Data Science with Python →

Lesson 01 - Course Introduction

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

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Lesson 02 - Introduction to Data Science

Embark on comprehensive journey through data science process, starting with introduction to fundamental concepts. Delve into Python’s role in data science, exploring essential packages & 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 skills necessary to effectively analyze and communicate insights from diverse datasets

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Lesson 03 - Numpy

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

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Lesson 04 - Working with Pandas

You will gain a comprehensive understanding of pandas, powerful library for data manipulation & 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

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Lesson 05 - Data Visualization

Through these topics, you will gain proficiency in data visualization using Matplotlib & 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.

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Lesson 06 - Maths and Statistics Fundamentals

This comprehensively explores linear algebra, calculus, & statistics — foundational pillars of data science. Grasp essential concepts such as scalars, vectors, matrices, & operations, along with understanding norms, ranks, determinants, inverses, eigenvalues, & eigenvectors. Furthermore, delve into application of calculus within linear algebra, establishing a solid mathematical framework for data analysis. Additionally, uncover importance of statistics in data science, mastering various types of data & 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

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Lesson 07 - Probability Distribution

You will explore core principles of probability theory essential for data science. Understand random variables, probability distributions (both discrete & continuous), & key concepts like probability density functions & 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.

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Lesson 08 - Advanced Statistics

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

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Lesson 09 - Data Wrangling

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

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Lesson 10 - Feature Engineering

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.

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