Description

Course Description:

The Predictive Analytics with Python & R Certified Course equips learners with practical knowledge of statistical modeling, machine learning techniques, and advanced analytics using two of the most popular languages in data science — Python and R. You will gain hands-on experience in data preprocessing, building predictive models, validating performance, and deploying solutions for real-world decision-making in domains such as finance, healthcare, marketing, and operations.

Key Features of Course Divine:

• Collaboration with E‑Cell IIT Tirupati
• 1:1 Online Mentorship Platform
• Credit-Based Certification
• Live Classes Led by Industry Experts
• Live, Real-World Projects
• 100% Placement Support
• Potential Interview Training
• Resume-Building Activities

Career Opportunities After Predictive Analytics with Python & R Certified Course:

• Data Scientist
• Predictive Analytics Specialist
• Machine Learning Engineer
• Business Intelligence Analyst
• Risk & Fraud Analyst
• Marketing Analytics Consultant

Essential Skills you will Develop Predictive Analytics with Python & R Certified Course:

• Data cleaning, preprocessing, and feature engineering
• Exploratory Data Analysis (EDA) with Python & R
• Building and tuning predictive models
• Applying machine learning algorithms
• Model evaluation using performance metrics
• Predictive modeling for business decision-making
• Visualization and reporting with Python

Tools Covered:

• Python: pandas, NumPy
• R: caret, randomForest, forecast, ggplot2, Shiny
• Jupyter Notebook, RStudio
• SQL integration for data handling
• Git/GitHub for version control
• scikit-learn, TensorFlow/Keras, Matplotlib, Seaborn

Syllabus:

Module 1: Introduction to Predictive Analytics What is Predictive Analytics? Applications in finance, healthcare, marketing, manufacturing, etc. Data-driven decision-making process Overview of Python and R for analytics.

Module 2: Data Exploration and Preparation Data collection and sources Data cleaning and preprocessing Handling missing values & outliers Data transformation and normalization Exploratory Data Analysis (EDA) with Python & R.

Module 3: Statistical Foundations Descriptive vs. inferential statistics Probability distributions Hypothesis testing Correlation & causation Feature selection and dimensionality reduction (PCA, LDA).

Module 4: Regression Models Simple and multiple linear regression Logistic regression Polynomial regression Model evaluation (RMSE, R², AUC, Precision, Recall) Implementation in Python (scikit-learn) & R.

Module 5: Machine Learning for Prediction Decision Trees & Random Forests Gradient Boosting (XGBoost, LightGBM) Support Vector Machines (SVM) k-Nearest Neighbors (k-NN) Model tuning and cross-validation.

Module 6: Time Series Forecasting Introduction to time series data AR, MA, ARIMA, SARIMA models Exponential smoothing methods Prophet (Facebook) model in Python & R Forecast accuracy measures.

Module 7: Advanced Predictive Techniques Ensemble learning techniques (bagging, boosting, stacking) Neural networks for predictive analytics Deep learning basics with Tensor Flow/Kera’s & R caret/koras packages Survival analysis and advanced regression techniques.

Module 8: Model Deployment and Interpretation Interpreting predictive models Model exploitability (SHAP, LIME) Deploying models with Flask (Python) & Shiny (R) Automating pipelines with Flow.

Module 9: Tools and Libraries Python: pandas, jumpy, Matplotlib, seaborn, sickie-learn, Statsmodels, tensor flow/Kera tidy verse, caret, forecast, random Forest, gillnet, ggplot2, koras.

Module 10: Industry Projects & Case Studies Predicting customer churn (Telecom/Banking) Fraud detection (Financial transactions) Sales forecasting (Retail) Predictive maintenance (Manufacturing/IoT)m Healthcare predictive analytics (Disease risk prediction).

Industry Projects:

• Customer Churn Prediction (Telecom/Banking)
• Fraud Detection in Financial Transactions
• Sales Forecasting (Retail/E-commerce)

 Who is this program for?

• Students & Fresh Graduates
• Data Enthusiasts & Beginners in Analytics
• Working Professionals in IT, Finance, Healthcare, or Retail

How To Apply:

Mobile: 9100348679

Email: coursedivine@gmail.com