Description: Python Mastery for Data Science & Statistical Modeling: Basics to Advanced Applications in Data Analysis, Visualization
What you’ll learn
Solid grasp of Python programming for Data Science & Statistics
Practical experience through hands-on projects and case studies
Ability to apply Statistical Modeling techniques using Python
Understanding of real-world applications in Data Analysis and Machine Learning
No prior knowledge or experience is required. Everything is explained from absolute basics.
Unlock the world of data science and statistical modeling with our comprehensive course, Python for Data Science & Statistical Modeling. Whether you’re a novice or looking to enhance your skills, this course provides a structured pathway to mastering Python for data science and delving into the fascinating world of statistical modeling.Module 1: Python Fundamentals for Data ScienceDive into the foundations of Python for data science, where you’ll learn the essentials that form the basis of your data journey.Session 1: Introduction to Python & Data ScienceSession 2: Python Syntax & Control FlowSession 3: Data Structures in PythonSession 4: Introduction to Numpy & Pandas for Data ManipulationModule 2: Data Science Essentials with PythonExplore the core components of data science using Python, including exploratory data analysis, visualization, and machine learning.Session 5: Exploratory Data Analysis with Pandas & NumpySession 6: Data Visualization with Matplotlib, Seaborn & BokehSession 7: Introduction to Scikit-Learn for Machine Learning in PythonModule 3: Mastering Probability, Statistics & Machine LearningGain in-depth knowledge of probability, statistics, and their seamless integration with Python’s powerful machine learning capabilities.Session 8: Difference between Probability and StatisticsSession 9: Set Theory and Probability ModelsSession 10: Random Variables and DistributionsSession 11: Expectation, Variance, and MomentsModule 4: Practical Statistical Modeling with PythonApply your understanding of probability and statistics to build statistical models and explore their real-world applications.Session 12: Probability and Statistical Modeling in PythonSession 13: Estimation Techniques & Maximum Likelihood EstimateSession 14: Logistic Regression and KL-DivergenceSession 15: Connecting Probability, Statistics & Machine Learning in PythonModule 5: Statistical Modeling Made EasySimplify statistical modeling with Python, covering summary statistics, hypothesis testing, correlation, and more.Session 16: Overview of Summary Statistics in PythonSession 17: Introduction to Hypothesis TestingSession 18: Null and Alternate Hypothesis with PythonSession 19: Correlation and Covariance in PythonModule 6: Implementing Statistical ModelsDelve deeper into implementing statistical models with Python, including linear regression, multiple regression, and custom models.Session 20: Linear Regression and CoefficientsSession 21: Testing for Correlation in PythonSession 22: Multiple Regression and F-TestSession 23: Building Custom Statistical Models with Python AlgorithmsModule 7: Capstone Projects & Real-World ApplicationsPut your skills to the test with hands-on projects, case studies, and real-world applications.Session 24: Mini-projects integrating Python, Data Science & StatisticsSession 25: Case Study 1: Real-world applications of Statistical ModelsSession 26: Case Study 2: Python-based Data Analysis & VisualizationModule 8: Conclusion & Next StepsWrap up your journey with a recap of key concepts and guidance on advancing your data science career.Session 27: Recap & Summary of Key ConceptsSession 28: Continuing Your Learning Path in Data Science & PythonJoin us on this transformative learning adventure, where you’ll gain the skills and knowledge to excel in data science, statistical modeling, and Python. Enroll now and embark on your path to data-driven success!Who Should Take This Course?Aspiring Data ScientistsData AnalystsBusiness AnalystsStudents pursuing a career in data-related fieldsAnyone interested in harnessing Python for data insightsWhy This Course?In today’s data-driven world, proficiency in Python and statistical modeling is a highly sought-after skillset. This course empowers you with the knowledge and practical experience needed to excel in data analysis, visualization, and modeling using Python. Whether you’re aiming to kickstart your career, enhance your current role, or simply explore the world of data, this course provides the foundation you need. What You Will Learn:This course is structured to take you from Python fundamentals to advanced statistical modeling, equipping you with the skills to:Master Python syntax and data structures for effective data manipulationExplore exploratory data analysis techniques using Pandas and NumpyCreate compelling data visualizations using Matplotlib, Seaborn, and BokehDive into Scikit-Learn for machine learning in PythonUnderstand key concepts in probability and statisticsApply statistical modeling techniques in real-world scenariosBuild custom statistical models using Python algorithmsPerform hypothesis testing and correlation analysisImplement linear and multiple regression modelsWork on hands-on projects and real-world case studiesKeywords:Python for Data Science, Statistical Modeling, Data Analysis, Data Visualization, Machine Learning, Pandas, Numpy, Matplotlib, Seaborn, Bokeh, Scikit-Learn, Probability, Statistics, Hypothesis Testing, Regression Analysis, Data Insights, Python Syntax, Data Manipulation…stical-modeling/

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