipelines.

Generative AI

LLMs, prompt engineering, RAG & building AI-powered apps.

MLOps & Deployment

Package, serve & monitor models with Docker, FastAPI & MLflow.

Curriculum

What Does the Data Science with Python Curriculum Cover?

Data Science with Python curriculum blends core Python syntax with statistical libraries. You will complete modules on NumPy array manipulation, pandas data frames, and Matplotlib visual storytelling.

Introduction to the Course

Course Help and Welcome

Python Environment Setup

Updates to Notebook Zip

Jupyter Notebooks

Optional: Virtual Environments

• Introduction to Python
- Installation and Working with Python
- Python Variables
- Python Operators
- Python Blocks & Syntax

• Python Keywords & Identifiers
- Comments & Indentation
- Arithmetic Operators
- Relational Operators
- Logical Operators
- Assignment Operators
- Membership Operators
- Identity Operators

• Variables & Data Types
- Global & Local Variables
- Type Casting
- Variable Scope
- Strings
- Lists
- Tuples
- Dictionaries
- Sets

• Control Flow & Loops
- If, Else, Elif
- Nested Conditions
- For & While Loops
- Break, Continue, Pass
- Range Function
- Pattern Programs
- Generators

• Data Structures
- Lists & Comprehensions
- Tuples & Operations
- Dictionaries & Methods
- Sets & Set Operations

• Functions, Modules & Packages
- Functions & Arguments
- Lambda Functions
- Recursive Functions
- Modules & Packages
- Map, Filter & Reduce

• Advanced Python
- Decorators
- Iterators
- Generators
- Exception Handling

• File Handling & OS Module
- File Operations
- Directory Management
- Path Handling
- System Operations

• Database & Excel Integration
- SQL Connectivity
- Database Operations
- Excel Read/Write
- Workbook Management

• AI & LLM Integration
- PandasAI
- OpenAI GPT APIs
- LangChain
- AutoGen & Code Interpreter

Welcome to the NumPy Section!

Introduction to Numpy

Numpy Arrays

Quick Note on Array Indexing

Numpy Array Indexing

Numpy Operations

Welcome to the Pandas Section!

Introduction to Pandas

Series

DataFrames - Part 1

DataFrames - Part 2

DataFrames - Part 3

Missing Data

Groupby

Merging Joining and Concatenating

Operations

Data Input and Output

Note on SF Salary Exercise

SF Salaries Exercise Overview

Welcome to the Data Visualization Section!

Introduction to Matplotlib

Matplotlib

Introduction to Seaborn

Distribution Plots

Distribution Plots Preview

Categorical Plots

Matrix Plots

Grids

Regression Plots

Style and Color

Pandas Built-in Data Visualization

Pandas Data Visualization

Introduction to Plotly and Cufflinks

Plotly and Cufflinks

Learn about Data Visualization with Plotly and Python!

Introduction to Geographical Plotting

Choropleth Maps

Choropleth Exercise

Welcome to the Data Capstone Projects!

Bank Data

Finance Data Project Overview

Finance Project

Welcome to Machine Learning. Here are a few resources to get you started!

Welcome to the Machine Learning Section!

Supervised Learning Overview

Evaluating Performance - Classification Error Metrics

Evaluating Performance - Regression Error Metrics

Machine Learning with Python

Linear Regression Theory

model_selection Updates for SciKit Learn

Linear Regression with Python

Linear Regression Project

KNN Theory

Learn K Nearest Neighbors with Python! (05:38)

KNN with Python (19:39)

KNN Project Overview

Introduction to Tree Methods

Decision Trees and Random Forest with Python

Decision Trees and Random Forest Project Overview

Decision Trees and Random Forest Solution

SVM Theory

Support Vector Machines with Python

SVM Project Overview

K Means Algorithm Theory

K Means with Python

K Means Project Overview

K Means Project Solutions

Natural Language Processing Theory

Learn about Natural Language Processing with Python!

NLP with Python - Part 1

Learn about Natural Language Processing with Python!

NLP with Python - Part 2

Learn about Natural Language Processing with Python!

NLP with Python - Part 3

Learn about Natural Language Processing with Python!

NLP Project Overview

Learn about Natural Language Processing with Python!

NLP Project Solutions

Download TensorFlow Notebooks Here

Quick Check for Notes

Welcome to the Deep Learning Section!

Introduction to Artificial Neural Networks (ANN)

Installing Tensorflow

Perceptron Model

Neural Networks

Activation Functions

Multi-Class Classification Considerations

Cost Functions and Gradient Descent

Backpropagation

TensorFlow vs Keras

TF Syntax Basics - Part One - Preparing the Data

TF Syntax Basics - Part Two - Creating and Training the Model

TF Syntax Basics - Part Three - Model Evaluation

TF Regression Code Along - Exploratory Data Analysis

TF Regression Code Along - Exploratory Data Analysis - Continued

TF Regression Code Along - Data Preprocessing and Creating a Model

TF Regression Code Along - Model Evaluation and Predictions

TF Classification Code Along - EDA and Preprocessing

TF Classification - Dealing with Overfitting and Evaluation

TensorFlow 2.0 Project Options Overview

TensorFlow 2.0 Project Notebook Overview

Keras Project Solutions - Dealing with Missing Data

Keras Project Solutions - Dealing with Missing Data - Part Two

Keras Project Solutions - Categorical Data

Keras Project Solutions - Data PreProcessing

Keras Project Solutions - Creating and Training a Model

Beyond Learning

We empower you with skills that land your dream job.

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Step by Step

Your Learning Journey

A structured path from absolute basics to deploying your own AI models — no prior coding needed.

Foundations

Python programming, problem-solving & the math that powers data science.

PythonStatisticsLinear Algebra

Data Analysis & Visualization

Wrangle data with Pandas & SQL, then tell stories with charts & dashboards.

PandasSQLPower BI

Machine Learning

Build, evaluate & tune supervised and unsupervised models on real data.

scikit-learnXGBoostModel Tuning

Deep Learning

Neural networks for images & text using TensorFlow and PyTorch.

TensorFlowPyTorchCNN / RNN

Generative AI & LLMs

Prompt engineering, RAG, fine-tuning & building apps on top of LLMs.

OpenAILangChainRAG

Capstone & Deployment

Ship an end-to-end AI project to production & build your portfolio.

DockerFastAPIPortfolio

Portfolio

Real-World AI Projects You'll Build

Graduate with a portfolio of deployable projects — the kind hiring managers actually want to see.

NLP

Sentiment Analysis Engine

Classify product reviews as positive/negative using transformers.

PythonNLTKBERT

Recommender

Movie Recommendation System

Build a collaborative-filtering engine like Netflix uses.

Pandasscikit-learnSurprise

Computer Vision

Image Classifier

Train a CNN to recognise objects from thousands of images.

TensorFlowKerasOpenCV

Forecasting

Sales Demand Forecasting

Predict future demand with time-series models for a retailer.

ProphetPandasXGBoost

Generative AI

LLM-Powered Chatbot

A RAG chatbot that answers from your own documents.

OpenAILangChainFAISS

Classification

Fraud Detection

Flag fraudulent transactions on imbalanced financial data.

scikit-learnSMOTEPython

Meet Your Mentors

Why Industry Advisors Guide the Data Science with Python Journey?

Data Science with Python instruction is overseen by senior data engineers from Fortune‑500 firms. Their mentorship includes weekly code reviews and feedback on model performance metrics.

Vinay

Workday HCM Trainer

Workday HCM expert with hands-on experience in core HR, supervisory organizations, business process configuration and condition rules. Trains learners on real-time tenant setup, security groups and end-to-end employee data management for global rollouts.

Sagorika

Workday HCM Trainer

Workday HCM specialist covering core HR, security administration, and absence and leave management. Brings practical project experience in domain and business process security, time-off plans and configurable validations, helping freshers become job-ready consultants.

Harrison Manoj

Workday HCM Trainer

Workday HCM consultant focusing on compensation, advanced compensation and talent management. Experienced in merit cycles, bonus and stock plans, performance reviews and goal management, with a strong emphasis on real interview scenarios and live tenant demos.

Bharath

Workday HCM Trainer

Workday HCM trainer experienced in the business process framework, condition rules, notifications and custom validations. Guides learners through tenant configuration, calculated fields and approval routing using real-world MNC implementation use cases.

Arramareddy Sindhu

Workday HCM Trainer

Workday HCM specialist covering staffing models, organization structures, job and position management, and reporting. Skilled in building custom and matrix reports, calculated fields and dashboards that mirror live client requirements.

Jhanvi

Workday HCM Trainer

Workday HCM expert in core HR, time tracking and employee and manager self-service. Trains on hire-to-retire processes, time entry templates, work schedules and mobile setup, combining theory with hands-on tenant practice.

Madhu

Workday HCM Trainer

Workday HCM consultant skilled in benefits administration, payroll integration and data conversion using EIB. Covers open enrollment, benefit event rules and inbound and outbound integrations, preparing learners for real implementation and support projects.

Nagaraju

Workday Techno Functional Trainer

Workday techno-functional expert covering core connectors, EIB, Workday Studio and web services. Experienced in building and debugging integrations, calculated fields and Report Writer, bridging functional HCM knowledge with strong technical integration skills.

Prerna

Workday HCM Trainer

Workday HCM trainer specializing in recruiting and onboarding. Covers job requisitions, candidate pipelines, offer and onboarding business processes and notifications, with practical sessions on configuring an end-to-end hire process.

Ekta

Workday HCM / HR Trainer

Workday HCM and HR training specialist covering the complete employee lifecycle from hire to termination. Combines core HCM configuration with HR domain knowledge, focusing on real-time scenarios, security and reporting for job-ready outcomes.

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