Course Overview
Data Science with Python is one of the most in-demand skills in today’s IT and business industries. From analyzing large datasets to building predictive models and deploying AI-powered applications, Data Science is at the core of decision-making.
At Techxeeria Technologies Pvt. Ltd., our Data Science with Python Training is designed to transform beginners into job-ready Data Scientists in just 6 months. The course covers Python programming, data analysis, statistics, machine learning, deep learning, and real-world projects, with hands-on assignments and industry-focused case studies.
Why Choose Data Science with Python Training?
Learn Python programming for Data Science (from basics to advanced)
Master NumPy, Pandas, Matplotlib, Seaborn, and Plotly for data analysis & visualization
Build strong foundations in Statistics, Probability, and Linear Algebra
Work on Machine Learning & Deep Learning projects with TensorFlow/Keras
Gain expertise in Supervised & Unsupervised ML algorithms
Hands-on Capstone Projects to boost your portfolio
Prepare for Data Scientist, ML Engineer, AI Engineer, and Analyst roles
6-Month Data Science with Python Syllabus
Month 1 – Python for Data Science (Basics + Advanced)
Week 1–2: Python Fundamentals
- Day 1: Introduction to Data Science & Python Setup (Primary Keyword: Data Science with Python Training)
- Day 2: Variables, Data Types, Operators
- Day 3: Conditional Statements & Loops
- Day 4: Functions (Built-in & User-defined)
- Day 5: String Handling & Methods
- Day 6: Lists, Tuples, Sets, Dictionaries
- Day 7: Hands-on Practice + Mini Project
Week 3–4: Advanced Python
- Day 8: File Handling
- Day 9: Exception Handling
- Day 10: Modules & Packages
- Day 11: OOP in Python (Classes, Objects)
- Day 12: Inheritance, Polymorphism
- Day 13: Python Libraries Overview – NumPy, Pandas, Matplotlib
- Day 14: Mini Project – Student Data Management
Month 2 – Data Analysis with Python
Week 5–6: NumPy & Pandas
- Day 15: NumPy Basics – Arrays, Indexing, Slicing
- Day 16: NumPy Functions & Operations
- Day 17: Pandas – Series & DataFrames
- Day 18: Data Cleaning & Handling Missing Values
- Day 19: Data Aggregation & Grouping
- Day 20: Data Transformation & Merging
- Day 21: Mini Project – Sales Data Analysis
Week 7–8: Data Visualization
- Day 22: Introduction to Matplotlib
- Day 23: Line, Bar, Pie, Histogram Charts
- Day 24: Seaborn Basics – Heatmaps, Pairplots
- Day 25: Advanced Visualization with Seaborn
- Day 26: Plotly for Interactive Dashboards
- Day 27: Case Study – Exploratory Data Analysis (EDA)
- Day 28: Mini Project – COVID-19 Data Visualization
Month 3 – Statistics & Probability for Data Science
Week 9–10: Statistics Fundamentals
- Day 29: Descriptive Statistics (Mean, Median, Mode, Variance, SD)
- Day 30: Probability Basics
- Day 31: Probability Distributions – Normal, Binomial, Poisson
- Day 32: Hypothesis Testing (t-test, chi-square, ANOVA)
- Day 33: Correlation & Covariance
- Day 34: Sampling Techniques
- Day 35: Mini Project – Hypothesis Testing on Real Dataset
Week 11–12: Linear Algebra & Data Preprocessing
- Day 36: Linear Algebra Basics – Vectors, Matrices
- Day 37: Matrix Operations with NumPy
- Day 38: Data Preprocessing – Encoding, Normalization, Standardization
- Day 39: Feature Scaling & Feature Engineering
- Day 40: Handling Outliers & Imbalanced Data
- Day 41: Data Pipeline Building
- Day 42: Mini Project – Data Cleaning + Preprocessing
Month 4 – Machine Learning with Python (Supervised ML)
Week 13–14: Regression Models
- Day 43: Introduction to Machine Learning
- Day 44: Linear Regression (Simple & Multiple)
- Day 45: Polynomial Regression
- Day 46: Evaluation Metrics (MSE, RMSE, R²)
- Day 47: Logistic Regression (Binary Classification)
- Day 48: Multi-Class Logistic Regression
- Day 49: Mini Project – House Price Prediction
Week 15–16: Classification Models
- Day 50: Decision Trees
- Day 51: Random Forests
- Day 52: Naïve Bayes Classifier
- Day 53: K-Nearest Neighbors (KNN)
- Day 54: Support Vector Machine (SVM)
- Day 55: Model Evaluation (Confusion Matrix, ROC, AUC)
- Day 56: Mini Project – Customer Churn Prediction
Month 5 – Machine Learning (Unsupervised + Advanced ML)
Week 17–18: Unsupervised Learning
- Day 57: K-Means Clustering
- Day 58: Hierarchical Clustering
- Day 59: PCA (Principal Component Analysis)
- Day 60: Dimensionality Reduction
- Day 61: Association Rule Mining (Apriori, FP-Growth)
- Day 62: Mini Project – Market Basket Analysis
Week 19–20: Ensemble & Advanced ML
- Day 63: Ensemble Learning – Bagging, Boosting
- Day 64: Gradient Boosting (XGBoost, LightGBM, CatBoost)
- Day 65: Hyperparameter Tuning (GridSearchCV, RandomSearchCV)
- Day 66: Cross-Validation Techniques
- Day 67: Model Deployment with Pickle & Joblib
- Day 68: Mini Project – Loan Default Prediction
Month 6 – Deep Learning + Final Projects
Week 21–22: Deep Learning with Python
- Day 69: Introduction to Neural Networks
- Day 70: Perceptron & Activation Functions
- Day 71: Forward & Backpropagation
- Day 72: Neural Networks with TensorFlow/Keras
- Day 73: CNN – Convolutional Neural Networks (Image Classification)
- Day 74: RNN – Recurrent Neural Networks (Text/Time Series)
- Day 75: Mini Project – Handwritten Digit Recognition
Week 23–24: Capstone Projects & Interview Prep
- Day 76–80: Capstone Project 1 – EDA + Machine Learning Model
- Day 81–85: Capstone Project 2 – Deep Learning Project
- Day 86–88: Resume Building + GitHub Portfolio Setup
- Day 89: Mock Interviews + Coding Test Prep
- Day 90: Final Assessment + Certification
Hands-On Training
- Mini Projects in every module (Python, ML, DL)
- 2 Major Capstone Projects for portfolio
- Resume & GitHub Portfolio Setup
- Interview Prep + Mock Interviews
Who Can Join?
- Beginners aiming to become Data Scientists
- Python learners looking to enter AI/ML fields
- Students preparing for IT/Analytics placements
- Developers interested in ML, AI, and Data Engineering
Career Opportunities After Course
- Data Scientist
- Machine Learning Engineer
- AI Engineer
- Data Analyst
- Business Intelligence Analyst
Instructor
