Beginner
Data Science with Python: Beginner to Advanced Professional Program
Master Data Science with Python in this comprehensive 80-hour program. Learn Python, data analysis, visualization, statistics, machine learning, deep learning, NLP, and model deployment through hands-on projects. Build industry-ready skills and prepare for successful Data Science and AI careers.
Recommended Course Audience
Pre-Required Skills
-
Basic computer knowledge
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No programming experience required
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Interest in AI, coding, and data beneficial
Course Highlights
19 modulesThis comprehensive 80-hour Data Science with Python program takes learners from fundamentals to advanced concepts. Participants will master Python programming, data analysis, visualization, statistics, machine learning, deep learning, NLP, model deployment, and MLOps. Through hands-on projects and real-world case studies, learners will develop industry-ready skills, build a professional portfolio, and confidently pursue Data Analyst, Data Scientist, and Machine Learning Engineer careers.
Explore the fundamentals of Data Science, industry applications, project lifecycle, Python ecosystem, development environments, Jupyter Notebook, Google Colab, essential libraries, and GitHub basics required for modern data-driven problem solving.
Learn Python programming concepts including variables, data types, operators, loops, functions, file handling, exception handling, object-oriented programming, modules, packages, and API integration for data science applications.
Master NumPy for numerical computing, array creation, indexing, slicing, mathematical operations, statistical functions, broadcasting, reshaping techniques, and linear algebra operations for efficient data processing.
Learn to work with datasets using Pandas, including data loading, cleaning, filtering, aggregation, grouping, merging, pivot tables, transformations, and handling missing values for analysis.
Create meaningful visualizations using Matplotlib and Seaborn. Learn charts, graphs, heatmaps, distributions, dashboards, and storytelling techniques that communicate insights effectively to stakeholders and decision-makers
Understand descriptive statistics, probability, distributions, sampling, hypothesis testing, confidence intervals, correlation, covariance, statistical significance, and A/B testing concepts essential for data-driven decision making.
Prepare high-quality datasets through missing value treatment, outlier handling, scaling, normalization, encoding, feature selection, dimensionality reduction, and feature engineering techniques that improve model performance
Analyze datasets using univariate, bivariate, and multivariate techniques. Discover patterns, trends, anomalies, relationships, and generate actionable business insights through structured exploratory analysis
Learn machine learning concepts, workflow, training and testing methodologies, cross-validation, model selection strategies, bias-variance tradeoff, and performance evaluation techniques for predictive analytics.
Build predictive models using regression and classification algorithms including Linear Regression, Logistic Regression, KNN, Naive Bayes, Decision Trees, and Random Forest with performance evaluation.
Discover hidden patterns using clustering and dimensionality reduction techniques including K-Means, Hierarchical Clustering, DBSCAN, PCA, customer segmentation, and association rule mining applications.
Explore ensemble learning methods such as Bagging, Boosting, AdaBoost, Gradient Boosting, XGBoost, LightGBM, SVMs, recommendation systems, and anomaly detection for advanced predictive modeling.
Learn forecasting techniques using trend analysis, seasonality detection, moving averages, exponential smoothing, ARIMA models, and business forecasting methods for future prediction tasks.
Understand neural networks, activation functions, backpropagation, TensorFlow, Keras, ANN architectures, and introductory CNN concepts for solving complex deep learning problems.
Learn text preprocessing, tokenization, stemming, lemmatization, vectorization techniques, Bag of Words, TF-IDF, sentiment analysis, and text classification using NLP methodologies.
Improve model performance through validation techniques, hyperparameter tuning, Grid Search, Random Search, feature importance analysis, interpretability methods, and optimization strategies
Deploy machine learning models using Flask and FastAPI. Learn Docker basics, REST APIs, Git workflows, model packaging, monitoring concepts, and deployment best practices.
Apply acquired skills through end-to-end industry projects, portfolio development, GitHub publishing, resume building, interview preparation, and career guidance for data science roles.
Course Instructor
Adhvaith Reddy
Manager of Research & Developement , Solix Technologies
$299.00
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Level
:
Beginner
Schedule
:
Aug 24 – Oct 16, 2026
Time
:
4:00 PM – 6:00 PM Asia/Kolkata
Sessions
:
1 session
Modules
:
19
Duration
:
80 hours