Data Science & Ai Mastery: 100 Days To Career Success

Master Data Science & AI in 100 Days with Hands-On Projects, Real Case Studies, and Career-Ready Skills

What you'll learn

Master Python programming, statistics, and data handling as the foundation for Data Science & AI

Perform data cleaning, feature engineering, and exploratory data analysis (EDA) with real-world datasets

Build and evaluate machine learning models for regression, classification, clustering, and forecasting

Apply deep learning with Neural Networks, CNNs, RNNs, and LSTMs using TensorFlow/Keras.

Work with Large Language Models (LLMs), practice prompt engineering, and explore generative AI use cases

Solve industry-level case studies such as churn prediction, sales forecasting, and recommendation systems.

Develop an end-to-end capstone project with data pipeline, model, dashboard, and business insights

Build a portfolio and resume that showcase your skills and prepare you for career opportunities in Data Science & AI.

Requirements

No prior experience in Data Science or AI is required — the course starts from the basics

A basic understanding of high-school level math (algebra, probability, and statistics) will be helpful

Familiarity with Python programming is a plus, but not mandatory — core concepts are covered early on

A computer with internet access and the ability to install software such as Python, Jupyter Notebook, and required libraries.

Most importantly: a growth mindset, curiosity, and commitment to completing the 100 days of structured learning.

Description

“This course contains the use of artificial intelligence.”This 100-Day Data Science & AI Program is a complete journey from foundations to advanced applications, designed to take you from beginner to career-ready professional. Across 100 days of structured learning, you will master Python programming, data handling, visualization, statistics, machine learning, deep learning, and generative AI.Each phase of the program includes hands-on labs where you apply concepts to real-world datasets, building skills that go beyond theory. You will work on case studies in areas like customer churn prediction, sales forecasting, recommendation systems, and business automation, ensuring practical exposure to industry use cases.The highlight of the program is the capstone project, where you design an end-to-end pipeline (data → model → dashboard → business insights) and present it as part of your portfolio. Along the way, you will also prepare a resume and personal brand that align with Data Science & AI roles.By the end of this program, you will have:• Completed 100 days of learning with a step-by-step roadmap.• Built multiple portfolio-ready projects.• Gained mastery through hands-on labs and applied case studies.• Delivered a capstone project that demonstrates industry-ready skills.• Positioned yourself for exciting career opportunities in Data Science, Machine Learning, and AI.This course is not just about learning—it’s about transforming your skills into career growth and new opportunities.

Overview

Section 1: Phase 1: Foundations of Data Science (Days 1–20)

Lecture 1 Day 1–5: Python basics (variables, loops, functions, OOP)

Lecture 2 Day 6–10: Data handling with NumPy & Pandas

Lecture 3 Day 11–15: Data visualization (Matplotlib, Seaborn)

Lecture 4 Day 16–20: Statistics & probability (mean, variance, distributions, hypothesis)

Lecture 5 Hands on Lab 1

Section 2: Phase 2 Data Wrangling & Exploration (Days 21–35)

Lecture 6 Day 21–25 Data cleaning (missing values, duplicates, outliers)

Lecture 7 Day 26–30 Feature engineering (encoding, scaling, transformations)

Lecture 8 Day 31–35 Exploratory Data Analysis (EDA) with case studies

Lecture 9 Hands on Lab 2

Section 3: Phase 3 Machine Learning Core (Days 36–55)

Lecture 10 Day 36–40 Intro to ML, traintest split, evaluation metrics

Lecture 11 Day 41–45 Regression models (Linear, Logistic, Ridge, Lasso)

Lecture 12 Day 46–50 Classification models (Decision Trees, Random Forest, SVM, KNN)

Lecture 13 Day 51–55 Unsupervised learning (K-Means, PCA, clustering use cases)

Lecture 14 Hands on Lab 3

Section 4: Phase 4 Applied ML & Projects (Days 56–70)

Lecture 15 Day 56–60 Case study — Predict customer churn (classification)

Lecture 16 Day 61–65 Case study — Sales forecasting (time-series)

Lecture 17 Day 66–70 Case study — Recommendation systems (collaborative filtering)

Lecture 18 Hands on Lab 4

Section 5: Phase 5 Deep Learning Foundations (Days 71–85)

Lecture 19 Day 71–75 Neural networks basics (perceptrons, forwardbackpropagation)

Lecture 20 Day 76–80 Deep learning with TensorFlowKeras

Lecture 21 Day 81–85 Applications — Image classification (CNN), text processing (RNNLSTM)

Lecture 22 Hands on Lab 5

Section 6: Phase 6 Generative AI & Advanced Applications (Days 86–95)

Lecture 23 Day 86–88 Introduction to LLMs (GPT, BERT, transformers)

Lecture 24 Day 89–91 Prompt engineering & fine-tuning basics

Lecture 25 Day 92–93 AI for business (automation, NLP, chatbots)

Lecture 26 Day 94–95 AI in industries (aviation, healthcare, finance)

Lecture 27 Hands on Lab 6

Section 7: Phase 7 Capstone & Future Path (Days 96–100)

Lecture 28 Day 96–98 Capstone project — End-to-end pipeline (data → ML model → dashboardAPI

Lecture 29 Day 99 Portfolio & resume building for Data ScienceAI roles

Lecture 30 Day 100 Presentation + “Next Steps” (MLOps, advanced DL, specialized domains)

Lecture 31 Hands on Lab 7

Beginners who want to start their journey into Data Science and AI with a structured, step-by-step roadmap,Students & graduates from any field looking to build portfolio-ready projects and land entry-level roles in Data Science, Machine Learning, or AI,Professionals in IT, business, or analytics who want to upskill and transition into high-demand AI-driven careers,Entrepreneurs & innovators interested in applying AI solutions to solve business or industry problems.,Lifelong learners passionate about technology and eager to complete a 100-day challenge that combines theory, hands-on labs, and a capstone project