The Data Science Course is designed to equip learners with the essential skills required to analyze, interpret, and visualize data effectively, enabling informed business and research decisions. The program covers key areas including Python programming, data preprocessing, statistical analysis, machine learning, data visualization, and big data tools. Through a combination of theoretical concepts and hands-on industry projects, participants gain practical experience in working with real-world datasets. By the end of the course, learners will be proficient in using data-driven techniques to solve complex problems, making them valuable assets in sectors such as IT, finance, healthcare, e-commerce, and research. This course is ideal for beginners looking to enter the field of data science as well as professionals aiming to enhance their analytical and technical capabilities.
Module 1: Introduction to Data Science & Python What is Data Science? Applications & career scope Python basics: syntax, variables, operators, loops, functions Python data structures: lists, tuples, dictionaries, sets Working with files in Python.
Module 2: Data Handling & Analysis Introduction to NumPy (arrays, matrix operations, broadcasting) Introduction to Pandas (Series, DataFrames, data selection, indexing) Data cleaning: handling missing values, duplicates, outliers Data transformation and feature engineering.
Module 3: Data Visualization Introduction to visualization Matplotlib: line, bar, scatter, histogram, pie charts Seaborn: heatmaps, pair plots, advanced plots Plotly / Dash (interactive visualizations).
Module 4: Statistics & Probability for Data Science Descriptive statistics: mean, median, mode, variance, std deviation Probability basics & distributions Hypothesis testing (t-test, chi-square, ANOVA) Correlation & covariance.
Module 5: Machine Learning with Python Introduction to Machine Learning Supervised Learning: Unsupervised Learning: Model evaluation: accuracy, precision, recall, F1-score, ROC curve.
Module 6: Advanced Machine Learning Ensemble methods: Bagging, Boosting (XGBoost, LightGBM) Time Series Analysis: ARIMA, SARIMA, Prophet Natural Language Processing (NLP) basics: text preprocessing, sentiment analysis Recommendation systems.
Module 7: Deep Learning (Optional Advanced) Introduction to Neural Networks TensorFlow / Keras basics Building simple deep learning models Applications: image recognition, NLP, predictive analytics.
Module 8: Databases & Big Data SQL basics for Data Science Querying structured data (MySQL/PostgreSQL) Introduction to NoSQL (MongoDB) Basics of Big Data (Hadoop, Spark overview).
Module 9: Data Science Project Lifecycle Problem definition & business understanding Data collection & preprocessing Model building & evaluation Model deployment (using Flask / Streamlet) Documentation & reporting
Module 10: Industry Projects & Case Studies Predictive analytics (sales/stock forecasting) Customer segmentation Sentiment analysis on social media data Fraud detection model.
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Email: coursedivine@gmail.com
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