Data Science

About Course

A Data Science course equips students with the skills to analyze and interpret complex data, enabling them to make data-driven decisions. It covers essential topics such as statistics, machine learning, data visualization, and programming languages like Python and R. Through hands-on projects, students learn to extract valuable insights from large datasets, which are crucial in various industries. The course often includes real-world case studies, providing practical experience in applying data science techniques to solve business problems.

In addition to technical skills, a Data Science course emphasizes critical thinking and problem-solving abilities. Students are taught to approach data with a curious and analytical mindset, identifying patterns and trends that can inform strategic decisions. The course also introduces ethical considerations, ensuring that students are aware of the responsibilities that come with handling sensitive data. By the end of the program, graduates are prepared to pursue careers in fields such as data analysis, business intelligence, and artificial intelligence, where data-driven insights are key to success.

Course Content

Module 1: Introduction to Data Science

Overview of the data science lifecycle: data collection, cleaning, analysis, and visualization.

00:00
Tools and technologies: Python, R, Jupyter Notebooks, and SQL.

00:00
Data ethics, privacy concerns, and responsible AI.

00:00

Module 2: Data Wrangling and Exploration

Module 3: Statistical Analysis and Hypothesis Testing

Module 4: Predictive Modeling and Machine Learning

Module 5: Big Data and Data Engineering

Module 6: Advanced Topics in Data Science

**Module 7: Project 1: Real-World Data Analysis and Visualization

**Module 8: Project 2: Predictive Analytics for Business Intelligence

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About Course

Course Content

Module 1: Introduction to Data Science

Overview of the data science lifecycle: data collection, cleaning, analysis, and visualization.

Tools and technologies: Python, R, Jupyter Notebooks, and SQL.

Data ethics, privacy concerns, and responsible AI.

Module 2: Data Wrangling and Exploration

Data cleaning techniques: handling missing data, duplicates, and outliers

Exploratory data analysis (EDA) using Pandas, NumPy, and Matplotlib.

Data visualization principles and best practices with Seaborn and Plotly.

Module 3: Statistical Analysis and Hypothesis Testing

Descriptive statistics: mean, median, mode, variance, and standard deviation.

Inferential statistics: confidence intervals, z-tests, t-tests, and chi-square tests.

Hypothesis testing and p-values for decision-making in data analysis.

Module 4: Predictive Modeling and Machine Learning

Supervised learning: regression and classification algorithms.

Model evaluation techniques: cross-validation, confusion matrix, and ROC curves.

Feature engineering and selection for improving model accuracy.

Module 5: Big Data and Data Engineering

Introduction to big data technologies: Hadoop, Spark, and NoSQL databases.

Data pipeline design and ETL processes for handling large datasets.

Real-time data processing and streaming with Apache Kafka and Spark Streaming.

Module 6: Advanced Topics in Data Science

Time series analysis: ARIMA, SARIMA, and LSTM models for forecasting.

Text mining and sentiment analysis using NLP techniques.

Introduction to deep learning and its applications in data science.

**Module 7: Project 1: Real-World Data Analysis and Visualization

End-to-end data analysis project using a real-world dataset.

Data cleaning, exploration, and visualization to derive insights.

Presentation of findings using dashboards (e.g., Tableau, Power BI).

**Module 8: Project 2: Predictive Analytics for Business Intelligence

Developing a predictive model to solve a business problem.

Feature selection, model building, and evaluation.

Deployment of the model and reporting of results.

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