Applied Data Science with Python

Get started with this program by understanding the course components and the topics covered. This will help you to be prepared for the upcoming sessions.

Embark on a comprehensive journey through the data science process, starting with an introduction to its fundamental concepts. Delve into Python’s role in data science, exploring essential packages and tools used for data manipulation, analysis, and visualization. By understanding the types of plots commonly used in data visualization, along with practical examples, you will acquire the skills necessary to effectively analyze and communicate insights from diverse datasets.

Through these topics, you will gain a comprehensive understanding of pandas, a powerful library for data manipulation and analysis in Python. Explore fundamental data structures such as Series and DataFrame, mastering essential statistical operations and handling techniques for dates, times, categorical data, and text data. Additionally, delve into advanced functionalities, including iteration, sorting, and plotting with Pandas, equipping them with the skills needed to process and analyze diverse datasets efficiently.

Through these topics, you will gain proficiency in data visualization using Matplotlib and Seaborn, two powerful libraries in Python. You will learn to create various types of plots, including line plots, scatter plots, bar charts, box plots, radar charts, area plots, polar plots, tree maps and pie charts using Matplotlib. Additionally, using Seaborn, you will explore advanced visualization techniques such as 3D visualization, violin plots, pair plots, heatmaps, joint plots, swarm plots and 3D graphs with multiple columns.

This comprehensively explores linear algebra, calculus, and statistics—the foundational pillars of data science. Grasp essential concepts such as scalars, vectors, matrices and their operations, along with understanding norms, ranks, determinants, inverses, eigenvalues and eigenvectors. Furthermore, delve into the application of calculus within linear algebra, establishing a solid mathematical framework for data analysis. Additionally, uncover the importance of statistics in data science, mastering various types of data and crucial statistical measures, including central tendency, dispersion, shape, covariance, and correlation. By mastering these concepts, you will be able to manipulate and analyze complex datasets, extract meaningful insights and make informed decisions in data-driven environments.

In this module, you will comprehensively understand NumPy, a fundamental library for numerical computing in Python. Explore the array object and its attributes, mastering essential array functions, arithmetic operations and statistical functions for efficient data manipulation and analysis. Additionally, you will delve into advanced topics such as string manipulation, array indexing, and slicing, equipping them with the necessary skills to work effectively with NumPy arrays in various data science applications.

In this module, you will explore the core principles of probability theory essential for data science. Understand random variables, probability distributions (both discrete and continuous) and key concepts like probability density functions and cumulative distribution functions. Additionally, delve into crucial theorems like the Central Limit Theorem and Bayes’ Theorem, along with estimation theory, equipping them to make informed statistical inferences and extract valuable insights from data.

In this module, you will master hypothesis testing methods essential for data analysis. You will understand concepts like null and alternative hypotheses, confidence intervals, margin of error, and confidence levels. Additionally, you will explore distributions, including the standard normal distribution (Z-distribution), t-distribution and chisquare distribution, along with associated tests like the t-test, z-test, and f-test. By understanding these techniques, you can make statistically sound decisions, analyze variance, and draw reliable conclusions from data.

Through these topics, you will acquire essential data preparation and manipulation skills, crucial steps in the data analysis pipeline. Learn the importance of thorough data collection and inspection, techniques to handle duplicates, and strategies for cleaning messy datasets. Additionally, delve into data transformation, binning and outlier detection methods to ensure data quality and reliability.

In this module, learners will explore the fundamentals of feature engineering, a critical aspect of data preprocessing in machine learning. They will learn various methods for transforming variables, including feature scaling, label encoding, one-hot encoding and hashing, essential for preparing categorical and numerical data for model training. Additionally, learners will delve into grouping operations, enabling them to aggregate and summarize data efficiently. By mastering these techniques, learners will be equipped to engineer informative features from raw data, enhancing machine learning models’ predictive power and performance.