📝
InternshipGitbook
  • Internship Onboarding 2025
  • Python Data Science Environment Setup
  • Learn and Install Jupyter Notebook
  • Learn From the Basic to Advanced
Powered by GitBook
On this page
  • 1. Introduction to Data Science and Machine Learning
  • 📌 Courses:
  • 2. Setting Up the Environment
  • 📌 Courses:
  • 3. Python for Data Science
  • 📌 Courses:
  • 4. Data Wrangling and Preprocessing
  • 📌 Courses:
  • 5. Exploratory Data Analysis (EDA)
  • 📌 Courses:
  • 6. SQL for Data Science
  • 📌 Courses:
  • 7. Machine Learning Basics
  • 📌 Courses:
  • 8. Supervised Learning Algorithms
  • 📌 Courses:
  • 9. Unsupervised Learning
  • 📌 Courses:
  • 10. Deep Learning Basics
  • 📌 Courses:
  • 11. Natural Language Processing (NLP)
  • 📌 Courses:
  • 12. Model Deployment
  • 📌 Courses:
  • 13. Real-World Projects & Case Studies
  • 📌 Courses:
  • 14. Best Practices in Data Science
  • 📌 Courses:
  • 15. Resume & Portfolio Building
  • 📌 Courses:

Was this helpful?

Internship Onboarding 2025

Below is a structured GitBook Content Outline for your Internship Training Materials in Data Science and Machine Learning. This will help interns build the necessary skills before starting a project.

NextPython Data Science Environment Setup

Last updated 3 months ago

Was this helpful?


GitBook: Data Science & Machine Learning Internship Training

1. Introduction to Data Science and Machine Learning

  • Overview of Data Science & ML

  • Role of a Data Science & ML Engineer

  • Applications in real-world projects

  • Required Tools & Technologies

📌 Courses:


2. Setting Up the Environment

  • Installing Python & Jupyter Notebook

  • Overview of Anaconda, VS Code, and Google Colab

  • Introduction to Git, GitHub, and Version Control

  • Setting up Virtual Environments (venv, conda)

📌 Courses:


3. Python for Data Science

  • Python Basics (Variables, Data Types, Control Flow)

  • Functions, Lambda, and List Comprehensions

  • Working with Libraries (NumPy, Pandas, Matplotlib, Seaborn)

  • File Handling (CSV, JSON, Excel, SQL)

📌 Courses:


4. Data Wrangling and Preprocessing

  • Handling Missing Values

  • Data Cleaning and Formatting

  • Feature Engineering and Selection

  • Encoding Categorical Variables

  • Handling Outliers and Scaling Data

📌 Courses:


5. Exploratory Data Analysis (EDA)

  • Understanding Data Distributions

  • Visualization Techniques (Matplotlib, Seaborn, Plotly)

  • Correlation Analysis

  • Hypothesis Testing

📌 Courses:


6. SQL for Data Science

  • Basics of SQL (Select, Insert, Update, Delete)

  • Joins, Subqueries, and Aggregations

  • Writing Efficient Queries for Large Datasets

  • Using SQL for Data Analysis

📌 Courses:


7. Machine Learning Basics

  • Understanding ML Workflow

  • Supervised vs. Unsupervised Learning

  • Model Selection and Evaluation Metrics

  • Bias-Variance Tradeoff

📌 Courses:


8. Supervised Learning Algorithms

  • Regression (Linear, Ridge, Lasso)

  • Classification (Logistic Regression, Decision Trees, Random Forest, XGBoost, SVM, k-NN)

  • Model Tuning (Hyperparameter Optimization)

📌 Courses:


9. Unsupervised Learning

  • Clustering (K-Means, DBSCAN, Hierarchical)

  • Dimensionality Reduction (PCA, t-SNE)

📌 Courses:


10. Deep Learning Basics

  • Introduction to Neural Networks

  • Basics of TensorFlow and PyTorch

  • Building a Simple Neural Network

  • Image Classification with CNNs

📌 Courses:


11. Natural Language Processing (NLP)

  • Text Preprocessing (Tokenization, Lemmatization, Stopwords)

  • Sentiment Analysis & Named Entity Recognition

  • Using Pretrained Models (BERT, GPT)

📌 Courses:


12. Model Deployment

  • Introduction to Flask & FastAPI for Model Deployment

  • Deploying ML Models using Streamlit

  • Working with Docker & Cloud Deployment (AWS, GCP)

📌 Courses:


13. Real-World Projects & Case Studies

  • Sentiment Analysis on Twitter Data

  • Customer Segmentation for a Retail Business

  • Predicting Loan Default using Credit Risk Data

  • Demand Forecasting for an E-commerce Company

📌 Courses:


14. Best Practices in Data Science

  • Model Interpretability (SHAP, LIME)

  • Data Science Ethics & Bias in AI

  • Writing Reproducible Code & Documentation

📌 Courses:


15. Resume & Portfolio Building

  • Creating an Impressive LinkedIn & GitHub Profile

  • Writing a Strong Resume for Data Science Roles

  • Preparing for Technical Interviews

📌 Courses:


This structured roadmap with courses ensures that you will gain strong theoretical knowledge and hands-on experience before working on projects. 🚀

IBM Data Science Professional Certificate (Coursera)
Introduction to Machine Learning (Udacity)
Python for Data Science (DataCamp)
Version Control with Git (Udacity)
Python for Data Science and Machine Learning Bootcamp (Udemy)
Data Manipulation with Pandas (DataCamp)
Feature Engineering for Machine Learning (Coursera)
Data Cleaning in Python (DataCamp)
Data Visualization with Python (Coursera)
Exploratory Data Analysis in Python (DataCamp)
SQL for Data Science (Coursera)
Advanced SQL for Data Analysis (Mode Analytics)
Machine Learning by Andrew Ng (Coursera)
Supervised Machine Learning: Regression and Classification (Coursera)
Supervised Learning with scikit-learn (DataCamp)
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow (Book)
Unsupervised Learning in Python (DataCamp)
Applied Unsupervised Learning (Coursera)
Deep Learning Specialization by Andrew Ng (Coursera)
TensorFlow for Deep Learning (Udacity)
Natural Language Processing with Deep Learning (Stanford)
NLP with Python (DataCamp)
Machine Learning Model Deployment (Udemy)
Serverless Machine Learning with AWS Lambda (DataCamp)
End-to-End Machine Learning Projects (Udemy)
Practical Data Science Specialization (Coursera)
Interpretable Machine Learning (Book)
Data Science Ethics (Coursera)
Data Science Career Guide (Udacity)
Cracking the Data Science Interview (Book)