The Data Science with Python Course is designed to equip learners with the skills to analyze, interpret, and visualize complex data to drive informed business decisions. The program combines the power of Python programming with essential data science techniques, including data cleaning, statistical analysis, machine learning, and data visualization. Through hands-on projects and real-world datasets, participants gain practical experience in extracting insights and building predictive models. By the end of the course, learners will be proficient in leveraging Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn to solve data-driven problems efficiently. This course is ideal for aspiring data scientists, analysts, and professionals looking to enhance their data analytics expertise in a fast-growing field.
Module 1: Introduction to Data Science & Python Overview of Data Science and its applications Setting up Python environment (Anaconda, Jupyter Notebook, Google Colab) Python basics: Data types, variables, operators, and control structures.
Module 2: Python for Data Analysis Lists, tuples, dictionaries, and sets Functions, loops, and conditional statements File handling and working with text/csv files.
Module 3: Data Manipulation with Pandas Series and DataFrames Data selection, filtering, and sorting Handling missing data and data cleaning techniques.
Module 4: Numerical Computing with NumPy Arrays and array operations Mathematical and statistical operations on arrays Broadcasting and vectorized operations.
Module 5: Data Visualization Visualization using Matplotlib: Line, bar, histogram, and scatter plots Advanced visualization with Seaborn Interactive dashboards using Plotly.
Module 6: Exploratory Data Analysis (EDA) Understanding data distribution and patterns Correlation and covariance analysis Feature selection and data preprocessing.
Module 7: Statistics for Data Science Descriptive and inferential statistics Probability distributions Hypothesis testing and statistical inference.
Module 8: Machine Learning with Python Supervised learning: Regression and classification Unsupervised learning: Clustering and dimensionality reduction Model evaluation metrics and validation techniques.
Module 9: Advanced Machine Learning & AI Basics Introduction to AI and deep learning concepts Decision trees, random forests, and ensemble techniques Introduction to neural networks and TensorFlow/Keras basics.
Module 10: Capstone Project & Deployment End-to-end real-world data science project Model deployment basics Presentation and reporting of insights.
Mobile: 9100348679
Email: coursedivine@gmail.com
You cannot copy content of this page
