--- title: Predictive Analytics Using Python Certificate Course | Amity Online description: Learn predictive modeling techniques with Python in this specialized certification from Amity Online. Build data-driven solutions for real-world applications. --- # Certificate in Predictive Analytics Using Python Post successful completion, apply for paid internship In Collaboration With Master data analytics with our Certificate in Predictive Analytics Using Python, your gateway to data-driven insights and building predictive models that solve real-world challenges. This course combines foundational learning, advanced machine learning techniques, and hands-on projects to equip you with job-ready skills. **Master the Essentials:** Learn data preprocessing, exploratory data analysis, and predictive modeling. **Hands-On Experience:** Apply your skills with real-world projects. **Proficiency in Tools:** Gain expertise in Python libraries like Pandas, NumPy, and Scikit-learn. **Future-Proof Your Career:** Make informed decisions and thrive in the analytics-driven era. **Flexible Learning:** Learn anytime, anywhere with our expert-led recorded lectures. **Internship Opportunities:** Post successful completion, apply for paid internship\*. \*Paid internship will be as per organization's norms or on first come first serve basis. Hurry! First batch intake closes on 30 Aug 2026 — only 5 internship spots left! ## Skills you'll gain ### Data Preprocessing Prepare, clean, and transform data for machine learning applications. ### Regression Techniques Learn and apply various regression models for predictions. ### Evaluation Methods Evaluate models using cross-validation, bootstrapping, and performance metrics. ### Classification Techniques Classify data using logistic regression, decision trees, and SVM. ### Unsupervised Clustering Group data through k-means, hierarchical and DBSCAN techniques. ### Dimensionality Reduction Reduce features using PCA, SVD, and t-SNE methods. ### Recommendation Analytics Build association rules and create user-based recommendation systems. ### Corporate Internships After successful completion, be eligible for a scheduled paid internship. ## Tools and Platforms covered ### 1\. Exploratory Data analysis and Data Preparation Master data preprocessing, transformation, scaling, and categorical conversions to prepare data for machine learning. 1. Data pre processing 2. Data Transformation, Data Reduction 3. Data Wrangling and Manipulation for Machine Learning 4. Feature Scaling 5. Categorical Conversions ### 2\. Introduction to Machine Learning and Regression Techniques  ### 3\. Evaluation Methods for Regression Techniques                   ### 4\. Classification techniques ### 5\. Evaluation Methods for Classification Techniques ### 6\. Introduction to Unsupervised Machine Learning and Clustering Techniques ### 7\. Feature Selection and Dimensionality Reduction  ### 8\. Association Rule and Recommender Systems           ## Earn and Share Your Certificate ## Meet our top-ranked faculty ### Pranab Das Consultant Advisory-Transformation-BE, KPMG India ### Kaushik Swaroop Consultant Advisory-Transformation-BE, KPMG India ## Amity Online Advantages: Why We're the Right Fit for You ### Learn from Analytics Experts and Industry Veterans Learn from experienced faculty and industry professionals who specialize in Predictive Analytics and Python programming. Our experts help you build strong analytical skills and knowledge that meet industry standards. ### Real-World Case Studies and Data-Driven Projects Gain hands-on experience with advanced ML techniques and business scenarios that reflect real-world challenges. This practical approach prepares you to make data-driven decisions in any business setting. ### Flexible Learning for Busy Professionals Study at your own pace with a flexible curriculum designed to fit around your schedule, making it ideal for working professionals looking to upskill. ### Certification with Industry Recognition Receive a certification in Predictive Analytics Using Python from Amity University Online, respected by employers across multiple sectors. - Total Modules: 8 Gain insight into a topic and learn the fundamentals. - Duration 32 hours - Level: Beginner No prior experience required - Internship Be eligible for internship (paid/unpaid) - Fee: ₹~~40,000~~ ₹25,000 ## Program Overview and Structure ### 1\. Exploratory Data analysis and Data Preparation Master data preprocessing, transformation, scaling, and categorical conversions to prepare data for machine learning. 1. Data pre processing 2. Data Transformation, Data Reduction 3. Data Wrangling and Manipulation for Machine Learning 4. Feature Scaling 5. Categorical Conversions ### 2\. Introduction to Machine Learning and Regression Techniques Learn and apply regression techniques like linear, decision tree, support vector, ridge, and lasso for predictive modeling. 1. Introduction to Machine Learning 2. Simple Linear Regression 3. Multiple Linear Regression 4. Support Vector Regressor 5. Decision Tree Regressor 6. Ridge and Lasso Regression ### 3\. Evaluation Methods for Regression Techniques Gain an understanding of hold-out, cross-validation, and bootstrapping techniques to evaluate model performance and ensure robust, unbiased predictions. 1. Hold-out method 2. Cross Validation Method 3. Boot Straping method ### 4\. Classification techniques Explore different classification techniques like Logistic Regression, Decision Trees, and SVM.  1. Introduction to Classification Techniques 2. Logistic Regression 3. Ensemble techniques 4. Decision trees 5. Random Forest  6. Naïve Bayes  7. K-Nearest Neighbours 8. SMOTE  9. Support Vector Machine ### 5\. Evaluation Methods for Classification Techniques Utilize confusion matrix, accuracy, precision, recall, and F1-score to effectively evaluate and interpret model performance. 1. Confusion Matrix 2. Accuracy 3. F1-Score, Precision, Recall ### 6\. Introduction to Unsupervised Machine Learning and Clustering Techniques Understand unsupervised learning and master clustering techniques, including hierarchical clustering, K-means, and DBSCAN, for uncovering patterns and grouping data effectively. 1. Introduction to Unsupervised Learning 2. Heirarchial Clustering 3. K-Means Clustering 4. DBSCAN (Density Based Spatial Clustering of Applications with Noise) ### 7\. Feature Selection and Dimensionality Reduction Leverage PCA, SVD, and t-SNE techniques for efficient dimensionality reduction and enhanced data analysis. 1. Principal Component Analysis (PCA) 2. Singular Value Decomposition (SVD) 3. T-distributed Stochastic Neighbor Embedding (t-SNE) ### 8\. Association Rule and Recommender Systems Learn to implement association rules using FP-Growth and Apriori algorithms and build effective recommender systems using weighted scores and user-based similarity techniques. 1. Association Rule, FP Growth 2. Case Study 3. Apriori Algorith in Python 4. Recommender Systems 5. Weighted Score Recommender System 6. User Based Similarity ## Frequently Asked Questions ### What makes these certifications unique? The "Predictive Analytics Using Python" certification is unique for its focus on practical Python applications in predictive analytics, providing hands-on experience with real-world data and industry-relevant tools. ### Who is this program for? This course is ideal for data analysts, business analysts, and anyone interested in leveraging data for predictive insights. ### What are the prerequisites for enrollment? While some basic knowledge of Python is helpful, the course starts with foundational concepts, making it accessible for beginners. ### What tools and technologies will I learn? You will learn Python with libraries like Pandas, NumPy, and Scikit-learn for data analysis and machine learning. ### How is the program structured? The certification course is structured as a 32-hour program that offers a self-paced learning format. It includes comprehensive video lectures, hands-on exercises, quizzes, and assignments designed to help learners build practical skills in predictive modeling using Python. ### Will I receive a certificate upon completion? Yes, students will receive a certification upon successfully completing the program. ### How can I apply what I learn to my career? You can apply what you learn by using predictive analytics and Python skills to analyze data, build predictive models, and make data-driven decisions, enhancing your value in roles like data analyst, business analyst, or data scientist across industries. ### Does this program offer a paid internship? Yes! Students who successfully complete the program may become eligible for a paid internship with KPMG or its associates, depending on selection criteria. ### What roles are available in the internship? Interns work in consulting, analytics, product development, and business intelligence, handling dashboard development, data analysis, and strategic problem-solving. ### How much is the stipend? The average internship stipend is ₹8,000 per month. ### Where are the paid internships conducted? Paid internships are conducted in-person at KPMG India offices in cities such as: Gurugram, Noida, Bangalore, Mumbai, Pune, Chennai, Kochi, Kolkata, Chandigarh, and Ahmedabad. ### When do internships start? Internships are offered quarterly, with the selection process taking place in the last week of each quarter. ### Can I reapply if I don’t get selected? Yes, students can reapply twice, but only in alternate quarters. If a student declines an internship after selection, they will not be eligible to reapply. ### How do I enroll and what are the payment options? You can enroll by visiting our website and following the enrollment instructions. We offer various payment options, including credit/debit cards, online banking, and installment plans. ### Can I access course materials after completion? Yes, students will have continued access to learning materials even after completing the course.