In this course, students gain an understanding of Data Science, exploring its process from data acquisition to analysis and visualization. It includes learning about essential tools and techniques, with practical applications such as predictive modeling and customer segmentation. The course aims to establish a solid foundational knowledge of Data Science and its real-world uses.

This course thoroughly introduces Python programming, covering syntax, data types, variables, control structures, functions, and key libraries. Students will learn to write basic Python code, utilize data structures like lists and dictionaries, and leverage scientific computing libraries such as NumPy and Pandas. These skills aim to enable students to effectively perform data analysis tasks in Python. 

Dive into the fundamentals of probability, descriptive, and inferential statistics. Students will explore various probability distributions (normal, binomial, Poisson) and their applications in modeling real-world phenomena. Key statistical techniques, including hypothesis testing and regression analysis, are introduced for practical use in Data Science. The goal is to equip students with the ability to apply statistical methods to real-world datasets effectively. 

These sessions focus on the essential process of data wrangling, transforming raw data into a usable format for analysis. You will navigate real-world data challenges, learning to address issues like missing values, inconsistent formats, and outliers using Python and its libraries, especially Pandas. The course also introduces regular expressions for text pattern manipulation. By its conclusion, you'll be proficient in the data wrangling process, and adept at preparing real-world datasets for thorough analysis. This skill is key for anyone stepping into the world of Data Science. 

This course introduces the fundamentals of SQL, a pivotal language for managing and querying data in relational databases. You'll learn how to craft SQL statements for data extraction, perform aggregations, and effectively join multiple tables. The course also covers the use of database management systems like MySQL and demonstrates connecting to databases using Python and libraries like SQLAlchemy. By the end, you'll be equipped to write basic SQL queries and understand how to employ SQL in Data Science for data preparation and analysis. 

Dive into data exploration and visualization with this course, where you'll learn to analyze data for insights and patterns using Python, Pandas, and Matplotlib. Gain skills in data summarization, hypothesis testing, and using a range of visualization techniques like bar charts and scatter plots. You'll also grasp basic statistical concepts to better interpret data sets. By the end of the course, you'll be adept at uncovering relationships between variables, spotting outliers, and conveying insights through compelling visualizations. 

This course introduces the fundamentals of machine learning, encompassing both supervised and unsupervised learning algorithms. You'll learn about key algorithms like linear regression, k-nearest neighbors, decision trees, and k-means clustering, and their application in solving real-world problems. The course also covers the basics of model evaluation, focusing on metrics like accuracy, precision, recall, and F1 score, to assess algorithm performance. By the end, you'll have the ability to apply various algorithms to real-world datasets. 

These sessions offer a primer on deep learning, emphasizing artificial neural networks. You'll learn to use frameworks like TensorFlow and PyTorch for tasks such as image classification and natural language processing. Advanced techniques, including convolutional and recurrent neural networks, are also covered. Upon completion, you'll be adept at building and training neural networks for various applications, a crucial skill in modern data science. 

Explore the realm of big data, focusing on its distinct challenges and processing techniques. Learn the differences between big data and traditional data, and how to solve complex problems using tools like Apache Spark for distributed computing. You'll be equipped with a thorough understanding of big data concepts and the skills to apply advanced processing techniques in real-world scenarios. 

This course addresses crucial ethical considerations in Data Science, including privacy, bias, and fairness. Students will explore the ethical implications of handling sensitive data and learn strategies to ensure responsible and secure data usage. The course also delves into the challenges of algorithmic bias, guiding students on creating fair and unbiased models. Students will know how to apply these best practices in a responsible and ethical manner.